diff --git a/README.md b/README.md
index 1d1d974..77f7aa6 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,7 @@
-# E-swish: A New Novel Activation Function
-Code for reproducibility of the experiments of [this paper](https://github.com/EricAlcaide/E-swish/blob/master/publishing/ABSTRACT.pdf)
+# E-swish: Adjusting Activations to Different Network Depths
+This repository will contain the code for reproducibility of the experiments of [this paper](https://arxiv.org/abs/1801.07145v1)
+
+Uploading soon.
 
 ## Formula
 <div style="text-align:center"><img src ="e_swish.PNG" /></div>
@@ -7,52 +9,21 @@ Code for reproducibility of the experiments of [this paper](https://github.com/E
 ## Abstract
 
 <p align="justify">
-	Activation functions have a notorious impact in neural networks on both training and testing the models against the desired problem. Currently, the most used activation function is the Rectified Linear Unit (ReLU). Although some alternatives have been proposed, none of them have managed to replace ReLU as the default activation due to inconstant improvements. This paper introduces a new activation function, closely related with the new activation Swish = x * sigmoid (x) (Ramachandran et al., 2017) which we call E-swish.
-	E-swish is just a Swish activation function mirrored across the identity for all positive values. We show that E-swish outperforms many other well-known activations on a variety of tasks and it also leads to a faster convergence. For example, replacing Relu by E-swish provided at 0.3% accuracy improvement on Cifar10 for the WRN 16-4.
+	
 </p>
 
 ## Results
 
-### MNIST
-
-| Activations      | % Accuracy (median of 3 runs) |
-| -------------    | -------------:|
-| Leaky Relu (0.3) | 97.71         |
-| Elu              | 97.92         |
-| Relu             | 98.30         |
-| Swish            | 98.31         |
-| **E-swish**      | **98.46**     |
-
 ### CIFAR10
 
-* **Simple CNN**
-
-| Activations      | % Accuracy (median of 3 runs) |
-| -------------    | -------------:|
-| Elu              | 81.81         |
-| Swish            | 81.88         |
-| Relu             | 82.06         |
-| Leaky Relu (0.3) | 82.22         |
-| **E-swish**      | **83.35**     |
-
-* **Deeper CNN**
-
-| Activations      | % Test error (median of 3 runs) |
-| -------------    | -------------:|
-| Leaky Relu (0.3) | 8.62          |
-| Swish            | 8.27          |
-| Relu             | 8.13          |
-| Elu              | 7.79          |
-| **E-swish**      | **7.61**      |
-
-<i>*Results are provided on a single model performance.</i>
+* **WRN 10-2**
 
-* **WRN 16-4**
+|| Activations      		| % Accuracy (median of 3 runs) |
+| -------------    			| -------------:|
+| Swish            			| 89.98         |
+| Relu             			| 91.52         |
+| **E-swish (beta =1.375)** | **91.89**     |
 
-| Activations      | % Test error (median of 5 runs) |
-| -------------    | -------------:|
-| Relu             | 5.02          |
-| **E-swish**      | **4.71**      |
 
 ## Contact
 
diff --git a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar10_deeper_cnn_elu-checkpoint.ipynb b/cifar/Deeper_CNN/.ipynb_checkpoints/cifar10_deeper_cnn_elu-checkpoint.ipynb
deleted file mode 100644
index 72b8eef..0000000
--- a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar10_deeper_cnn_elu-checkpoint.ipynb
+++ /dev/null
@@ -1,1608 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "import keras.regularizers as regularizers\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 2.0441 - acc: 0.3842 - val_loss: 1.4719 - val_acc: 0.5211\n",
-      "Epoch 2/250\n",
-      "390/390 [==============================] - 145s 372ms/step - loss: 1.4238 - acc: 0.5356 - val_loss: 1.0515 - val_acc: 0.6488\n",
-      "Epoch 3/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 1.2189 - acc: 0.6031 - val_loss: 0.9504 - val_acc: 0.7042\n",
-      "Epoch 4/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 1.0990 - acc: 0.6478 - val_loss: 0.8618 - val_acc: 0.7261\n",
-      "Epoch 5/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 1.0263 - acc: 0.6726 - val_loss: 0.8084 - val_acc: 0.7480\n",
-      "Epoch 6/250\n",
-      "390/390 [==============================] - 145s 372ms/step - loss: 0.9630 - acc: 0.6924 - val_loss: 0.7860 - val_acc: 0.7603\n",
-      "Epoch 7/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.9235 - acc: 0.7081 - val_loss: 0.6973 - val_acc: 0.7842\n",
-      "Epoch 8/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8852 - acc: 0.7216 - val_loss: 0.6893 - val_acc: 0.7946\n",
-      "Epoch 9/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8515 - acc: 0.7357 - val_loss: 0.6928 - val_acc: 0.7892\n",
-      "Epoch 10/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8301 - acc: 0.7441 - val_loss: 0.6603 - val_acc: 0.8061\n",
-      "Epoch 11/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8091 - acc: 0.7520 - val_loss: 0.6253 - val_acc: 0.8187\n",
-      "Epoch 12/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7971 - acc: 0.7569 - val_loss: 0.6954 - val_acc: 0.8001\n",
-      "Epoch 13/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7799 - acc: 0.7634 - val_loss: 0.5979 - val_acc: 0.8261\n",
-      "Epoch 14/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7614 - acc: 0.7706 - val_loss: 0.6250 - val_acc: 0.8186\n",
-      "Epoch 15/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7527 - acc: 0.7751 - val_loss: 0.5733 - val_acc: 0.8386\n",
-      "Epoch 16/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7396 - acc: 0.7779 - val_loss: 0.5829 - val_acc: 0.8377\n",
-      "Epoch 17/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7265 - acc: 0.7854 - val_loss: 0.5548 - val_acc: 0.8456\n",
-      "Epoch 18/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.7186 - acc: 0.7886 - val_loss: 0.5842 - val_acc: 0.8381\n",
-      "Epoch 19/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7163 - acc: 0.7896 - val_loss: 0.5739 - val_acc: 0.8398\n",
-      "Epoch 20/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.7048 - acc: 0.7939 - val_loss: 0.6158 - val_acc: 0.8335\n",
-      "Epoch 21/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.6969 - acc: 0.7984 - val_loss: 0.5708 - val_acc: 0.8421\n",
-      "Epoch 22/250\n",
-      "390/390 [==============================] - 144s 371ms/step - loss: 0.6889 - acc: 0.8009 - val_loss: 0.5664 - val_acc: 0.8473\n",
-      "Epoch 23/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.6866 - acc: 0.8027 - val_loss: 0.5506 - val_acc: 0.8510\n",
-      "Epoch 24/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6791 - acc: 0.8056 - val_loss: 0.5621 - val_acc: 0.8458\n",
-      "Epoch 25/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6746 - acc: 0.8072 - val_loss: 0.5372 - val_acc: 0.8546\n",
-      "Epoch 26/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6681 - acc: 0.8083 - val_loss: 0.5762 - val_acc: 0.8479\n",
-      "Epoch 27/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6676 - acc: 0.8107 - val_loss: 0.5505 - val_acc: 0.8512\n",
-      "Epoch 28/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6618 - acc: 0.8104 - val_loss: 0.5256 - val_acc: 0.8609\n",
-      "Epoch 29/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.6612 - acc: 0.8126 - val_loss: 0.5718 - val_acc: 0.8452\n",
-      "Epoch 30/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6555 - acc: 0.8128 - val_loss: 0.5674 - val_acc: 0.8545\n",
-      "Epoch 31/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6514 - acc: 0.8168 - val_loss: 0.5625 - val_acc: 0.8516\n",
-      "Epoch 32/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6471 - acc: 0.8175 - val_loss: 0.5254 - val_acc: 0.8627\n",
-      "Epoch 33/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6470 - acc: 0.8197 - val_loss: 0.5403 - val_acc: 0.8556\n",
-      "Epoch 34/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6451 - acc: 0.8187 - val_loss: 0.5181 - val_acc: 0.8650\n",
-      "Epoch 35/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6431 - acc: 0.8182 - val_loss: 0.5238 - val_acc: 0.8636\n",
-      "Epoch 36/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6379 - acc: 0.8209 - val_loss: 0.5368 - val_acc: 0.8582\n",
-      "Epoch 37/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6329 - acc: 0.8231 - val_loss: 0.5277 - val_acc: 0.8629\n",
-      "Epoch 38/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6327 - acc: 0.8235 - val_loss: 0.5277 - val_acc: 0.8651\n",
-      "Epoch 39/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6304 - acc: 0.8246 - val_loss: 0.5318 - val_acc: 0.8627\n",
-      "Epoch 40/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6307 - acc: 0.8248 - val_loss: 0.5306 - val_acc: 0.8656\n",
-      "Epoch 41/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6274 - acc: 0.8278 - val_loss: 0.5323 - val_acc: 0.8643\n",
-      "Epoch 42/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6223 - acc: 0.8277 - val_loss: 0.5071 - val_acc: 0.8714\n",
-      "Epoch 43/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6218 - acc: 0.8302 - val_loss: 0.5173 - val_acc: 0.8649\n",
-      "Epoch 44/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6146 - acc: 0.8301 - val_loss: 0.5140 - val_acc: 0.8694\n",
-      "Epoch 45/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6198 - acc: 0.8296 - val_loss: 0.5175 - val_acc: 0.8683\n",
-      "Epoch 46/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6170 - acc: 0.8324 - val_loss: 0.4919 - val_acc: 0.8749\n",
-      "Epoch 47/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6108 - acc: 0.8323 - val_loss: 0.5256 - val_acc: 0.8659\n",
-      "Epoch 48/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6161 - acc: 0.8301 - val_loss: 0.5015 - val_acc: 0.8734\n",
-      "Epoch 49/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6109 - acc: 0.8328 - val_loss: 0.5102 - val_acc: 0.8717\n",
-      "Epoch 50/250\n",
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-      "Epoch 51/250\n",
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-      "Epoch 54/250\n",
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-      "Epoch 64/250\n",
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-      "Epoch 66/250\n",
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-      "Epoch 72/250\n",
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-      "Epoch 74/250\n",
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-      "Epoch 75/250\n",
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-      "Epoch 77/250\n",
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-      "Epoch 79/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5188 - acc: 0.8634 - val_loss: 0.4519 - val_acc: 0.8911\n",
-      "Epoch 80/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5147 - acc: 0.8639 - val_loss: 0.4607 - val_acc: 0.8902\n",
-      "Epoch 81/250\n",
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-      "Epoch 82/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5039 - acc: 0.8663 - val_loss: 0.4351 - val_acc: 0.8945\n",
-      "Epoch 83/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5048 - acc: 0.8671 - val_loss: 0.4459 - val_acc: 0.8901\n",
-      "Epoch 84/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5002 - acc: 0.8674 - val_loss: 0.4368 - val_acc: 0.8954\n",
-      "Epoch 85/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.5013 - acc: 0.8669 - val_loss: 0.4411 - val_acc: 0.8957\n",
-      "Epoch 86/250\n",
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-      "Epoch 87/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4984 - acc: 0.8678 - val_loss: 0.4317 - val_acc: 0.8958\n",
-      "Epoch 88/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4963 - acc: 0.8674 - val_loss: 0.4217 - val_acc: 0.8972\n",
-      "Epoch 89/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4971 - acc: 0.8665 - val_loss: 0.4300 - val_acc: 0.8946\n",
-      "Epoch 90/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4877 - acc: 0.8701 - val_loss: 0.4226 - val_acc: 0.8973\n",
-      "Epoch 91/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4855 - acc: 0.8720 - val_loss: 0.4363 - val_acc: 0.8944\n",
-      "Epoch 92/250\n"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4901 - acc: 0.8698 - val_loss: 0.4319 - val_acc: 0.8958\n",
-      "Epoch 93/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4876 - acc: 0.8706 - val_loss: 0.4279 - val_acc: 0.8962\n",
-      "Epoch 94/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4809 - acc: 0.8711 - val_loss: 0.4216 - val_acc: 0.8980\n",
-      "Epoch 95/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4864 - acc: 0.8692 - val_loss: 0.4304 - val_acc: 0.8971\n",
-      "Epoch 96/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4841 - acc: 0.8692 - val_loss: 0.4368 - val_acc: 0.8938\n",
-      "Epoch 97/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4802 - acc: 0.8717 - val_loss: 0.4323 - val_acc: 0.8953\n",
-      "Epoch 98/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4823 - acc: 0.8695 - val_loss: 0.4223 - val_acc: 0.8978\n",
-      "Epoch 99/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4767 - acc: 0.8717 - val_loss: 0.4190 - val_acc: 0.8988\n",
-      "Epoch 100/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4779 - acc: 0.8706 - val_loss: 0.4294 - val_acc: 0.8920\n",
-      "Epoch 101/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4594 - acc: 0.8772 - val_loss: 0.4142 - val_acc: 0.8980\n",
-      "Epoch 102/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4542 - acc: 0.8781 - val_loss: 0.4034 - val_acc: 0.9022\n",
-      "Epoch 103/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4450 - acc: 0.8830 - val_loss: 0.4006 - val_acc: 0.9026\n",
-      "Epoch 104/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4517 - acc: 0.8792 - val_loss: 0.4006 - val_acc: 0.9025\n",
-      "Epoch 105/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4433 - acc: 0.8827 - val_loss: 0.4007 - val_acc: 0.9026\n",
-      "Epoch 106/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4448 - acc: 0.8809 - val_loss: 0.3926 - val_acc: 0.9059\n",
-      "Epoch 107/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4449 - acc: 0.8825 - val_loss: 0.4019 - val_acc: 0.9019\n",
-      "Epoch 108/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4400 - acc: 0.8811 - val_loss: 0.4080 - val_acc: 0.9015\n",
-      "Epoch 109/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4418 - acc: 0.8804 - val_loss: 0.3966 - val_acc: 0.9051\n",
-      "Epoch 110/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4382 - acc: 0.8818 - val_loss: 0.4004 - val_acc: 0.9008\n",
-      "Epoch 111/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4407 - acc: 0.8812 - val_loss: 0.3947 - val_acc: 0.9044\n",
-      "Epoch 112/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4398 - acc: 0.8814 - val_loss: 0.4007 - val_acc: 0.9036\n",
-      "Epoch 113/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4357 - acc: 0.8835 - val_loss: 0.4011 - val_acc: 0.9025\n",
-      "Epoch 114/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4339 - acc: 0.8834 - val_loss: 0.3874 - val_acc: 0.9038\n",
-      "Epoch 115/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4319 - acc: 0.8833 - val_loss: 0.3963 - val_acc: 0.9025\n",
-      "Epoch 116/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4266 - acc: 0.8861 - val_loss: 0.3837 - val_acc: 0.9057\n",
-      "Epoch 117/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4330 - acc: 0.8819 - val_loss: 0.3996 - val_acc: 0.9010\n",
-      "Epoch 118/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4281 - acc: 0.8832 - val_loss: 0.3858 - val_acc: 0.9062\n",
-      "Epoch 119/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4251 - acc: 0.8856 - val_loss: 0.3886 - val_acc: 0.9044\n",
-      "Epoch 120/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4245 - acc: 0.8865 - val_loss: 0.4014 - val_acc: 0.9011\n",
-      "Epoch 121/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4242 - acc: 0.8857 - val_loss: 0.3973 - val_acc: 0.9031\n",
-      "Epoch 122/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4293 - acc: 0.8832 - val_loss: 0.3985 - val_acc: 0.9017\n",
-      "Epoch 123/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4222 - acc: 0.8854 - val_loss: 0.3964 - val_acc: 0.9017\n",
-      "Epoch 124/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4207 - acc: 0.8852 - val_loss: 0.3904 - val_acc: 0.9041\n",
-      "Epoch 125/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4229 - acc: 0.8852 - val_loss: 0.3934 - val_acc: 0.9033\n",
-      "Epoch 126/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4062 - acc: 0.8913 - val_loss: 0.3796 - val_acc: 0.9083\n",
-      "Epoch 127/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4117 - acc: 0.8895 - val_loss: 0.3836 - val_acc: 0.9078\n",
-      "Epoch 128/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4012 - acc: 0.8913 - val_loss: 0.3852 - val_acc: 0.9050\n",
-      "Epoch 129/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4029 - acc: 0.8912 - val_loss: 0.3786 - val_acc: 0.9083\n",
-      "Epoch 130/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4041 - acc: 0.8909 - val_loss: 0.3794 - val_acc: 0.9067\n",
-      "Epoch 131/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3971 - acc: 0.8930 - val_loss: 0.3796 - val_acc: 0.9054\n",
-      "Epoch 132/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3993 - acc: 0.8931 - val_loss: 0.3711 - val_acc: 0.9096\n",
-      "Epoch 133/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3976 - acc: 0.8927 - val_loss: 0.3741 - val_acc: 0.9085\n",
-      "Epoch 134/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3926 - acc: 0.8939 - val_loss: 0.3732 - val_acc: 0.9095\n",
-      "Epoch 135/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3918 - acc: 0.8939 - val_loss: 0.3751 - val_acc: 0.9085\n",
-      "Epoch 136/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4003 - acc: 0.8903 - val_loss: 0.3719 - val_acc: 0.9092\n",
-      "Epoch 137/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3933 - acc: 0.8928 - val_loss: 0.3764 - val_acc: 0.9086\n",
-      "Epoch 138/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3967 - acc: 0.8937 - val_loss: 0.3722 - val_acc: 0.9072\n",
-      "Epoch 139/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3876 - acc: 0.8957 - val_loss: 0.3736 - val_acc: 0.9068\n",
-      "Epoch 140/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3858 - acc: 0.8963 - val_loss: 0.3749 - val_acc: 0.9071\n",
-      "Epoch 141/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3851 - acc: 0.8974 - val_loss: 0.3740 - val_acc: 0.9064\n",
-      "Epoch 142/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3885 - acc: 0.8944 - val_loss: 0.3702 - val_acc: 0.9087\n",
-      "Epoch 143/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3848 - acc: 0.8981 - val_loss: 0.3763 - val_acc: 0.9074\n",
-      "Epoch 144/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3908 - acc: 0.8927 - val_loss: 0.3703 - val_acc: 0.9080\n",
-      "Epoch 145/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3864 - acc: 0.8963 - val_loss: 0.3789 - val_acc: 0.9059\n",
-      "Epoch 146/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3863 - acc: 0.8945 - val_loss: 0.3679 - val_acc: 0.9067\n",
-      "Epoch 147/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3830 - acc: 0.8970 - val_loss: 0.3714 - val_acc: 0.9078\n",
-      "Epoch 148/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3825 - acc: 0.8972 - val_loss: 0.3729 - val_acc: 0.9081\n",
-      "Epoch 149/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3864 - acc: 0.8962 - val_loss: 0.3684 - val_acc: 0.9077\n",
-      "Epoch 150/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3819 - acc: 0.8967 - val_loss: 0.3705 - val_acc: 0.9082\n",
-      "Epoch 151/250\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3798 - acc: 0.8975 - val_loss: 0.3664 - val_acc: 0.9089\n",
-      "Epoch 152/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3735 - acc: 0.8973 - val_loss: 0.3659 - val_acc: 0.9083\n",
-      "Epoch 153/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3717 - acc: 0.8992 - val_loss: 0.3662 - val_acc: 0.9100\n",
-      "Epoch 154/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3753 - acc: 0.8977 - val_loss: 0.3625 - val_acc: 0.9095\n",
-      "Epoch 155/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3735 - acc: 0.8987 - val_loss: 0.3633 - val_acc: 0.9116\n",
-      "Epoch 156/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3626 - acc: 0.9030 - val_loss: 0.3689 - val_acc: 0.9097\n",
-      "Epoch 157/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3709 - acc: 0.8987 - val_loss: 0.3671 - val_acc: 0.9090\n",
-      "Epoch 158/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3681 - acc: 0.8996 - val_loss: 0.3662 - val_acc: 0.9102\n",
-      "Epoch 159/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3658 - acc: 0.9003 - val_loss: 0.3619 - val_acc: 0.9114\n",
-      "Epoch 160/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3726 - acc: 0.8985 - val_loss: 0.3648 - val_acc: 0.9097\n",
-      "Epoch 161/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3644 - acc: 0.9008 - val_loss: 0.3606 - val_acc: 0.9104\n",
-      "Epoch 162/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3685 - acc: 0.8993 - val_loss: 0.3623 - val_acc: 0.9108\n",
-      "Epoch 163/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3685 - acc: 0.9008 - val_loss: 0.3645 - val_acc: 0.9113\n",
-      "Epoch 164/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3629 - acc: 0.9016 - val_loss: 0.3614 - val_acc: 0.9101\n",
-      "Epoch 165/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3651 - acc: 0.9024 - val_loss: 0.3647 - val_acc: 0.9084\n",
-      "Epoch 166/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3660 - acc: 0.9010 - val_loss: 0.3606 - val_acc: 0.9104\n",
-      "Epoch 167/250\n",
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-      "Epoch 168/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3649 - acc: 0.9008 - val_loss: 0.3616 - val_acc: 0.9075\n",
-      "Epoch 169/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3630 - acc: 0.9018 - val_loss: 0.3633 - val_acc: 0.9100\n",
-      "Epoch 170/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3680 - acc: 0.9001 - val_loss: 0.3641 - val_acc: 0.9091\n",
-      "Epoch 171/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3610 - acc: 0.9028 - val_loss: 0.3647 - val_acc: 0.9101\n",
-      "Epoch 172/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3680 - acc: 0.8991 - val_loss: 0.3599 - val_acc: 0.9102\n",
-      "Epoch 173/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3671 - acc: 0.9002 - val_loss: 0.3618 - val_acc: 0.9108\n",
-      "Epoch 174/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3610 - acc: 0.9027 - val_loss: 0.3623 - val_acc: 0.9107\n",
-      "Epoch 175/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3593 - acc: 0.9033 - val_loss: 0.3603 - val_acc: 0.9114\n",
-      "Epoch 176/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3629 - acc: 0.8998 - val_loss: 0.3590 - val_acc: 0.9111\n",
-      "Epoch 177/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3536 - acc: 0.9047 - val_loss: 0.3570 - val_acc: 0.9115\n",
-      "Epoch 178/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3582 - acc: 0.9030 - val_loss: 0.3554 - val_acc: 0.9118\n",
-      "Epoch 179/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3546 - acc: 0.9038 - val_loss: 0.3578 - val_acc: 0.9123\n",
-      "Epoch 180/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3512 - acc: 0.9063 - val_loss: 0.3602 - val_acc: 0.9123\n",
-      "Epoch 181/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3535 - acc: 0.9036 - val_loss: 0.3587 - val_acc: 0.9122\n",
-      "Epoch 182/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3536 - acc: 0.9045 - val_loss: 0.3577 - val_acc: 0.9117\n",
-      "Epoch 183/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3526 - acc: 0.9051 - val_loss: 0.3570 - val_acc: 0.9120\n",
-      "Epoch 184/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3529 - acc: 0.9043 - val_loss: 0.3576 - val_acc: 0.9122\n",
-      "Epoch 185/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3532 - acc: 0.9040 - val_loss: 0.3559 - val_acc: 0.9122\n",
-      "Epoch 186/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3550 - acc: 0.9033 - val_loss: 0.3572 - val_acc: 0.9117\n",
-      "Epoch 187/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3556 - acc: 0.9025 - val_loss: 0.3555 - val_acc: 0.9121\n",
-      "Epoch 188/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3511 - acc: 0.9049 - val_loss: 0.3542 - val_acc: 0.9124\n",
-      "Epoch 189/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3535 - acc: 0.9050 - val_loss: 0.3540 - val_acc: 0.9118\n",
-      "Epoch 190/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3499 - acc: 0.9055 - val_loss: 0.3572 - val_acc: 0.9113\n",
-      "Epoch 191/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3486 - acc: 0.9057 - val_loss: 0.3572 - val_acc: 0.9124\n",
-      "Epoch 192/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3518 - acc: 0.9048 - val_loss: 0.3552 - val_acc: 0.9117\n",
-      "Epoch 193/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3482 - acc: 0.9055 - val_loss: 0.3564 - val_acc: 0.9126\n",
-      "Epoch 194/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3545 - acc: 0.9039 - val_loss: 0.3591 - val_acc: 0.9126\n",
-      "Epoch 195/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3492 - acc: 0.9067 - val_loss: 0.3602 - val_acc: 0.9124\n",
-      "Epoch 196/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3489 - acc: 0.9060 - val_loss: 0.3614 - val_acc: 0.9120\n",
-      "Epoch 197/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3529 - acc: 0.9040 - val_loss: 0.3562 - val_acc: 0.9123\n",
-      "Epoch 198/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3500 - acc: 0.9058 - val_loss: 0.3563 - val_acc: 0.9131\n",
-      "Epoch 199/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3472 - acc: 0.9052 - val_loss: 0.3557 - val_acc: 0.9138\n",
-      "Epoch 200/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3528 - acc: 0.9040 - val_loss: 0.3571 - val_acc: 0.9120\n",
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3497 - acc: 0.9047 - val_loss: 0.3574 - val_acc: 0.9128\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3499 - acc: 0.9052 - val_loss: 0.3576 - val_acc: 0.9130\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3486 - acc: 0.9059 - val_loss: 0.3552 - val_acc: 0.9125\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3488 - acc: 0.9051 - val_loss: 0.3553 - val_acc: 0.9134\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3490 - acc: 0.9058 - val_loss: 0.3567 - val_acc: 0.9129\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3453 - acc: 0.9063 - val_loss: 0.3544 - val_acc: 0.9121\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3421 - acc: 0.9072 - val_loss: 0.3544 - val_acc: 0.9129\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3450 - acc: 0.9070 - val_loss: 0.3546 - val_acc: 0.9129\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3394 - acc: 0.9089 - val_loss: 0.3529 - val_acc: 0.9130\n",
-      "Epoch 210/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3492 - acc: 0.9059 - val_loss: 0.3545 - val_acc: 0.9130\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3455 - acc: 0.9068 - val_loss: 0.3543 - val_acc: 0.9129\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3464 - acc: 0.9073 - val_loss: 0.3538 - val_acc: 0.9133\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3447 - acc: 0.9067 - val_loss: 0.3537 - val_acc: 0.9136\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3458 - acc: 0.9064 - val_loss: 0.3564 - val_acc: 0.9132\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3503 - acc: 0.9051 - val_loss: 0.3545 - val_acc: 0.9141\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3438 - acc: 0.9045 - val_loss: 0.3542 - val_acc: 0.9129\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3457 - acc: 0.9066 - val_loss: 0.3537 - val_acc: 0.9135\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3424 - acc: 0.9074 - val_loss: 0.3548 - val_acc: 0.9132\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3528 - acc: 0.9035 - val_loss: 0.3546 - val_acc: 0.9127\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3428 - acc: 0.9066 - val_loss: 0.3552 - val_acc: 0.9130\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3465 - acc: 0.9078 - val_loss: 0.3540 - val_acc: 0.9131\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3475 - acc: 0.9046 - val_loss: 0.3535 - val_acc: 0.9133\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3500 - acc: 0.9031 - val_loss: 0.3548 - val_acc: 0.9126\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3434 - acc: 0.9074 - val_loss: 0.3535 - val_acc: 0.9128\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3490 - acc: 0.9055 - val_loss: 0.3539 - val_acc: 0.9129\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3395 - acc: 0.9078 - val_loss: 0.3538 - val_acc: 0.9128\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3440 - acc: 0.9076 - val_loss: 0.3531 - val_acc: 0.9129\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3424 - acc: 0.9075 - val_loss: 0.3537 - val_acc: 0.9130\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3439 - acc: 0.9086 - val_loss: 0.3539 - val_acc: 0.9120\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3475 - acc: 0.9047 - val_loss: 0.3524 - val_acc: 0.9124\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3366 - acc: 0.9087 - val_loss: 0.3533 - val_acc: 0.9128\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3450 - acc: 0.9072 - val_loss: 0.3535 - val_acc: 0.9127\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3454 - acc: 0.9062 - val_loss: 0.3536 - val_acc: 0.9131\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3420 - acc: 0.9076 - val_loss: 0.3534 - val_acc: 0.9134\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3473 - acc: 0.9059 - val_loss: 0.3530 - val_acc: 0.9127\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3404 - acc: 0.9078 - val_loss: 0.3533 - val_acc: 0.9125\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3433 - acc: 0.9066 - val_loss: 0.3526 - val_acc: 0.9130\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3413 - acc: 0.9084 - val_loss: 0.3523 - val_acc: 0.9137\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3371 - acc: 0.9084 - val_loss: 0.3528 - val_acc: 0.9135\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3409 - acc: 0.9073 - val_loss: 0.3522 - val_acc: 0.9135\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3405 - acc: 0.9081 - val_loss: 0.3531 - val_acc: 0.9130\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3371 - acc: 0.9082 - val_loss: 0.3538 - val_acc: 0.9136\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3472 - acc: 0.9062 - val_loss: 0.3519 - val_acc: 0.9135\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3375 - acc: 0.9084 - val_loss: 0.3533 - val_acc: 0.9130\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3408 - acc: 0.9088 - val_loss: 0.3549 - val_acc: 0.9130\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3400 - acc: 0.9076 - val_loss: 0.3528 - val_acc: 0.9129\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3383 - acc: 0.9073 - val_loss: 0.3532 - val_acc: 0.9134\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3399 - acc: 0.9073 - val_loss: 0.3541 - val_acc: 0.9132\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3405 - acc: 0.9086 - val_loss: 0.3534 - val_acc: 0.9132\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3421 - acc: 0.9075 - val_loss: 0.3538 - val_acc: 0.9128\n",
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-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"elu\", \"elu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('elu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "241/390 [=================>............] - ETA: 55s - loss: 2.2732 - acc: 0.3320"
-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"elu\", \"elu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('models/elu/elu_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9187\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(\"relu\")\n",
-    "        model.load_weights('relu_at_'+str(i+1)+'.h5')\n",
-    "        model.save('relu_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('relu_at_'+str(i+1)+'.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 2.0201 - acc: 0.3906 - val_loss: 1.3828 - val_acc: 0.5563\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.4731 - acc: 0.5308 - val_loss: 1.1149 - val_acc: 0.6337\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.2980 - acc: 0.5922 - val_loss: 1.0801 - val_acc: 0.6678\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 1.2151 - acc: 0.6298 - val_loss: 0.9754 - val_acc: 0.6856\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1390 - acc: 0.6510 - val_loss: 1.5834 - val_acc: 0.6358\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1029 - acc: 0.6706 - val_loss: 0.7973 - val_acc: 0.7567\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0241 - acc: 0.6928 - val_loss: 0.7856 - val_acc: 0.7628\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9956 - acc: 0.7041 - val_loss: 1.2215 - val_acc: 0.7283\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0520 - acc: 0.7052 - val_loss: 1.6160 - val_acc: 0.7280\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0199 - acc: 0.7127 - val_loss: 0.7939 - val_acc: 0.7845\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0037 - acc: 0.7170 - val_loss: 0.7445 - val_acc: 0.7803\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9561 - acc: 0.7259 - val_loss: 0.7351 - val_acc: 0.7896\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9214 - acc: 0.7366 - val_loss: 0.6897 - val_acc: 0.8018\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9240 - acc: 0.7409 - val_loss: 0.7333 - val_acc: 0.7836\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9455 - acc: 0.7394 - val_loss: 0.6761 - val_acc: 0.8085\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9299 - acc: 0.7462 - val_loss: 0.6588 - val_acc: 0.8135\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9478 - acc: 0.7482 - val_loss: 0.7012 - val_acc: 0.8151\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9236 - acc: 0.7535 - val_loss: 0.6909 - val_acc: 0.8094\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9234 - acc: 0.7549 - val_loss: 0.6782 - val_acc: 0.8189\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8722 - acc: 0.7605 - val_loss: 0.6582 - val_acc: 0.8214\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8591 - acc: 0.7642 - val_loss: 0.6625 - val_acc: 0.8172\n",
-      "Epoch 22/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8269 - acc: 0.7725 - val_loss: 0.6077 - val_acc: 0.8343\n",
-      "Epoch 23/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8260 - acc: 0.7745 - val_loss: 0.6269 - val_acc: 0.8280\n",
-      "Epoch 24/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8197 - acc: 0.7769 - val_loss: 0.6442 - val_acc: 0.8197\n",
-      "Epoch 25/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8047 - acc: 0.7790 - val_loss: 0.6241 - val_acc: 0.8293\n",
-      "Epoch 26/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8188 - acc: 0.7806 - val_loss: 0.6076 - val_acc: 0.8352\n",
-      "Epoch 27/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8053 - acc: 0.7823 - val_loss: 0.6519 - val_acc: 0.8270\n",
-      "Epoch 28/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8434 - acc: 0.7808 - val_loss: 0.6330 - val_acc: 0.8318\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7910 - acc: 0.7879 - val_loss: 0.6119 - val_acc: 0.8378\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7811 - acc: 0.7891 - val_loss: 0.5965 - val_acc: 0.8451\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7707 - acc: 0.7902 - val_loss: 0.6395 - val_acc: 0.8271\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7675 - acc: 0.7932 - val_loss: 0.5810 - val_acc: 0.8464\n",
-      "Epoch 33/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7828 - acc: 0.7923 - val_loss: 0.6336 - val_acc: 0.8310\n",
-      "Epoch 34/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7781 - acc: 0.7948 - val_loss: 0.5732 - val_acc: 0.8480\n",
-      "Epoch 35/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7599 - acc: 0.7993 - val_loss: 0.5962 - val_acc: 0.8421\n",
-      "Epoch 36/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7571 - acc: 0.7990 - val_loss: 0.6151 - val_acc: 0.8365\n",
-      "Epoch 37/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7565 - acc: 0.7981 - val_loss: 0.5864 - val_acc: 0.8464\n",
-      "Epoch 38/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7765 - acc: 0.7984 - val_loss: 0.6086 - val_acc: 0.8371\n",
-      "Epoch 39/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7530 - acc: 0.7973 - val_loss: 0.5760 - val_acc: 0.8520\n",
-      "Epoch 40/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7489 - acc: 0.8030 - val_loss: 0.6164 - val_acc: 0.8407\n",
-      "Epoch 41/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7573 - acc: 0.8010 - val_loss: 0.6014 - val_acc: 0.8493\n",
-      "Epoch 42/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7443 - acc: 0.8053 - val_loss: 0.5915 - val_acc: 0.8531\n",
-      "Epoch 43/200\n",
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-      "Epoch 87/200\n",
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-      "Epoch 88/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5667 - acc: 0.8520 - val_loss: 0.4694 - val_acc: 0.8878\n",
-      "Epoch 89/200\n",
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-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5565 - acc: 0.8538 - val_loss: 0.4680 - val_acc: 0.8862\n",
-      "Epoch 91/200\n",
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-      "Epoch 92/200\n"
-     ]
-    },
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-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5423 - acc: 0.8576 - val_loss: 0.4586 - val_acc: 0.8902\n",
-      "Epoch 93/200\n",
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-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5499 - acc: 0.8535 - val_loss: 0.4542 - val_acc: 0.8877\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5362 - acc: 0.8586 - val_loss: 0.4561 - val_acc: 0.8869\n",
-      "Epoch 96/200\n",
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-      "Epoch 97/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5360 - acc: 0.8568 - val_loss: 0.4597 - val_acc: 0.8866\n",
-      "Epoch 98/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5389 - acc: 0.8566 - val_loss: 0.4537 - val_acc: 0.8865\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5435 - acc: 0.8535 - val_loss: 0.4490 - val_acc: 0.8913\n",
-      "Epoch 100/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5295 - acc: 0.8574 - val_loss: 0.4588 - val_acc: 0.8837\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5126 - acc: 0.8649 - val_loss: 0.4349 - val_acc: 0.8947\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5037 - acc: 0.8662 - val_loss: 0.4344 - val_acc: 0.8927\n",
-      "Epoch 103/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5049 - acc: 0.8671 - val_loss: 0.4230 - val_acc: 0.8981\n",
-      "Epoch 104/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4946 - acc: 0.8680 - val_loss: 0.4370 - val_acc: 0.8915\n",
-      "Epoch 105/200\n",
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-      "Epoch 106/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4964 - acc: 0.8688 - val_loss: 0.4269 - val_acc: 0.8958\n",
-      "Epoch 107/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4917 - acc: 0.8703 - val_loss: 0.4228 - val_acc: 0.8984\n",
-      "Epoch 108/200\n",
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-      "Epoch 111/200\n",
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-      "Epoch 112/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.4821 - acc: 0.8717 - val_loss: 0.4369 - val_acc: 0.8953\n",
-      "Epoch 113/200\n",
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-      "Epoch 114/200\n",
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-      "Epoch 123/200\n",
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-      "Epoch 124/200\n",
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-      "Epoch 126/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4521 - acc: 0.8776 - val_loss: 0.4075 - val_acc: 0.8980\n",
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-      "Epoch 128/200\n",
-      "390/390 [==============================] - 181s 464ms/step - loss: 0.4431 - acc: 0.8817 - val_loss: 0.4046 - val_acc: 0.9000\n",
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-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4360 - acc: 0.8839 - val_loss: 0.3950 - val_acc: 0.9007\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4390 - acc: 0.8811 - val_loss: 0.3987 - val_acc: 0.9011\n",
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-      "390/390 [==============================] - 178s 457ms/step - loss: 0.4398 - acc: 0.8813 - val_loss: 0.3989 - val_acc: 0.8995\n",
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-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4388 - acc: 0.8825 - val_loss: 0.4080 - val_acc: 0.8963\n",
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-      "Epoch 135/200\n",
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-      "390/390 [==============================] - 177s 454ms/step - loss: 0.4277 - acc: 0.8842 - val_loss: 0.3929 - val_acc: 0.9014\n",
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-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4341 - acc: 0.8825 - val_loss: 0.4060 - val_acc: 0.9006\n",
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-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4285 - acc: 0.8839 - val_loss: 0.3915 - val_acc: 0.9013\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4296 - acc: 0.8833 - val_loss: 0.4029 - val_acc: 0.9015\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4305 - acc: 0.8839 - val_loss: 0.4011 - val_acc: 0.9009\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4307 - acc: 0.8837 - val_loss: 0.3954 - val_acc: 0.9019\n",
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-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4214 - acc: 0.8852 - val_loss: 0.3977 - val_acc: 0.9002\n",
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-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4228 - acc: 0.8851 - val_loss: 0.3914 - val_acc: 0.9003\n",
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-      "Epoch 151/200\n"
-     ]
-    },
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-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4115 - acc: 0.8871 - val_loss: 0.3882 - val_acc: 0.9029\n",
-      "Epoch 152/200\n",
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-      "Epoch 155/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4095 - acc: 0.8899 - val_loss: 0.3948 - val_acc: 0.9024\n",
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-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4084 - acc: 0.8893 - val_loss: 0.3909 - val_acc: 0.9042\n",
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-      "Epoch 158/200\n",
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-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4055 - acc: 0.8913 - val_loss: 0.3808 - val_acc: 0.9062\n",
-      "Epoch 162/200\n",
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-      "Epoch 163/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8910 - val_loss: 0.3853 - val_acc: 0.9044\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4027 - acc: 0.8910 - val_loss: 0.3823 - val_acc: 0.9061\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3982 - acc: 0.8928 - val_loss: 0.3831 - val_acc: 0.9062\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8903 - val_loss: 0.3881 - val_acc: 0.9056\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3985 - acc: 0.8912 - val_loss: 0.3836 - val_acc: 0.9070\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3989 - acc: 0.8925 - val_loss: 0.3874 - val_acc: 0.9035\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4048 - acc: 0.8895 - val_loss: 0.3850 - val_acc: 0.9034\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3951 - acc: 0.8937 - val_loss: 0.3849 - val_acc: 0.9045\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3978 - acc: 0.8920 - val_loss: 0.3835 - val_acc: 0.9052\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3997 - acc: 0.8901 - val_loss: 0.3865 - val_acc: 0.9044\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3989 - acc: 0.8913 - val_loss: 0.3794 - val_acc: 0.9049\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3992 - acc: 0.8915 - val_loss: 0.3835 - val_acc: 0.9040\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3927 - acc: 0.8924 - val_loss: 0.3854 - val_acc: 0.9039\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3928 - acc: 0.8938 - val_loss: 0.3804 - val_acc: 0.9056\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3919 - acc: 0.8931 - val_loss: 0.3817 - val_acc: 0.9057\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3892 - acc: 0.8952 - val_loss: 0.3801 - val_acc: 0.9065\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3919 - acc: 0.8941 - val_loss: 0.3808 - val_acc: 0.9056\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3892 - acc: 0.8936 - val_loss: 0.3816 - val_acc: 0.9055\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.3855 - acc: 0.8959 - val_loss: 0.3810 - val_acc: 0.9069\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3895 - acc: 0.8943 - val_loss: 0.3812 - val_acc: 0.9051\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3922 - acc: 0.8943 - val_loss: 0.3776 - val_acc: 0.9068\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3883 - acc: 0.8951 - val_loss: 0.3796 - val_acc: 0.9074\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 180s 461ms/step - loss: 0.3942 - acc: 0.8928 - val_loss: 0.3794 - val_acc: 0.9056\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 179s 459ms/step - loss: 0.3870 - acc: 0.8960 - val_loss: 0.3777 - val_acc: 0.9072\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3899 - acc: 0.8941 - val_loss: 0.3780 - val_acc: 0.9066\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 178s 456ms/step - loss: 0.3853 - acc: 0.8971 - val_loss: 0.3778 - val_acc: 0.9066\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 178s 458ms/step - loss: 0.3878 - acc: 0.8958 - val_loss: 0.3792 - val_acc: 0.9076\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3873 - acc: 0.8956 - val_loss: 0.3781 - val_acc: 0.9085\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3876 - acc: 0.8948 - val_loss: 0.3769 - val_acc: 0.9078\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3837 - acc: 0.8973 - val_loss: 0.3768 - val_acc: 0.9075\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3876 - acc: 0.8946 - val_loss: 0.3755 - val_acc: 0.9085\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3926 - acc: 0.8941 - val_loss: 0.3756 - val_acc: 0.9087\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3839 - acc: 0.8957 - val_loss: 0.3782 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3867 - acc: 0.8934 - val_loss: 0.3760 - val_acc: 0.9079\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3884 - acc: 0.8948 - val_loss: 0.3788 - val_acc: 0.9064\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3857 - acc: 0.8943 - val_loss: 0.3773 - val_acc: 0.9082\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3882 - acc: 0.8937 - val_loss: 0.3750 - val_acc: 0.9078\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.3839 - acc: 0.8945 - val_loss: 0.3787 - val_acc: 0.9079\n",
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0.37935949974060057, 0.38349194710254669, 0.38540293841362, 0.38042077803611757, 0.38166472558975217, 0.38014628505706788, 0.38078072862625123, 0.38159099376201627, 0.38099342994689939, 0.38124294857978819, 0.37758006939888, 0.37961545908451078, 0.37935590014457704, 0.3776896659374237, 0.37800100386142732, 0.37784726963043214, 0.37919219954013822, 0.37809970812797544, 0.37685366349220278, 0.37684274215698244, 0.37554384021759035, 0.37560194835662841, 0.37815707368850709, 0.37599482469558715, 0.37884682497978212, 0.37729874968528748, 0.37496673526763918, 0.37871543755531312], 'acc': [0.39059993583573949, 0.53091915301263892, 0.59221607316637936, 0.62987247357048737, 0.65096647413564024, 0.67065688157869452, 0.69281360284234994, 0.7040623997050981, 0.70522537694590803, 0.71272457493089658, 0.71711581647109246, 0.72589829964709651, 0.73664581332024681, 0.7409167468528699, 0.73947305104883199, 0.74629050370856742, 0.74821543148552949, 0.75348893170330744, 0.75489252488277336, 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0.89125722545266683, 0.89230429900545394, 0.89100096244478522, 0.89108116782778612, 0.89276547962784725, 0.89037937115200216, 0.89132178378556448, 0.89246470963759872, 0.8894369586331744, 0.89372996794871795, 0.89201991004521219, 0.8900841346153846, 0.89127729615877282, 0.89152644230769229, 0.89234104044328688, 0.89383012820512819, 0.89312640363144347, 0.89525128454065661, 0.89409054487179485, 0.89360549132947975, 0.89591346153846152, 0.89434953480911128, 0.89426932944523285, 0.89509143403297742, 0.89268224145176323, 0.89601362179487176, 0.89410725750828213, 0.89719682384998245, 0.89575312796945483, 0.8955929487179487, 0.89474951832357241, 0.89725560897435896, 0.89458895307668296, 0.89405048076923077, 0.89571290942209225, 0.89348732759679472, 0.89475160256410258, 0.89432948347770291, 0.89376804621739003, 0.89452874109235114]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"relu\", \"relu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - \n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    model = load_model('relu_at_+'str(i)'+.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar10_deeper_cnn_leaky-checkpoint.ipynb b/cifar/Deeper_CNN/.ipynb_checkpoints/cifar10_deeper_cnn_leaky-checkpoint.ipynb
deleted file mode 100644
index 5e91459..0000000
--- a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar10_deeper_cnn_leaky-checkpoint.ipynb
+++ /dev/null
@@ -1,1534 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "import keras.regularizers as regularizers\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def leaky(x):\n",
-    "    return K.maximum(0.3*x, x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "390/390 [==============================] - 160s 411ms/step - loss: 2.0497 - acc: 0.3803 - val_loss: 1.5434 - val_acc: 0.5072\n",
-      "Epoch 2/250\n",
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-      "390/390 [==============================] - 158s 404ms/step - loss: 1.1404 - acc: 0.6505 - val_loss: 0.9167 - val_acc: 0.7123\n",
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-      "390/390 [==============================] - 154s 396ms/step - loss: 0.9539 - acc: 0.7103 - val_loss: 0.7404 - val_acc: 0.7859\n",
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-      "Epoch 18/250\n",
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-      "Epoch 20/250\n",
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-      "Epoch 21/250\n",
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-      "Epoch 22/250\n",
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-      "Epoch 23/250\n",
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-      "Epoch 24/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.8064 - acc: 0.7531 - val_loss: 0.6624 - val_acc: 0.8098\n",
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-      "Epoch 26/250\n",
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-      "Epoch 28/250\n",
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-      "Epoch 29/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7733 - acc: 0.7653 - val_loss: 0.5992 - val_acc: 0.8281\n",
-      "Epoch 30/250\n",
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-      "Epoch 31/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7620 - acc: 0.7694 - val_loss: 0.6001 - val_acc: 0.8287\n",
-      "Epoch 32/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7569 - acc: 0.7742 - val_loss: 0.5742 - val_acc: 0.8391\n",
-      "Epoch 33/250\n"
-     ]
-    },
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-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7549 - acc: 0.7730 - val_loss: 0.6329 - val_acc: 0.8161\n",
-      "Epoch 34/250\n",
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-      "Epoch 35/250\n",
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-      "Epoch 36/250\n",
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-      "Epoch 37/250\n",
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-      "Epoch 38/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7299 - acc: 0.7807 - val_loss: 0.5777 - val_acc: 0.8365\n",
-      "Epoch 39/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7374 - acc: 0.7797 - val_loss: 0.5976 - val_acc: 0.8302\n",
-      "Epoch 40/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7291 - acc: 0.7819 - val_loss: 0.5783 - val_acc: 0.8350\n",
-      "Epoch 41/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7284 - acc: 0.7834 - val_loss: 0.5807 - val_acc: 0.8346\n",
-      "Epoch 42/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7224 - acc: 0.7850 - val_loss: 0.5847 - val_acc: 0.8347\n",
-      "Epoch 43/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7256 - acc: 0.7839 - val_loss: 0.5669 - val_acc: 0.8382\n",
-      "Epoch 44/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7215 - acc: 0.7846 - val_loss: 0.5972 - val_acc: 0.8302\n",
-      "Epoch 45/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7183 - acc: 0.7882 - val_loss: 0.5575 - val_acc: 0.8439\n",
-      "Epoch 46/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7098 - acc: 0.7892 - val_loss: 0.6131 - val_acc: 0.8286\n",
-      "Epoch 47/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7129 - acc: 0.7903 - val_loss: 0.5823 - val_acc: 0.8370\n",
-      "Epoch 48/250\n",
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-      "Epoch 49/250\n",
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-      "Epoch 50/250\n",
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-      "Epoch 51/250\n",
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-      "Epoch 52/250\n",
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-      "Epoch 53/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6999 - acc: 0.7948 - val_loss: 0.5853 - val_acc: 0.8342\n",
-      "Epoch 54/250\n",
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-      "Epoch 55/250\n",
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-      "Epoch 56/250\n",
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-      "Epoch 63/250\n",
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-      "Epoch 64/250\n",
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-      "Epoch 65/250\n",
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-      "Epoch 81/250\n",
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-      "Epoch 85/250\n",
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-      "Epoch 89/250\n",
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-      "Epoch 91/250\n",
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-      "Epoch 92/250\n"
-     ]
-    },
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-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5747 - acc: 0.8312 - val_loss: 0.4679 - val_acc: 0.8708\n",
-      "Epoch 93/250\n",
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-      "Epoch 95/250\n",
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-      "Epoch 96/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5691 - acc: 0.8330 - val_loss: 0.4679 - val_acc: 0.8702\n",
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-      "Epoch 98/250\n",
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-      "Epoch 99/250\n",
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-      "Epoch 101/250\n",
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-      "Epoch 103/250\n",
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-      "Epoch 104/250\n",
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-      "Epoch 105/250\n",
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-      "Epoch 106/250\n",
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-      "Epoch 107/250\n",
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-      "Epoch 108/250\n",
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-      "Epoch 111/250\n",
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-      "Epoch 112/250\n",
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-      "Epoch 113/250\n",
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-      "Epoch 114/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5144 - acc: 0.8496 - val_loss: 0.4336 - val_acc: 0.8832\n",
-      "Epoch 115/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5250 - acc: 0.8467 - val_loss: 0.4232 - val_acc: 0.8832\n",
-      "Epoch 116/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.5100 - acc: 0.8510 - val_loss: 0.4285 - val_acc: 0.8799\n",
-      "Epoch 117/250\n",
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-      "Epoch 118/250\n",
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-      "Epoch 119/250\n",
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-      "Epoch 120/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5160 - acc: 0.8490 - val_loss: 0.4122 - val_acc: 0.8845\n",
-      "Epoch 121/250\n",
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-      "Epoch 122/250\n",
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-      "Epoch 123/250\n",
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-      "Epoch 144/250\n",
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-      "Epoch 147/250\n",
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-      "Epoch 148/250\n",
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-      "Epoch 150/250\n",
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-      "Epoch 151/250\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4586 - acc: 0.8655 - val_loss: 0.3817 - val_acc: 0.8969\n",
-      "Epoch 152/250\n",
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-      "Epoch 153/250\n",
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-      "Epoch 154/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4518 - acc: 0.8685 - val_loss: 0.3760 - val_acc: 0.8980\n",
-      "Epoch 155/250\n",
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-      "Epoch 156/250\n",
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-      "Epoch 157/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4478 - acc: 0.8706 - val_loss: 0.3755 - val_acc: 0.8989\n",
-      "Epoch 158/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4446 - acc: 0.8705 - val_loss: 0.3769 - val_acc: 0.8982\n",
-      "Epoch 159/250\n",
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-      "Epoch 160/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4476 - acc: 0.8693 - val_loss: 0.3771 - val_acc: 0.8990\n",
-      "Epoch 161/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4427 - acc: 0.8704 - val_loss: 0.3721 - val_acc: 0.8991\n",
-      "Epoch 162/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4460 - acc: 0.8706 - val_loss: 0.3780 - val_acc: 0.8976\n",
-      "Epoch 163/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4436 - acc: 0.8711 - val_loss: 0.3802 - val_acc: 0.8969\n",
-      "Epoch 164/250\n",
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-      "Epoch 165/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4442 - acc: 0.8696 - val_loss: 0.3760 - val_acc: 0.8965\n",
-      "Epoch 166/250\n",
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-      "Epoch 167/250\n",
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-      "Epoch 168/250\n",
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-      "Epoch 170/250\n",
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-      "Epoch 171/250\n",
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-      "Epoch 172/250\n",
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-      "Epoch 173/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4438 - acc: 0.8710 - val_loss: 0.3753 - val_acc: 0.8989\n",
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-      "Epoch 181/250\n",
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-      "Epoch 182/250\n",
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-      "Epoch 183/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4260 - acc: 0.8765 - val_loss: 0.3721 - val_acc: 0.8990\n",
-      "Epoch 184/250\n",
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-      "Epoch 185/250\n",
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-      "Epoch 186/250\n",
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-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4346 - acc: 0.8747 - val_loss: 0.3759 - val_acc: 0.8986\n",
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-      "Epoch 189/250\n",
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-      "Epoch 198/250\n",
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-      "Epoch 199/250\n",
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-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4273 - acc: 0.8751 - val_loss: 0.3668 - val_acc: 0.9025\n",
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-      "390/390 [==============================] - 157s 401ms/step - loss: 0.4217 - acc: 0.8764 - val_loss: 0.3680 - val_acc: 0.9023\n",
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-      "390/390 [==============================] - 156s 400ms/step - loss: 0.4236 - acc: 0.8761 - val_loss: 0.3667 - val_acc: 0.9018\n",
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-      "390/390 [==============================] - 156s 399ms/step - loss: 0.4253 - acc: 0.8764 - val_loss: 0.3664 - val_acc: 0.9013\n",
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-      "Epoch 210/250\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4194 - acc: 0.8785 - val_loss: 0.3655 - val_acc: 0.9033\n",
-      "Epoch 211/250\n",
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-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4196 - acc: 0.8778 - val_loss: 0.3645 - val_acc: 0.9022\n",
-      "Epoch 213/250\n",
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-      "Epoch 214/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4190 - acc: 0.8764 - val_loss: 0.3657 - val_acc: 0.9009\n",
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-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4227 - acc: 0.8781 - val_loss: 0.3676 - val_acc: 0.9025\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4229 - acc: 0.8768 - val_loss: 0.3646 - val_acc: 0.9020\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4231 - acc: 0.8765 - val_loss: 0.3667 - val_acc: 0.9016\n",
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-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4169 - acc: 0.8784 - val_loss: 0.3659 - val_acc: 0.9037\n",
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-      "Epoch 221/250\n",
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-      "Epoch 222/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4208 - acc: 0.8762 - val_loss: 0.3650 - val_acc: 0.9012\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4188 - acc: 0.8785 - val_loss: 0.3650 - val_acc: 0.9010\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4206 - acc: 0.8769 - val_loss: 0.3642 - val_acc: 0.9022\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4205 - acc: 0.8748 - val_loss: 0.3668 - val_acc: 0.9015\n",
-      "Epoch 226/250\n",
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-      "Epoch 227/250\n",
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-      "Epoch 228/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4146 - acc: 0.8783 - val_loss: 0.3676 - val_acc: 0.9019\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4170 - acc: 0.8766 - val_loss: 0.3669 - val_acc: 0.9019\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4195 - acc: 0.8782 - val_loss: 0.3663 - val_acc: 0.9020\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4146 - acc: 0.8785 - val_loss: 0.3658 - val_acc: 0.9014\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4188 - acc: 0.8797 - val_loss: 0.3658 - val_acc: 0.9026\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4184 - acc: 0.8780 - val_loss: 0.3656 - val_acc: 0.9027\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4215 - acc: 0.8773 - val_loss: 0.3656 - val_acc: 0.9011\n",
-      "Epoch 235/250\n",
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-      "Epoch 236/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4128 - acc: 0.8797 - val_loss: 0.3658 - val_acc: 0.9013\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4186 - acc: 0.8783 - val_loss: 0.3660 - val_acc: 0.9012\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4155 - acc: 0.8792 - val_loss: 0.3656 - val_acc: 0.9015\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4203 - acc: 0.8767 - val_loss: 0.3668 - val_acc: 0.9011\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4194 - acc: 0.8783 - val_loss: 0.3657 - val_acc: 0.9011\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4211 - acc: 0.8770 - val_loss: 0.3655 - val_acc: 0.9018\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4116 - acc: 0.8804 - val_loss: 0.3649 - val_acc: 0.9025\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4201 - acc: 0.8775 - val_loss: 0.3655 - val_acc: 0.9025\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4179 - acc: 0.8777 - val_loss: 0.3649 - val_acc: 0.9017\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4219 - acc: 0.8759 - val_loss: 0.3649 - val_acc: 0.9017\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4117 - acc: 0.8801 - val_loss: 0.3654 - val_acc: 0.9023\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4194 - acc: 0.8772 - val_loss: 0.3641 - val_acc: 0.9034\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4160 - acc: 0.8783 - val_loss: 0.3630 - val_acc: 0.9038\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4231 - acc: 0.8750 - val_loss: 0.3637 - val_acc: 0.9022\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4183 - acc: 0.8786 - val_loss: 0.3645 - val_acc: 0.9013\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = leaky, \"leaky\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('models/leaky_relu/leaky_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "390/390 [==============================] - 158s 405ms/step - loss: 2.0187 - acc: 0.3877 - val_loss: 1.6984 - val_acc: 0.4242\n",
-      "Epoch 2/250\n",
-      "390/390 [==============================] - 155s 398ms/step - loss: 1.4728 - acc: 0.5273 - val_loss: 1.1026 - val_acc: 0.6552\n",
-      "Epoch 3/250\n",
-      "390/390 [==============================] - 155s 398ms/step - loss: 1.3274 - acc: 0.5788 - val_loss: 1.0162 - val_acc: 0.6786\n",
-      "Epoch 4/250\n",
-      "390/390 [==============================] - 156s 399ms/step - loss: 1.2513 - acc: 0.6113 - val_loss: 0.9912 - val_acc: 0.6954\n",
-      "Epoch 5/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 1.1921 - acc: 0.6330 - val_loss: 0.9877 - val_acc: 0.7016\n",
-      "Epoch 6/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 1.1351 - acc: 0.6514 - val_loss: 0.9224 - val_acc: 0.7261\n",
-      "Epoch 7/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 1.1040 - acc: 0.6649 - val_loss: 0.8577 - val_acc: 0.7413\n",
-      "Epoch 8/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 1.0846 - acc: 0.6734 - val_loss: 0.8276 - val_acc: 0.7465\n",
-      "Epoch 9/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 1.0717 - acc: 0.6793 - val_loss: 0.7919 - val_acc: 0.7667\n",
-      "Epoch 10/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 1.0318 - acc: 0.6882 - val_loss: 0.8241 - val_acc: 0.7489\n",
-      "Epoch 11/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 1.0091 - acc: 0.6976 - val_loss: 0.7637 - val_acc: 0.7735\n",
-      "Epoch 12/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 1.0045 - acc: 0.7013 - val_loss: 0.7834 - val_acc: 0.7789\n",
-      "Epoch 13/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9775 - acc: 0.7078 - val_loss: 0.7633 - val_acc: 0.7755\n",
-      "Epoch 14/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9667 - acc: 0.7118 - val_loss: 0.7498 - val_acc: 0.7761\n",
-      "Epoch 15/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9531 - acc: 0.7171 - val_loss: 0.7334 - val_acc: 0.7829\n",
-      "Epoch 16/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9309 - acc: 0.7235 - val_loss: 0.7671 - val_acc: 0.7907\n",
-      "Epoch 17/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9220 - acc: 0.7249 - val_loss: 0.7211 - val_acc: 0.7929\n",
-      "Epoch 18/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9041 - acc: 0.7303 - val_loss: 0.7120 - val_acc: 0.7940\n",
-      "Epoch 19/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9014 - acc: 0.7297 - val_loss: 0.7206 - val_acc: 0.7921\n",
-      "Epoch 20/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8709 - acc: 0.7373 - val_loss: 0.6885 - val_acc: 0.8060\n",
-      "Epoch 21/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8559 - acc: 0.7386 - val_loss: 0.6510 - val_acc: 0.8108\n",
-      "Epoch 22/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8403 - acc: 0.7442 - val_loss: 0.6884 - val_acc: 0.8009\n",
-      "Epoch 23/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8309 - acc: 0.7474 - val_loss: 0.6261 - val_acc: 0.8188\n",
-      "Epoch 24/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8249 - acc: 0.7464 - val_loss: 0.6359 - val_acc: 0.8186\n",
-      "Epoch 25/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8077 - acc: 0.7553 - val_loss: 0.6508 - val_acc: 0.8112\n",
-      "Epoch 26/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7998 - acc: 0.7557 - val_loss: 0.5940 - val_acc: 0.8323\n",
-      "Epoch 27/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7990 - acc: 0.7580 - val_loss: 0.6385 - val_acc: 0.8179\n",
-      "Epoch 28/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7833 - acc: 0.7616 - val_loss: 0.6023 - val_acc: 0.8304\n",
-      "Epoch 29/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7806 - acc: 0.7629 - val_loss: 0.6287 - val_acc: 0.8172\n",
-      "Epoch 30/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7735 - acc: 0.7659 - val_loss: 0.6216 - val_acc: 0.8211\n",
-      "Epoch 31/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7695 - acc: 0.7668 - val_loss: 0.6119 - val_acc: 0.8238\n",
-      "Epoch 32/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7598 - acc: 0.7697 - val_loss: 0.5801 - val_acc: 0.8383\n",
-      "Epoch 33/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7559 - acc: 0.7708 - val_loss: 0.6157 - val_acc: 0.8208\n",
-      "Epoch 34/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7564 - acc: 0.7719 - val_loss: 0.5777 - val_acc: 0.8366\n",
-      "Epoch 35/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7415 - acc: 0.7770 - val_loss: 0.5707 - val_acc: 0.8409\n",
-      "Epoch 36/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7410 - acc: 0.7778 - val_loss: 0.5970 - val_acc: 0.8287\n",
-      "Epoch 37/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7365 - acc: 0.7777 - val_loss: 0.6265 - val_acc: 0.8236\n",
-      "Epoch 38/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7386 - acc: 0.7800 - val_loss: 0.6287 - val_acc: 0.8206\n",
-      "Epoch 39/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7345 - acc: 0.7794 - val_loss: 0.6056 - val_acc: 0.8251\n",
-      "Epoch 40/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7334 - acc: 0.7794 - val_loss: 0.5592 - val_acc: 0.8403\n",
-      "Epoch 41/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7323 - acc: 0.7794 - val_loss: 0.6101 - val_acc: 0.8220\n",
-      "Epoch 42/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7238 - acc: 0.7826 - val_loss: 0.5687 - val_acc: 0.8396\n",
-      "Epoch 43/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7235 - acc: 0.7849 - val_loss: 0.5998 - val_acc: 0.8275\n",
-      "Epoch 44/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7207 - acc: 0.7847 - val_loss: 0.5663 - val_acc: 0.8386\n",
-      "Epoch 45/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7185 - acc: 0.7866 - val_loss: 0.5845 - val_acc: 0.8363\n",
-      "Epoch 46/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7163 - acc: 0.7861 - val_loss: 0.5689 - val_acc: 0.8395\n",
-      "Epoch 47/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7154 - acc: 0.7882 - val_loss: 0.5389 - val_acc: 0.8545\n",
-      "Epoch 48/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7077 - acc: 0.7898 - val_loss: 0.5888 - val_acc: 0.8328\n",
-      "Epoch 49/250\n",
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-      "Epoch 89/250\n",
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-      "Epoch 91/250\n",
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-      "Epoch 92/250\n"
-     ]
-    },
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-     "text": [
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-      "Epoch 96/250\n",
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-      "Epoch 98/250\n",
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-      "Epoch 99/250\n",
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-      "Epoch 101/250\n",
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-      "Epoch 102/250\n",
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-      "Epoch 103/250\n",
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-      "Epoch 104/250\n",
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-      "Epoch 107/250\n",
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-      "Epoch 111/250\n",
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-      "Epoch 113/250\n",
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-      "Epoch 114/250\n",
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-      "Epoch 139/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4691 - acc: 0.8638 - val_loss: 0.3870 - val_acc: 0.8955\n",
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-      "Epoch 151/250\n"
-     ]
-    },
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-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4508 - acc: 0.8674 - val_loss: 0.3747 - val_acc: 0.8983\n",
-      "Epoch 152/250\n",
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-      "Epoch 154/250\n",
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-      "Epoch 155/250\n",
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-      "Epoch 162/250\n",
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-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4446 - acc: 0.8696 - val_loss: 0.3705 - val_acc: 0.8993\n",
-      "Epoch 170/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4371 - acc: 0.8723 - val_loss: 0.3727 - val_acc: 0.8996\n",
-      "Epoch 171/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4399 - acc: 0.8705 - val_loss: 0.3696 - val_acc: 0.8983\n",
-      "Epoch 172/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4431 - acc: 0.8718 - val_loss: 0.3706 - val_acc: 0.8995\n",
-      "Epoch 173/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4406 - acc: 0.8699 - val_loss: 0.3718 - val_acc: 0.9002\n",
-      "Epoch 174/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4363 - acc: 0.8748 - val_loss: 0.3708 - val_acc: 0.8987\n",
-      "Epoch 175/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4431 - acc: 0.8698 - val_loss: 0.3684 - val_acc: 0.9018\n",
-      "Epoch 176/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4340 - acc: 0.8736 - val_loss: 0.3629 - val_acc: 0.9028\n",
-      "Epoch 177/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4333 - acc: 0.8743 - val_loss: 0.3659 - val_acc: 0.9018\n",
-      "Epoch 178/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4313 - acc: 0.8731 - val_loss: 0.3638 - val_acc: 0.9013\n",
-      "Epoch 179/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4285 - acc: 0.8745 - val_loss: 0.3638 - val_acc: 0.9016\n",
-      "Epoch 180/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4359 - acc: 0.8729 - val_loss: 0.3668 - val_acc: 0.8998\n",
-      "Epoch 181/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4301 - acc: 0.8775 - val_loss: 0.3630 - val_acc: 0.9008\n",
-      "Epoch 182/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4323 - acc: 0.8723 - val_loss: 0.3651 - val_acc: 0.9016\n",
-      "Epoch 183/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4235 - acc: 0.8757 - val_loss: 0.3654 - val_acc: 0.9017\n",
-      "Epoch 184/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4366 - acc: 0.8735 - val_loss: 0.3641 - val_acc: 0.9015\n",
-      "Epoch 185/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4233 - acc: 0.8770 - val_loss: 0.3638 - val_acc: 0.9016\n",
-      "Epoch 186/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4293 - acc: 0.8755 - val_loss: 0.3637 - val_acc: 0.9006\n",
-      "Epoch 187/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4299 - acc: 0.8730 - val_loss: 0.3661 - val_acc: 0.9003\n",
-      "Epoch 188/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4300 - acc: 0.8730 - val_loss: 0.3629 - val_acc: 0.9020\n",
-      "Epoch 189/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4273 - acc: 0.8756 - val_loss: 0.3606 - val_acc: 0.9022\n",
-      "Epoch 190/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4270 - acc: 0.8752 - val_loss: 0.3617 - val_acc: 0.9025\n",
-      "Epoch 191/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4269 - acc: 0.8765 - val_loss: 0.3629 - val_acc: 0.9008\n",
-      "Epoch 192/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4243 - acc: 0.8764 - val_loss: 0.3652 - val_acc: 0.9006\n",
-      "Epoch 193/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4301 - acc: 0.8764 - val_loss: 0.3647 - val_acc: 0.9016\n",
-      "Epoch 194/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4267 - acc: 0.8763 - val_loss: 0.3645 - val_acc: 0.9022\n",
-      "Epoch 195/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4189 - acc: 0.8788 - val_loss: 0.3646 - val_acc: 0.9010\n",
-      "Epoch 196/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4214 - acc: 0.8784 - val_loss: 0.3616 - val_acc: 0.9032\n",
-      "Epoch 197/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4298 - acc: 0.8745 - val_loss: 0.3649 - val_acc: 0.9007\n",
-      "Epoch 198/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4275 - acc: 0.8737 - val_loss: 0.3647 - val_acc: 0.9019\n",
-      "Epoch 199/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4266 - acc: 0.8759 - val_loss: 0.3628 - val_acc: 0.9020\n",
-      "Epoch 200/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4250 - acc: 0.8770 - val_loss: 0.3624 - val_acc: 0.9003\n",
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4267 - acc: 0.8755 - val_loss: 0.3625 - val_acc: 0.9021\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4228 - acc: 0.8771 - val_loss: 0.3623 - val_acc: 0.9031\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4228 - acc: 0.8753 - val_loss: 0.3599 - val_acc: 0.9021\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4209 - acc: 0.8764 - val_loss: 0.3619 - val_acc: 0.9029\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4260 - acc: 0.8760 - val_loss: 0.3597 - val_acc: 0.9030\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4255 - acc: 0.8763 - val_loss: 0.3605 - val_acc: 0.9030\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4246 - acc: 0.8737 - val_loss: 0.3609 - val_acc: 0.9034\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4227 - acc: 0.8758 - val_loss: 0.3623 - val_acc: 0.9029\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4202 - acc: 0.8783 - val_loss: 0.3602 - val_acc: 0.9028\n",
-      "Epoch 210/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4192 - acc: 0.8769 - val_loss: 0.3605 - val_acc: 0.9026\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4259 - acc: 0.8754 - val_loss: 0.3601 - val_acc: 0.9043\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4221 - acc: 0.8769 - val_loss: 0.3610 - val_acc: 0.9035\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4216 - acc: 0.8767 - val_loss: 0.3593 - val_acc: 0.9041\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4192 - acc: 0.8782 - val_loss: 0.3621 - val_acc: 0.9037\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4226 - acc: 0.8766 - val_loss: 0.3615 - val_acc: 0.9028\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4167 - acc: 0.8776 - val_loss: 0.3610 - val_acc: 0.9020\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4214 - acc: 0.8763 - val_loss: 0.3619 - val_acc: 0.9017\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4186 - acc: 0.8772 - val_loss: 0.3593 - val_acc: 0.9037\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4206 - acc: 0.8757 - val_loss: 0.3622 - val_acc: 0.9026\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4214 - acc: 0.8755 - val_loss: 0.3602 - val_acc: 0.9019\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4148 - acc: 0.8795 - val_loss: 0.3605 - val_acc: 0.9033\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4223 - acc: 0.8751 - val_loss: 0.3587 - val_acc: 0.9031\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4173 - acc: 0.8779 - val_loss: 0.3598 - val_acc: 0.9030\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4185 - acc: 0.8767 - val_loss: 0.3573 - val_acc: 0.9047\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4214 - acc: 0.8768 - val_loss: 0.3585 - val_acc: 0.9035\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4181 - acc: 0.8769 - val_loss: 0.3583 - val_acc: 0.9034\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4141 - acc: 0.8804 - val_loss: 0.3583 - val_acc: 0.9021\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4136 - acc: 0.8809 - val_loss: 0.3588 - val_acc: 0.9035\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4133 - acc: 0.8782 - val_loss: 0.3595 - val_acc: 0.9032\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4223 - acc: 0.8776 - val_loss: 0.3595 - val_acc: 0.9036\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 153s 394ms/step - loss: 0.4159 - acc: 0.8796 - val_loss: 0.3593 - val_acc: 0.9020\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4213 - acc: 0.8769 - val_loss: 0.3590 - val_acc: 0.9032\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4140 - acc: 0.8785 - val_loss: 0.3591 - val_acc: 0.9030\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4160 - acc: 0.8787 - val_loss: 0.3588 - val_acc: 0.9033\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4162 - acc: 0.8797 - val_loss: 0.3582 - val_acc: 0.9028\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4176 - acc: 0.8759 - val_loss: 0.3595 - val_acc: 0.9023\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4183 - acc: 0.8766 - val_loss: 0.3578 - val_acc: 0.9032\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4218 - acc: 0.8763 - val_loss: 0.3598 - val_acc: 0.9023\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4122 - acc: 0.8801 - val_loss: 0.3581 - val_acc: 0.9029\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 0.4158 - acc: 0.8780 - val_loss: 0.3574 - val_acc: 0.9036\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 0.4185 - acc: 0.8769 - val_loss: 0.3578 - val_acc: 0.9017\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 0.4185 - acc: 0.8777 - val_loss: 0.3583 - val_acc: 0.9038\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 155s 396ms/step - loss: 0.4181 - acc: 0.8785 - val_loss: 0.3591 - val_acc: 0.9035\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4131 - acc: 0.8802 - val_loss: 0.3593 - val_acc: 0.9032\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4169 - acc: 0.8781 - val_loss: 0.3590 - val_acc: 0.9036\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4229 - acc: 0.8756 - val_loss: 0.3575 - val_acc: 0.9037\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4151 - acc: 0.8804 - val_loss: 0.3577 - val_acc: 0.9033\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4190 - acc: 0.8774 - val_loss: 0.3573 - val_acc: 0.9033\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4117 - acc: 0.8800 - val_loss: 0.3579 - val_acc: 0.9040\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4163 - acc: 0.8794 - val_loss: 0.3575 - val_acc: 0.9041\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = leaky, \"leaky\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('models/leaky_relu/leaky_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_25 (Conv2D)           (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_25 (Activation)   (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_25 (Batc (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_26 (Conv2D)           (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_26 (Activation)   (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_26 (Batc (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_13 (MaxPooling (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_13 (Dropout)         (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_27 (Conv2D)           (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_27 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_27 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_28 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_28 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_28 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_14 (MaxPooling (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_14 (Dropout)         (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_29 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_29 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_29 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_30 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_30 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_30 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_15 (MaxPooling (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_15 (Dropout)         (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_5 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_5 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9138\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(leaky)\n",
-    "        model.load_weights('models/leaky_relu/leaky_at_'+str(i+1)+'.h5')\n",
-    "        model.save('models/leaky_relu/leaky_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('models/leaky_relu/leaky_at_'+str(i+1)+'.h5', custom_objects={\"leaky\": leaky})\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_elu-checkpoint.ipynb b/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_elu-checkpoint.ipynb
deleted file mode 100644
index 25d55ce..0000000
--- a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_elu-checkpoint.ipynb
+++ /dev/null
@@ -1,1629 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "import keras.regularizers as regularizers\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "224/390 [================>.............] - ETA: 1:02 - loss: 2.3091 - acc: 0.3331"
-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"elu\", \"elu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('elu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 2.0326 - acc: 0.3921 - val_loss: 1.4817 - val_acc: 0.4821\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.4682 - acc: 0.5383 - val_loss: 1.1022 - val_acc: 0.6379\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.2809 - acc: 0.6011 - val_loss: 1.0062 - val_acc: 0.6743\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.2080 - acc: 0.6357 - val_loss: 0.9366 - val_acc: 0.7097\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1744 - acc: 0.6544 - val_loss: 0.8131 - val_acc: 0.7433\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1130 - acc: 0.6762 - val_loss: 0.8670 - val_acc: 0.7193\n",
-      "Epoch 7/200\n",
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-      "Epoch 18/200\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8130 - acc: 0.7782 - val_loss: 0.6456 - val_acc: 0.8283\n",
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-      "Epoch 26/200\n",
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-      "Epoch 27/200\n",
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-      "Epoch 28/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8108 - acc: 0.7843 - val_loss: 0.7208 - val_acc: 0.8022\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7996 - acc: 0.7860 - val_loss: 0.6415 - val_acc: 0.8277\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7843 - acc: 0.7877 - val_loss: 0.6660 - val_acc: 0.8384\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7805 - acc: 0.7894 - val_loss: 0.6038 - val_acc: 0.8442\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7730 - acc: 0.7919 - val_loss: 0.5988 - val_acc: 0.8400\n",
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-      "Epoch 35/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7517 - acc: 0.7981 - val_loss: 0.6077 - val_acc: 0.8391\n",
-      "Epoch 36/200\n",
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-      "Epoch 37/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7471 - acc: 0.7993 - val_loss: 0.6064 - val_acc: 0.8429\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7408 - acc: 0.8023 - val_loss: 0.6010 - val_acc: 0.8422\n",
-      "Epoch 39/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7437 - acc: 0.8013 - val_loss: 0.5859 - val_acc: 0.8505\n",
-      "Epoch 40/200\n",
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-      "Epoch 42/200\n",
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-      "Epoch 43/200\n",
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-      "Epoch 44/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7297 - acc: 0.8091 - val_loss: 0.5748 - val_acc: 0.8528\n",
-      "Epoch 45/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7233 - acc: 0.8078 - val_loss: 0.5769 - val_acc: 0.8545\n",
-      "Epoch 46/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7283 - acc: 0.8070 - val_loss: 0.5843 - val_acc: 0.8478\n",
-      "Epoch 47/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7184 - acc: 0.8080 - val_loss: 0.5578 - val_acc: 0.8565\n",
-      "Epoch 48/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7086 - acc: 0.8104 - val_loss: 0.5498 - val_acc: 0.8616\n",
-      "Epoch 49/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7126 - acc: 0.8135 - val_loss: 0.6244 - val_acc: 0.8465\n",
-      "Epoch 50/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7045 - acc: 0.8147 - val_loss: 0.5479 - val_acc: 0.8629\n",
-      "Epoch 51/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7065 - acc: 0.8150 - val_loss: 0.5739 - val_acc: 0.8522\n",
-      "Epoch 52/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6940 - acc: 0.8170 - val_loss: 0.5590 - val_acc: 0.8596\n",
-      "Epoch 53/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7054 - acc: 0.8132 - val_loss: 0.6061 - val_acc: 0.8495\n",
-      "Epoch 54/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7009 - acc: 0.8155 - val_loss: 0.5657 - val_acc: 0.8578\n",
-      "Epoch 55/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6990 - acc: 0.8145 - val_loss: 0.5317 - val_acc: 0.8656\n",
-      "Epoch 56/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7062 - acc: 0.8147 - val_loss: 0.6053 - val_acc: 0.8487\n",
-      "Epoch 57/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6888 - acc: 0.8170 - val_loss: 0.6077 - val_acc: 0.8561\n",
-      "Epoch 58/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6931 - acc: 0.8184 - val_loss: 0.5667 - val_acc: 0.8618\n",
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-      "Epoch 60/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6856 - acc: 0.8206 - val_loss: 0.5573 - val_acc: 0.8632\n",
-      "Epoch 61/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6886 - acc: 0.8198 - val_loss: 0.5442 - val_acc: 0.8641\n",
-      "Epoch 62/200\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6800 - acc: 0.8222 - val_loss: 0.5288 - val_acc: 0.8694\n",
-      "Epoch 64/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6731 - acc: 0.8224 - val_loss: 0.5377 - val_acc: 0.8684\n",
-      "Epoch 65/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6754 - acc: 0.8234 - val_loss: 0.5518 - val_acc: 0.8619\n",
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-      "Epoch 71/200\n",
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-      "Epoch 72/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6543 - acc: 0.8292 - val_loss: 0.5594 - val_acc: 0.8599\n",
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-      "Epoch 77/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5945 - acc: 0.8465 - val_loss: 0.4939 - val_acc: 0.8794\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5821 - acc: 0.8478 - val_loss: 0.5027 - val_acc: 0.8825\n",
-      "Epoch 79/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5862 - acc: 0.8483 - val_loss: 0.4798 - val_acc: 0.8845\n",
-      "Epoch 80/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5698 - acc: 0.8505 - val_loss: 0.4997 - val_acc: 0.8808\n",
-      "Epoch 81/200\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.5741 - acc: 0.8499 - val_loss: 0.4760 - val_acc: 0.8859\n",
-      "Epoch 82/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.5663 - acc: 0.8511 - val_loss: 0.4825 - val_acc: 0.8811\n",
-      "Epoch 83/200\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.5628 - acc: 0.8545 - val_loss: 0.4869 - val_acc: 0.8809\n",
-      "Epoch 84/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.5641 - acc: 0.8530 - val_loss: 0.4695 - val_acc: 0.8848\n",
-      "Epoch 85/200\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.5537 - acc: 0.8525 - val_loss: 0.4896 - val_acc: 0.8811\n",
-      "Epoch 86/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5576 - acc: 0.8526 - val_loss: 0.4719 - val_acc: 0.8862\n",
-      "Epoch 87/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5509 - acc: 0.8540 - val_loss: 0.4778 - val_acc: 0.8833\n",
-      "Epoch 88/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5570 - acc: 0.8514 - val_loss: 0.4550 - val_acc: 0.8900\n",
-      "Epoch 89/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5473 - acc: 0.8573 - val_loss: 0.4710 - val_acc: 0.8835\n",
-      "Epoch 90/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5494 - acc: 0.8554 - val_loss: 0.4685 - val_acc: 0.8880\n",
-      "Epoch 91/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5463 - acc: 0.8543 - val_loss: 0.4723 - val_acc: 0.8840\n",
-      "Epoch 92/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5442 - acc: 0.8552 - val_loss: 0.4543 - val_acc: 0.8901\n",
-      "Epoch 93/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5357 - acc: 0.8575 - val_loss: 0.4981 - val_acc: 0.8768\n",
-      "Epoch 94/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5400 - acc: 0.8552 - val_loss: 0.4722 - val_acc: 0.8852\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5358 - acc: 0.8576 - val_loss: 0.4596 - val_acc: 0.8872\n",
-      "Epoch 96/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5374 - acc: 0.8553 - val_loss: 0.4757 - val_acc: 0.8844\n",
-      "Epoch 97/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5378 - acc: 0.8568 - val_loss: 0.4681 - val_acc: 0.8855\n",
-      "Epoch 98/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5306 - acc: 0.8582 - val_loss: 0.4627 - val_acc: 0.8852\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5301 - acc: 0.8599 - val_loss: 0.4680 - val_acc: 0.8808\n",
-      "Epoch 100/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5248 - acc: 0.8596 - val_loss: 0.4600 - val_acc: 0.8830\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5085 - acc: 0.8656 - val_loss: 0.4352 - val_acc: 0.8921\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5019 - acc: 0.8673 - val_loss: 0.4438 - val_acc: 0.8926\n",
-      "Epoch 103/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4995 - acc: 0.8670 - val_loss: 0.4378 - val_acc: 0.8933\n",
-      "Epoch 104/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4978 - acc: 0.8669 - val_loss: 0.4247 - val_acc: 0.8946\n",
-      "Epoch 105/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4911 - acc: 0.8687 - val_loss: 0.4311 - val_acc: 0.8956\n",
-      "Epoch 106/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4940 - acc: 0.8673 - val_loss: 0.4279 - val_acc: 0.8960\n",
-      "Epoch 107/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4898 - acc: 0.8695 - val_loss: 0.4480 - val_acc: 0.8925\n",
-      "Epoch 108/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4902 - acc: 0.8697 - val_loss: 0.4291 - val_acc: 0.8935\n",
-      "Epoch 109/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4894 - acc: 0.8699 - val_loss: 0.4355 - val_acc: 0.8929\n",
-      "Epoch 110/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4804 - acc: 0.8720 - val_loss: 0.4436 - val_acc: 0.8898\n",
-      "Epoch 111/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4871 - acc: 0.8690 - val_loss: 0.4317 - val_acc: 0.8931\n",
-      "Epoch 112/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4856 - acc: 0.8698 - val_loss: 0.4276 - val_acc: 0.8955\n",
-      "Epoch 113/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4784 - acc: 0.8699 - val_loss: 0.4421 - val_acc: 0.8893\n",
-      "Epoch 114/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4840 - acc: 0.8699 - val_loss: 0.4389 - val_acc: 0.8912\n",
-      "Epoch 115/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4792 - acc: 0.8709 - val_loss: 0.4253 - val_acc: 0.8942\n",
-      "Epoch 116/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4818 - acc: 0.8688 - val_loss: 0.4287 - val_acc: 0.8932\n",
-      "Epoch 117/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4731 - acc: 0.8741 - val_loss: 0.4260 - val_acc: 0.8953\n",
-      "Epoch 118/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4812 - acc: 0.8696 - val_loss: 0.4195 - val_acc: 0.8971\n",
-      "Epoch 119/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4746 - acc: 0.8734 - val_loss: 0.4200 - val_acc: 0.8967\n",
-      "Epoch 120/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4787 - acc: 0.8691 - val_loss: 0.4251 - val_acc: 0.8956\n",
-      "Epoch 121/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4789 - acc: 0.8709 - val_loss: 0.4351 - val_acc: 0.8917\n",
-      "Epoch 122/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4668 - acc: 0.8738 - val_loss: 0.4215 - val_acc: 0.8934\n",
-      "Epoch 123/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4745 - acc: 0.8721 - val_loss: 0.4282 - val_acc: 0.8941\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4697 - acc: 0.8724 - val_loss: 0.4236 - val_acc: 0.8956\n",
-      "Epoch 125/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4647 - acc: 0.8734 - val_loss: 0.4166 - val_acc: 0.8981\n",
-      "Epoch 126/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4470 - acc: 0.8802 - val_loss: 0.4197 - val_acc: 0.8976\n",
-      "Epoch 127/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4504 - acc: 0.8784 - val_loss: 0.4146 - val_acc: 0.8977\n",
-      "Epoch 128/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4421 - acc: 0.8825 - val_loss: 0.4006 - val_acc: 0.9012\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4430 - acc: 0.8809 - val_loss: 0.4088 - val_acc: 0.9012\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4426 - acc: 0.8796 - val_loss: 0.3972 - val_acc: 0.9036\n",
-      "Epoch 131/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4401 - acc: 0.8820 - val_loss: 0.4090 - val_acc: 0.8994\n",
-      "Epoch 132/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4415 - acc: 0.8814 - val_loss: 0.4023 - val_acc: 0.8999\n",
-      "Epoch 133/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4330 - acc: 0.8836 - val_loss: 0.4026 - val_acc: 0.9020\n",
-      "Epoch 134/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4357 - acc: 0.8839 - val_loss: 0.4062 - val_acc: 0.9011\n",
-      "Epoch 135/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4370 - acc: 0.8833 - val_loss: 0.4024 - val_acc: 0.9026\n",
-      "Epoch 136/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4332 - acc: 0.8837 - val_loss: 0.4024 - val_acc: 0.9018\n",
-      "Epoch 137/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4383 - acc: 0.8818 - val_loss: 0.3979 - val_acc: 0.9018\n",
-      "Epoch 138/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4330 - acc: 0.8829 - val_loss: 0.3998 - val_acc: 0.9018\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4335 - acc: 0.8838 - val_loss: 0.4034 - val_acc: 0.9006\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4290 - acc: 0.8851 - val_loss: 0.4015 - val_acc: 0.9014\n",
-      "Epoch 141/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4363 - acc: 0.8832 - val_loss: 0.3968 - val_acc: 0.9022\n",
-      "Epoch 142/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4345 - acc: 0.8837 - val_loss: 0.3999 - val_acc: 0.8991\n",
-      "Epoch 143/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4284 - acc: 0.8850 - val_loss: 0.3960 - val_acc: 0.9024\n",
-      "Epoch 144/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4292 - acc: 0.8843 - val_loss: 0.3952 - val_acc: 0.9020\n",
-      "Epoch 145/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4246 - acc: 0.8859 - val_loss: 0.3941 - val_acc: 0.9022\n",
-      "Epoch 146/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4324 - acc: 0.8841 - val_loss: 0.3938 - val_acc: 0.9021\n",
-      "Epoch 147/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4269 - acc: 0.8861 - val_loss: 0.3979 - val_acc: 0.9015\n",
-      "Epoch 148/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4257 - acc: 0.8854 - val_loss: 0.3911 - val_acc: 0.9040\n",
-      "Epoch 149/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4287 - acc: 0.8847 - val_loss: 0.3994 - val_acc: 0.8991\n",
-      "Epoch 150/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4235 - acc: 0.8852 - val_loss: 0.3994 - val_acc: 0.9004\n",
-      "Epoch 151/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4195 - acc: 0.8864 - val_loss: 0.3925 - val_acc: 0.9012\n",
-      "Epoch 152/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4110 - acc: 0.8892 - val_loss: 0.3940 - val_acc: 0.9026\n",
-      "Epoch 153/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4105 - acc: 0.8887 - val_loss: 0.3894 - val_acc: 0.9039\n",
-      "Epoch 154/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4097 - acc: 0.8892 - val_loss: 0.3901 - val_acc: 0.9009\n",
-      "Epoch 155/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4117 - acc: 0.8895 - val_loss: 0.3905 - val_acc: 0.9030\n",
-      "Epoch 156/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4097 - acc: 0.8905 - val_loss: 0.3883 - val_acc: 0.9034\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4152 - acc: 0.8880 - val_loss: 0.3860 - val_acc: 0.9038\n",
-      "Epoch 158/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4096 - acc: 0.8902 - val_loss: 0.3841 - val_acc: 0.9033\n",
-      "Epoch 159/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4085 - acc: 0.8896 - val_loss: 0.3871 - val_acc: 0.9028\n",
-      "Epoch 160/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4083 - acc: 0.8894 - val_loss: 0.3865 - val_acc: 0.9028\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4077 - acc: 0.8897 - val_loss: 0.3902 - val_acc: 0.9034\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4021 - acc: 0.8926 - val_loss: 0.3883 - val_acc: 0.9036\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4060 - acc: 0.8904 - val_loss: 0.3884 - val_acc: 0.9056\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3993 - acc: 0.8924 - val_loss: 0.3864 - val_acc: 0.9050\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4065 - acc: 0.8905 - val_loss: 0.3847 - val_acc: 0.9048\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4058 - acc: 0.8902 - val_loss: 0.3851 - val_acc: 0.9038\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3974 - acc: 0.8915 - val_loss: 0.3848 - val_acc: 0.9042\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4063 - acc: 0.8911 - val_loss: 0.3877 - val_acc: 0.9023\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4016 - acc: 0.8925 - val_loss: 0.3808 - val_acc: 0.9050\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4053 - acc: 0.8902 - val_loss: 0.3829 - val_acc: 0.9053\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3988 - acc: 0.8925 - val_loss: 0.3859 - val_acc: 0.9042\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4022 - acc: 0.8913 - val_loss: 0.3841 - val_acc: 0.9045\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4016 - acc: 0.8918 - val_loss: 0.3855 - val_acc: 0.9035\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3982 - acc: 0.8930 - val_loss: 0.3838 - val_acc: 0.9043\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3952 - acc: 0.8930 - val_loss: 0.3787 - val_acc: 0.9067\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3981 - acc: 0.8924 - val_loss: 0.3826 - val_acc: 0.9041\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3961 - acc: 0.8943 - val_loss: 0.3800 - val_acc: 0.9064\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3946 - acc: 0.8919 - val_loss: 0.3803 - val_acc: 0.9063\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3942 - acc: 0.8933 - val_loss: 0.3813 - val_acc: 0.9050\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3902 - acc: 0.8968 - val_loss: 0.3807 - val_acc: 0.9065\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3958 - acc: 0.8948 - val_loss: 0.3791 - val_acc: 0.9063\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3918 - acc: 0.8952 - val_loss: 0.3788 - val_acc: 0.9065\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3884 - acc: 0.8958 - val_loss: 0.3794 - val_acc: 0.9065\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3927 - acc: 0.8942 - val_loss: 0.3800 - val_acc: 0.9054\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3904 - acc: 0.8956 - val_loss: 0.3766 - val_acc: 0.9073\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3896 - acc: 0.8947 - val_loss: 0.3790 - val_acc: 0.9069\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3942 - acc: 0.8931 - val_loss: 0.3800 - val_acc: 0.9056\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3892 - acc: 0.8950 - val_loss: 0.3794 - val_acc: 0.9046\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3877 - acc: 0.8960 - val_loss: 0.3780 - val_acc: 0.9055\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3900 - acc: 0.8948 - val_loss: 0.3801 - val_acc: 0.9051\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3933 - acc: 0.8944 - val_loss: 0.3789 - val_acc: 0.9054\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3880 - acc: 0.8963 - val_loss: 0.3795 - val_acc: 0.9067\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3876 - acc: 0.8956 - val_loss: 0.3770 - val_acc: 0.9068\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3868 - acc: 0.8958 - val_loss: 0.3809 - val_acc: 0.9061\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3873 - acc: 0.8954 - val_loss: 0.3792 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3908 - acc: 0.8949 - val_loss: 0.3801 - val_acc: 0.9061\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3882 - acc: 0.8946 - val_loss: 0.3801 - val_acc: 0.9055\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3909 - acc: 0.8946 - val_loss: 0.3779 - val_acc: 0.9064\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3852 - acc: 0.8948 - val_loss: 0.3775 - val_acc: 0.9056\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3839 - acc: 0.8976 - val_loss: 0.3770 - val_acc: 0.9053\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"elu\", \"elu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=200\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('elu_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3826 - acc: 0.8967 - val_loss: 0.3779 - val_acc: 0.9063\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3823 - acc: 0.8968 - val_loss: 0.3773 - val_acc: 0.9072\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3856 - acc: 0.8974 - val_loss: 0.3766 - val_acc: 0.9069\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3803 - acc: 0.8979 - val_loss: 0.3789 - val_acc: 0.9064\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3843 - acc: 0.8954 - val_loss: 0.3786 - val_acc: 0.9070\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3842 - acc: 0.8968 - val_loss: 0.3771 - val_acc: 0.9068\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3845 - acc: 0.8976 - val_loss: 0.3755 - val_acc: 0.9065\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3851 - acc: 0.8966 - val_loss: 0.3754 - val_acc: 0.9078\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3873 - acc: 0.8942 - val_loss: 0.3758 - val_acc: 0.9072\n",
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3837 - acc: 0.8959 - val_loss: 0.3763 - val_acc: 0.9069\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3820 - acc: 0.8957 - val_loss: 0.3774 - val_acc: 0.9067\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3842 - acc: 0.8968 - val_loss: 0.3767 - val_acc: 0.9061\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3859 - acc: 0.8962 - val_loss: 0.3766 - val_acc: 0.9065\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3843 - acc: 0.8959 - val_loss: 0.3776 - val_acc: 0.9067\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3846 - acc: 0.8970 - val_loss: 0.3769 - val_acc: 0.9072\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3800 - acc: 0.8991 - val_loss: 0.3770 - val_acc: 0.9064\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3789 - acc: 0.8969 - val_loss: 0.3756 - val_acc: 0.9069\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3825 - acc: 0.8966 - val_loss: 0.3777 - val_acc: 0.9066\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3831 - acc: 0.8972 - val_loss: 0.3775 - val_acc: 0.9072\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3818 - acc: 0.8947 - val_loss: 0.3779 - val_acc: 0.9064\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3799 - acc: 0.8964 - val_loss: 0.3785 - val_acc: 0.9076\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3795 - acc: 0.8969 - val_loss: 0.3765 - val_acc: 0.9073\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3875 - acc: 0.8962 - val_loss: 0.3764 - val_acc: 0.9063\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3825 - acc: 0.8974 - val_loss: 0.3755 - val_acc: 0.9063\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3804 - acc: 0.8966 - val_loss: 0.3768 - val_acc: 0.9060\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3770 - acc: 0.8976 - val_loss: 0.3766 - val_acc: 0.9070\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3802 - acc: 0.8958 - val_loss: 0.3753 - val_acc: 0.9071\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3870 - acc: 0.8952 - val_loss: 0.3754 - val_acc: 0.9071\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3774 - acc: 0.8971 - val_loss: 0.3733 - val_acc: 0.9075\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3805 - acc: 0.8970 - val_loss: 0.3756 - val_acc: 0.9073\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3788 - acc: 0.8975 - val_loss: 0.3744 - val_acc: 0.9075\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 148s 380ms/step - loss: 0.3777 - acc: 0.8977 - val_loss: 0.3760 - val_acc: 0.9066\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 148s 381ms/step - loss: 0.3792 - acc: 0.8988 - val_loss: 0.3735 - val_acc: 0.9073\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.3801 - acc: 0.8968 - val_loss: 0.3741 - val_acc: 0.9080\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 150s 384ms/step - loss: 0.3789 - acc: 0.8979 - val_loss: 0.3754 - val_acc: 0.9071\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 146s 375ms/step - loss: 0.3765 - acc: 0.8977 - val_loss: 0.3757 - val_acc: 0.9076\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.3844 - acc: 0.8950 - val_loss: 0.3754 - val_acc: 0.9070\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 147s 376ms/step - loss: 0.3815 - acc: 0.8966 - val_loss: 0.3750 - val_acc: 0.9068\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3807 - acc: 0.8982 - val_loss: 0.3771 - val_acc: 0.9076\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 147s 376ms/step - loss: 0.3778 - acc: 0.9002 - val_loss: 0.3751 - val_acc: 0.9069\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 145s 372ms/step - loss: 0.3801 - acc: 0.8978 - val_loss: 0.3748 - val_acc: 0.9075\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3804 - acc: 0.8981 - val_loss: 0.3731 - val_acc: 0.9084\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 144s 368ms/step - loss: 0.3800 - acc: 0.8987 - val_loss: 0.3758 - val_acc: 0.9079\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3797 - acc: 0.8984 - val_loss: 0.3763 - val_acc: 0.9081\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3752 - acc: 0.8986 - val_loss: 0.3758 - val_acc: 0.9079\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3762 - acc: 0.8980 - val_loss: 0.3746 - val_acc: 0.9073\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3797 - acc: 0.8976 - val_loss: 0.3741 - val_acc: 0.9079\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3765 - acc: 0.8988 - val_loss: 0.3761 - val_acc: 0.9071\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3791 - acc: 0.8964 - val_loss: 0.3758 - val_acc: 0.9065\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 0.3796 - acc: 0.8989 - val_loss: 0.3770 - val_acc: 0.9069\n",
-      "{'val_loss': [0.37785097351074221, 0.37731364712715149, 0.37661438734531405, 0.37886159641742706, 0.37856462571620941, 0.37713949706554412, 0.37550454359054564, 0.37540296201705931, 0.37580586643218994, 0.37626878676414488, 0.3773591064929962, 0.37666828496456145, 0.37660351555347443, 0.37759391674995424, 0.37693574063777924, 0.37701914649009705, 0.37559629876613615, 0.37769697990417478, 0.37752313163280488, 0.37790378603935243, 0.3785025860309601, 0.37651138377189636, 0.37637851953506468, 0.3754533318758011, 0.37683316841125486, 0.37656428048610685, 0.37534701485633848, 0.37539958257675171, 0.37330716643333434, 0.375648184299469, 0.37437661690711976, 0.37600070507526395, 0.37349254970550538, 0.37409095671176912, 0.37544303872585294, 0.37571356678009032, 0.37537829804420469, 0.37495623900890351, 0.37714893469810484, 0.3750675146341324, 0.37481870002746581, 0.37313218743801119, 0.37582568845748904, 0.37627374584674833, 0.37577459394931795, 0.37462998654842378, 0.37410167636871339, 0.37613337566852567, 0.37578551383018494, 0.37696433467864993], 'val_acc': [0.90629999999999999, 0.90720000000000001, 0.90690000000000004, 0.90639999999999998, 0.90700000000000003, 0.90680000000000005, 0.90649999999999997, 0.90780000000000005, 0.90720000000000001, 0.90690000000000004, 0.90669999999999995, 0.90610000000000002, 0.90649999999999997, 0.90669999999999995, 0.90720000000000001, 0.90639999999999998, 0.90690000000000004, 0.90659999999999996, 0.90720000000000001, 0.90639999999999998, 0.90759999999999996, 0.9073, 0.90629999999999999, 0.90629999999999999, 0.90600000000000003, 0.90700000000000003, 0.90710000000000002, 0.90710000000000002, 0.90749999999999997, 0.9073, 0.90749999999999997, 0.90659999999999996, 0.9073, 0.90800000000000003, 0.90710000000000002, 0.90759999999999996, 0.90700000000000003, 0.90680000000000005, 0.90759999999999996, 0.90690000000000004, 0.90749999999999997, 0.90839999999999999, 0.90790000000000004, 0.90810000000000002, 0.90790000000000004, 0.9073, 0.90790000000000004, 0.90710000000000002, 0.90649999999999997, 0.90690000000000004], 'acc': [0.89669554058389478, 0.89681584861059005, 0.89745749117741414, 0.89785851782470472, 0.89541225539287927, 0.89679579722181435, 0.89753769650304782, 0.89659455128205123, 0.89422768143866405, 0.89585338464561926, 0.8957330766954138, 0.89681584859146768, 0.89619425731780711, 0.89601379527776415, 0.89697625926185731, 0.89908164900237264, 0.89693615652255076, 0.89655518128315836, 0.89719682390734978, 0.89475056139903453, 0.8963747192431154, 0.89679579720269187, 0.89621394230769236, 0.89743898522800258, 0.89663538662791442, 0.89751764515251697, 0.89583333333333337, 0.8951910725563248, 0.89703641323696015, 0.89703641317959282, 0.89751764513339449, 0.89765800445325339, 0.89880093042002218, 0.89685595121603956, 0.89791867181892993, 0.8977181585239683, 0.8949911774333027, 0.89649502723156582, 0.89813923644529992, 0.90028472894424427, 0.89781841516188787, 0.89809695512820509, 0.89864322414900455, 0.89837985240307838, 0.89858036577452982, 0.89797882579403276, 0.89763795312184491, 0.89882098173230818, 0.89641426282051284, 0.89886108435688017], 'loss': [0.38247765058082078, 0.38237306306405749, 0.38536368729711001, 0.38022890837099982, 0.38418068871843841, 0.38421753206656917, 0.38468616945566869, 0.38509517396107701, 0.38704997230280497, 0.38390190780334915, 0.38193131318297341, 0.38425878466686303, 0.38578086314118748, 0.38412407446947855, 0.38460791421732077, 0.37987503716300841, 0.378891275389515, 0.38247182903130694, 0.38302359059524721, 0.38172237312598684, 0.38006335567655491, 0.37968259891581757, 0.38745315521955492, 0.38236816986371253, 0.38026291069825335, 0.37708598499616292, 0.38020494740742905, 0.38690472353553651, 0.37743982981518748, 0.38049827865541536, 0.37883050631245324, 0.37772402394877286, 0.3792561863644367, 0.38013887082226278, 0.3788666189347929, 0.37660440887669017, 0.38446455074305103, 0.38157797130323734, 0.38074120329403133, 0.37764427879509116, 0.38006463212809349, 0.38043843278518091, 0.38019017297140512, 0.37979160633116227, 0.37523847440706448, 0.3762182190435836, 0.37966022781733716, 0.37660038402381141, 0.37906198035448024, 0.37974316005068864]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "epochs = 250\n",
-    "model = load_model('relu_at_3.h5')\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000017,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=200)\n",
-    "# Save the model\n",
-    "model.save('relu_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9187\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(\"relu\")\n",
-    "        model.load_weights('relu_at_'+str(i+1)+'.h5')\n",
-    "        model.save('relu_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('relu_at_'+str(i+1)+'.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 2.0201 - acc: 0.3906 - val_loss: 1.3828 - val_acc: 0.5563\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.4731 - acc: 0.5308 - val_loss: 1.1149 - val_acc: 0.6337\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.2980 - acc: 0.5922 - val_loss: 1.0801 - val_acc: 0.6678\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 1.2151 - acc: 0.6298 - val_loss: 0.9754 - val_acc: 0.6856\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1390 - acc: 0.6510 - val_loss: 1.5834 - val_acc: 0.6358\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1029 - acc: 0.6706 - val_loss: 0.7973 - val_acc: 0.7567\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0241 - acc: 0.6928 - val_loss: 0.7856 - val_acc: 0.7628\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9956 - acc: 0.7041 - val_loss: 1.2215 - val_acc: 0.7283\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0520 - acc: 0.7052 - val_loss: 1.6160 - val_acc: 0.7280\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0199 - acc: 0.7127 - val_loss: 0.7939 - val_acc: 0.7845\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0037 - acc: 0.7170 - val_loss: 0.7445 - val_acc: 0.7803\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9561 - acc: 0.7259 - val_loss: 0.7351 - val_acc: 0.7896\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9214 - acc: 0.7366 - val_loss: 0.6897 - val_acc: 0.8018\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9240 - acc: 0.7409 - val_loss: 0.7333 - val_acc: 0.7836\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9455 - acc: 0.7394 - val_loss: 0.6761 - val_acc: 0.8085\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9299 - acc: 0.7462 - val_loss: 0.6588 - val_acc: 0.8135\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9478 - acc: 0.7482 - val_loss: 0.7012 - val_acc: 0.8151\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9236 - acc: 0.7535 - val_loss: 0.6909 - val_acc: 0.8094\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9234 - acc: 0.7549 - val_loss: 0.6782 - val_acc: 0.8189\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8722 - acc: 0.7605 - val_loss: 0.6582 - val_acc: 0.8214\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8591 - acc: 0.7642 - val_loss: 0.6625 - val_acc: 0.8172\n",
-      "Epoch 22/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8269 - acc: 0.7725 - val_loss: 0.6077 - val_acc: 0.8343\n",
-      "Epoch 23/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8260 - acc: 0.7745 - val_loss: 0.6269 - val_acc: 0.8280\n",
-      "Epoch 24/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8197 - acc: 0.7769 - val_loss: 0.6442 - val_acc: 0.8197\n",
-      "Epoch 25/200\n",
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-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7146 - acc: 0.8112 - val_loss: 0.5766 - val_acc: 0.8543\n",
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-      "Epoch 51/200\n",
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-      "Epoch 55/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7108 - acc: 0.8152 - val_loss: 0.5700 - val_acc: 0.8593\n",
-      "Epoch 56/200\n",
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-      "Epoch 60/200\n",
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-      "Epoch 77/200\n",
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-      "Epoch 79/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5962 - acc: 0.8474 - val_loss: 0.4920 - val_acc: 0.8826\n",
-      "Epoch 80/200\n",
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-      "Epoch 81/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5869 - acc: 0.8470 - val_loss: 0.4761 - val_acc: 0.8886\n",
-      "Epoch 82/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5713 - acc: 0.8523 - val_loss: 0.4877 - val_acc: 0.8820\n",
-      "Epoch 83/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5799 - acc: 0.8501 - val_loss: 0.4911 - val_acc: 0.8843\n",
-      "Epoch 84/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5708 - acc: 0.8521 - val_loss: 0.4748 - val_acc: 0.8885\n",
-      "Epoch 85/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5667 - acc: 0.8513 - val_loss: 0.4805 - val_acc: 0.8842\n",
-      "Epoch 86/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5677 - acc: 0.8520 - val_loss: 0.4675 - val_acc: 0.8858\n",
-      "Epoch 87/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5537 - acc: 0.8549 - val_loss: 0.4713 - val_acc: 0.8847\n",
-      "Epoch 88/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5667 - acc: 0.8520 - val_loss: 0.4694 - val_acc: 0.8878\n",
-      "Epoch 89/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5627 - acc: 0.8526 - val_loss: 0.4810 - val_acc: 0.8814\n",
-      "Epoch 90/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5565 - acc: 0.8538 - val_loss: 0.4680 - val_acc: 0.8862\n",
-      "Epoch 91/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5535 - acc: 0.8546 - val_loss: 0.4712 - val_acc: 0.8847\n",
-      "Epoch 92/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5423 - acc: 0.8576 - val_loss: 0.4586 - val_acc: 0.8902\n",
-      "Epoch 93/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5474 - acc: 0.8566 - val_loss: 0.4638 - val_acc: 0.8858\n",
-      "Epoch 94/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5499 - acc: 0.8535 - val_loss: 0.4542 - val_acc: 0.8877\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5362 - acc: 0.8586 - val_loss: 0.4561 - val_acc: 0.8869\n",
-      "Epoch 96/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5433 - acc: 0.8543 - val_loss: 0.4426 - val_acc: 0.8915\n",
-      "Epoch 97/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5360 - acc: 0.8568 - val_loss: 0.4597 - val_acc: 0.8866\n",
-      "Epoch 98/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5389 - acc: 0.8566 - val_loss: 0.4537 - val_acc: 0.8865\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5435 - acc: 0.8535 - val_loss: 0.4490 - val_acc: 0.8913\n",
-      "Epoch 100/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5295 - acc: 0.8574 - val_loss: 0.4588 - val_acc: 0.8837\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5126 - acc: 0.8649 - val_loss: 0.4349 - val_acc: 0.8947\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5037 - acc: 0.8662 - val_loss: 0.4344 - val_acc: 0.8927\n",
-      "Epoch 103/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5049 - acc: 0.8671 - val_loss: 0.4230 - val_acc: 0.8981\n",
-      "Epoch 104/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4946 - acc: 0.8680 - val_loss: 0.4370 - val_acc: 0.8915\n",
-      "Epoch 105/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4985 - acc: 0.8676 - val_loss: 0.4267 - val_acc: 0.8978\n",
-      "Epoch 106/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4964 - acc: 0.8688 - val_loss: 0.4269 - val_acc: 0.8958\n",
-      "Epoch 107/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4917 - acc: 0.8703 - val_loss: 0.4228 - val_acc: 0.8984\n",
-      "Epoch 108/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4929 - acc: 0.8693 - val_loss: 0.4239 - val_acc: 0.8957\n",
-      "Epoch 109/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4857 - acc: 0.8693 - val_loss: 0.4249 - val_acc: 0.8974\n",
-      "Epoch 110/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4896 - acc: 0.8700 - val_loss: 0.4235 - val_acc: 0.8971\n",
-      "Epoch 111/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4832 - acc: 0.8697 - val_loss: 0.4233 - val_acc: 0.8974\n",
-      "Epoch 112/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.4821 - acc: 0.8717 - val_loss: 0.4369 - val_acc: 0.8953\n",
-      "Epoch 113/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4878 - acc: 0.8698 - val_loss: 0.4254 - val_acc: 0.8970\n",
-      "Epoch 114/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4785 - acc: 0.8715 - val_loss: 0.4166 - val_acc: 0.8970\n",
-      "Epoch 115/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4844 - acc: 0.8698 - val_loss: 0.4253 - val_acc: 0.8967\n",
-      "Epoch 116/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4799 - acc: 0.8711 - val_loss: 0.4380 - val_acc: 0.8919\n",
-      "Epoch 117/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4786 - acc: 0.8706 - val_loss: 0.4211 - val_acc: 0.8948\n",
-      "Epoch 118/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4725 - acc: 0.8729 - val_loss: 0.4296 - val_acc: 0.8936\n",
-      "Epoch 119/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4752 - acc: 0.8712 - val_loss: 0.4253 - val_acc: 0.8922\n",
-      "Epoch 120/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4740 - acc: 0.8720 - val_loss: 0.4342 - val_acc: 0.8906\n",
-      "Epoch 121/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4714 - acc: 0.8740 - val_loss: 0.4373 - val_acc: 0.8936\n",
-      "Epoch 122/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4708 - acc: 0.8729 - val_loss: 0.4101 - val_acc: 0.8962\n",
-      "Epoch 123/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4622 - acc: 0.8761 - val_loss: 0.4203 - val_acc: 0.8945\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4684 - acc: 0.8729 - val_loss: 0.4186 - val_acc: 0.8958\n",
-      "Epoch 125/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4613 - acc: 0.8746 - val_loss: 0.4081 - val_acc: 0.8997\n",
-      "Epoch 126/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4521 - acc: 0.8776 - val_loss: 0.4075 - val_acc: 0.8980\n",
-      "Epoch 127/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4516 - acc: 0.8782 - val_loss: 0.4002 - val_acc: 0.8997\n",
-      "Epoch 128/200\n",
-      "390/390 [==============================] - 181s 464ms/step - loss: 0.4431 - acc: 0.8817 - val_loss: 0.4046 - val_acc: 0.9000\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4360 - acc: 0.8839 - val_loss: 0.3950 - val_acc: 0.9007\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4390 - acc: 0.8811 - val_loss: 0.3987 - val_acc: 0.9011\n",
-      "Epoch 131/200\n",
-      "390/390 [==============================] - 178s 457ms/step - loss: 0.4398 - acc: 0.8813 - val_loss: 0.3989 - val_acc: 0.8995\n",
-      "Epoch 132/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4388 - acc: 0.8825 - val_loss: 0.4080 - val_acc: 0.8963\n",
-      "Epoch 133/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4423 - acc: 0.8800 - val_loss: 0.4072 - val_acc: 0.8986\n",
-      "Epoch 134/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4373 - acc: 0.8807 - val_loss: 0.4090 - val_acc: 0.8962\n",
-      "Epoch 135/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4330 - acc: 0.8823 - val_loss: 0.4040 - val_acc: 0.8990\n",
-      "Epoch 136/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.4277 - acc: 0.8842 - val_loss: 0.3929 - val_acc: 0.9014\n",
-      "Epoch 137/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4341 - acc: 0.8825 - val_loss: 0.4060 - val_acc: 0.9006\n",
-      "Epoch 138/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4285 - acc: 0.8839 - val_loss: 0.3915 - val_acc: 0.9013\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4296 - acc: 0.8833 - val_loss: 0.4029 - val_acc: 0.9015\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4305 - acc: 0.8839 - val_loss: 0.4011 - val_acc: 0.9009\n",
-      "Epoch 141/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4266 - acc: 0.8849 - val_loss: 0.3881 - val_acc: 0.9016\n",
-      "Epoch 142/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4307 - acc: 0.8837 - val_loss: 0.3954 - val_acc: 0.9019\n",
-      "Epoch 143/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4255 - acc: 0.8869 - val_loss: 0.3977 - val_acc: 0.9002\n",
-      "Epoch 144/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4284 - acc: 0.8838 - val_loss: 0.4010 - val_acc: 0.9011\n",
-      "Epoch 145/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4236 - acc: 0.8841 - val_loss: 0.3886 - val_acc: 0.9028\n",
-      "Epoch 146/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4223 - acc: 0.8849 - val_loss: 0.3961 - val_acc: 0.9010\n",
-      "Epoch 147/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4214 - acc: 0.8852 - val_loss: 0.3977 - val_acc: 0.9002\n",
-      "Epoch 148/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4208 - acc: 0.8858 - val_loss: 0.3911 - val_acc: 0.9024\n",
-      "Epoch 149/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4228 - acc: 0.8851 - val_loss: 0.3914 - val_acc: 0.9003\n",
-      "Epoch 150/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4217 - acc: 0.8861 - val_loss: 0.3957 - val_acc: 0.9004\n",
-      "Epoch 151/200\n"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4115 - acc: 0.8871 - val_loss: 0.3882 - val_acc: 0.9029\n",
-      "Epoch 152/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4149 - acc: 0.8877 - val_loss: 0.3919 - val_acc: 0.9037\n",
-      "Epoch 153/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4113 - acc: 0.8888 - val_loss: 0.3920 - val_acc: 0.9013\n",
-      "Epoch 154/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4110 - acc: 0.8882 - val_loss: 0.3838 - val_acc: 0.9035\n",
-      "Epoch 155/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4095 - acc: 0.8899 - val_loss: 0.3948 - val_acc: 0.9024\n",
-      "Epoch 156/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4084 - acc: 0.8893 - val_loss: 0.3909 - val_acc: 0.9042\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4045 - acc: 0.8903 - val_loss: 0.3851 - val_acc: 0.9046\n",
-      "Epoch 158/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4040 - acc: 0.8916 - val_loss: 0.3865 - val_acc: 0.9032\n",
-      "Epoch 159/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4052 - acc: 0.8896 - val_loss: 0.3857 - val_acc: 0.9046\n",
-      "Epoch 160/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.4019 - acc: 0.8913 - val_loss: 0.3871 - val_acc: 0.9056\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4055 - acc: 0.8913 - val_loss: 0.3808 - val_acc: 0.9062\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4025 - acc: 0.8923 - val_loss: 0.3901 - val_acc: 0.9044\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8910 - val_loss: 0.3853 - val_acc: 0.9044\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4027 - acc: 0.8910 - val_loss: 0.3823 - val_acc: 0.9061\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3982 - acc: 0.8928 - val_loss: 0.3831 - val_acc: 0.9062\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8903 - val_loss: 0.3881 - val_acc: 0.9056\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3985 - acc: 0.8912 - val_loss: 0.3836 - val_acc: 0.9070\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3989 - acc: 0.8925 - val_loss: 0.3874 - val_acc: 0.9035\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4048 - acc: 0.8895 - val_loss: 0.3850 - val_acc: 0.9034\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3951 - acc: 0.8937 - val_loss: 0.3849 - val_acc: 0.9045\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3978 - acc: 0.8920 - val_loss: 0.3835 - val_acc: 0.9052\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3997 - acc: 0.8901 - val_loss: 0.3865 - val_acc: 0.9044\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3989 - acc: 0.8913 - val_loss: 0.3794 - val_acc: 0.9049\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3992 - acc: 0.8915 - val_loss: 0.3835 - val_acc: 0.9040\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3927 - acc: 0.8924 - val_loss: 0.3854 - val_acc: 0.9039\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3928 - acc: 0.8938 - val_loss: 0.3804 - val_acc: 0.9056\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3919 - acc: 0.8931 - val_loss: 0.3817 - val_acc: 0.9057\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3892 - acc: 0.8952 - val_loss: 0.3801 - val_acc: 0.9065\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3919 - acc: 0.8941 - val_loss: 0.3808 - val_acc: 0.9056\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3892 - acc: 0.8936 - val_loss: 0.3816 - val_acc: 0.9055\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.3855 - acc: 0.8959 - val_loss: 0.3810 - val_acc: 0.9069\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3895 - acc: 0.8943 - val_loss: 0.3812 - val_acc: 0.9051\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3922 - acc: 0.8943 - val_loss: 0.3776 - val_acc: 0.9068\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3883 - acc: 0.8951 - val_loss: 0.3796 - val_acc: 0.9074\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 180s 461ms/step - loss: 0.3942 - acc: 0.8928 - val_loss: 0.3794 - val_acc: 0.9056\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 179s 459ms/step - loss: 0.3870 - acc: 0.8960 - val_loss: 0.3777 - val_acc: 0.9072\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3899 - acc: 0.8941 - val_loss: 0.3780 - val_acc: 0.9066\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 178s 456ms/step - loss: 0.3853 - acc: 0.8971 - val_loss: 0.3778 - val_acc: 0.9066\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 178s 458ms/step - loss: 0.3878 - acc: 0.8958 - val_loss: 0.3792 - val_acc: 0.9076\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3873 - acc: 0.8956 - val_loss: 0.3781 - val_acc: 0.9085\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3876 - acc: 0.8948 - val_loss: 0.3769 - val_acc: 0.9078\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3837 - acc: 0.8973 - val_loss: 0.3768 - val_acc: 0.9075\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3876 - acc: 0.8946 - val_loss: 0.3755 - val_acc: 0.9085\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3926 - acc: 0.8941 - val_loss: 0.3756 - val_acc: 0.9087\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3839 - acc: 0.8957 - val_loss: 0.3782 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3867 - acc: 0.8934 - val_loss: 0.3760 - val_acc: 0.9079\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3884 - acc: 0.8948 - val_loss: 0.3788 - val_acc: 0.9064\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3857 - acc: 0.8943 - val_loss: 0.3773 - val_acc: 0.9082\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3882 - acc: 0.8937 - val_loss: 0.3750 - val_acc: 0.9078\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.3839 - acc: 0.8945 - val_loss: 0.3787 - val_acc: 0.9079\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"relu\", \"relu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - \n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    model = load_model('relu_at_+'str(i)'+.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_relu-checkpoint.ipynb b/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_relu-checkpoint.ipynb
deleted file mode 100644
index c22d83c..0000000
--- a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_relu-checkpoint.ipynb
+++ /dev/null
@@ -1,2206 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "import keras.regularizers as regularizers\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/125\n",
-      "390/390 [==============================] - 146s 375ms/step - loss: 1.9621 - acc: 0.4016 - val_loss: 1.4939 - val_acc: 0.4811\n",
-      "Epoch 2/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 1.5067 - acc: 0.5318 - val_loss: 1.1457 - val_acc: 0.6151\n",
-      "Epoch 3/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 1.3314 - acc: 0.5865 - val_loss: 0.9872 - val_acc: 0.6775\n",
-      "Epoch 4/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 1.2071 - acc: 0.6284 - val_loss: 1.2618 - val_acc: 0.6857\n",
-      "Epoch 5/125\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 1.1495 - acc: 0.6520 - val_loss: 0.8920 - val_acc: 0.7103\n",
-      "Epoch 6/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1091 - acc: 0.6689 - val_loss: 0.8913 - val_acc: 0.7200\n",
-      "Epoch 7/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0341 - acc: 0.6907 - val_loss: 0.7908 - val_acc: 0.7604\n",
-      "Epoch 8/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0400 - acc: 0.6987 - val_loss: 0.8802 - val_acc: 0.7294\n",
-      "Epoch 9/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9719 - acc: 0.7125 - val_loss: 0.7907 - val_acc: 0.7680\n",
-      "Epoch 10/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9527 - acc: 0.7223 - val_loss: 0.7163 - val_acc: 0.7884\n",
-      "Epoch 11/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9404 - acc: 0.7334 - val_loss: 0.6833 - val_acc: 0.7984\n",
-      "Epoch 12/125\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.9198 - acc: 0.7346 - val_loss: 0.6708 - val_acc: 0.8057\n",
-      "Epoch 13/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.8923 - acc: 0.7455 - val_loss: 0.6519 - val_acc: 0.8101\n",
-      "Epoch 14/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.8747 - acc: 0.7513 - val_loss: 0.6905 - val_acc: 0.8073\n",
-      "Epoch 15/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.8910 - acc: 0.7484 - val_loss: 0.6858 - val_acc: 0.8061\n",
-      "Epoch 16/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.9040 - acc: 0.7514 - val_loss: 0.6874 - val_acc: 0.8148\n",
-      "Epoch 17/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.8576 - acc: 0.7604 - val_loss: 0.6712 - val_acc: 0.8145\n",
-      "Epoch 18/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8816 - acc: 0.7589 - val_loss: 0.6655 - val_acc: 0.8074\n",
-      "Epoch 19/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8236 - acc: 0.7680 - val_loss: 0.6724 - val_acc: 0.8163\n",
-      "Epoch 20/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8232 - acc: 0.7696 - val_loss: 0.6804 - val_acc: 0.8120\n",
-      "Epoch 21/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8158 - acc: 0.7723 - val_loss: 0.6547 - val_acc: 0.8204\n",
-      "Epoch 22/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8126 - acc: 0.7763 - val_loss: 0.6153 - val_acc: 0.8299\n",
-      "Epoch 23/125\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.8032 - acc: 0.7786 - val_loss: 0.6098 - val_acc: 0.8344\n",
-      "Epoch 24/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7968 - acc: 0.7807 - val_loss: 0.6179 - val_acc: 0.8299\n",
-      "Epoch 25/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8056 - acc: 0.7837 - val_loss: 0.6230 - val_acc: 0.8284\n",
-      "Epoch 26/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8150 - acc: 0.7807 - val_loss: 0.5978 - val_acc: 0.8442\n",
-      "Epoch 27/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7985 - acc: 0.7839 - val_loss: 0.6175 - val_acc: 0.8330\n",
-      "Epoch 28/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7816 - acc: 0.7865 - val_loss: 0.6203 - val_acc: 0.8445\n",
-      "Epoch 29/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7693 - acc: 0.7911 - val_loss: 0.6177 - val_acc: 0.8387\n",
-      "Epoch 30/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8150 - acc: 0.7875 - val_loss: 0.5981 - val_acc: 0.8404\n",
-      "Epoch 31/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8255 - acc: 0.7864 - val_loss: 0.6219 - val_acc: 0.8306\n",
-      "Epoch 32/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7817 - acc: 0.7916 - val_loss: 0.5718 - val_acc: 0.8497\n",
-      "Epoch 33/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7612 - acc: 0.7958 - val_loss: 0.6599 - val_acc: 0.8408\n",
-      "Epoch 34/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8247 - acc: 0.7897 - val_loss: 0.6348 - val_acc: 0.8374\n",
-      "Epoch 35/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7594 - acc: 0.7983 - val_loss: 0.6220 - val_acc: 0.8367\n",
-      "Epoch 36/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7541 - acc: 0.7980 - val_loss: 0.5908 - val_acc: 0.8478\n",
-      "Epoch 37/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7514 - acc: 0.8012 - val_loss: 0.5862 - val_acc: 0.8510\n",
-      "Epoch 38/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7468 - acc: 0.8026 - val_loss: 0.6233 - val_acc: 0.8364\n",
-      "Epoch 39/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7378 - acc: 0.8034 - val_loss: 0.5990 - val_acc: 0.8416\n",
-      "Epoch 40/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7554 - acc: 0.8037 - val_loss: 0.5806 - val_acc: 0.8475\n",
-      "Epoch 41/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7492 - acc: 0.8021 - val_loss: 0.6153 - val_acc: 0.8409\n",
-      "Epoch 42/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7543 - acc: 0.8030 - val_loss: 0.5969 - val_acc: 0.8475\n",
-      "Epoch 43/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7350 - acc: 0.8066 - val_loss: 0.5890 - val_acc: 0.8553\n",
-      "Epoch 44/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7237 - acc: 0.8072 - val_loss: 0.6020 - val_acc: 0.8447\n",
-      "Epoch 45/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7277 - acc: 0.8085 - val_loss: 0.5714 - val_acc: 0.8580\n",
-      "Epoch 46/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7123 - acc: 0.8115 - val_loss: 0.5915 - val_acc: 0.8435\n",
-      "Epoch 47/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7434 - acc: 0.8069 - val_loss: 0.5516 - val_acc: 0.8622\n",
-      "Epoch 48/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7106 - acc: 0.8124 - val_loss: 0.5625 - val_acc: 0.8581\n",
-      "Epoch 49/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7221 - acc: 0.8119 - val_loss: 0.5998 - val_acc: 0.8554\n",
-      "Epoch 50/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7249 - acc: 0.8101 - val_loss: 0.5772 - val_acc: 0.8547\n",
-      "Epoch 51/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7213 - acc: 0.8130 - val_loss: 0.5706 - val_acc: 0.8511\n",
-      "Epoch 52/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7211 - acc: 0.8145 - val_loss: 0.5483 - val_acc: 0.8580\n",
-      "Epoch 53/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7152 - acc: 0.8146 - val_loss: 0.5521 - val_acc: 0.8583\n",
-      "Epoch 54/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7081 - acc: 0.8170 - val_loss: 0.5966 - val_acc: 0.8452\n",
-      "Epoch 55/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7176 - acc: 0.8147 - val_loss: 0.6625 - val_acc: 0.8547\n",
-      "Epoch 56/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7040 - acc: 0.8169 - val_loss: 0.5779 - val_acc: 0.8493\n",
-      "Epoch 57/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7076 - acc: 0.8171 - val_loss: 0.5624 - val_acc: 0.8556\n",
-      "Epoch 58/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.6955 - acc: 0.8202 - val_loss: 0.5591 - val_acc: 0.8575\n",
-      "Epoch 59/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7046 - acc: 0.8191 - val_loss: 0.5517 - val_acc: 0.8609\n",
-      "Epoch 60/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7191 - acc: 0.8194 - val_loss: 0.5530 - val_acc: 0.8630\n",
-      "Epoch 61/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7056 - acc: 0.8189 - val_loss: 0.6082 - val_acc: 0.8457\n",
-      "Epoch 62/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7118 - acc: 0.8174 - val_loss: 0.5708 - val_acc: 0.8565\n",
-      "Epoch 63/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7015 - acc: 0.8229 - val_loss: 0.5680 - val_acc: 0.8549\n",
-      "Epoch 64/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7110 - acc: 0.8192 - val_loss: 0.5639 - val_acc: 0.8560\n",
-      "Epoch 65/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6944 - acc: 0.8225 - val_loss: 0.5434 - val_acc: 0.8657\n",
-      "Epoch 66/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7065 - acc: 0.8209 - val_loss: 0.5615 - val_acc: 0.8599\n",
-      "Epoch 67/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7077 - acc: 0.8198 - val_loss: 0.6333 - val_acc: 0.8404\n",
-      "Epoch 68/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7029 - acc: 0.8212 - val_loss: 0.5489 - val_acc: 0.8644\n",
-      "Epoch 69/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.6911 - acc: 0.8249 - val_loss: 0.5461 - val_acc: 0.8615\n",
-      "Epoch 70/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6913 - acc: 0.8224 - val_loss: 0.5886 - val_acc: 0.8538\n",
-      "Epoch 71/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6985 - acc: 0.8230 - val_loss: 0.5514 - val_acc: 0.8669\n",
-      "Epoch 72/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6773 - acc: 0.8290 - val_loss: 0.5306 - val_acc: 0.8692\n",
-      "Epoch 73/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6831 - acc: 0.8243 - val_loss: 0.5528 - val_acc: 0.8715\n",
-      "Epoch 74/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.6717 - acc: 0.8278 - val_loss: 0.5222 - val_acc: 0.8700\n",
-      "Epoch 75/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6835 - acc: 0.8249 - val_loss: 0.5600 - val_acc: 0.8611\n",
-      "Epoch 76/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6396 - acc: 0.8380 - val_loss: 0.5075 - val_acc: 0.8776\n",
-      "Epoch 77/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6297 - acc: 0.8446 - val_loss: 0.4977 - val_acc: 0.8806\n",
-      "Epoch 78/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6111 - acc: 0.8483 - val_loss: 0.4977 - val_acc: 0.8845\n",
-      "Epoch 79/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6137 - acc: 0.8453 - val_loss: 0.5036 - val_acc: 0.8797\n",
-      "Epoch 80/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6007 - acc: 0.8488 - val_loss: 0.4972 - val_acc: 0.8782\n",
-      "Epoch 81/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5993 - acc: 0.8480 - val_loss: 0.4925 - val_acc: 0.8806\n",
-      "Epoch 82/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6030 - acc: 0.8474 - val_loss: 0.4848 - val_acc: 0.8846\n",
-      "Epoch 83/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5981 - acc: 0.8483 - val_loss: 0.4782 - val_acc: 0.8828\n",
-      "Epoch 84/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5964 - acc: 0.8504 - val_loss: 0.4847 - val_acc: 0.8840\n",
-      "Epoch 85/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5902 - acc: 0.8484 - val_loss: 0.4850 - val_acc: 0.8813\n",
-      "Epoch 86/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6004 - acc: 0.8488 - val_loss: 0.5161 - val_acc: 0.8711\n",
-      "Epoch 87/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5769 - acc: 0.8524 - val_loss: 0.4756 - val_acc: 0.8833\n",
-      "Epoch 88/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5900 - acc: 0.8519 - val_loss: 0.4779 - val_acc: 0.8847\n",
-      "Epoch 89/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5801 - acc: 0.8538 - val_loss: 0.4774 - val_acc: 0.8826\n",
-      "Epoch 90/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5851 - acc: 0.8502 - val_loss: 0.4810 - val_acc: 0.8806\n",
-      "Epoch 91/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5725 - acc: 0.8546 - val_loss: 0.4734 - val_acc: 0.8839\n",
-      "Epoch 92/125\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5766 - acc: 0.8513 - val_loss: 0.4702 - val_acc: 0.8843\n",
-      "Epoch 93/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5672 - acc: 0.8561 - val_loss: 0.4829 - val_acc: 0.8812\n",
-      "Epoch 94/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5708 - acc: 0.8523 - val_loss: 0.4743 - val_acc: 0.8816\n",
-      "Epoch 95/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5769 - acc: 0.8535 - val_loss: 0.4854 - val_acc: 0.8795\n",
-      "Epoch 96/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5685 - acc: 0.8564 - val_loss: 0.4765 - val_acc: 0.8840\n",
-      "Epoch 97/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5668 - acc: 0.8556 - val_loss: 0.4792 - val_acc: 0.8811\n",
-      "Epoch 98/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5623 - acc: 0.8547 - val_loss: 0.4645 - val_acc: 0.8857\n",
-      "Epoch 99/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5675 - acc: 0.8545 - val_loss: 0.4627 - val_acc: 0.8856\n",
-      "Epoch 100/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5555 - acc: 0.8561 - val_loss: 0.4856 - val_acc: 0.8813\n",
-      "Epoch 101/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5419 - acc: 0.8616 - val_loss: 0.4488 - val_acc: 0.8868\n",
-      "Epoch 102/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5284 - acc: 0.8620 - val_loss: 0.4432 - val_acc: 0.8904\n",
-      "Epoch 103/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5261 - acc: 0.8658 - val_loss: 0.4591 - val_acc: 0.8864\n",
-      "Epoch 104/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5330 - acc: 0.8643 - val_loss: 0.4532 - val_acc: 0.8870\n",
-      "Epoch 105/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5299 - acc: 0.8644 - val_loss: 0.4517 - val_acc: 0.8890\n",
-      "Epoch 106/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5213 - acc: 0.8674 - val_loss: 0.4434 - val_acc: 0.8907\n",
-      "Epoch 107/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5239 - acc: 0.8646 - val_loss: 0.4397 - val_acc: 0.8933\n",
-      "Epoch 108/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5194 - acc: 0.8656 - val_loss: 0.4463 - val_acc: 0.8870\n",
-      "Epoch 109/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5232 - acc: 0.8671 - val_loss: 0.4365 - val_acc: 0.8936\n",
-      "Epoch 110/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5229 - acc: 0.8658 - val_loss: 0.4443 - val_acc: 0.8900\n",
-      "Epoch 111/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5215 - acc: 0.8660 - val_loss: 0.4420 - val_acc: 0.8920\n",
-      "Epoch 112/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5095 - acc: 0.8711 - val_loss: 0.4391 - val_acc: 0.8916\n",
-      "Epoch 113/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5077 - acc: 0.8692 - val_loss: 0.4451 - val_acc: 0.8884\n",
-      "Epoch 114/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5166 - acc: 0.8676 - val_loss: 0.4258 - val_acc: 0.8937\n",
-      "Epoch 115/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5121 - acc: 0.8689 - val_loss: 0.4405 - val_acc: 0.8921\n",
-      "Epoch 116/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5200 - acc: 0.8675 - val_loss: 0.4283 - val_acc: 0.8937\n",
-      "Epoch 117/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5042 - acc: 0.8695 - val_loss: 0.4554 - val_acc: 0.8851\n",
-      "Epoch 118/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5047 - acc: 0.8706 - val_loss: 0.4282 - val_acc: 0.8930\n",
-      "Epoch 119/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5128 - acc: 0.8673 - val_loss: 0.4449 - val_acc: 0.8920\n",
-      "Epoch 120/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4991 - acc: 0.8706 - val_loss: 0.4303 - val_acc: 0.8950\n",
-      "Epoch 121/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5151 - acc: 0.8684 - val_loss: 0.4401 - val_acc: 0.8922\n",
-      "Epoch 122/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5105 - acc: 0.8699 - val_loss: 0.4391 - val_acc: 0.8890\n",
-      "Epoch 123/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5075 - acc: 0.8694 - val_loss: 0.4322 - val_acc: 0.8912\n",
-      "Epoch 124/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5027 - acc: 0.8698 - val_loss: 0.4223 - val_acc: 0.8909\n",
-      "Epoch 125/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5075 - acc: 0.8699 - val_loss: 0.4211 - val_acc: 0.8933\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"relu\", \"relu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=125\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 126/150\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4875 - acc: 0.8745 - val_loss: 0.4247 - val_acc: 0.8959\n",
-      "Epoch 127/150\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4853 - acc: 0.8769 - val_loss: 0.4142 - val_acc: 0.8995\n",
-      "Epoch 128/150\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4909 - acc: 0.8755 - val_loss: 0.4278 - val_acc: 0.8981\n",
-      "Epoch 129/150\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4765 - acc: 0.8781 - val_loss: 0.4252 - val_acc: 0.8962\n",
-      "Epoch 130/150\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4871 - acc: 0.8743 - val_loss: 0.4140 - val_acc: 0.8983\n",
-      "Epoch 131/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4754 - acc: 0.8785 - val_loss: 0.4117 - val_acc: 0.9006\n",
-      "Epoch 132/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4788 - acc: 0.8788 - val_loss: 0.4254 - val_acc: 0.8969\n",
-      "Epoch 133/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4734 - acc: 0.8776 - val_loss: 0.4189 - val_acc: 0.8973\n",
-      "Epoch 134/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4772 - acc: 0.8777 - val_loss: 0.4208 - val_acc: 0.8977\n",
-      "Epoch 135/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4681 - acc: 0.8801 - val_loss: 0.4138 - val_acc: 0.8959\n",
-      "Epoch 136/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4753 - acc: 0.8788 - val_loss: 0.4117 - val_acc: 0.8989\n",
-      "Epoch 137/150\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4707 - acc: 0.8794 - val_loss: 0.4113 - val_acc: 0.8995\n",
-      "Epoch 138/150\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4698 - acc: 0.8785 - val_loss: 0.4103 - val_acc: 0.8987\n",
-      "Epoch 139/150\n",
-      "390/390 [==============================] - 144s 368ms/step - loss: 0.4758 - acc: 0.8794 - val_loss: 0.4211 - val_acc: 0.8972\n",
-      "Epoch 140/150\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4689 - acc: 0.8800 - val_loss: 0.4116 - val_acc: 0.9001\n",
-      "Epoch 141/150\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4636 - acc: 0.8819 - val_loss: 0.4126 - val_acc: 0.8972\n",
-      "Epoch 142/150\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4676 - acc: 0.8795 - val_loss: 0.4149 - val_acc: 0.8976\n",
-      "Epoch 143/150\n",
-      "390/390 [==============================] - 144s 368ms/step - loss: 0.4665 - acc: 0.8799 - val_loss: 0.4090 - val_acc: 0.8995\n",
-      "Epoch 144/150\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4640 - acc: 0.8793 - val_loss: 0.4077 - val_acc: 0.8991\n",
-      "Epoch 145/150\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4636 - acc: 0.8807 - val_loss: 0.4054 - val_acc: 0.8982\n",
-      "Epoch 146/150\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4657 - acc: 0.8805 - val_loss: 0.4114 - val_acc: 0.9003\n",
-      "Epoch 147/150\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.4612 - acc: 0.8816 - val_loss: 0.4021 - val_acc: 0.9011\n",
-      "Epoch 148/150\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.4566 - acc: 0.8812 - val_loss: 0.3988 - val_acc: 0.8991\n",
-      "Epoch 149/150\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4568 - acc: 0.8809 - val_loss: 0.4044 - val_acc: 0.8998\n",
-      "Epoch 150/150\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.4591 - acc: 0.8818 - val_loss: 0.4018 - val_acc: 0.8999\n",
-      "{'acc': [0.874478665364259, 0.87690487646467608, 0.87552133465486337, 0.87806785370548601, 0.87431825469386937, 0.8784488290404906, 0.87884985564953633, 0.87758662178992919, 0.87772698107154312, 0.88021334616618541, 0.87870949631055506, 0.87935113887737915, 0.87852903430875695, 0.8794513956109109, 0.88005293551491814, 0.88189765796624664, 0.87957170352287162, 0.87997273022752942, 0.87929098494052127, 0.8806544754189255, 0.88047401349361709, 0.88163699071530466, 0.881229967948718, 0.88094091208683967, 0.88185755537991961], 'val_acc': [0.89590000000000003, 0.89949999999999997, 0.89810000000000001, 0.8962, 0.89829999999999999, 0.90059999999999996, 0.89690000000000003, 0.89729999999999999, 0.89770000000000005, 0.89590000000000003, 0.89890000000000003, 0.89949999999999997, 0.89870000000000005, 0.8972, 0.90010000000000001, 0.8972, 0.89759999999999995, 0.89949999999999997, 0.89910000000000001, 0.8982, 0.90029999999999999, 0.90110000000000001, 0.89910000000000001, 0.89980000000000004, 0.89990000000000003], 'loss': [0.48735690415192384, 0.48509300074441181, 0.49076752481760411, 0.4762809316164261, 0.48713528641455062, 0.47547251031036974, 0.47876259538276017, 0.47331453350671104, 0.47724341407279613, 0.46761857245187938, 0.47525001708077513, 0.47088334544713889, 0.46990926435141367, 0.47536514581378009, 0.46891183380060253, 0.46364587774864174, 0.46747687480982808, 0.46656295007951065, 0.4640342264765151, 0.46359020543282026, 0.46583449418895856, 0.46073419578495339, 0.45663233292408478, 0.45680606752806319, 0.45876933880482573], 'val_loss': [0.42471205363273623, 0.41424565310478212, 0.4277682622909546, 0.42521119308471678, 0.4140267830848694, 0.41170915389060975, 0.42535561351776124, 0.41889373297691346, 0.42084854388236997, 0.41376996846199038, 0.41165704236030581, 0.41126591663360595, 0.41026577069759368, 0.4210781241416931, 0.41164072477817537, 0.41259382781982423, 0.41487700324058535, 0.40902580733299254, 0.4077197432041168, 0.40536285705566405, 0.4114024616718292, 0.40205375580787661, 0.39884623870849611, 0.40435921936035157, 0.40175050497055054]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Fit the model\n",
-    "epochs = 150\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=125)\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 151/250\n",
-      "390/390 [==============================] - 148s 379ms/step - loss: 0.4485 - acc: 0.8838 - val_loss: 0.4056 - val_acc: 0.9003\n",
-      "Epoch 152/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.4527 - acc: 0.8834 - val_loss: 0.4075 - val_acc: 0.8992\n",
-      "Epoch 153/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.4425 - acc: 0.8855 - val_loss: 0.4035 - val_acc: 0.9007\n",
-      "Epoch 154/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.4423 - acc: 0.8861 - val_loss: 0.4013 - val_acc: 0.9009\n",
-      "Epoch 155/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4474 - acc: 0.8845 - val_loss: 0.4014 - val_acc: 0.9007\n",
-      "Epoch 156/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4438 - acc: 0.8841 - val_loss: 0.4067 - val_acc: 0.8999\n",
-      "Epoch 157/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4473 - acc: 0.8848 - val_loss: 0.4016 - val_acc: 0.9004\n",
-      "Epoch 158/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4472 - acc: 0.8844 - val_loss: 0.4033 - val_acc: 0.8999\n",
-      "Epoch 159/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4436 - acc: 0.8845 - val_loss: 0.4004 - val_acc: 0.9011\n",
-      "Epoch 160/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4413 - acc: 0.8849 - val_loss: 0.3993 - val_acc: 0.9011\n",
-      "Epoch 161/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4426 - acc: 0.8856 - val_loss: 0.4032 - val_acc: 0.8993\n",
-      "Epoch 162/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4472 - acc: 0.8853 - val_loss: 0.4002 - val_acc: 0.9005\n",
-      "Epoch 163/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4429 - acc: 0.8856 - val_loss: 0.3979 - val_acc: 0.9004\n",
-      "Epoch 164/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4376 - acc: 0.8861 - val_loss: 0.3984 - val_acc: 0.9009\n",
-      "Epoch 165/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4389 - acc: 0.8856 - val_loss: 0.3996 - val_acc: 0.9016\n",
-      "Epoch 166/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4424 - acc: 0.8859 - val_loss: 0.3930 - val_acc: 0.9013\n",
-      "Epoch 167/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4371 - acc: 0.8868 - val_loss: 0.3988 - val_acc: 0.9005\n",
-      "Epoch 168/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4465 - acc: 0.8863 - val_loss: 0.3958 - val_acc: 0.9003\n",
-      "Epoch 169/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4351 - acc: 0.8864 - val_loss: 0.3986 - val_acc: 0.9007\n",
-      "Epoch 170/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4361 - acc: 0.8872 - val_loss: 0.3977 - val_acc: 0.8997\n",
-      "Epoch 171/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4305 - acc: 0.8881 - val_loss: 0.3993 - val_acc: 0.8996\n",
-      "Epoch 172/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4433 - acc: 0.8861 - val_loss: 0.3993 - val_acc: 0.9005\n",
-      "Epoch 173/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4368 - acc: 0.8875 - val_loss: 0.3972 - val_acc: 0.8991\n",
-      "Epoch 174/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4306 - acc: 0.8895 - val_loss: 0.4012 - val_acc: 0.8998\n",
-      "Epoch 175/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4388 - acc: 0.8865 - val_loss: 0.3993 - val_acc: 0.9003\n",
-      "Epoch 176/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4399 - acc: 0.8867 - val_loss: 0.3964 - val_acc: 0.9009\n",
-      "Epoch 177/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4331 - acc: 0.8889 - val_loss: 0.3961 - val_acc: 0.8996\n",
-      "Epoch 178/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4315 - acc: 0.8885 - val_loss: 0.3941 - val_acc: 0.8998\n",
-      "Epoch 179/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4270 - acc: 0.8909 - val_loss: 0.3971 - val_acc: 0.9006\n",
-      "Epoch 180/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4333 - acc: 0.8880 - val_loss: 0.3954 - val_acc: 0.9013\n",
-      "Epoch 181/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4233 - acc: 0.8912 - val_loss: 0.3964 - val_acc: 0.9019\n",
-      "Epoch 182/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4299 - acc: 0.8890 - val_loss: 0.3920 - val_acc: 0.9006\n",
-      "Epoch 183/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4305 - acc: 0.8889 - val_loss: 0.3953 - val_acc: 0.9002\n",
-      "Epoch 184/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4213 - acc: 0.8898 - val_loss: 0.3989 - val_acc: 0.9014\n",
-      "Epoch 185/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4272 - acc: 0.8904 - val_loss: 0.3964 - val_acc: 0.9012\n",
-      "Epoch 186/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4317 - acc: 0.8894 - val_loss: 0.3967 - val_acc: 0.9005\n",
-      "Epoch 187/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4252 - acc: 0.8892 - val_loss: 0.3950 - val_acc: 0.9005\n",
-      "Epoch 188/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4224 - acc: 0.8907 - val_loss: 0.3905 - val_acc: 0.9015\n",
-      "Epoch 189/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4210 - acc: 0.8917 - val_loss: 0.3939 - val_acc: 0.9008\n",
-      "Epoch 190/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4223 - acc: 0.8897 - val_loss: 0.3949 - val_acc: 0.9022\n",
-      "Epoch 191/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4334 - acc: 0.8894 - val_loss: 0.3893 - val_acc: 0.9031\n",
-      "Epoch 192/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4160 - acc: 0.8921 - val_loss: 0.3944 - val_acc: 0.9002\n",
-      "Epoch 193/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4240 - acc: 0.8892 - val_loss: 0.3939 - val_acc: 0.9011\n",
-      "Epoch 194/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4234 - acc: 0.8898 - val_loss: 0.3930 - val_acc: 0.9008\n",
-      "Epoch 195/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4216 - acc: 0.8897 - val_loss: 0.3916 - val_acc: 0.9025\n",
-      "Epoch 196/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4191 - acc: 0.8914 - val_loss: 0.3937 - val_acc: 0.9025\n",
-      "Epoch 197/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4267 - acc: 0.8890 - val_loss: 0.3936 - val_acc: 0.9020\n",
-      "Epoch 198/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4223 - acc: 0.8908 - val_loss: 0.3945 - val_acc: 0.9008\n",
-      "Epoch 199/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4211 - acc: 0.8895 - val_loss: 0.3941 - val_acc: 0.9017\n",
-      "Epoch 200/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4188 - acc: 0.8904 - val_loss: 0.3949 - val_acc: 0.9020\n",
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4152 - acc: 0.8913 - val_loss: 0.3943 - val_acc: 0.9018\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4366 - acc: 0.8885 - val_loss: 0.3964 - val_acc: 0.9012\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 143s 365ms/step - loss: 0.4158 - acc: 0.8921 - val_loss: 0.3944 - val_acc: 0.9017\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 143s 365ms/step - loss: 0.4203 - acc: 0.8907 - val_loss: 0.3913 - val_acc: 0.9015\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4202 - acc: 0.8901 - val_loss: 0.3911 - val_acc: 0.9031\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4139 - acc: 0.8913 - val_loss: 0.3923 - val_acc: 0.9020\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4225 - acc: 0.8912 - val_loss: 0.3918 - val_acc: 0.9012\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 143s 365ms/step - loss: 0.4239 - acc: 0.8915 - val_loss: 0.3914 - val_acc: 0.9025\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 143s 365ms/step - loss: 0.4144 - acc: 0.8928 - val_loss: 0.3926 - val_acc: 0.9031\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4161 - acc: 0.8918 - val_loss: 0.3911 - val_acc: 0.9026\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4184 - acc: 0.8928 - val_loss: 0.3927 - val_acc: 0.9019\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4162 - acc: 0.8927 - val_loss: 0.3938 - val_acc: 0.9017\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4182 - acc: 0.8925 - val_loss: 0.3945 - val_acc: 0.9012\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4164 - acc: 0.8923 - val_loss: 0.3919 - val_acc: 0.9011\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4135 - acc: 0.8941 - val_loss: 0.3926 - val_acc: 0.9018\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4133 - acc: 0.8925 - val_loss: 0.3919 - val_acc: 0.9022\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.4222 - acc: 0.8918 - val_loss: 0.3933 - val_acc: 0.9025\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4183 - acc: 0.8932 - val_loss: 0.3925 - val_acc: 0.9027\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4179 - acc: 0.8919 - val_loss: 0.3903 - val_acc: 0.9029\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4146 - acc: 0.8940 - val_loss: 0.3935 - val_acc: 0.9021\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4196 - acc: 0.8922 - val_loss: 0.3914 - val_acc: 0.9031\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4195 - acc: 0.8945 - val_loss: 0.3883 - val_acc: 0.9044\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4175 - acc: 0.8938 - val_loss: 0.3898 - val_acc: 0.9035\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4134 - acc: 0.8944 - val_loss: 0.3878 - val_acc: 0.9043\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4131 - acc: 0.8938 - val_loss: 0.3913 - val_acc: 0.9034\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.4121 - acc: 0.8920 - val_loss: 0.3891 - val_acc: 0.9039\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4117 - acc: 0.8931 - val_loss: 0.3903 - val_acc: 0.9030\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4135 - acc: 0.8925 - val_loss: 0.3929 - val_acc: 0.9028\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4124 - acc: 0.8939 - val_loss: 0.3906 - val_acc: 0.9017\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4171 - acc: 0.8927 - val_loss: 0.3904 - val_acc: 0.9031\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4168 - acc: 0.8929 - val_loss: 0.3905 - val_acc: 0.9017\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4110 - acc: 0.8929 - val_loss: 0.3920 - val_acc: 0.9031\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.4106 - acc: 0.8943 - val_loss: 0.3924 - val_acc: 0.9028\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4088 - acc: 0.8927 - val_loss: 0.3904 - val_acc: 0.9038\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4094 - acc: 0.8938 - val_loss: 0.3920 - val_acc: 0.9027\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4165 - acc: 0.8916 - val_loss: 0.3915 - val_acc: 0.9017\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4056 - acc: 0.8941 - val_loss: 0.3889 - val_acc: 0.9022\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4192 - acc: 0.8911 - val_loss: 0.3890 - val_acc: 0.9036\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4153 - acc: 0.8929 - val_loss: 0.3914 - val_acc: 0.9027\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.4154 - acc: 0.8930 - val_loss: 0.3886 - val_acc: 0.9024\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4149 - acc: 0.8942 - val_loss: 0.3916 - val_acc: 0.9015\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4144 - acc: 0.8921 - val_loss: 0.3902 - val_acc: 0.9014\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4111 - acc: 0.8924 - val_loss: 0.3901 - val_acc: 0.9022\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4209 - acc: 0.8922 - val_loss: 0.3894 - val_acc: 0.9041\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4128 - acc: 0.8932 - val_loss: 0.3899 - val_acc: 0.9032\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4119 - acc: 0.8933 - val_loss: 0.3884 - val_acc: 0.9034\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.4146 - acc: 0.8910 - val_loss: 0.3898 - val_acc: 0.9032\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4113 - acc: 0.8941 - val_loss: 0.3904 - val_acc: 0.9026\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4105 - acc: 0.8930 - val_loss: 0.3909 - val_acc: 0.9028\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4130 - acc: 0.8939 - val_loss: 0.3892 - val_acc: 0.9030\n",
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-     ]
-    }
-   ],
-   "source": [
-    "model = load_model('relu_at_2.h5')\n",
-    "# Fit the model\n",
-    "batch_size = 128\n",
-    "epochs = 250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "#Compile the model again\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000075,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=150)\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 2.0326 - acc: 0.3921 - val_loss: 1.4817 - val_acc: 0.4821\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.4682 - acc: 0.5383 - val_loss: 1.1022 - val_acc: 0.6379\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.2809 - acc: 0.6011 - val_loss: 1.0062 - val_acc: 0.6743\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.2080 - acc: 0.6357 - val_loss: 0.9366 - val_acc: 0.7097\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1744 - acc: 0.6544 - val_loss: 0.8131 - val_acc: 0.7433\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1130 - acc: 0.6762 - val_loss: 0.8670 - val_acc: 0.7193\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0570 - acc: 0.6917 - val_loss: 0.8936 - val_acc: 0.7333\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0653 - acc: 0.6989 - val_loss: 0.7705 - val_acc: 0.7695\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0051 - acc: 0.7101 - val_loss: 0.8274 - val_acc: 0.7668\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9963 - acc: 0.7193 - val_loss: 0.7916 - val_acc: 0.7715\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9746 - acc: 0.7258 - val_loss: 0.6929 - val_acc: 0.7960\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9489 - acc: 0.7309 - val_loss: 0.6966 - val_acc: 0.7995\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9129 - acc: 0.7412 - val_loss: 0.7122 - val_acc: 0.7965\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8949 - acc: 0.7477 - val_loss: 0.8933 - val_acc: 0.7692\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8833 - acc: 0.7503 - val_loss: 0.6525 - val_acc: 0.8149\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8926 - acc: 0.7532 - val_loss: 0.7042 - val_acc: 0.8006\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8922 - acc: 0.7571 - val_loss: 0.6713 - val_acc: 0.8192\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8520 - acc: 0.7604 - val_loss: 0.6556 - val_acc: 0.8152\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8443 - acc: 0.7646 - val_loss: 0.6444 - val_acc: 0.8202\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8402 - acc: 0.7689 - val_loss: 0.6360 - val_acc: 0.8229\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8367 - acc: 0.7703 - val_loss: 0.6278 - val_acc: 0.8332\n",
-      "Epoch 22/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8195 - acc: 0.7762 - val_loss: 0.6078 - val_acc: 0.8331\n",
-      "Epoch 23/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8249 - acc: 0.7757 - val_loss: 0.6614 - val_acc: 0.8244\n",
-      "Epoch 24/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8130 - acc: 0.7782 - val_loss: 0.6456 - val_acc: 0.8283\n",
-      "Epoch 25/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8167 - acc: 0.7785 - val_loss: 0.6081 - val_acc: 0.8375\n",
-      "Epoch 26/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7999 - acc: 0.7823 - val_loss: 0.6737 - val_acc: 0.8130\n",
-      "Epoch 27/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8070 - acc: 0.7832 - val_loss: 0.6138 - val_acc: 0.8360\n",
-      "Epoch 28/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8108 - acc: 0.7843 - val_loss: 0.7208 - val_acc: 0.8022\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7996 - acc: 0.7860 - val_loss: 0.6415 - val_acc: 0.8277\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7843 - acc: 0.7877 - val_loss: 0.6660 - val_acc: 0.8384\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7805 - acc: 0.7894 - val_loss: 0.6038 - val_acc: 0.8442\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7730 - acc: 0.7919 - val_loss: 0.5988 - val_acc: 0.8400\n",
-      "Epoch 33/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7532 - acc: 0.7979 - val_loss: 0.6017 - val_acc: 0.8417\n",
-      "Epoch 34/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7705 - acc: 0.7946 - val_loss: 0.6732 - val_acc: 0.8328\n",
-      "Epoch 35/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7517 - acc: 0.7981 - val_loss: 0.6077 - val_acc: 0.8391\n",
-      "Epoch 36/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7458 - acc: 0.7984 - val_loss: 0.5767 - val_acc: 0.8505\n",
-      "Epoch 37/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7471 - acc: 0.7993 - val_loss: 0.6064 - val_acc: 0.8429\n",
-      "Epoch 38/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7408 - acc: 0.8023 - val_loss: 0.6010 - val_acc: 0.8422\n",
-      "Epoch 39/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7437 - acc: 0.8013 - val_loss: 0.5859 - val_acc: 0.8505\n",
-      "Epoch 40/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7425 - acc: 0.8042 - val_loss: 0.6082 - val_acc: 0.8435\n",
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-      "Epoch 85/200\n",
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-      "Epoch 88/200\n",
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-      "Epoch 89/200\n",
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-      "Epoch 91/200\n",
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-      "Epoch 92/200\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5442 - acc: 0.8552 - val_loss: 0.4543 - val_acc: 0.8901\n",
-      "Epoch 93/200\n",
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-      "Epoch 94/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5400 - acc: 0.8552 - val_loss: 0.4722 - val_acc: 0.8852\n",
-      "Epoch 95/200\n",
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-      "Epoch 96/200\n",
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-      "Epoch 98/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5306 - acc: 0.8582 - val_loss: 0.4627 - val_acc: 0.8852\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5301 - acc: 0.8599 - val_loss: 0.4680 - val_acc: 0.8808\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5248 - acc: 0.8596 - val_loss: 0.4600 - val_acc: 0.8830\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5085 - acc: 0.8656 - val_loss: 0.4352 - val_acc: 0.8921\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5019 - acc: 0.8673 - val_loss: 0.4438 - val_acc: 0.8926\n",
-      "Epoch 103/200\n",
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-      "Epoch 104/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4978 - acc: 0.8669 - val_loss: 0.4247 - val_acc: 0.8946\n",
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-      "Epoch 106/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4940 - acc: 0.8673 - val_loss: 0.4279 - val_acc: 0.8960\n",
-      "Epoch 107/200\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4804 - acc: 0.8720 - val_loss: 0.4436 - val_acc: 0.8898\n",
-      "Epoch 111/200\n",
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-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4856 - acc: 0.8698 - val_loss: 0.4276 - val_acc: 0.8955\n",
-      "Epoch 113/200\n",
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-      "Epoch 114/200\n",
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-      "Epoch 115/200\n",
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-      "Epoch 123/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4745 - acc: 0.8721 - val_loss: 0.4282 - val_acc: 0.8941\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4697 - acc: 0.8724 - val_loss: 0.4236 - val_acc: 0.8956\n",
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-      "Epoch 126/200\n",
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-      "Epoch 128/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4421 - acc: 0.8825 - val_loss: 0.4006 - val_acc: 0.9012\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4430 - acc: 0.8809 - val_loss: 0.4088 - val_acc: 0.9012\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4426 - acc: 0.8796 - val_loss: 0.3972 - val_acc: 0.9036\n",
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-      "Epoch 132/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4415 - acc: 0.8814 - val_loss: 0.4023 - val_acc: 0.8999\n",
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-      "Epoch 134/200\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4332 - acc: 0.8837 - val_loss: 0.4024 - val_acc: 0.9018\n",
-      "Epoch 137/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4383 - acc: 0.8818 - val_loss: 0.3979 - val_acc: 0.9018\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4330 - acc: 0.8829 - val_loss: 0.3998 - val_acc: 0.9018\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4335 - acc: 0.8838 - val_loss: 0.4034 - val_acc: 0.9006\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4290 - acc: 0.8851 - val_loss: 0.4015 - val_acc: 0.9014\n",
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-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4363 - acc: 0.8832 - val_loss: 0.3968 - val_acc: 0.9022\n",
-      "Epoch 142/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4345 - acc: 0.8837 - val_loss: 0.3999 - val_acc: 0.8991\n",
-      "Epoch 143/200\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4287 - acc: 0.8847 - val_loss: 0.3994 - val_acc: 0.8991\n",
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-      "Epoch 151/200\n"
-     ]
-    },
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4195 - acc: 0.8864 - val_loss: 0.3925 - val_acc: 0.9012\n",
-      "Epoch 152/200\n",
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-      "Epoch 154/200\n",
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-      "Epoch 155/200\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4097 - acc: 0.8905 - val_loss: 0.3883 - val_acc: 0.9034\n",
-      "Epoch 157/200\n",
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-      "Epoch 158/200\n",
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-      "Epoch 160/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4083 - acc: 0.8894 - val_loss: 0.3865 - val_acc: 0.9028\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4077 - acc: 0.8897 - val_loss: 0.3902 - val_acc: 0.9034\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4021 - acc: 0.8926 - val_loss: 0.3883 - val_acc: 0.9036\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4060 - acc: 0.8904 - val_loss: 0.3884 - val_acc: 0.9056\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3993 - acc: 0.8924 - val_loss: 0.3864 - val_acc: 0.9050\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4065 - acc: 0.8905 - val_loss: 0.3847 - val_acc: 0.9048\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4058 - acc: 0.8902 - val_loss: 0.3851 - val_acc: 0.9038\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3974 - acc: 0.8915 - val_loss: 0.3848 - val_acc: 0.9042\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4063 - acc: 0.8911 - val_loss: 0.3877 - val_acc: 0.9023\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4016 - acc: 0.8925 - val_loss: 0.3808 - val_acc: 0.9050\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4053 - acc: 0.8902 - val_loss: 0.3829 - val_acc: 0.9053\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3988 - acc: 0.8925 - val_loss: 0.3859 - val_acc: 0.9042\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4022 - acc: 0.8913 - val_loss: 0.3841 - val_acc: 0.9045\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4016 - acc: 0.8918 - val_loss: 0.3855 - val_acc: 0.9035\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3982 - acc: 0.8930 - val_loss: 0.3838 - val_acc: 0.9043\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3952 - acc: 0.8930 - val_loss: 0.3787 - val_acc: 0.9067\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3981 - acc: 0.8924 - val_loss: 0.3826 - val_acc: 0.9041\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3961 - acc: 0.8943 - val_loss: 0.3800 - val_acc: 0.9064\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3946 - acc: 0.8919 - val_loss: 0.3803 - val_acc: 0.9063\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3942 - acc: 0.8933 - val_loss: 0.3813 - val_acc: 0.9050\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3902 - acc: 0.8968 - val_loss: 0.3807 - val_acc: 0.9065\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3958 - acc: 0.8948 - val_loss: 0.3791 - val_acc: 0.9063\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3918 - acc: 0.8952 - val_loss: 0.3788 - val_acc: 0.9065\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3884 - acc: 0.8958 - val_loss: 0.3794 - val_acc: 0.9065\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3927 - acc: 0.8942 - val_loss: 0.3800 - val_acc: 0.9054\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3904 - acc: 0.8956 - val_loss: 0.3766 - val_acc: 0.9073\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3896 - acc: 0.8947 - val_loss: 0.3790 - val_acc: 0.9069\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3942 - acc: 0.8931 - val_loss: 0.3800 - val_acc: 0.9056\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3892 - acc: 0.8950 - val_loss: 0.3794 - val_acc: 0.9046\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3877 - acc: 0.8960 - val_loss: 0.3780 - val_acc: 0.9055\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3900 - acc: 0.8948 - val_loss: 0.3801 - val_acc: 0.9051\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3933 - acc: 0.8944 - val_loss: 0.3789 - val_acc: 0.9054\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3880 - acc: 0.8963 - val_loss: 0.3795 - val_acc: 0.9067\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3876 - acc: 0.8956 - val_loss: 0.3770 - val_acc: 0.9068\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3868 - acc: 0.8958 - val_loss: 0.3809 - val_acc: 0.9061\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3873 - acc: 0.8954 - val_loss: 0.3792 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3908 - acc: 0.8949 - val_loss: 0.3801 - val_acc: 0.9061\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3882 - acc: 0.8946 - val_loss: 0.3801 - val_acc: 0.9055\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3909 - acc: 0.8946 - val_loss: 0.3779 - val_acc: 0.9064\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3852 - acc: 0.8948 - val_loss: 0.3775 - val_acc: 0.9056\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3839 - acc: 0.8976 - val_loss: 0.3770 - val_acc: 0.9053\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"relu\", \"relu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=200\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('relu_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3826 - acc: 0.8967 - val_loss: 0.3779 - val_acc: 0.9063\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3823 - acc: 0.8968 - val_loss: 0.3773 - val_acc: 0.9072\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3856 - acc: 0.8974 - val_loss: 0.3766 - val_acc: 0.9069\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3803 - acc: 0.8979 - val_loss: 0.3789 - val_acc: 0.9064\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3843 - acc: 0.8954 - val_loss: 0.3786 - val_acc: 0.9070\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3842 - acc: 0.8968 - val_loss: 0.3771 - val_acc: 0.9068\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3845 - acc: 0.8976 - val_loss: 0.3755 - val_acc: 0.9065\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3851 - acc: 0.8966 - val_loss: 0.3754 - val_acc: 0.9078\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3873 - acc: 0.8942 - val_loss: 0.3758 - val_acc: 0.9072\n",
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3837 - acc: 0.8959 - val_loss: 0.3763 - val_acc: 0.9069\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3820 - acc: 0.8957 - val_loss: 0.3774 - val_acc: 0.9067\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3842 - acc: 0.8968 - val_loss: 0.3767 - val_acc: 0.9061\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3859 - acc: 0.8962 - val_loss: 0.3766 - val_acc: 0.9065\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3843 - acc: 0.8959 - val_loss: 0.3776 - val_acc: 0.9067\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3846 - acc: 0.8970 - val_loss: 0.3769 - val_acc: 0.9072\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3800 - acc: 0.8991 - val_loss: 0.3770 - val_acc: 0.9064\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3789 - acc: 0.8969 - val_loss: 0.3756 - val_acc: 0.9069\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3825 - acc: 0.8966 - val_loss: 0.3777 - val_acc: 0.9066\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3831 - acc: 0.8972 - val_loss: 0.3775 - val_acc: 0.9072\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3818 - acc: 0.8947 - val_loss: 0.3779 - val_acc: 0.9064\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3799 - acc: 0.8964 - val_loss: 0.3785 - val_acc: 0.9076\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3795 - acc: 0.8969 - val_loss: 0.3765 - val_acc: 0.9073\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3875 - acc: 0.8962 - val_loss: 0.3764 - val_acc: 0.9063\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3825 - acc: 0.8974 - val_loss: 0.3755 - val_acc: 0.9063\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3804 - acc: 0.8966 - val_loss: 0.3768 - val_acc: 0.9060\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3770 - acc: 0.8976 - val_loss: 0.3766 - val_acc: 0.9070\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3802 - acc: 0.8958 - val_loss: 0.3753 - val_acc: 0.9071\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3870 - acc: 0.8952 - val_loss: 0.3754 - val_acc: 0.9071\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3774 - acc: 0.8971 - val_loss: 0.3733 - val_acc: 0.9075\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3805 - acc: 0.8970 - val_loss: 0.3756 - val_acc: 0.9073\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3788 - acc: 0.8975 - val_loss: 0.3744 - val_acc: 0.9075\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 148s 380ms/step - loss: 0.3777 - acc: 0.8977 - val_loss: 0.3760 - val_acc: 0.9066\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 148s 381ms/step - loss: 0.3792 - acc: 0.8988 - val_loss: 0.3735 - val_acc: 0.9073\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.3801 - acc: 0.8968 - val_loss: 0.3741 - val_acc: 0.9080\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 150s 384ms/step - loss: 0.3789 - acc: 0.8979 - val_loss: 0.3754 - val_acc: 0.9071\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 146s 375ms/step - loss: 0.3765 - acc: 0.8977 - val_loss: 0.3757 - val_acc: 0.9076\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.3844 - acc: 0.8950 - val_loss: 0.3754 - val_acc: 0.9070\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 147s 376ms/step - loss: 0.3815 - acc: 0.8966 - val_loss: 0.3750 - val_acc: 0.9068\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3807 - acc: 0.8982 - val_loss: 0.3771 - val_acc: 0.9076\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 147s 376ms/step - loss: 0.3778 - acc: 0.9002 - val_loss: 0.3751 - val_acc: 0.9069\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 145s 372ms/step - loss: 0.3801 - acc: 0.8978 - val_loss: 0.3748 - val_acc: 0.9075\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3804 - acc: 0.8981 - val_loss: 0.3731 - val_acc: 0.9084\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 144s 368ms/step - loss: 0.3800 - acc: 0.8987 - val_loss: 0.3758 - val_acc: 0.9079\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3797 - acc: 0.8984 - val_loss: 0.3763 - val_acc: 0.9081\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3752 - acc: 0.8986 - val_loss: 0.3758 - val_acc: 0.9079\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3762 - acc: 0.8980 - val_loss: 0.3746 - val_acc: 0.9073\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3797 - acc: 0.8976 - val_loss: 0.3741 - val_acc: 0.9079\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3765 - acc: 0.8988 - val_loss: 0.3761 - val_acc: 0.9071\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3791 - acc: 0.8964 - val_loss: 0.3758 - val_acc: 0.9065\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 0.3796 - acc: 0.8989 - val_loss: 0.3770 - val_acc: 0.9069\n",
-      "{'val_loss': [0.37785097351074221, 0.37731364712715149, 0.37661438734531405, 0.37886159641742706, 0.37856462571620941, 0.37713949706554412, 0.37550454359054564, 0.37540296201705931, 0.37580586643218994, 0.37626878676414488, 0.3773591064929962, 0.37666828496456145, 0.37660351555347443, 0.37759391674995424, 0.37693574063777924, 0.37701914649009705, 0.37559629876613615, 0.37769697990417478, 0.37752313163280488, 0.37790378603935243, 0.3785025860309601, 0.37651138377189636, 0.37637851953506468, 0.3754533318758011, 0.37683316841125486, 0.37656428048610685, 0.37534701485633848, 0.37539958257675171, 0.37330716643333434, 0.375648184299469, 0.37437661690711976, 0.37600070507526395, 0.37349254970550538, 0.37409095671176912, 0.37544303872585294, 0.37571356678009032, 0.37537829804420469, 0.37495623900890351, 0.37714893469810484, 0.3750675146341324, 0.37481870002746581, 0.37313218743801119, 0.37582568845748904, 0.37627374584674833, 0.37577459394931795, 0.37462998654842378, 0.37410167636871339, 0.37613337566852567, 0.37578551383018494, 0.37696433467864993], 'val_acc': [0.90629999999999999, 0.90720000000000001, 0.90690000000000004, 0.90639999999999998, 0.90700000000000003, 0.90680000000000005, 0.90649999999999997, 0.90780000000000005, 0.90720000000000001, 0.90690000000000004, 0.90669999999999995, 0.90610000000000002, 0.90649999999999997, 0.90669999999999995, 0.90720000000000001, 0.90639999999999998, 0.90690000000000004, 0.90659999999999996, 0.90720000000000001, 0.90639999999999998, 0.90759999999999996, 0.9073, 0.90629999999999999, 0.90629999999999999, 0.90600000000000003, 0.90700000000000003, 0.90710000000000002, 0.90710000000000002, 0.90749999999999997, 0.9073, 0.90749999999999997, 0.90659999999999996, 0.9073, 0.90800000000000003, 0.90710000000000002, 0.90759999999999996, 0.90700000000000003, 0.90680000000000005, 0.90759999999999996, 0.90690000000000004, 0.90749999999999997, 0.90839999999999999, 0.90790000000000004, 0.90810000000000002, 0.90790000000000004, 0.9073, 0.90790000000000004, 0.90710000000000002, 0.90649999999999997, 0.90690000000000004], 'acc': [0.89669554058389478, 0.89681584861059005, 0.89745749117741414, 0.89785851782470472, 0.89541225539287927, 0.89679579722181435, 0.89753769650304782, 0.89659455128205123, 0.89422768143866405, 0.89585338464561926, 0.8957330766954138, 0.89681584859146768, 0.89619425731780711, 0.89601379527776415, 0.89697625926185731, 0.89908164900237264, 0.89693615652255076, 0.89655518128315836, 0.89719682390734978, 0.89475056139903453, 0.8963747192431154, 0.89679579720269187, 0.89621394230769236, 0.89743898522800258, 0.89663538662791442, 0.89751764515251697, 0.89583333333333337, 0.8951910725563248, 0.89703641323696015, 0.89703641317959282, 0.89751764513339449, 0.89765800445325339, 0.89880093042002218, 0.89685595121603956, 0.89791867181892993, 0.8977181585239683, 0.8949911774333027, 0.89649502723156582, 0.89813923644529992, 0.90028472894424427, 0.89781841516188787, 0.89809695512820509, 0.89864322414900455, 0.89837985240307838, 0.89858036577452982, 0.89797882579403276, 0.89763795312184491, 0.89882098173230818, 0.89641426282051284, 0.89886108435688017], 'loss': [0.38247765058082078, 0.38237306306405749, 0.38536368729711001, 0.38022890837099982, 0.38418068871843841, 0.38421753206656917, 0.38468616945566869, 0.38509517396107701, 0.38704997230280497, 0.38390190780334915, 0.38193131318297341, 0.38425878466686303, 0.38578086314118748, 0.38412407446947855, 0.38460791421732077, 0.37987503716300841, 0.378891275389515, 0.38247182903130694, 0.38302359059524721, 0.38172237312598684, 0.38006335567655491, 0.37968259891581757, 0.38745315521955492, 0.38236816986371253, 0.38026291069825335, 0.37708598499616292, 0.38020494740742905, 0.38690472353553651, 0.37743982981518748, 0.38049827865541536, 0.37883050631245324, 0.37772402394877286, 0.3792561863644367, 0.38013887082226278, 0.3788666189347929, 0.37660440887669017, 0.38446455074305103, 0.38157797130323734, 0.38074120329403133, 0.37764427879509116, 0.38006463212809349, 0.38043843278518091, 0.38019017297140512, 0.37979160633116227, 0.37523847440706448, 0.3762182190435836, 0.37966022781733716, 0.37660038402381141, 0.37906198035448024, 0.37974316005068864]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "epochs = 250\n",
-    "model = load_model('relu_at_3.h5')\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000017,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=200)\n",
-    "# Save the model\n",
-    "model.save('relu_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9187\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(\"relu\")\n",
-    "        model.load_weights('relu_at_'+str(i+1)+'.h5')\n",
-    "        model.save('relu_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('relu_at_'+str(i+1)+'.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 2.0201 - acc: 0.3906 - val_loss: 1.3828 - val_acc: 0.5563\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.4731 - acc: 0.5308 - val_loss: 1.1149 - val_acc: 0.6337\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.2980 - acc: 0.5922 - val_loss: 1.0801 - val_acc: 0.6678\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 1.2151 - acc: 0.6298 - val_loss: 0.9754 - val_acc: 0.6856\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1390 - acc: 0.6510 - val_loss: 1.5834 - val_acc: 0.6358\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1029 - acc: 0.6706 - val_loss: 0.7973 - val_acc: 0.7567\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0241 - acc: 0.6928 - val_loss: 0.7856 - val_acc: 0.7628\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9956 - acc: 0.7041 - val_loss: 1.2215 - val_acc: 0.7283\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0520 - acc: 0.7052 - val_loss: 1.6160 - val_acc: 0.7280\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0199 - acc: 0.7127 - val_loss: 0.7939 - val_acc: 0.7845\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0037 - acc: 0.7170 - val_loss: 0.7445 - val_acc: 0.7803\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9561 - acc: 0.7259 - val_loss: 0.7351 - val_acc: 0.7896\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9214 - acc: 0.7366 - val_loss: 0.6897 - val_acc: 0.8018\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9240 - acc: 0.7409 - val_loss: 0.7333 - val_acc: 0.7836\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9455 - acc: 0.7394 - val_loss: 0.6761 - val_acc: 0.8085\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9299 - acc: 0.7462 - val_loss: 0.6588 - val_acc: 0.8135\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9478 - acc: 0.7482 - val_loss: 0.7012 - val_acc: 0.8151\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9236 - acc: 0.7535 - val_loss: 0.6909 - val_acc: 0.8094\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9234 - acc: 0.7549 - val_loss: 0.6782 - val_acc: 0.8189\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8722 - acc: 0.7605 - val_loss: 0.6582 - val_acc: 0.8214\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8591 - acc: 0.7642 - val_loss: 0.6625 - val_acc: 0.8172\n",
-      "Epoch 22/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8269 - acc: 0.7725 - val_loss: 0.6077 - val_acc: 0.8343\n",
-      "Epoch 23/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8260 - acc: 0.7745 - val_loss: 0.6269 - val_acc: 0.8280\n",
-      "Epoch 24/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8197 - acc: 0.7769 - val_loss: 0.6442 - val_acc: 0.8197\n",
-      "Epoch 25/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8047 - acc: 0.7790 - val_loss: 0.6241 - val_acc: 0.8293\n",
-      "Epoch 26/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8188 - acc: 0.7806 - val_loss: 0.6076 - val_acc: 0.8352\n",
-      "Epoch 27/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8053 - acc: 0.7823 - val_loss: 0.6519 - val_acc: 0.8270\n",
-      "Epoch 28/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8434 - acc: 0.7808 - val_loss: 0.6330 - val_acc: 0.8318\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7910 - acc: 0.7879 - val_loss: 0.6119 - val_acc: 0.8378\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7811 - acc: 0.7891 - val_loss: 0.5965 - val_acc: 0.8451\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7707 - acc: 0.7902 - val_loss: 0.6395 - val_acc: 0.8271\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7675 - acc: 0.7932 - val_loss: 0.5810 - val_acc: 0.8464\n",
-      "Epoch 33/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7828 - acc: 0.7923 - val_loss: 0.6336 - val_acc: 0.8310\n",
-      "Epoch 34/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7781 - acc: 0.7948 - val_loss: 0.5732 - val_acc: 0.8480\n",
-      "Epoch 35/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7599 - acc: 0.7993 - val_loss: 0.5962 - val_acc: 0.8421\n",
-      "Epoch 36/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7571 - acc: 0.7990 - val_loss: 0.6151 - val_acc: 0.8365\n",
-      "Epoch 37/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7565 - acc: 0.7981 - val_loss: 0.5864 - val_acc: 0.8464\n",
-      "Epoch 38/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7765 - acc: 0.7984 - val_loss: 0.6086 - val_acc: 0.8371\n",
-      "Epoch 39/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7530 - acc: 0.7973 - val_loss: 0.5760 - val_acc: 0.8520\n",
-      "Epoch 40/200\n",
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-      "Epoch 88/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5667 - acc: 0.8520 - val_loss: 0.4694 - val_acc: 0.8878\n",
-      "Epoch 89/200\n",
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-      "Epoch 91/200\n",
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-      "Epoch 92/200\n"
-     ]
-    },
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-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5423 - acc: 0.8576 - val_loss: 0.4586 - val_acc: 0.8902\n",
-      "Epoch 93/200\n",
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-      "Epoch 94/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5499 - acc: 0.8535 - val_loss: 0.4542 - val_acc: 0.8877\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5362 - acc: 0.8586 - val_loss: 0.4561 - val_acc: 0.8869\n",
-      "Epoch 96/200\n",
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-      "Epoch 97/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5360 - acc: 0.8568 - val_loss: 0.4597 - val_acc: 0.8866\n",
-      "Epoch 98/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5389 - acc: 0.8566 - val_loss: 0.4537 - val_acc: 0.8865\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5435 - acc: 0.8535 - val_loss: 0.4490 - val_acc: 0.8913\n",
-      "Epoch 100/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5295 - acc: 0.8574 - val_loss: 0.4588 - val_acc: 0.8837\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5126 - acc: 0.8649 - val_loss: 0.4349 - val_acc: 0.8947\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5037 - acc: 0.8662 - val_loss: 0.4344 - val_acc: 0.8927\n",
-      "Epoch 103/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5049 - acc: 0.8671 - val_loss: 0.4230 - val_acc: 0.8981\n",
-      "Epoch 104/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4946 - acc: 0.8680 - val_loss: 0.4370 - val_acc: 0.8915\n",
-      "Epoch 105/200\n",
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-      "Epoch 106/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4964 - acc: 0.8688 - val_loss: 0.4269 - val_acc: 0.8958\n",
-      "Epoch 107/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4917 - acc: 0.8703 - val_loss: 0.4228 - val_acc: 0.8984\n",
-      "Epoch 108/200\n",
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-      "Epoch 110/200\n",
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-      "Epoch 111/200\n",
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-      "Epoch 112/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.4821 - acc: 0.8717 - val_loss: 0.4369 - val_acc: 0.8953\n",
-      "Epoch 113/200\n",
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-      "Epoch 114/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4785 - acc: 0.8715 - val_loss: 0.4166 - val_acc: 0.8970\n",
-      "Epoch 115/200\n",
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-      "Epoch 118/200\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4740 - acc: 0.8720 - val_loss: 0.4342 - val_acc: 0.8906\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4708 - acc: 0.8729 - val_loss: 0.4101 - val_acc: 0.8962\n",
-      "Epoch 123/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4622 - acc: 0.8761 - val_loss: 0.4203 - val_acc: 0.8945\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4684 - acc: 0.8729 - val_loss: 0.4186 - val_acc: 0.8958\n",
-      "Epoch 125/200\n",
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-      "Epoch 126/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4521 - acc: 0.8776 - val_loss: 0.4075 - val_acc: 0.8980\n",
-      "Epoch 127/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4516 - acc: 0.8782 - val_loss: 0.4002 - val_acc: 0.8997\n",
-      "Epoch 128/200\n",
-      "390/390 [==============================] - 181s 464ms/step - loss: 0.4431 - acc: 0.8817 - val_loss: 0.4046 - val_acc: 0.9000\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4360 - acc: 0.8839 - val_loss: 0.3950 - val_acc: 0.9007\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4390 - acc: 0.8811 - val_loss: 0.3987 - val_acc: 0.9011\n",
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-      "390/390 [==============================] - 178s 457ms/step - loss: 0.4398 - acc: 0.8813 - val_loss: 0.3989 - val_acc: 0.8995\n",
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-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4388 - acc: 0.8825 - val_loss: 0.4080 - val_acc: 0.8963\n",
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-      "Epoch 134/200\n",
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-      "Epoch 135/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4330 - acc: 0.8823 - val_loss: 0.4040 - val_acc: 0.8990\n",
-      "Epoch 136/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.4277 - acc: 0.8842 - val_loss: 0.3929 - val_acc: 0.9014\n",
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-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4341 - acc: 0.8825 - val_loss: 0.4060 - val_acc: 0.9006\n",
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-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4285 - acc: 0.8839 - val_loss: 0.3915 - val_acc: 0.9013\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4296 - acc: 0.8833 - val_loss: 0.4029 - val_acc: 0.9015\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4305 - acc: 0.8839 - val_loss: 0.4011 - val_acc: 0.9009\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4266 - acc: 0.8849 - val_loss: 0.3881 - val_acc: 0.9016\n",
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-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4214 - acc: 0.8852 - val_loss: 0.3977 - val_acc: 0.9002\n",
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-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4228 - acc: 0.8851 - val_loss: 0.3914 - val_acc: 0.9003\n",
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-      "Epoch 151/200\n"
-     ]
-    },
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-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4115 - acc: 0.8871 - val_loss: 0.3882 - val_acc: 0.9029\n",
-      "Epoch 152/200\n",
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-      "Epoch 155/200\n",
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-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4084 - acc: 0.8893 - val_loss: 0.3909 - val_acc: 0.9042\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4045 - acc: 0.8903 - val_loss: 0.3851 - val_acc: 0.9046\n",
-      "Epoch 158/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4040 - acc: 0.8916 - val_loss: 0.3865 - val_acc: 0.9032\n",
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-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4052 - acc: 0.8896 - val_loss: 0.3857 - val_acc: 0.9046\n",
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-      "390/390 [==============================] - 175s 450ms/step - loss: 0.4019 - acc: 0.8913 - val_loss: 0.3871 - val_acc: 0.9056\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4055 - acc: 0.8913 - val_loss: 0.3808 - val_acc: 0.9062\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4025 - acc: 0.8923 - val_loss: 0.3901 - val_acc: 0.9044\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8910 - val_loss: 0.3853 - val_acc: 0.9044\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4027 - acc: 0.8910 - val_loss: 0.3823 - val_acc: 0.9061\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3982 - acc: 0.8928 - val_loss: 0.3831 - val_acc: 0.9062\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8903 - val_loss: 0.3881 - val_acc: 0.9056\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3985 - acc: 0.8912 - val_loss: 0.3836 - val_acc: 0.9070\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3989 - acc: 0.8925 - val_loss: 0.3874 - val_acc: 0.9035\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4048 - acc: 0.8895 - val_loss: 0.3850 - val_acc: 0.9034\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3951 - acc: 0.8937 - val_loss: 0.3849 - val_acc: 0.9045\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3978 - acc: 0.8920 - val_loss: 0.3835 - val_acc: 0.9052\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3997 - acc: 0.8901 - val_loss: 0.3865 - val_acc: 0.9044\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3989 - acc: 0.8913 - val_loss: 0.3794 - val_acc: 0.9049\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3992 - acc: 0.8915 - val_loss: 0.3835 - val_acc: 0.9040\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3927 - acc: 0.8924 - val_loss: 0.3854 - val_acc: 0.9039\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3928 - acc: 0.8938 - val_loss: 0.3804 - val_acc: 0.9056\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3919 - acc: 0.8931 - val_loss: 0.3817 - val_acc: 0.9057\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3892 - acc: 0.8952 - val_loss: 0.3801 - val_acc: 0.9065\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3919 - acc: 0.8941 - val_loss: 0.3808 - val_acc: 0.9056\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3892 - acc: 0.8936 - val_loss: 0.3816 - val_acc: 0.9055\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.3855 - acc: 0.8959 - val_loss: 0.3810 - val_acc: 0.9069\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3895 - acc: 0.8943 - val_loss: 0.3812 - val_acc: 0.9051\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3922 - acc: 0.8943 - val_loss: 0.3776 - val_acc: 0.9068\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3883 - acc: 0.8951 - val_loss: 0.3796 - val_acc: 0.9074\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 180s 461ms/step - loss: 0.3942 - acc: 0.8928 - val_loss: 0.3794 - val_acc: 0.9056\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 179s 459ms/step - loss: 0.3870 - acc: 0.8960 - val_loss: 0.3777 - val_acc: 0.9072\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3899 - acc: 0.8941 - val_loss: 0.3780 - val_acc: 0.9066\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 178s 456ms/step - loss: 0.3853 - acc: 0.8971 - val_loss: 0.3778 - val_acc: 0.9066\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 178s 458ms/step - loss: 0.3878 - acc: 0.8958 - val_loss: 0.3792 - val_acc: 0.9076\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3873 - acc: 0.8956 - val_loss: 0.3781 - val_acc: 0.9085\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3876 - acc: 0.8948 - val_loss: 0.3769 - val_acc: 0.9078\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3837 - acc: 0.8973 - val_loss: 0.3768 - val_acc: 0.9075\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3876 - acc: 0.8946 - val_loss: 0.3755 - val_acc: 0.9085\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3926 - acc: 0.8941 - val_loss: 0.3756 - val_acc: 0.9087\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3839 - acc: 0.8957 - val_loss: 0.3782 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3867 - acc: 0.8934 - val_loss: 0.3760 - val_acc: 0.9079\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3884 - acc: 0.8948 - val_loss: 0.3788 - val_acc: 0.9064\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3857 - acc: 0.8943 - val_loss: 0.3773 - val_acc: 0.9082\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3882 - acc: 0.8937 - val_loss: 0.3750 - val_acc: 0.9078\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.3839 - acc: 0.8945 - val_loss: 0.3787 - val_acc: 0.9079\n",
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0.37935949974060057, 0.38349194710254669, 0.38540293841362, 0.38042077803611757, 0.38166472558975217, 0.38014628505706788, 0.38078072862625123, 0.38159099376201627, 0.38099342994689939, 0.38124294857978819, 0.37758006939888, 0.37961545908451078, 0.37935590014457704, 0.3776896659374237, 0.37800100386142732, 0.37784726963043214, 0.37919219954013822, 0.37809970812797544, 0.37685366349220278, 0.37684274215698244, 0.37554384021759035, 0.37560194835662841, 0.37815707368850709, 0.37599482469558715, 0.37884682497978212, 0.37729874968528748, 0.37496673526763918, 0.37871543755531312], 'acc': [0.39059993583573949, 0.53091915301263892, 0.59221607316637936, 0.62987247357048737, 0.65096647413564024, 0.67065688157869452, 0.69281360284234994, 0.7040623997050981, 0.70522537694590803, 0.71272457493089658, 0.71711581647109246, 0.72589829964709651, 0.73664581332024681, 0.7409167468528699, 0.73947305104883199, 0.74629050370856742, 0.74821543148552949, 0.75348893170330744, 0.75489252488277336, 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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"relu\", \"relu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - \n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    model = load_model('relu_at_+'str(i)'+.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_swish-checkpoint.ipynb b/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_swish-checkpoint.ipynb
deleted file mode 100644
index 510eb8b..0000000
--- a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_swish-checkpoint.ipynb
+++ /dev/null
@@ -1,1884 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "import keras.regularizers as regularizers\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/125\n",
-      "390/390 [==============================] - 146s 375ms/step - loss: 1.9621 - acc: 0.4016 - val_loss: 1.4939 - val_acc: 0.4811\n",
-      "Epoch 2/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 1.5067 - acc: 0.5318 - val_loss: 1.1457 - val_acc: 0.6151\n",
-      "Epoch 3/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 1.3314 - acc: 0.5865 - val_loss: 0.9872 - val_acc: 0.6775\n",
-      "Epoch 4/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 1.2071 - acc: 0.6284 - val_loss: 1.2618 - val_acc: 0.6857\n",
-      "Epoch 5/125\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 1.1495 - acc: 0.6520 - val_loss: 0.8920 - val_acc: 0.7103\n",
-      "Epoch 6/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1091 - acc: 0.6689 - val_loss: 0.8913 - val_acc: 0.7200\n",
-      "Epoch 7/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0341 - acc: 0.6907 - val_loss: 0.7908 - val_acc: 0.7604\n",
-      "Epoch 8/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0400 - acc: 0.6987 - val_loss: 0.8802 - val_acc: 0.7294\n",
-      "Epoch 9/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9719 - acc: 0.7125 - val_loss: 0.7907 - val_acc: 0.7680\n",
-      "Epoch 10/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9527 - acc: 0.7223 - val_loss: 0.7163 - val_acc: 0.7884\n",
-      "Epoch 11/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9404 - acc: 0.7334 - val_loss: 0.6833 - val_acc: 0.7984\n",
-      "Epoch 12/125\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.9198 - acc: 0.7346 - val_loss: 0.6708 - val_acc: 0.8057\n",
-      "Epoch 13/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.8923 - acc: 0.7455 - val_loss: 0.6519 - val_acc: 0.8101\n",
-      "Epoch 14/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.8747 - acc: 0.7513 - val_loss: 0.6905 - val_acc: 0.8073\n",
-      "Epoch 15/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.8910 - acc: 0.7484 - val_loss: 0.6858 - val_acc: 0.8061\n",
-      "Epoch 16/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.9040 - acc: 0.7514 - val_loss: 0.6874 - val_acc: 0.8148\n",
-      "Epoch 17/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.8576 - acc: 0.7604 - val_loss: 0.6712 - val_acc: 0.8145\n",
-      "Epoch 18/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8816 - acc: 0.7589 - val_loss: 0.6655 - val_acc: 0.8074\n",
-      "Epoch 19/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8236 - acc: 0.7680 - val_loss: 0.6724 - val_acc: 0.8163\n",
-      "Epoch 20/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8232 - acc: 0.7696 - val_loss: 0.6804 - val_acc: 0.8120\n",
-      "Epoch 21/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8158 - acc: 0.7723 - val_loss: 0.6547 - val_acc: 0.8204\n",
-      "Epoch 22/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8126 - acc: 0.7763 - val_loss: 0.6153 - val_acc: 0.8299\n",
-      "Epoch 23/125\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.8032 - acc: 0.7786 - val_loss: 0.6098 - val_acc: 0.8344\n",
-      "Epoch 24/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7968 - acc: 0.7807 - val_loss: 0.6179 - val_acc: 0.8299\n",
-      "Epoch 25/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8056 - acc: 0.7837 - val_loss: 0.6230 - val_acc: 0.8284\n",
-      "Epoch 26/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8150 - acc: 0.7807 - val_loss: 0.5978 - val_acc: 0.8442\n",
-      "Epoch 27/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7985 - acc: 0.7839 - val_loss: 0.6175 - val_acc: 0.8330\n",
-      "Epoch 28/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7816 - acc: 0.7865 - val_loss: 0.6203 - val_acc: 0.8445\n",
-      "Epoch 29/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7693 - acc: 0.7911 - val_loss: 0.6177 - val_acc: 0.8387\n",
-      "Epoch 30/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8150 - acc: 0.7875 - val_loss: 0.5981 - val_acc: 0.8404\n",
-      "Epoch 31/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8255 - acc: 0.7864 - val_loss: 0.6219 - val_acc: 0.8306\n",
-      "Epoch 32/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7817 - acc: 0.7916 - val_loss: 0.5718 - val_acc: 0.8497\n",
-      "Epoch 33/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7612 - acc: 0.7958 - val_loss: 0.6599 - val_acc: 0.8408\n",
-      "Epoch 34/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8247 - acc: 0.7897 - val_loss: 0.6348 - val_acc: 0.8374\n",
-      "Epoch 35/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7594 - acc: 0.7983 - val_loss: 0.6220 - val_acc: 0.8367\n",
-      "Epoch 36/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7541 - acc: 0.7980 - val_loss: 0.5908 - val_acc: 0.8478\n",
-      "Epoch 37/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7514 - acc: 0.8012 - val_loss: 0.5862 - val_acc: 0.8510\n",
-      "Epoch 38/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7468 - acc: 0.8026 - val_loss: 0.6233 - val_acc: 0.8364\n",
-      "Epoch 39/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7378 - acc: 0.8034 - val_loss: 0.5990 - val_acc: 0.8416\n",
-      "Epoch 40/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7554 - acc: 0.8037 - val_loss: 0.5806 - val_acc: 0.8475\n",
-      "Epoch 41/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7492 - acc: 0.8021 - val_loss: 0.6153 - val_acc: 0.8409\n",
-      "Epoch 42/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7543 - acc: 0.8030 - val_loss: 0.5969 - val_acc: 0.8475\n",
-      "Epoch 43/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7350 - acc: 0.8066 - val_loss: 0.5890 - val_acc: 0.8553\n",
-      "Epoch 44/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7237 - acc: 0.8072 - val_loss: 0.6020 - val_acc: 0.8447\n",
-      "Epoch 45/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7277 - acc: 0.8085 - val_loss: 0.5714 - val_acc: 0.8580\n",
-      "Epoch 46/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7123 - acc: 0.8115 - val_loss: 0.5915 - val_acc: 0.8435\n",
-      "Epoch 47/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7434 - acc: 0.8069 - val_loss: 0.5516 - val_acc: 0.8622\n",
-      "Epoch 48/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7106 - acc: 0.8124 - val_loss: 0.5625 - val_acc: 0.8581\n",
-      "Epoch 49/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7221 - acc: 0.8119 - val_loss: 0.5998 - val_acc: 0.8554\n",
-      "Epoch 50/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7249 - acc: 0.8101 - val_loss: 0.5772 - val_acc: 0.8547\n",
-      "Epoch 51/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7213 - acc: 0.8130 - val_loss: 0.5706 - val_acc: 0.8511\n",
-      "Epoch 52/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7211 - acc: 0.8145 - val_loss: 0.5483 - val_acc: 0.8580\n",
-      "Epoch 53/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7152 - acc: 0.8146 - val_loss: 0.5521 - val_acc: 0.8583\n",
-      "Epoch 54/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7081 - acc: 0.8170 - val_loss: 0.5966 - val_acc: 0.8452\n",
-      "Epoch 55/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7176 - acc: 0.8147 - val_loss: 0.6625 - val_acc: 0.8547\n",
-      "Epoch 56/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7040 - acc: 0.8169 - val_loss: 0.5779 - val_acc: 0.8493\n",
-      "Epoch 57/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7076 - acc: 0.8171 - val_loss: 0.5624 - val_acc: 0.8556\n",
-      "Epoch 58/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.6955 - acc: 0.8202 - val_loss: 0.5591 - val_acc: 0.8575\n",
-      "Epoch 59/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7046 - acc: 0.8191 - val_loss: 0.5517 - val_acc: 0.8609\n",
-      "Epoch 60/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7191 - acc: 0.8194 - val_loss: 0.5530 - val_acc: 0.8630\n",
-      "Epoch 61/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7056 - acc: 0.8189 - val_loss: 0.6082 - val_acc: 0.8457\n",
-      "Epoch 62/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7118 - acc: 0.8174 - val_loss: 0.5708 - val_acc: 0.8565\n",
-      "Epoch 63/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7015 - acc: 0.8229 - val_loss: 0.5680 - val_acc: 0.8549\n",
-      "Epoch 64/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7110 - acc: 0.8192 - val_loss: 0.5639 - val_acc: 0.8560\n",
-      "Epoch 65/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6944 - acc: 0.8225 - val_loss: 0.5434 - val_acc: 0.8657\n",
-      "Epoch 66/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7065 - acc: 0.8209 - val_loss: 0.5615 - val_acc: 0.8599\n",
-      "Epoch 67/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7077 - acc: 0.8198 - val_loss: 0.6333 - val_acc: 0.8404\n",
-      "Epoch 68/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7029 - acc: 0.8212 - val_loss: 0.5489 - val_acc: 0.8644\n",
-      "Epoch 69/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.6911 - acc: 0.8249 - val_loss: 0.5461 - val_acc: 0.8615\n",
-      "Epoch 70/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6913 - acc: 0.8224 - val_loss: 0.5886 - val_acc: 0.8538\n",
-      "Epoch 71/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6985 - acc: 0.8230 - val_loss: 0.5514 - val_acc: 0.8669\n",
-      "Epoch 72/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6773 - acc: 0.8290 - val_loss: 0.5306 - val_acc: 0.8692\n",
-      "Epoch 73/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6831 - acc: 0.8243 - val_loss: 0.5528 - val_acc: 0.8715\n",
-      "Epoch 74/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.6717 - acc: 0.8278 - val_loss: 0.5222 - val_acc: 0.8700\n",
-      "Epoch 75/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6835 - acc: 0.8249 - val_loss: 0.5600 - val_acc: 0.8611\n",
-      "Epoch 76/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6396 - acc: 0.8380 - val_loss: 0.5075 - val_acc: 0.8776\n",
-      "Epoch 77/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6297 - acc: 0.8446 - val_loss: 0.4977 - val_acc: 0.8806\n",
-      "Epoch 78/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6111 - acc: 0.8483 - val_loss: 0.4977 - val_acc: 0.8845\n",
-      "Epoch 79/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6137 - acc: 0.8453 - val_loss: 0.5036 - val_acc: 0.8797\n",
-      "Epoch 80/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6007 - acc: 0.8488 - val_loss: 0.4972 - val_acc: 0.8782\n",
-      "Epoch 81/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5993 - acc: 0.8480 - val_loss: 0.4925 - val_acc: 0.8806\n",
-      "Epoch 82/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6030 - acc: 0.8474 - val_loss: 0.4848 - val_acc: 0.8846\n",
-      "Epoch 83/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5981 - acc: 0.8483 - val_loss: 0.4782 - val_acc: 0.8828\n",
-      "Epoch 84/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5964 - acc: 0.8504 - val_loss: 0.4847 - val_acc: 0.8840\n",
-      "Epoch 85/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5902 - acc: 0.8484 - val_loss: 0.4850 - val_acc: 0.8813\n",
-      "Epoch 86/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6004 - acc: 0.8488 - val_loss: 0.5161 - val_acc: 0.8711\n",
-      "Epoch 87/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5769 - acc: 0.8524 - val_loss: 0.4756 - val_acc: 0.8833\n",
-      "Epoch 88/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5900 - acc: 0.8519 - val_loss: 0.4779 - val_acc: 0.8847\n",
-      "Epoch 89/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5801 - acc: 0.8538 - val_loss: 0.4774 - val_acc: 0.8826\n",
-      "Epoch 90/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5851 - acc: 0.8502 - val_loss: 0.4810 - val_acc: 0.8806\n",
-      "Epoch 91/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5725 - acc: 0.8546 - val_loss: 0.4734 - val_acc: 0.8839\n",
-      "Epoch 92/125\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5766 - acc: 0.8513 - val_loss: 0.4702 - val_acc: 0.8843\n",
-      "Epoch 93/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5672 - acc: 0.8561 - val_loss: 0.4829 - val_acc: 0.8812\n",
-      "Epoch 94/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5708 - acc: 0.8523 - val_loss: 0.4743 - val_acc: 0.8816\n",
-      "Epoch 95/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5769 - acc: 0.8535 - val_loss: 0.4854 - val_acc: 0.8795\n",
-      "Epoch 96/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5685 - acc: 0.8564 - val_loss: 0.4765 - val_acc: 0.8840\n",
-      "Epoch 97/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5668 - acc: 0.8556 - val_loss: 0.4792 - val_acc: 0.8811\n",
-      "Epoch 98/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5623 - acc: 0.8547 - val_loss: 0.4645 - val_acc: 0.8857\n",
-      "Epoch 99/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5675 - acc: 0.8545 - val_loss: 0.4627 - val_acc: 0.8856\n",
-      "Epoch 100/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5555 - acc: 0.8561 - val_loss: 0.4856 - val_acc: 0.8813\n",
-      "Epoch 101/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5419 - acc: 0.8616 - val_loss: 0.4488 - val_acc: 0.8868\n",
-      "Epoch 102/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5284 - acc: 0.8620 - val_loss: 0.4432 - val_acc: 0.8904\n",
-      "Epoch 103/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5261 - acc: 0.8658 - val_loss: 0.4591 - val_acc: 0.8864\n",
-      "Epoch 104/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5330 - acc: 0.8643 - val_loss: 0.4532 - val_acc: 0.8870\n",
-      "Epoch 105/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5299 - acc: 0.8644 - val_loss: 0.4517 - val_acc: 0.8890\n",
-      "Epoch 106/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5213 - acc: 0.8674 - val_loss: 0.4434 - val_acc: 0.8907\n",
-      "Epoch 107/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5239 - acc: 0.8646 - val_loss: 0.4397 - val_acc: 0.8933\n",
-      "Epoch 108/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5194 - acc: 0.8656 - val_loss: 0.4463 - val_acc: 0.8870\n",
-      "Epoch 109/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5232 - acc: 0.8671 - val_loss: 0.4365 - val_acc: 0.8936\n",
-      "Epoch 110/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5229 - acc: 0.8658 - val_loss: 0.4443 - val_acc: 0.8900\n",
-      "Epoch 111/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5215 - acc: 0.8660 - val_loss: 0.4420 - val_acc: 0.8920\n",
-      "Epoch 112/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5095 - acc: 0.8711 - val_loss: 0.4391 - val_acc: 0.8916\n",
-      "Epoch 113/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5077 - acc: 0.8692 - val_loss: 0.4451 - val_acc: 0.8884\n",
-      "Epoch 114/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5166 - acc: 0.8676 - val_loss: 0.4258 - val_acc: 0.8937\n",
-      "Epoch 115/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5121 - acc: 0.8689 - val_loss: 0.4405 - val_acc: 0.8921\n",
-      "Epoch 116/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5200 - acc: 0.8675 - val_loss: 0.4283 - val_acc: 0.8937\n",
-      "Epoch 117/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5042 - acc: 0.8695 - val_loss: 0.4554 - val_acc: 0.8851\n",
-      "Epoch 118/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5047 - acc: 0.8706 - val_loss: 0.4282 - val_acc: 0.8930\n",
-      "Epoch 119/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5128 - acc: 0.8673 - val_loss: 0.4449 - val_acc: 0.8920\n",
-      "Epoch 120/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4991 - acc: 0.8706 - val_loss: 0.4303 - val_acc: 0.8950\n",
-      "Epoch 121/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5151 - acc: 0.8684 - val_loss: 0.4401 - val_acc: 0.8922\n",
-      "Epoch 122/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5105 - acc: 0.8699 - val_loss: 0.4391 - val_acc: 0.8890\n",
-      "Epoch 123/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5075 - acc: 0.8694 - val_loss: 0.4322 - val_acc: 0.8912\n",
-      "Epoch 124/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5027 - acc: 0.8698 - val_loss: 0.4223 - val_acc: 0.8909\n",
-      "Epoch 125/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5075 - acc: 0.8699 - val_loss: 0.4211 - val_acc: 0.8933\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = swish, \"swish\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('swish_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 2.0326 - acc: 0.3921 - val_loss: 1.4817 - val_acc: 0.4821\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.4682 - acc: 0.5383 - val_loss: 1.1022 - val_acc: 0.6379\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.2809 - acc: 0.6011 - val_loss: 1.0062 - val_acc: 0.6743\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.2080 - acc: 0.6357 - val_loss: 0.9366 - val_acc: 0.7097\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1744 - acc: 0.6544 - val_loss: 0.8131 - val_acc: 0.7433\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1130 - acc: 0.6762 - val_loss: 0.8670 - val_acc: 0.7193\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0570 - acc: 0.6917 - val_loss: 0.8936 - val_acc: 0.7333\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0653 - acc: 0.6989 - val_loss: 0.7705 - val_acc: 0.7695\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0051 - acc: 0.7101 - val_loss: 0.8274 - val_acc: 0.7668\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9963 - acc: 0.7193 - val_loss: 0.7916 - val_acc: 0.7715\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9746 - acc: 0.7258 - val_loss: 0.6929 - val_acc: 0.7960\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9489 - acc: 0.7309 - val_loss: 0.6966 - val_acc: 0.7995\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9129 - acc: 0.7412 - val_loss: 0.7122 - val_acc: 0.7965\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8949 - acc: 0.7477 - val_loss: 0.8933 - val_acc: 0.7692\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8833 - acc: 0.7503 - val_loss: 0.6525 - val_acc: 0.8149\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8926 - acc: 0.7532 - val_loss: 0.7042 - val_acc: 0.8006\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8922 - acc: 0.7571 - val_loss: 0.6713 - val_acc: 0.8192\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8520 - acc: 0.7604 - val_loss: 0.6556 - val_acc: 0.8152\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8443 - acc: 0.7646 - val_loss: 0.6444 - val_acc: 0.8202\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8402 - acc: 0.7689 - val_loss: 0.6360 - val_acc: 0.8229\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8367 - acc: 0.7703 - val_loss: 0.6278 - val_acc: 0.8332\n",
-      "Epoch 22/200\n",
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-      "Epoch 55/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6990 - acc: 0.8145 - val_loss: 0.5317 - val_acc: 0.8656\n",
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-      "Epoch 77/200\n",
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-      "Epoch 79/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5862 - acc: 0.8483 - val_loss: 0.4798 - val_acc: 0.8845\n",
-      "Epoch 80/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5698 - acc: 0.8505 - val_loss: 0.4997 - val_acc: 0.8808\n",
-      "Epoch 81/200\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.5741 - acc: 0.8499 - val_loss: 0.4760 - val_acc: 0.8859\n",
-      "Epoch 82/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.5663 - acc: 0.8511 - val_loss: 0.4825 - val_acc: 0.8811\n",
-      "Epoch 83/200\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.5628 - acc: 0.8545 - val_loss: 0.4869 - val_acc: 0.8809\n",
-      "Epoch 84/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.5641 - acc: 0.8530 - val_loss: 0.4695 - val_acc: 0.8848\n",
-      "Epoch 85/200\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.5537 - acc: 0.8525 - val_loss: 0.4896 - val_acc: 0.8811\n",
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-      "Epoch 87/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5509 - acc: 0.8540 - val_loss: 0.4778 - val_acc: 0.8833\n",
-      "Epoch 88/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5570 - acc: 0.8514 - val_loss: 0.4550 - val_acc: 0.8900\n",
-      "Epoch 89/200\n",
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-      "Epoch 90/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5494 - acc: 0.8554 - val_loss: 0.4685 - val_acc: 0.8880\n",
-      "Epoch 91/200\n",
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-      "Epoch 92/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5442 - acc: 0.8552 - val_loss: 0.4543 - val_acc: 0.8901\n",
-      "Epoch 93/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5357 - acc: 0.8575 - val_loss: 0.4981 - val_acc: 0.8768\n",
-      "Epoch 94/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5400 - acc: 0.8552 - val_loss: 0.4722 - val_acc: 0.8852\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5358 - acc: 0.8576 - val_loss: 0.4596 - val_acc: 0.8872\n",
-      "Epoch 96/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.5374 - acc: 0.8553 - val_loss: 0.4757 - val_acc: 0.8844\n",
-      "Epoch 97/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5378 - acc: 0.8568 - val_loss: 0.4681 - val_acc: 0.8855\n",
-      "Epoch 98/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5306 - acc: 0.8582 - val_loss: 0.4627 - val_acc: 0.8852\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5301 - acc: 0.8599 - val_loss: 0.4680 - val_acc: 0.8808\n",
-      "Epoch 100/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5248 - acc: 0.8596 - val_loss: 0.4600 - val_acc: 0.8830\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5085 - acc: 0.8656 - val_loss: 0.4352 - val_acc: 0.8921\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5019 - acc: 0.8673 - val_loss: 0.4438 - val_acc: 0.8926\n",
-      "Epoch 103/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4995 - acc: 0.8670 - val_loss: 0.4378 - val_acc: 0.8933\n",
-      "Epoch 104/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4978 - acc: 0.8669 - val_loss: 0.4247 - val_acc: 0.8946\n",
-      "Epoch 105/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4911 - acc: 0.8687 - val_loss: 0.4311 - val_acc: 0.8956\n",
-      "Epoch 106/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4940 - acc: 0.8673 - val_loss: 0.4279 - val_acc: 0.8960\n",
-      "Epoch 107/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4898 - acc: 0.8695 - val_loss: 0.4480 - val_acc: 0.8925\n",
-      "Epoch 108/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4902 - acc: 0.8697 - val_loss: 0.4291 - val_acc: 0.8935\n",
-      "Epoch 109/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4894 - acc: 0.8699 - val_loss: 0.4355 - val_acc: 0.8929\n",
-      "Epoch 110/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4804 - acc: 0.8720 - val_loss: 0.4436 - val_acc: 0.8898\n",
-      "Epoch 111/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4871 - acc: 0.8690 - val_loss: 0.4317 - val_acc: 0.8931\n",
-      "Epoch 112/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4856 - acc: 0.8698 - val_loss: 0.4276 - val_acc: 0.8955\n",
-      "Epoch 113/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4784 - acc: 0.8699 - val_loss: 0.4421 - val_acc: 0.8893\n",
-      "Epoch 114/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4840 - acc: 0.8699 - val_loss: 0.4389 - val_acc: 0.8912\n",
-      "Epoch 115/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4792 - acc: 0.8709 - val_loss: 0.4253 - val_acc: 0.8942\n",
-      "Epoch 116/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4818 - acc: 0.8688 - val_loss: 0.4287 - val_acc: 0.8932\n",
-      "Epoch 117/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4731 - acc: 0.8741 - val_loss: 0.4260 - val_acc: 0.8953\n",
-      "Epoch 118/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4812 - acc: 0.8696 - val_loss: 0.4195 - val_acc: 0.8971\n",
-      "Epoch 119/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4746 - acc: 0.8734 - val_loss: 0.4200 - val_acc: 0.8967\n",
-      "Epoch 120/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4787 - acc: 0.8691 - val_loss: 0.4251 - val_acc: 0.8956\n",
-      "Epoch 121/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4789 - acc: 0.8709 - val_loss: 0.4351 - val_acc: 0.8917\n",
-      "Epoch 122/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4668 - acc: 0.8738 - val_loss: 0.4215 - val_acc: 0.8934\n",
-      "Epoch 123/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4745 - acc: 0.8721 - val_loss: 0.4282 - val_acc: 0.8941\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4697 - acc: 0.8724 - val_loss: 0.4236 - val_acc: 0.8956\n",
-      "Epoch 125/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4647 - acc: 0.8734 - val_loss: 0.4166 - val_acc: 0.8981\n",
-      "Epoch 126/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4470 - acc: 0.8802 - val_loss: 0.4197 - val_acc: 0.8976\n",
-      "Epoch 127/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4504 - acc: 0.8784 - val_loss: 0.4146 - val_acc: 0.8977\n",
-      "Epoch 128/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4421 - acc: 0.8825 - val_loss: 0.4006 - val_acc: 0.9012\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4430 - acc: 0.8809 - val_loss: 0.4088 - val_acc: 0.9012\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4426 - acc: 0.8796 - val_loss: 0.3972 - val_acc: 0.9036\n",
-      "Epoch 131/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4401 - acc: 0.8820 - val_loss: 0.4090 - val_acc: 0.8994\n",
-      "Epoch 132/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4415 - acc: 0.8814 - val_loss: 0.4023 - val_acc: 0.8999\n",
-      "Epoch 133/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4330 - acc: 0.8836 - val_loss: 0.4026 - val_acc: 0.9020\n",
-      "Epoch 134/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4357 - acc: 0.8839 - val_loss: 0.4062 - val_acc: 0.9011\n",
-      "Epoch 135/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4370 - acc: 0.8833 - val_loss: 0.4024 - val_acc: 0.9026\n",
-      "Epoch 136/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4332 - acc: 0.8837 - val_loss: 0.4024 - val_acc: 0.9018\n",
-      "Epoch 137/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4383 - acc: 0.8818 - val_loss: 0.3979 - val_acc: 0.9018\n",
-      "Epoch 138/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4330 - acc: 0.8829 - val_loss: 0.3998 - val_acc: 0.9018\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4335 - acc: 0.8838 - val_loss: 0.4034 - val_acc: 0.9006\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4290 - acc: 0.8851 - val_loss: 0.4015 - val_acc: 0.9014\n",
-      "Epoch 141/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4363 - acc: 0.8832 - val_loss: 0.3968 - val_acc: 0.9022\n",
-      "Epoch 142/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4345 - acc: 0.8837 - val_loss: 0.3999 - val_acc: 0.8991\n",
-      "Epoch 143/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4284 - acc: 0.8850 - val_loss: 0.3960 - val_acc: 0.9024\n",
-      "Epoch 144/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4292 - acc: 0.8843 - val_loss: 0.3952 - val_acc: 0.9020\n",
-      "Epoch 145/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4246 - acc: 0.8859 - val_loss: 0.3941 - val_acc: 0.9022\n",
-      "Epoch 146/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4324 - acc: 0.8841 - val_loss: 0.3938 - val_acc: 0.9021\n",
-      "Epoch 147/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4269 - acc: 0.8861 - val_loss: 0.3979 - val_acc: 0.9015\n",
-      "Epoch 148/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4257 - acc: 0.8854 - val_loss: 0.3911 - val_acc: 0.9040\n",
-      "Epoch 149/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4287 - acc: 0.8847 - val_loss: 0.3994 - val_acc: 0.8991\n",
-      "Epoch 150/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4235 - acc: 0.8852 - val_loss: 0.3994 - val_acc: 0.9004\n",
-      "Epoch 151/200\n"
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4195 - acc: 0.8864 - val_loss: 0.3925 - val_acc: 0.9012\n",
-      "Epoch 152/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4110 - acc: 0.8892 - val_loss: 0.3940 - val_acc: 0.9026\n",
-      "Epoch 153/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4105 - acc: 0.8887 - val_loss: 0.3894 - val_acc: 0.9039\n",
-      "Epoch 154/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4097 - acc: 0.8892 - val_loss: 0.3901 - val_acc: 0.9009\n",
-      "Epoch 155/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4117 - acc: 0.8895 - val_loss: 0.3905 - val_acc: 0.9030\n",
-      "Epoch 156/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4097 - acc: 0.8905 - val_loss: 0.3883 - val_acc: 0.9034\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4152 - acc: 0.8880 - val_loss: 0.3860 - val_acc: 0.9038\n",
-      "Epoch 158/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4096 - acc: 0.8902 - val_loss: 0.3841 - val_acc: 0.9033\n",
-      "Epoch 159/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4085 - acc: 0.8896 - val_loss: 0.3871 - val_acc: 0.9028\n",
-      "Epoch 160/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4083 - acc: 0.8894 - val_loss: 0.3865 - val_acc: 0.9028\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4077 - acc: 0.8897 - val_loss: 0.3902 - val_acc: 0.9034\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4021 - acc: 0.8926 - val_loss: 0.3883 - val_acc: 0.9036\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4060 - acc: 0.8904 - val_loss: 0.3884 - val_acc: 0.9056\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3993 - acc: 0.8924 - val_loss: 0.3864 - val_acc: 0.9050\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4065 - acc: 0.8905 - val_loss: 0.3847 - val_acc: 0.9048\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4058 - acc: 0.8902 - val_loss: 0.3851 - val_acc: 0.9038\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3974 - acc: 0.8915 - val_loss: 0.3848 - val_acc: 0.9042\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4063 - acc: 0.8911 - val_loss: 0.3877 - val_acc: 0.9023\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4016 - acc: 0.8925 - val_loss: 0.3808 - val_acc: 0.9050\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4053 - acc: 0.8902 - val_loss: 0.3829 - val_acc: 0.9053\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3988 - acc: 0.8925 - val_loss: 0.3859 - val_acc: 0.9042\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4022 - acc: 0.8913 - val_loss: 0.3841 - val_acc: 0.9045\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4016 - acc: 0.8918 - val_loss: 0.3855 - val_acc: 0.9035\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3982 - acc: 0.8930 - val_loss: 0.3838 - val_acc: 0.9043\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3952 - acc: 0.8930 - val_loss: 0.3787 - val_acc: 0.9067\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3981 - acc: 0.8924 - val_loss: 0.3826 - val_acc: 0.9041\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3961 - acc: 0.8943 - val_loss: 0.3800 - val_acc: 0.9064\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3946 - acc: 0.8919 - val_loss: 0.3803 - val_acc: 0.9063\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3942 - acc: 0.8933 - val_loss: 0.3813 - val_acc: 0.9050\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3902 - acc: 0.8968 - val_loss: 0.3807 - val_acc: 0.9065\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3958 - acc: 0.8948 - val_loss: 0.3791 - val_acc: 0.9063\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3918 - acc: 0.8952 - val_loss: 0.3788 - val_acc: 0.9065\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3884 - acc: 0.8958 - val_loss: 0.3794 - val_acc: 0.9065\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3927 - acc: 0.8942 - val_loss: 0.3800 - val_acc: 0.9054\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3904 - acc: 0.8956 - val_loss: 0.3766 - val_acc: 0.9073\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3896 - acc: 0.8947 - val_loss: 0.3790 - val_acc: 0.9069\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3942 - acc: 0.8931 - val_loss: 0.3800 - val_acc: 0.9056\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3892 - acc: 0.8950 - val_loss: 0.3794 - val_acc: 0.9046\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3877 - acc: 0.8960 - val_loss: 0.3780 - val_acc: 0.9055\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3900 - acc: 0.8948 - val_loss: 0.3801 - val_acc: 0.9051\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3933 - acc: 0.8944 - val_loss: 0.3789 - val_acc: 0.9054\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3880 - acc: 0.8963 - val_loss: 0.3795 - val_acc: 0.9067\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3876 - acc: 0.8956 - val_loss: 0.3770 - val_acc: 0.9068\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3868 - acc: 0.8958 - val_loss: 0.3809 - val_acc: 0.9061\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3873 - acc: 0.8954 - val_loss: 0.3792 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3908 - acc: 0.8949 - val_loss: 0.3801 - val_acc: 0.9061\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3882 - acc: 0.8946 - val_loss: 0.3801 - val_acc: 0.9055\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3909 - acc: 0.8946 - val_loss: 0.3779 - val_acc: 0.9064\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3852 - acc: 0.8948 - val_loss: 0.3775 - val_acc: 0.9056\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3839 - acc: 0.8976 - val_loss: 0.3770 - val_acc: 0.9053\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = swish, \"swish\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=200\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('swish_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3826 - acc: 0.8967 - val_loss: 0.3779 - val_acc: 0.9063\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3823 - acc: 0.8968 - val_loss: 0.3773 - val_acc: 0.9072\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3856 - acc: 0.8974 - val_loss: 0.3766 - val_acc: 0.9069\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3803 - acc: 0.8979 - val_loss: 0.3789 - val_acc: 0.9064\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3843 - acc: 0.8954 - val_loss: 0.3786 - val_acc: 0.9070\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3842 - acc: 0.8968 - val_loss: 0.3771 - val_acc: 0.9068\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3845 - acc: 0.8976 - val_loss: 0.3755 - val_acc: 0.9065\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3851 - acc: 0.8966 - val_loss: 0.3754 - val_acc: 0.9078\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3873 - acc: 0.8942 - val_loss: 0.3758 - val_acc: 0.9072\n",
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3837 - acc: 0.8959 - val_loss: 0.3763 - val_acc: 0.9069\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3820 - acc: 0.8957 - val_loss: 0.3774 - val_acc: 0.9067\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3842 - acc: 0.8968 - val_loss: 0.3767 - val_acc: 0.9061\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3859 - acc: 0.8962 - val_loss: 0.3766 - val_acc: 0.9065\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3843 - acc: 0.8959 - val_loss: 0.3776 - val_acc: 0.9067\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3846 - acc: 0.8970 - val_loss: 0.3769 - val_acc: 0.9072\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3800 - acc: 0.8991 - val_loss: 0.3770 - val_acc: 0.9064\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3789 - acc: 0.8969 - val_loss: 0.3756 - val_acc: 0.9069\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3825 - acc: 0.8966 - val_loss: 0.3777 - val_acc: 0.9066\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3831 - acc: 0.8972 - val_loss: 0.3775 - val_acc: 0.9072\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3818 - acc: 0.8947 - val_loss: 0.3779 - val_acc: 0.9064\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3799 - acc: 0.8964 - val_loss: 0.3785 - val_acc: 0.9076\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3795 - acc: 0.8969 - val_loss: 0.3765 - val_acc: 0.9073\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3875 - acc: 0.8962 - val_loss: 0.3764 - val_acc: 0.9063\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3825 - acc: 0.8974 - val_loss: 0.3755 - val_acc: 0.9063\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3804 - acc: 0.8966 - val_loss: 0.3768 - val_acc: 0.9060\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3770 - acc: 0.8976 - val_loss: 0.3766 - val_acc: 0.9070\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3802 - acc: 0.8958 - val_loss: 0.3753 - val_acc: 0.9071\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3870 - acc: 0.8952 - val_loss: 0.3754 - val_acc: 0.9071\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3774 - acc: 0.8971 - val_loss: 0.3733 - val_acc: 0.9075\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3805 - acc: 0.8970 - val_loss: 0.3756 - val_acc: 0.9073\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3788 - acc: 0.8975 - val_loss: 0.3744 - val_acc: 0.9075\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 148s 380ms/step - loss: 0.3777 - acc: 0.8977 - val_loss: 0.3760 - val_acc: 0.9066\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 148s 381ms/step - loss: 0.3792 - acc: 0.8988 - val_loss: 0.3735 - val_acc: 0.9073\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.3801 - acc: 0.8968 - val_loss: 0.3741 - val_acc: 0.9080\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 150s 384ms/step - loss: 0.3789 - acc: 0.8979 - val_loss: 0.3754 - val_acc: 0.9071\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 146s 375ms/step - loss: 0.3765 - acc: 0.8977 - val_loss: 0.3757 - val_acc: 0.9076\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.3844 - acc: 0.8950 - val_loss: 0.3754 - val_acc: 0.9070\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 147s 376ms/step - loss: 0.3815 - acc: 0.8966 - val_loss: 0.3750 - val_acc: 0.9068\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3807 - acc: 0.8982 - val_loss: 0.3771 - val_acc: 0.9076\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 147s 376ms/step - loss: 0.3778 - acc: 0.9002 - val_loss: 0.3751 - val_acc: 0.9069\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 145s 372ms/step - loss: 0.3801 - acc: 0.8978 - val_loss: 0.3748 - val_acc: 0.9075\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3804 - acc: 0.8981 - val_loss: 0.3731 - val_acc: 0.9084\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 144s 368ms/step - loss: 0.3800 - acc: 0.8987 - val_loss: 0.3758 - val_acc: 0.9079\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3797 - acc: 0.8984 - val_loss: 0.3763 - val_acc: 0.9081\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3752 - acc: 0.8986 - val_loss: 0.3758 - val_acc: 0.9079\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3762 - acc: 0.8980 - val_loss: 0.3746 - val_acc: 0.9073\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3797 - acc: 0.8976 - val_loss: 0.3741 - val_acc: 0.9079\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3765 - acc: 0.8988 - val_loss: 0.3761 - val_acc: 0.9071\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3791 - acc: 0.8964 - val_loss: 0.3758 - val_acc: 0.9065\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 0.3796 - acc: 0.8989 - val_loss: 0.3770 - val_acc: 0.9069\n",
-      "{'val_loss': [0.37785097351074221, 0.37731364712715149, 0.37661438734531405, 0.37886159641742706, 0.37856462571620941, 0.37713949706554412, 0.37550454359054564, 0.37540296201705931, 0.37580586643218994, 0.37626878676414488, 0.3773591064929962, 0.37666828496456145, 0.37660351555347443, 0.37759391674995424, 0.37693574063777924, 0.37701914649009705, 0.37559629876613615, 0.37769697990417478, 0.37752313163280488, 0.37790378603935243, 0.3785025860309601, 0.37651138377189636, 0.37637851953506468, 0.3754533318758011, 0.37683316841125486, 0.37656428048610685, 0.37534701485633848, 0.37539958257675171, 0.37330716643333434, 0.375648184299469, 0.37437661690711976, 0.37600070507526395, 0.37349254970550538, 0.37409095671176912, 0.37544303872585294, 0.37571356678009032, 0.37537829804420469, 0.37495623900890351, 0.37714893469810484, 0.3750675146341324, 0.37481870002746581, 0.37313218743801119, 0.37582568845748904, 0.37627374584674833, 0.37577459394931795, 0.37462998654842378, 0.37410167636871339, 0.37613337566852567, 0.37578551383018494, 0.37696433467864993], 'val_acc': [0.90629999999999999, 0.90720000000000001, 0.90690000000000004, 0.90639999999999998, 0.90700000000000003, 0.90680000000000005, 0.90649999999999997, 0.90780000000000005, 0.90720000000000001, 0.90690000000000004, 0.90669999999999995, 0.90610000000000002, 0.90649999999999997, 0.90669999999999995, 0.90720000000000001, 0.90639999999999998, 0.90690000000000004, 0.90659999999999996, 0.90720000000000001, 0.90639999999999998, 0.90759999999999996, 0.9073, 0.90629999999999999, 0.90629999999999999, 0.90600000000000003, 0.90700000000000003, 0.90710000000000002, 0.90710000000000002, 0.90749999999999997, 0.9073, 0.90749999999999997, 0.90659999999999996, 0.9073, 0.90800000000000003, 0.90710000000000002, 0.90759999999999996, 0.90700000000000003, 0.90680000000000005, 0.90759999999999996, 0.90690000000000004, 0.90749999999999997, 0.90839999999999999, 0.90790000000000004, 0.90810000000000002, 0.90790000000000004, 0.9073, 0.90790000000000004, 0.90710000000000002, 0.90649999999999997, 0.90690000000000004], 'acc': [0.89669554058389478, 0.89681584861059005, 0.89745749117741414, 0.89785851782470472, 0.89541225539287927, 0.89679579722181435, 0.89753769650304782, 0.89659455128205123, 0.89422768143866405, 0.89585338464561926, 0.8957330766954138, 0.89681584859146768, 0.89619425731780711, 0.89601379527776415, 0.89697625926185731, 0.89908164900237264, 0.89693615652255076, 0.89655518128315836, 0.89719682390734978, 0.89475056139903453, 0.8963747192431154, 0.89679579720269187, 0.89621394230769236, 0.89743898522800258, 0.89663538662791442, 0.89751764515251697, 0.89583333333333337, 0.8951910725563248, 0.89703641323696015, 0.89703641317959282, 0.89751764513339449, 0.89765800445325339, 0.89880093042002218, 0.89685595121603956, 0.89791867181892993, 0.8977181585239683, 0.8949911774333027, 0.89649502723156582, 0.89813923644529992, 0.90028472894424427, 0.89781841516188787, 0.89809695512820509, 0.89864322414900455, 0.89837985240307838, 0.89858036577452982, 0.89797882579403276, 0.89763795312184491, 0.89882098173230818, 0.89641426282051284, 0.89886108435688017], 'loss': [0.38247765058082078, 0.38237306306405749, 0.38536368729711001, 0.38022890837099982, 0.38418068871843841, 0.38421753206656917, 0.38468616945566869, 0.38509517396107701, 0.38704997230280497, 0.38390190780334915, 0.38193131318297341, 0.38425878466686303, 0.38578086314118748, 0.38412407446947855, 0.38460791421732077, 0.37987503716300841, 0.378891275389515, 0.38247182903130694, 0.38302359059524721, 0.38172237312598684, 0.38006335567655491, 0.37968259891581757, 0.38745315521955492, 0.38236816986371253, 0.38026291069825335, 0.37708598499616292, 0.38020494740742905, 0.38690472353553651, 0.37743982981518748, 0.38049827865541536, 0.37883050631245324, 0.37772402394877286, 0.3792561863644367, 0.38013887082226278, 0.3788666189347929, 0.37660440887669017, 0.38446455074305103, 0.38157797130323734, 0.38074120329403133, 0.37764427879509116, 0.38006463212809349, 0.38043843278518091, 0.38019017297140512, 0.37979160633116227, 0.37523847440706448, 0.3762182190435836, 0.37966022781733716, 0.37660038402381141, 0.37906198035448024, 0.37974316005068864]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "epochs = 250\n",
-    "model = load_model('relu_at_3.h5')\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000017,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=200)\n",
-    "# Save the model\n",
-    "model.save('relu_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9187\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(\"relu\")\n",
-    "        model.load_weights('relu_at_'+str(i+1)+'.h5')\n",
-    "        model.save('relu_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('relu_at_'+str(i+1)+'.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 2.0201 - acc: 0.3906 - val_loss: 1.3828 - val_acc: 0.5563\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.4731 - acc: 0.5308 - val_loss: 1.1149 - val_acc: 0.6337\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.2980 - acc: 0.5922 - val_loss: 1.0801 - val_acc: 0.6678\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 1.2151 - acc: 0.6298 - val_loss: 0.9754 - val_acc: 0.6856\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1390 - acc: 0.6510 - val_loss: 1.5834 - val_acc: 0.6358\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1029 - acc: 0.6706 - val_loss: 0.7973 - val_acc: 0.7567\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0241 - acc: 0.6928 - val_loss: 0.7856 - val_acc: 0.7628\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9956 - acc: 0.7041 - val_loss: 1.2215 - val_acc: 0.7283\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0520 - acc: 0.7052 - val_loss: 1.6160 - val_acc: 0.7280\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0199 - acc: 0.7127 - val_loss: 0.7939 - val_acc: 0.7845\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0037 - acc: 0.7170 - val_loss: 0.7445 - val_acc: 0.7803\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9561 - acc: 0.7259 - val_loss: 0.7351 - val_acc: 0.7896\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9214 - acc: 0.7366 - val_loss: 0.6897 - val_acc: 0.8018\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9240 - acc: 0.7409 - val_loss: 0.7333 - val_acc: 0.7836\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9455 - acc: 0.7394 - val_loss: 0.6761 - val_acc: 0.8085\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9299 - acc: 0.7462 - val_loss: 0.6588 - val_acc: 0.8135\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9478 - acc: 0.7482 - val_loss: 0.7012 - val_acc: 0.8151\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9236 - acc: 0.7535 - val_loss: 0.6909 - val_acc: 0.8094\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9234 - acc: 0.7549 - val_loss: 0.6782 - val_acc: 0.8189\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8722 - acc: 0.7605 - val_loss: 0.6582 - val_acc: 0.8214\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8591 - acc: 0.7642 - val_loss: 0.6625 - val_acc: 0.8172\n",
-      "Epoch 22/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8269 - acc: 0.7725 - val_loss: 0.6077 - val_acc: 0.8343\n",
-      "Epoch 23/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8260 - acc: 0.7745 - val_loss: 0.6269 - val_acc: 0.8280\n",
-      "Epoch 24/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8197 - acc: 0.7769 - val_loss: 0.6442 - val_acc: 0.8197\n",
-      "Epoch 25/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8047 - acc: 0.7790 - val_loss: 0.6241 - val_acc: 0.8293\n",
-      "Epoch 26/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8188 - acc: 0.7806 - val_loss: 0.6076 - val_acc: 0.8352\n",
-      "Epoch 27/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8053 - acc: 0.7823 - val_loss: 0.6519 - val_acc: 0.8270\n",
-      "Epoch 28/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8434 - acc: 0.7808 - val_loss: 0.6330 - val_acc: 0.8318\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7910 - acc: 0.7879 - val_loss: 0.6119 - val_acc: 0.8378\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7811 - acc: 0.7891 - val_loss: 0.5965 - val_acc: 0.8451\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7707 - acc: 0.7902 - val_loss: 0.6395 - val_acc: 0.8271\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7675 - acc: 0.7932 - val_loss: 0.5810 - val_acc: 0.8464\n",
-      "Epoch 33/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7828 - acc: 0.7923 - val_loss: 0.6336 - val_acc: 0.8310\n",
-      "Epoch 34/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7781 - acc: 0.7948 - val_loss: 0.5732 - val_acc: 0.8480\n",
-      "Epoch 35/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7599 - acc: 0.7993 - val_loss: 0.5962 - val_acc: 0.8421\n",
-      "Epoch 36/200\n",
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-      "Epoch 87/200\n",
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-      "Epoch 88/200\n",
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-      "Epoch 89/200\n",
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-      "Epoch 91/200\n",
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-      "Epoch 92/200\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5423 - acc: 0.8576 - val_loss: 0.4586 - val_acc: 0.8902\n",
-      "Epoch 93/200\n",
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-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5499 - acc: 0.8535 - val_loss: 0.4542 - val_acc: 0.8877\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5362 - acc: 0.8586 - val_loss: 0.4561 - val_acc: 0.8869\n",
-      "Epoch 96/200\n",
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-      "Epoch 98/200\n",
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-      "Epoch 99/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5435 - acc: 0.8535 - val_loss: 0.4490 - val_acc: 0.8913\n",
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-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5295 - acc: 0.8574 - val_loss: 0.4588 - val_acc: 0.8837\n",
-      "Epoch 101/200\n",
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-      "Epoch 102/200\n",
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-      "Epoch 103/200\n",
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-      "Epoch 106/200\n",
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-      "Epoch 107/200\n",
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-      "Epoch 111/200\n",
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-      "Epoch 112/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.4821 - acc: 0.8717 - val_loss: 0.4369 - val_acc: 0.8953\n",
-      "Epoch 113/200\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4785 - acc: 0.8715 - val_loss: 0.4166 - val_acc: 0.8970\n",
-      "Epoch 115/200\n",
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-      "Epoch 116/200\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4740 - acc: 0.8720 - val_loss: 0.4342 - val_acc: 0.8906\n",
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-      "Epoch 123/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4622 - acc: 0.8761 - val_loss: 0.4203 - val_acc: 0.8945\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4684 - acc: 0.8729 - val_loss: 0.4186 - val_acc: 0.8958\n",
-      "Epoch 125/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4613 - acc: 0.8746 - val_loss: 0.4081 - val_acc: 0.8997\n",
-      "Epoch 126/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4521 - acc: 0.8776 - val_loss: 0.4075 - val_acc: 0.8980\n",
-      "Epoch 127/200\n",
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-      "Epoch 128/200\n",
-      "390/390 [==============================] - 181s 464ms/step - loss: 0.4431 - acc: 0.8817 - val_loss: 0.4046 - val_acc: 0.9000\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4360 - acc: 0.8839 - val_loss: 0.3950 - val_acc: 0.9007\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4390 - acc: 0.8811 - val_loss: 0.3987 - val_acc: 0.9011\n",
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-      "390/390 [==============================] - 178s 457ms/step - loss: 0.4398 - acc: 0.8813 - val_loss: 0.3989 - val_acc: 0.8995\n",
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-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4388 - acc: 0.8825 - val_loss: 0.4080 - val_acc: 0.8963\n",
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-      "Epoch 135/200\n",
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-      "390/390 [==============================] - 177s 454ms/step - loss: 0.4277 - acc: 0.8842 - val_loss: 0.3929 - val_acc: 0.9014\n",
-      "Epoch 137/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4341 - acc: 0.8825 - val_loss: 0.4060 - val_acc: 0.9006\n",
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-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4285 - acc: 0.8839 - val_loss: 0.3915 - val_acc: 0.9013\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4296 - acc: 0.8833 - val_loss: 0.4029 - val_acc: 0.9015\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4305 - acc: 0.8839 - val_loss: 0.4011 - val_acc: 0.9009\n",
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-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4228 - acc: 0.8851 - val_loss: 0.3914 - val_acc: 0.9003\n",
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-      "Epoch 151/200\n"
-     ]
-    },
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-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4115 - acc: 0.8871 - val_loss: 0.3882 - val_acc: 0.9029\n",
-      "Epoch 152/200\n",
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-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4095 - acc: 0.8899 - val_loss: 0.3948 - val_acc: 0.9024\n",
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-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4084 - acc: 0.8893 - val_loss: 0.3909 - val_acc: 0.9042\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4045 - acc: 0.8903 - val_loss: 0.3851 - val_acc: 0.9046\n",
-      "Epoch 158/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4040 - acc: 0.8916 - val_loss: 0.3865 - val_acc: 0.9032\n",
-      "Epoch 159/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4052 - acc: 0.8896 - val_loss: 0.3857 - val_acc: 0.9046\n",
-      "Epoch 160/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.4019 - acc: 0.8913 - val_loss: 0.3871 - val_acc: 0.9056\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4055 - acc: 0.8913 - val_loss: 0.3808 - val_acc: 0.9062\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4025 - acc: 0.8923 - val_loss: 0.3901 - val_acc: 0.9044\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8910 - val_loss: 0.3853 - val_acc: 0.9044\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4027 - acc: 0.8910 - val_loss: 0.3823 - val_acc: 0.9061\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3982 - acc: 0.8928 - val_loss: 0.3831 - val_acc: 0.9062\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8903 - val_loss: 0.3881 - val_acc: 0.9056\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3985 - acc: 0.8912 - val_loss: 0.3836 - val_acc: 0.9070\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3989 - acc: 0.8925 - val_loss: 0.3874 - val_acc: 0.9035\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4048 - acc: 0.8895 - val_loss: 0.3850 - val_acc: 0.9034\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3951 - acc: 0.8937 - val_loss: 0.3849 - val_acc: 0.9045\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3978 - acc: 0.8920 - val_loss: 0.3835 - val_acc: 0.9052\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3997 - acc: 0.8901 - val_loss: 0.3865 - val_acc: 0.9044\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3989 - acc: 0.8913 - val_loss: 0.3794 - val_acc: 0.9049\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3992 - acc: 0.8915 - val_loss: 0.3835 - val_acc: 0.9040\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3927 - acc: 0.8924 - val_loss: 0.3854 - val_acc: 0.9039\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3928 - acc: 0.8938 - val_loss: 0.3804 - val_acc: 0.9056\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3919 - acc: 0.8931 - val_loss: 0.3817 - val_acc: 0.9057\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3892 - acc: 0.8952 - val_loss: 0.3801 - val_acc: 0.9065\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3919 - acc: 0.8941 - val_loss: 0.3808 - val_acc: 0.9056\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3892 - acc: 0.8936 - val_loss: 0.3816 - val_acc: 0.9055\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.3855 - acc: 0.8959 - val_loss: 0.3810 - val_acc: 0.9069\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3895 - acc: 0.8943 - val_loss: 0.3812 - val_acc: 0.9051\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3922 - acc: 0.8943 - val_loss: 0.3776 - val_acc: 0.9068\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3883 - acc: 0.8951 - val_loss: 0.3796 - val_acc: 0.9074\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 180s 461ms/step - loss: 0.3942 - acc: 0.8928 - val_loss: 0.3794 - val_acc: 0.9056\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 179s 459ms/step - loss: 0.3870 - acc: 0.8960 - val_loss: 0.3777 - val_acc: 0.9072\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3899 - acc: 0.8941 - val_loss: 0.3780 - val_acc: 0.9066\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 178s 456ms/step - loss: 0.3853 - acc: 0.8971 - val_loss: 0.3778 - val_acc: 0.9066\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 178s 458ms/step - loss: 0.3878 - acc: 0.8958 - val_loss: 0.3792 - val_acc: 0.9076\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3873 - acc: 0.8956 - val_loss: 0.3781 - val_acc: 0.9085\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3876 - acc: 0.8948 - val_loss: 0.3769 - val_acc: 0.9078\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3837 - acc: 0.8973 - val_loss: 0.3768 - val_acc: 0.9075\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3876 - acc: 0.8946 - val_loss: 0.3755 - val_acc: 0.9085\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3926 - acc: 0.8941 - val_loss: 0.3756 - val_acc: 0.9087\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3839 - acc: 0.8957 - val_loss: 0.3782 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3867 - acc: 0.8934 - val_loss: 0.3760 - val_acc: 0.9079\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3884 - acc: 0.8948 - val_loss: 0.3788 - val_acc: 0.9064\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3857 - acc: 0.8943 - val_loss: 0.3773 - val_acc: 0.9082\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3882 - acc: 0.8937 - val_loss: 0.3750 - val_acc: 0.9078\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.3839 - acc: 0.8945 - val_loss: 0.3787 - val_acc: 0.9079\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"relu\", \"relu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - \n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    model = load_model('relu_at_+'str(i)'+.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_test-checkpoint.ipynb b/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_test-checkpoint.ipynb
deleted file mode 100644
index b377fcb..0000000
--- a/cifar/Deeper_CNN/.ipynb_checkpoints/cifar_kaggle_test-checkpoint.ipynb
+++ /dev/null
@@ -1,1713 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "from keras.models import load_model\n",
-    "import keras.regularizers as regularizers\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 181s 464ms/step - loss: 2.0738 - acc: 0.3787 - val_loss: 1.3396 - val_acc: 0.5477\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 1.4269 - acc: 0.5314 - val_loss: 1.0368 - val_acc: 0.6504\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.2076 - acc: 0.6036 - val_loss: 0.8844 - val_acc: 0.7165\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.0982 - acc: 0.6431 - val_loss: 0.9312 - val_acc: 0.7087\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.0170 - acc: 0.6697 - val_loss: 0.8638 - val_acc: 0.7204\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.9570 - acc: 0.6918 - val_loss: 0.7709 - val_acc: 0.7666\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9127 - acc: 0.7112 - val_loss: 0.7347 - val_acc: 0.7747\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8836 - acc: 0.7216 - val_loss: 0.6990 - val_acc: 0.7934\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.8563 - acc: 0.7334 - val_loss: 0.6695 - val_acc: 0.8014\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.8293 - acc: 0.7423 - val_loss: 0.6457 - val_acc: 0.8079\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.8102 - acc: 0.7501 - val_loss: 0.6209 - val_acc: 0.8144\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.7946 - acc: 0.7554 - val_loss: 0.6253 - val_acc: 0.8169\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.7806 - acc: 0.7630 - val_loss: 0.6238 - val_acc: 0.8160\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7676 - acc: 0.7677 - val_loss: 0.6359 - val_acc: 0.8151\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7511 - acc: 0.7760 - val_loss: 0.5995 - val_acc: 0.8298\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7422 - acc: 0.7781 - val_loss: 0.5971 - val_acc: 0.8316\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7293 - acc: 0.7822 - val_loss: 0.5970 - val_acc: 0.8326\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7216 - acc: 0.7869 - val_loss: 0.5847 - val_acc: 0.8387\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7170 - acc: 0.7887 - val_loss: 0.5724 - val_acc: 0.8416\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7001 - acc: 0.7967 - val_loss: 0.5934 - val_acc: 0.8340\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7073 - acc: 0.7944 - val_loss: 0.5727 - val_acc: 0.8420\n",
-      "Epoch 22/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6971 - acc: 0.7980 - val_loss: 0.5887 - val_acc: 0.8398\n",
-      "Epoch 23/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6881 - acc: 0.8010 - val_loss: 0.5795 - val_acc: 0.8377\n",
-      "Epoch 24/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6834 - acc: 0.8002 - val_loss: 0.5555 - val_acc: 0.8478\n",
-      "Epoch 25/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6780 - acc: 0.8056 - val_loss: 0.5808 - val_acc: 0.8450\n",
-      "Epoch 26/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6694 - acc: 0.8087 - val_loss: 0.5288 - val_acc: 0.8604\n",
-      "Epoch 27/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6664 - acc: 0.8102 - val_loss: 0.5656 - val_acc: 0.8519\n",
-      "Epoch 28/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6637 - acc: 0.8117 - val_loss: 0.5846 - val_acc: 0.8441\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6652 - acc: 0.8115 - val_loss: 0.5603 - val_acc: 0.8541\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.6602 - acc: 0.8138 - val_loss: 0.5834 - val_acc: 0.8460\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.6539 - acc: 0.8143 - val_loss: 0.5562 - val_acc: 0.8533\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.6479 - acc: 0.8185 - val_loss: 0.5478 - val_acc: 0.8554\n",
-      "Epoch 33/200\n",
-      "390/390 [==============================] - 176s 453ms/step - loss: 0.6480 - acc: 0.8174 - val_loss: 0.5290 - val_acc: 0.8623\n",
-      "Epoch 34/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.6456 - acc: 0.8186 - val_loss: 0.5237 - val_acc: 0.8662\n",
-      "Epoch 35/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.6409 - acc: 0.8200 - val_loss: 0.5190 - val_acc: 0.8657\n",
-      "Epoch 36/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.6417 - acc: 0.8206 - val_loss: 0.5265 - val_acc: 0.8661\n",
-      "Epoch 37/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6397 - acc: 0.8211 - val_loss: 0.5297 - val_acc: 0.8629\n",
-      "Epoch 38/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6375 - acc: 0.8255 - val_loss: 0.5341 - val_acc: 0.8622\n",
-      "Epoch 39/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6396 - acc: 0.8250 - val_loss: 0.5483 - val_acc: 0.8583\n",
-      "Epoch 40/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6333 - acc: 0.8252 - val_loss: 0.5127 - val_acc: 0.8709\n",
-      "Epoch 41/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6323 - acc: 0.8232 - val_loss: 0.5198 - val_acc: 0.8644\n",
-      "Epoch 42/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6191 - acc: 0.8302 - val_loss: 0.5101 - val_acc: 0.8725\n",
-      "Epoch 43/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6240 - acc: 0.8282 - val_loss: 0.5391 - val_acc: 0.8662\n",
-      "Epoch 44/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6241 - acc: 0.8276 - val_loss: 0.5139 - val_acc: 0.8679\n",
-      "Epoch 45/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6245 - acc: 0.8274 - val_loss: 0.5194 - val_acc: 0.8676\n",
-      "Epoch 46/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6174 - acc: 0.8309 - val_loss: 0.5196 - val_acc: 0.8658\n",
-      "Epoch 47/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6248 - acc: 0.8296 - val_loss: 0.5104 - val_acc: 0.8718\n",
-      "Epoch 48/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6160 - acc: 0.8336 - val_loss: 0.5210 - val_acc: 0.8695\n",
-      "Epoch 49/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6149 - acc: 0.8317 - val_loss: 0.5055 - val_acc: 0.8755\n",
-      "Epoch 50/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6095 - acc: 0.8353 - val_loss: 0.5209 - val_acc: 0.8665\n",
-      "Epoch 51/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6137 - acc: 0.8347 - val_loss: 0.5293 - val_acc: 0.8643\n",
-      "Epoch 52/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6149 - acc: 0.8335 - val_loss: 0.5048 - val_acc: 0.8716\n",
-      "Epoch 53/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6100 - acc: 0.8361 - val_loss: 0.5130 - val_acc: 0.8726\n",
-      "Epoch 54/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6075 - acc: 0.8358 - val_loss: 0.5101 - val_acc: 0.8688\n",
-      "Epoch 55/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6033 - acc: 0.8362 - val_loss: 0.5189 - val_acc: 0.8666\n",
-      "Epoch 56/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6120 - acc: 0.8348 - val_loss: 0.5211 - val_acc: 0.8709\n",
-      "Epoch 57/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5973 - acc: 0.8390 - val_loss: 0.5192 - val_acc: 0.8670\n",
-      "Epoch 58/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6051 - acc: 0.8386 - val_loss: 0.5078 - val_acc: 0.8772\n",
-      "Epoch 59/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5959 - acc: 0.8397 - val_loss: 0.5162 - val_acc: 0.8681\n",
-      "Epoch 60/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6022 - acc: 0.8385 - val_loss: 0.5355 - val_acc: 0.8681\n",
-      "Epoch 61/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5930 - acc: 0.8403 - val_loss: 0.5117 - val_acc: 0.8745\n",
-      "Epoch 62/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5985 - acc: 0.8409 - val_loss: 0.5003 - val_acc: 0.8769\n",
-      "Epoch 63/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5990 - acc: 0.8394 - val_loss: 0.4987 - val_acc: 0.8739\n",
-      "Epoch 64/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5972 - acc: 0.8404 - val_loss: 0.5158 - val_acc: 0.8737\n",
-      "Epoch 65/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5946 - acc: 0.8408 - val_loss: 0.5010 - val_acc: 0.8760\n",
-      "Epoch 66/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5931 - acc: 0.8412 - val_loss: 0.5097 - val_acc: 0.8718\n",
-      "Epoch 67/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5990 - acc: 0.8399 - val_loss: 0.4921 - val_acc: 0.8805\n",
-      "Epoch 68/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5945 - acc: 0.8424 - val_loss: 0.4981 - val_acc: 0.8723\n",
-      "Epoch 69/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5967 - acc: 0.8404 - val_loss: 0.5170 - val_acc: 0.8749\n",
-      "Epoch 70/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5886 - acc: 0.8450 - val_loss: 0.4881 - val_acc: 0.8829\n",
-      "Epoch 71/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5883 - acc: 0.8448 - val_loss: 0.5076 - val_acc: 0.8767\n",
-      "Epoch 72/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5882 - acc: 0.8442 - val_loss: 0.4994 - val_acc: 0.8809\n",
-      "Epoch 73/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5904 - acc: 0.8448 - val_loss: 0.5110 - val_acc: 0.8738\n",
-      "Epoch 74/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5892 - acc: 0.8439 - val_loss: 0.5284 - val_acc: 0.8652\n",
-      "Epoch 75/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5921 - acc: 0.8422 - val_loss: 0.5199 - val_acc: 0.8696\n",
-      "Epoch 76/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5479 - acc: 0.8577 - val_loss: 0.4723 - val_acc: 0.8866\n",
-      "Epoch 77/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5350 - acc: 0.8624 - val_loss: 0.4661 - val_acc: 0.8872\n",
-      "Epoch 78/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5290 - acc: 0.8610 - val_loss: 0.4749 - val_acc: 0.8869\n",
-      "Epoch 79/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5201 - acc: 0.8647 - val_loss: 0.4587 - val_acc: 0.8902\n",
-      "Epoch 80/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5172 - acc: 0.8647 - val_loss: 0.4545 - val_acc: 0.8921\n",
-      "Epoch 81/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5120 - acc: 0.8671 - val_loss: 0.4536 - val_acc: 0.8896\n",
-      "Epoch 82/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5096 - acc: 0.8673 - val_loss: 0.4476 - val_acc: 0.8930\n",
-      "Epoch 83/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5096 - acc: 0.8676 - val_loss: 0.4417 - val_acc: 0.8924\n",
-      "Epoch 84/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5062 - acc: 0.8658 - val_loss: 0.4310 - val_acc: 0.8963\n",
-      "Epoch 85/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4995 - acc: 0.8675 - val_loss: 0.4582 - val_acc: 0.8878\n",
-      "Epoch 86/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5029 - acc: 0.8681 - val_loss: 0.4409 - val_acc: 0.8950\n",
-      "Epoch 87/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5016 - acc: 0.8660 - val_loss: 0.4393 - val_acc: 0.8952\n",
-      "Epoch 88/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4947 - acc: 0.8676 - val_loss: 0.4402 - val_acc: 0.8957\n",
-      "Epoch 89/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4988 - acc: 0.8681 - val_loss: 0.4408 - val_acc: 0.8913\n",
-      "Epoch 90/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4920 - acc: 0.8706 - val_loss: 0.4598 - val_acc: 0.8858\n",
-      "Epoch 91/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4922 - acc: 0.8701 - val_loss: 0.4346 - val_acc: 0.8926\n",
-      "Epoch 92/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4867 - acc: 0.8718 - val_loss: 0.4358 - val_acc: 0.8934\n",
-      "Epoch 93/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4882 - acc: 0.8698 - val_loss: 0.4363 - val_acc: 0.8902\n",
-      "Epoch 94/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4898 - acc: 0.8692 - val_loss: 0.4398 - val_acc: 0.8910\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4860 - acc: 0.8715 - val_loss: 0.4347 - val_acc: 0.8945\n",
-      "Epoch 96/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4859 - acc: 0.8704 - val_loss: 0.4294 - val_acc: 0.8947\n",
-      "Epoch 97/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4858 - acc: 0.8717 - val_loss: 0.4414 - val_acc: 0.8877\n",
-      "Epoch 98/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4723 - acc: 0.8761 - val_loss: 0.4346 - val_acc: 0.8959\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4806 - acc: 0.8716 - val_loss: 0.4317 - val_acc: 0.8894\n",
-      "Epoch 100/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4771 - acc: 0.8725 - val_loss: 0.4303 - val_acc: 0.8930\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4691 - acc: 0.8759 - val_loss: 0.4073 - val_acc: 0.9027\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4557 - acc: 0.8786 - val_loss: 0.4046 - val_acc: 0.9021\n",
-      "Epoch 103/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4461 - acc: 0.8828 - val_loss: 0.4059 - val_acc: 0.9004\n",
-      "Epoch 104/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4506 - acc: 0.8798 - val_loss: 0.4070 - val_acc: 0.9018\n",
-      "Epoch 105/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4484 - acc: 0.8827 - val_loss: 0.4061 - val_acc: 0.9037\n",
-      "Epoch 106/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4457 - acc: 0.8809 - val_loss: 0.3990 - val_acc: 0.9041\n",
-      "Epoch 107/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4471 - acc: 0.8810 - val_loss: 0.4061 - val_acc: 0.9027\n",
-      "Epoch 108/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4377 - acc: 0.8840 - val_loss: 0.3989 - val_acc: 0.9050\n",
-      "Epoch 109/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4420 - acc: 0.8824 - val_loss: 0.4043 - val_acc: 0.9026\n",
-      "Epoch 110/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4411 - acc: 0.8820 - val_loss: 0.3997 - val_acc: 0.9013\n",
-      "Epoch 111/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4390 - acc: 0.8825 - val_loss: 0.4076 - val_acc: 0.9025\n",
-      "Epoch 112/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4378 - acc: 0.8818 - val_loss: 0.4060 - val_acc: 0.9019\n",
-      "Epoch 113/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4362 - acc: 0.8830 - val_loss: 0.3946 - val_acc: 0.9047\n",
-      "Epoch 114/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4329 - acc: 0.8838 - val_loss: 0.3950 - val_acc: 0.9034\n",
-      "Epoch 115/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4353 - acc: 0.8835 - val_loss: 0.3903 - val_acc: 0.9013\n",
-      "Epoch 116/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4328 - acc: 0.8837 - val_loss: 0.4027 - val_acc: 0.9007\n",
-      "Epoch 117/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4329 - acc: 0.8836 - val_loss: 0.3977 - val_acc: 0.9026\n",
-      "Epoch 118/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4287 - acc: 0.8850 - val_loss: 0.3907 - val_acc: 0.9043\n",
-      "Epoch 119/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4311 - acc: 0.8829 - val_loss: 0.4071 - val_acc: 0.8989\n",
-      "Epoch 120/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4293 - acc: 0.8838 - val_loss: 0.4066 - val_acc: 0.8984\n",
-      "Epoch 121/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4245 - acc: 0.8864 - val_loss: 0.4008 - val_acc: 0.9011\n",
-      "Epoch 122/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4226 - acc: 0.8862 - val_loss: 0.3902 - val_acc: 0.9007\n",
-      "Epoch 123/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4295 - acc: 0.8836 - val_loss: 0.4070 - val_acc: 0.8995\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4223 - acc: 0.8860 - val_loss: 0.3952 - val_acc: 0.9024\n",
-      "Epoch 125/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4224 - acc: 0.8862 - val_loss: 0.3888 - val_acc: 0.9026\n",
-      "Epoch 126/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4120 - acc: 0.8894 - val_loss: 0.3801 - val_acc: 0.9060\n",
-      "Epoch 127/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4033 - acc: 0.8921 - val_loss: 0.3826 - val_acc: 0.9075\n",
-      "Epoch 128/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4016 - acc: 0.8919 - val_loss: 0.3783 - val_acc: 0.9093\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3960 - acc: 0.8961 - val_loss: 0.3833 - val_acc: 0.9066\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4076 - acc: 0.8898 - val_loss: 0.3781 - val_acc: 0.9081\n",
-      "Epoch 131/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3994 - acc: 0.8931 - val_loss: 0.3823 - val_acc: 0.9070\n",
-      "Epoch 132/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3933 - acc: 0.8942 - val_loss: 0.3825 - val_acc: 0.9071\n",
-      "Epoch 133/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3984 - acc: 0.8931 - val_loss: 0.3786 - val_acc: 0.9066\n",
-      "Epoch 134/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3906 - acc: 0.8955 - val_loss: 0.3804 - val_acc: 0.9079\n",
-      "Epoch 135/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3942 - acc: 0.8936 - val_loss: 0.3854 - val_acc: 0.9065\n",
-      "Epoch 136/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3917 - acc: 0.8940 - val_loss: 0.3771 - val_acc: 0.9087\n",
-      "Epoch 137/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3895 - acc: 0.8944 - val_loss: 0.3792 - val_acc: 0.9085\n",
-      "Epoch 138/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3935 - acc: 0.8935 - val_loss: 0.3757 - val_acc: 0.9068\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3915 - acc: 0.8926 - val_loss: 0.3780 - val_acc: 0.9084\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3897 - acc: 0.8946 - val_loss: 0.3734 - val_acc: 0.9093\n",
-      "Epoch 141/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3893 - acc: 0.8962 - val_loss: 0.3782 - val_acc: 0.9086\n",
-      "Epoch 142/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3918 - acc: 0.8945 - val_loss: 0.3733 - val_acc: 0.9096\n",
-      "Epoch 143/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3825 - acc: 0.8968 - val_loss: 0.3733 - val_acc: 0.9079\n",
-      "Epoch 144/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3880 - acc: 0.8968 - val_loss: 0.3754 - val_acc: 0.9064\n",
-      "Epoch 145/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3837 - acc: 0.8958 - val_loss: 0.3733 - val_acc: 0.9075\n",
-      "Epoch 146/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3863 - acc: 0.8965 - val_loss: 0.3781 - val_acc: 0.9082\n",
-      "Epoch 147/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3806 - acc: 0.8982 - val_loss: 0.3708 - val_acc: 0.9096\n",
-      "Epoch 148/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3798 - acc: 0.8983 - val_loss: 0.3827 - val_acc: 0.9062\n",
-      "Epoch 149/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3790 - acc: 0.8982 - val_loss: 0.3792 - val_acc: 0.9059\n",
-      "Epoch 150/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3845 - acc: 0.8958 - val_loss: 0.3769 - val_acc: 0.9065\n",
-      "Epoch 151/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3757 - acc: 0.8990 - val_loss: 0.3710 - val_acc: 0.9084\n",
-      "Epoch 152/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3753 - acc: 0.8992 - val_loss: 0.3667 - val_acc: 0.9089\n",
-      "Epoch 153/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3777 - acc: 0.8979 - val_loss: 0.3694 - val_acc: 0.9082\n",
-      "Epoch 154/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3701 - acc: 0.9016 - val_loss: 0.3693 - val_acc: 0.9076\n",
-      "Epoch 155/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3694 - acc: 0.9005 - val_loss: 0.3673 - val_acc: 0.9090\n",
-      "Epoch 156/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3658 - acc: 0.9016 - val_loss: 0.3685 - val_acc: 0.9088\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3661 - acc: 0.9015 - val_loss: 0.3656 - val_acc: 0.9093\n",
-      "Epoch 158/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3675 - acc: 0.9023 - val_loss: 0.3692 - val_acc: 0.9102\n",
-      "Epoch 159/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3704 - acc: 0.8999 - val_loss: 0.3653 - val_acc: 0.9095\n",
-      "Epoch 160/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3600 - acc: 0.9032 - val_loss: 0.3660 - val_acc: 0.9105\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3649 - acc: 0.9033 - val_loss: 0.3668 - val_acc: 0.9091\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3672 - acc: 0.9009 - val_loss: 0.3667 - val_acc: 0.9109\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3621 - acc: 0.9018 - val_loss: 0.3613 - val_acc: 0.9107\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3634 - acc: 0.9027 - val_loss: 0.3636 - val_acc: 0.9114\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3643 - acc: 0.9029 - val_loss: 0.3636 - val_acc: 0.9086\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3633 - acc: 0.9015 - val_loss: 0.3660 - val_acc: 0.9109\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3656 - acc: 0.9013 - val_loss: 0.3653 - val_acc: 0.9101\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3562 - acc: 0.9058 - val_loss: 0.3678 - val_acc: 0.9097\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3578 - acc: 0.9045 - val_loss: 0.3601 - val_acc: 0.9119\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3656 - acc: 0.9004 - val_loss: 0.3641 - val_acc: 0.9106\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3629 - acc: 0.9014 - val_loss: 0.3638 - val_acc: 0.9112\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3626 - acc: 0.9024 - val_loss: 0.3631 - val_acc: 0.9109\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3599 - acc: 0.9024 - val_loss: 0.3660 - val_acc: 0.9090\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3592 - acc: 0.9043 - val_loss: 0.3651 - val_acc: 0.9098\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3592 - acc: 0.9028 - val_loss: 0.3653 - val_acc: 0.9098\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3566 - acc: 0.9041 - val_loss: 0.3640 - val_acc: 0.9092\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3496 - acc: 0.9073 - val_loss: 0.3636 - val_acc: 0.9112\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3600 - acc: 0.9033 - val_loss: 0.3636 - val_acc: 0.9116\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3510 - acc: 0.9057 - val_loss: 0.3639 - val_acc: 0.9126\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3477 - acc: 0.9073 - val_loss: 0.3602 - val_acc: 0.9134\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3510 - acc: 0.9048 - val_loss: 0.3616 - val_acc: 0.9120\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3527 - acc: 0.9043 - val_loss: 0.3637 - val_acc: 0.9100\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3509 - acc: 0.9055 - val_loss: 0.3614 - val_acc: 0.9127\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3560 - acc: 0.9039 - val_loss: 0.3578 - val_acc: 0.9119\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3473 - acc: 0.9077 - val_loss: 0.3626 - val_acc: 0.9117\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3477 - acc: 0.9048 - val_loss: 0.3616 - val_acc: 0.9120\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3479 - acc: 0.9064 - val_loss: 0.3593 - val_acc: 0.9135\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3538 - acc: 0.9041 - val_loss: 0.3615 - val_acc: 0.9104\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3488 - acc: 0.9061 - val_loss: 0.3621 - val_acc: 0.9110\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3521 - acc: 0.9066 - val_loss: 0.3612 - val_acc: 0.9110\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3498 - acc: 0.9060 - val_loss: 0.3584 - val_acc: 0.9119\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3525 - acc: 0.9046 - val_loss: 0.3589 - val_acc: 0.9116\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3438 - acc: 0.9087 - val_loss: 0.3593 - val_acc: 0.9112\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3505 - acc: 0.9053 - val_loss: 0.3601 - val_acc: 0.9108\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3490 - acc: 0.9054 - val_loss: 0.3583 - val_acc: 0.9119\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3462 - acc: 0.9072 - val_loss: 0.3578 - val_acc: 0.9129\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3523 - acc: 0.9055 - val_loss: 0.3582 - val_acc: 0.9116\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3487 - acc: 0.9055 - val_loss: 0.3586 - val_acc: 0.9110\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3506 - acc: 0.9060 - val_loss: 0.3575 - val_acc: 0.9119\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3483 - acc: 0.9067 - val_loss: 0.3584 - val_acc: 0.9115\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = e_swish_2, \"e_swish_2\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training\n",
-    "batch_size = 128\n",
-    "epochs=200\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('e_swish_2_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 2.0201 - acc: 0.3906 - val_loss: 1.3828 - val_acc: 0.5563\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.4731 - acc: 0.5308 - val_loss: 1.1149 - val_acc: 0.6337\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.2980 - acc: 0.5922 - val_loss: 1.0801 - val_acc: 0.6678\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 1.2151 - acc: 0.6298 - val_loss: 0.9754 - val_acc: 0.6856\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1390 - acc: 0.6510 - val_loss: 1.5834 - val_acc: 0.6358\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1029 - acc: 0.6706 - val_loss: 0.7973 - val_acc: 0.7567\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0241 - acc: 0.6928 - val_loss: 0.7856 - val_acc: 0.7628\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9956 - acc: 0.7041 - val_loss: 1.2215 - val_acc: 0.7283\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0520 - acc: 0.7052 - val_loss: 1.6160 - val_acc: 0.7280\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0199 - acc: 0.7127 - val_loss: 0.7939 - val_acc: 0.7845\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0037 - acc: 0.7170 - val_loss: 0.7445 - val_acc: 0.7803\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9561 - acc: 0.7259 - val_loss: 0.7351 - val_acc: 0.7896\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9214 - acc: 0.7366 - val_loss: 0.6897 - val_acc: 0.8018\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9240 - acc: 0.7409 - val_loss: 0.7333 - val_acc: 0.7836\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9455 - acc: 0.7394 - val_loss: 0.6761 - val_acc: 0.8085\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9299 - acc: 0.7462 - val_loss: 0.6588 - val_acc: 0.8135\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9478 - acc: 0.7482 - val_loss: 0.7012 - val_acc: 0.8151\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9236 - acc: 0.7535 - val_loss: 0.6909 - val_acc: 0.8094\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9234 - acc: 0.7549 - val_loss: 0.6782 - val_acc: 0.8189\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8722 - acc: 0.7605 - val_loss: 0.6582 - val_acc: 0.8214\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8591 - acc: 0.7642 - val_loss: 0.6625 - val_acc: 0.8172\n",
-      "Epoch 22/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8269 - acc: 0.7725 - val_loss: 0.6077 - val_acc: 0.8343\n",
-      "Epoch 23/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8260 - acc: 0.7745 - val_loss: 0.6269 - val_acc: 0.8280\n",
-      "Epoch 24/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8197 - acc: 0.7769 - val_loss: 0.6442 - val_acc: 0.8197\n",
-      "Epoch 25/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8047 - acc: 0.7790 - val_loss: 0.6241 - val_acc: 0.8293\n",
-      "Epoch 26/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8188 - acc: 0.7806 - val_loss: 0.6076 - val_acc: 0.8352\n",
-      "Epoch 27/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8053 - acc: 0.7823 - val_loss: 0.6519 - val_acc: 0.8270\n",
-      "Epoch 28/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8434 - acc: 0.7808 - val_loss: 0.6330 - val_acc: 0.8318\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7910 - acc: 0.7879 - val_loss: 0.6119 - val_acc: 0.8378\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7811 - acc: 0.7891 - val_loss: 0.5965 - val_acc: 0.8451\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7707 - acc: 0.7902 - val_loss: 0.6395 - val_acc: 0.8271\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7675 - acc: 0.7932 - val_loss: 0.5810 - val_acc: 0.8464\n",
-      "Epoch 33/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7828 - acc: 0.7923 - val_loss: 0.6336 - val_acc: 0.8310\n",
-      "Epoch 34/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7781 - acc: 0.7948 - val_loss: 0.5732 - val_acc: 0.8480\n",
-      "Epoch 35/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7599 - acc: 0.7993 - val_loss: 0.5962 - val_acc: 0.8421\n",
-      "Epoch 36/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7571 - acc: 0.7990 - val_loss: 0.6151 - val_acc: 0.8365\n",
-      "Epoch 37/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7565 - acc: 0.7981 - val_loss: 0.5864 - val_acc: 0.8464\n",
-      "Epoch 38/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7765 - acc: 0.7984 - val_loss: 0.6086 - val_acc: 0.8371\n",
-      "Epoch 39/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7530 - acc: 0.7973 - val_loss: 0.5760 - val_acc: 0.8520\n",
-      "Epoch 40/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7489 - acc: 0.8030 - val_loss: 0.6164 - val_acc: 0.8407\n",
-      "Epoch 41/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7573 - acc: 0.8010 - val_loss: 0.6014 - val_acc: 0.8493\n",
-      "Epoch 42/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7443 - acc: 0.8053 - val_loss: 0.5915 - val_acc: 0.8531\n",
-      "Epoch 43/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7395 - acc: 0.8055 - val_loss: 0.5853 - val_acc: 0.8527\n",
-      "Epoch 44/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7227 - acc: 0.8074 - val_loss: 0.5628 - val_acc: 0.8596\n",
-      "Epoch 45/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7235 - acc: 0.8086 - val_loss: 0.5740 - val_acc: 0.8546\n",
-      "Epoch 46/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7244 - acc: 0.8098 - val_loss: 0.5851 - val_acc: 0.8470\n",
-      "Epoch 47/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7256 - acc: 0.8081 - val_loss: 0.5751 - val_acc: 0.8535\n",
-      "Epoch 48/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7238 - acc: 0.8093 - val_loss: 0.5957 - val_acc: 0.8468\n",
-      "Epoch 49/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7146 - acc: 0.8112 - val_loss: 0.5766 - val_acc: 0.8543\n",
-      "Epoch 50/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7047 - acc: 0.8129 - val_loss: 0.5858 - val_acc: 0.8507\n",
-      "Epoch 51/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7186 - acc: 0.8132 - val_loss: 0.5443 - val_acc: 0.8671\n",
-      "Epoch 52/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7213 - acc: 0.8124 - val_loss: 0.5546 - val_acc: 0.8629\n",
-      "Epoch 53/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7064 - acc: 0.8124 - val_loss: 0.5631 - val_acc: 0.8606\n",
-      "Epoch 54/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7087 - acc: 0.8195 - val_loss: 0.5347 - val_acc: 0.8653\n",
-      "Epoch 55/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7108 - acc: 0.8152 - val_loss: 0.5700 - val_acc: 0.8593\n",
-      "Epoch 56/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7082 - acc: 0.8148 - val_loss: 0.5746 - val_acc: 0.8537\n",
-      "Epoch 57/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6987 - acc: 0.8188 - val_loss: 0.5549 - val_acc: 0.8612\n",
-      "Epoch 58/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6983 - acc: 0.8193 - val_loss: 0.5453 - val_acc: 0.8599\n",
-      "Epoch 59/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7052 - acc: 0.8172 - val_loss: 0.5706 - val_acc: 0.8608\n",
-      "Epoch 60/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6905 - acc: 0.8199 - val_loss: 0.5770 - val_acc: 0.8546\n",
-      "Epoch 61/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6847 - acc: 0.8224 - val_loss: 0.5566 - val_acc: 0.8653\n",
-      "Epoch 62/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7043 - acc: 0.8194 - val_loss: 0.5629 - val_acc: 0.8581\n",
-      "Epoch 63/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6949 - acc: 0.8193 - val_loss: 0.5522 - val_acc: 0.8611\n",
-      "Epoch 64/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6857 - acc: 0.8212 - val_loss: 0.5427 - val_acc: 0.8663\n",
-      "Epoch 65/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6768 - acc: 0.8262 - val_loss: 0.5607 - val_acc: 0.8605\n",
-      "Epoch 66/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6842 - acc: 0.8230 - val_loss: 0.5553 - val_acc: 0.8618\n",
-      "Epoch 67/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6684 - acc: 0.8280 - val_loss: 0.5316 - val_acc: 0.8725\n",
-      "Epoch 68/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6789 - acc: 0.8240 - val_loss: 0.5659 - val_acc: 0.8556\n",
-      "Epoch 69/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6752 - acc: 0.8236 - val_loss: 0.5545 - val_acc: 0.8627\n",
-      "Epoch 70/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6796 - acc: 0.8253 - val_loss: 0.5433 - val_acc: 0.8695\n",
-      "Epoch 71/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6776 - acc: 0.8265 - val_loss: 0.5272 - val_acc: 0.8718\n",
-      "Epoch 72/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6764 - acc: 0.8266 - val_loss: 0.5555 - val_acc: 0.8612\n",
-      "Epoch 73/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6706 - acc: 0.8262 - val_loss: 0.5707 - val_acc: 0.8638\n",
-      "Epoch 74/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6767 - acc: 0.8271 - val_loss: 0.5402 - val_acc: 0.8703\n",
-      "Epoch 75/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6697 - acc: 0.8265 - val_loss: 0.5153 - val_acc: 0.8720\n",
-      "Epoch 76/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6226 - acc: 0.8408 - val_loss: 0.4931 - val_acc: 0.8828\n",
-      "Epoch 77/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6095 - acc: 0.8447 - val_loss: 0.4853 - val_acc: 0.8847\n",
-      "Epoch 78/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5955 - acc: 0.8486 - val_loss: 0.4940 - val_acc: 0.8829\n",
-      "Epoch 79/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5962 - acc: 0.8474 - val_loss: 0.4920 - val_acc: 0.8826\n",
-      "Epoch 80/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5897 - acc: 0.8493 - val_loss: 0.4860 - val_acc: 0.8858\n",
-      "Epoch 81/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5869 - acc: 0.8470 - val_loss: 0.4761 - val_acc: 0.8886\n",
-      "Epoch 82/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5713 - acc: 0.8523 - val_loss: 0.4877 - val_acc: 0.8820\n",
-      "Epoch 83/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5799 - acc: 0.8501 - val_loss: 0.4911 - val_acc: 0.8843\n",
-      "Epoch 84/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5708 - acc: 0.8521 - val_loss: 0.4748 - val_acc: 0.8885\n",
-      "Epoch 85/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5667 - acc: 0.8513 - val_loss: 0.4805 - val_acc: 0.8842\n",
-      "Epoch 86/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5677 - acc: 0.8520 - val_loss: 0.4675 - val_acc: 0.8858\n",
-      "Epoch 87/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5537 - acc: 0.8549 - val_loss: 0.4713 - val_acc: 0.8847\n",
-      "Epoch 88/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5667 - acc: 0.8520 - val_loss: 0.4694 - val_acc: 0.8878\n",
-      "Epoch 89/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5627 - acc: 0.8526 - val_loss: 0.4810 - val_acc: 0.8814\n",
-      "Epoch 90/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5565 - acc: 0.8538 - val_loss: 0.4680 - val_acc: 0.8862\n",
-      "Epoch 91/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5535 - acc: 0.8546 - val_loss: 0.4712 - val_acc: 0.8847\n",
-      "Epoch 92/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5423 - acc: 0.8576 - val_loss: 0.4586 - val_acc: 0.8902\n",
-      "Epoch 93/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5474 - acc: 0.8566 - val_loss: 0.4638 - val_acc: 0.8858\n",
-      "Epoch 94/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5499 - acc: 0.8535 - val_loss: 0.4542 - val_acc: 0.8877\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5362 - acc: 0.8586 - val_loss: 0.4561 - val_acc: 0.8869\n",
-      "Epoch 96/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5433 - acc: 0.8543 - val_loss: 0.4426 - val_acc: 0.8915\n",
-      "Epoch 97/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5360 - acc: 0.8568 - val_loss: 0.4597 - val_acc: 0.8866\n",
-      "Epoch 98/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5389 - acc: 0.8566 - val_loss: 0.4537 - val_acc: 0.8865\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5435 - acc: 0.8535 - val_loss: 0.4490 - val_acc: 0.8913\n",
-      "Epoch 100/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5295 - acc: 0.8574 - val_loss: 0.4588 - val_acc: 0.8837\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5126 - acc: 0.8649 - val_loss: 0.4349 - val_acc: 0.8947\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5037 - acc: 0.8662 - val_loss: 0.4344 - val_acc: 0.8927\n",
-      "Epoch 103/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5049 - acc: 0.8671 - val_loss: 0.4230 - val_acc: 0.8981\n",
-      "Epoch 104/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4946 - acc: 0.8680 - val_loss: 0.4370 - val_acc: 0.8915\n",
-      "Epoch 105/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4985 - acc: 0.8676 - val_loss: 0.4267 - val_acc: 0.8978\n",
-      "Epoch 106/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4964 - acc: 0.8688 - val_loss: 0.4269 - val_acc: 0.8958\n",
-      "Epoch 107/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4917 - acc: 0.8703 - val_loss: 0.4228 - val_acc: 0.8984\n",
-      "Epoch 108/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4929 - acc: 0.8693 - val_loss: 0.4239 - val_acc: 0.8957\n",
-      "Epoch 109/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4857 - acc: 0.8693 - val_loss: 0.4249 - val_acc: 0.8974\n",
-      "Epoch 110/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4896 - acc: 0.8700 - val_loss: 0.4235 - val_acc: 0.8971\n",
-      "Epoch 111/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4832 - acc: 0.8697 - val_loss: 0.4233 - val_acc: 0.8974\n",
-      "Epoch 112/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.4821 - acc: 0.8717 - val_loss: 0.4369 - val_acc: 0.8953\n",
-      "Epoch 113/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4878 - acc: 0.8698 - val_loss: 0.4254 - val_acc: 0.8970\n",
-      "Epoch 114/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4785 - acc: 0.8715 - val_loss: 0.4166 - val_acc: 0.8970\n",
-      "Epoch 115/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4844 - acc: 0.8698 - val_loss: 0.4253 - val_acc: 0.8967\n",
-      "Epoch 116/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4799 - acc: 0.8711 - val_loss: 0.4380 - val_acc: 0.8919\n",
-      "Epoch 117/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4786 - acc: 0.8706 - val_loss: 0.4211 - val_acc: 0.8948\n",
-      "Epoch 118/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4725 - acc: 0.8729 - val_loss: 0.4296 - val_acc: 0.8936\n",
-      "Epoch 119/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4752 - acc: 0.8712 - val_loss: 0.4253 - val_acc: 0.8922\n",
-      "Epoch 120/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4740 - acc: 0.8720 - val_loss: 0.4342 - val_acc: 0.8906\n",
-      "Epoch 121/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4714 - acc: 0.8740 - val_loss: 0.4373 - val_acc: 0.8936\n",
-      "Epoch 122/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4708 - acc: 0.8729 - val_loss: 0.4101 - val_acc: 0.8962\n",
-      "Epoch 123/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4622 - acc: 0.8761 - val_loss: 0.4203 - val_acc: 0.8945\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4684 - acc: 0.8729 - val_loss: 0.4186 - val_acc: 0.8958\n",
-      "Epoch 125/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4613 - acc: 0.8746 - val_loss: 0.4081 - val_acc: 0.8997\n",
-      "Epoch 126/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4521 - acc: 0.8776 - val_loss: 0.4075 - val_acc: 0.8980\n",
-      "Epoch 127/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4516 - acc: 0.8782 - val_loss: 0.4002 - val_acc: 0.8997\n",
-      "Epoch 128/200\n",
-      "390/390 [==============================] - 181s 464ms/step - loss: 0.4431 - acc: 0.8817 - val_loss: 0.4046 - val_acc: 0.9000\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4360 - acc: 0.8839 - val_loss: 0.3950 - val_acc: 0.9007\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4390 - acc: 0.8811 - val_loss: 0.3987 - val_acc: 0.9011\n",
-      "Epoch 131/200\n",
-      "390/390 [==============================] - 178s 457ms/step - loss: 0.4398 - acc: 0.8813 - val_loss: 0.3989 - val_acc: 0.8995\n",
-      "Epoch 132/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4388 - acc: 0.8825 - val_loss: 0.4080 - val_acc: 0.8963\n",
-      "Epoch 133/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4423 - acc: 0.8800 - val_loss: 0.4072 - val_acc: 0.8986\n",
-      "Epoch 134/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4373 - acc: 0.8807 - val_loss: 0.4090 - val_acc: 0.8962\n",
-      "Epoch 135/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4330 - acc: 0.8823 - val_loss: 0.4040 - val_acc: 0.8990\n",
-      "Epoch 136/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.4277 - acc: 0.8842 - val_loss: 0.3929 - val_acc: 0.9014\n",
-      "Epoch 137/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4341 - acc: 0.8825 - val_loss: 0.4060 - val_acc: 0.9006\n",
-      "Epoch 138/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4285 - acc: 0.8839 - val_loss: 0.3915 - val_acc: 0.9013\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4296 - acc: 0.8833 - val_loss: 0.4029 - val_acc: 0.9015\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4305 - acc: 0.8839 - val_loss: 0.4011 - val_acc: 0.9009\n",
-      "Epoch 141/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4266 - acc: 0.8849 - val_loss: 0.3881 - val_acc: 0.9016\n",
-      "Epoch 142/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4307 - acc: 0.8837 - val_loss: 0.3954 - val_acc: 0.9019\n",
-      "Epoch 143/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4255 - acc: 0.8869 - val_loss: 0.3977 - val_acc: 0.9002\n",
-      "Epoch 144/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4284 - acc: 0.8838 - val_loss: 0.4010 - val_acc: 0.9011\n",
-      "Epoch 145/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4236 - acc: 0.8841 - val_loss: 0.3886 - val_acc: 0.9028\n",
-      "Epoch 146/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4223 - acc: 0.8849 - val_loss: 0.3961 - val_acc: 0.9010\n",
-      "Epoch 147/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4214 - acc: 0.8852 - val_loss: 0.3977 - val_acc: 0.9002\n",
-      "Epoch 148/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4208 - acc: 0.8858 - val_loss: 0.3911 - val_acc: 0.9024\n",
-      "Epoch 149/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4228 - acc: 0.8851 - val_loss: 0.3914 - val_acc: 0.9003\n",
-      "Epoch 150/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4217 - acc: 0.8861 - val_loss: 0.3957 - val_acc: 0.9004\n",
-      "Epoch 151/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4115 - acc: 0.8871 - val_loss: 0.3882 - val_acc: 0.9029\n",
-      "Epoch 152/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4149 - acc: 0.8877 - val_loss: 0.3919 - val_acc: 0.9037\n",
-      "Epoch 153/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4113 - acc: 0.8888 - val_loss: 0.3920 - val_acc: 0.9013\n",
-      "Epoch 154/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4110 - acc: 0.8882 - val_loss: 0.3838 - val_acc: 0.9035\n",
-      "Epoch 155/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4095 - acc: 0.8899 - val_loss: 0.3948 - val_acc: 0.9024\n",
-      "Epoch 156/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4084 - acc: 0.8893 - val_loss: 0.3909 - val_acc: 0.9042\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4045 - acc: 0.8903 - val_loss: 0.3851 - val_acc: 0.9046\n",
-      "Epoch 158/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4040 - acc: 0.8916 - val_loss: 0.3865 - val_acc: 0.9032\n",
-      "Epoch 159/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4052 - acc: 0.8896 - val_loss: 0.3857 - val_acc: 0.9046\n",
-      "Epoch 160/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.4019 - acc: 0.8913 - val_loss: 0.3871 - val_acc: 0.9056\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4055 - acc: 0.8913 - val_loss: 0.3808 - val_acc: 0.9062\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4025 - acc: 0.8923 - val_loss: 0.3901 - val_acc: 0.9044\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8910 - val_loss: 0.3853 - val_acc: 0.9044\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4027 - acc: 0.8910 - val_loss: 0.3823 - val_acc: 0.9061\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3982 - acc: 0.8928 - val_loss: 0.3831 - val_acc: 0.9062\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8903 - val_loss: 0.3881 - val_acc: 0.9056\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3985 - acc: 0.8912 - val_loss: 0.3836 - val_acc: 0.9070\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3989 - acc: 0.8925 - val_loss: 0.3874 - val_acc: 0.9035\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4048 - acc: 0.8895 - val_loss: 0.3850 - val_acc: 0.9034\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3951 - acc: 0.8937 - val_loss: 0.3849 - val_acc: 0.9045\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3978 - acc: 0.8920 - val_loss: 0.3835 - val_acc: 0.9052\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3997 - acc: 0.8901 - val_loss: 0.3865 - val_acc: 0.9044\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3989 - acc: 0.8913 - val_loss: 0.3794 - val_acc: 0.9049\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3992 - acc: 0.8915 - val_loss: 0.3835 - val_acc: 0.9040\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3927 - acc: 0.8924 - val_loss: 0.3854 - val_acc: 0.9039\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3928 - acc: 0.8938 - val_loss: 0.3804 - val_acc: 0.9056\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3919 - acc: 0.8931 - val_loss: 0.3817 - val_acc: 0.9057\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3892 - acc: 0.8952 - val_loss: 0.3801 - val_acc: 0.9065\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3919 - acc: 0.8941 - val_loss: 0.3808 - val_acc: 0.9056\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3892 - acc: 0.8936 - val_loss: 0.3816 - val_acc: 0.9055\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.3855 - acc: 0.8959 - val_loss: 0.3810 - val_acc: 0.9069\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3895 - acc: 0.8943 - val_loss: 0.3812 - val_acc: 0.9051\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3922 - acc: 0.8943 - val_loss: 0.3776 - val_acc: 0.9068\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3883 - acc: 0.8951 - val_loss: 0.3796 - val_acc: 0.9074\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 180s 461ms/step - loss: 0.3942 - acc: 0.8928 - val_loss: 0.3794 - val_acc: 0.9056\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 179s 459ms/step - loss: 0.3870 - acc: 0.8960 - val_loss: 0.3777 - val_acc: 0.9072\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3899 - acc: 0.8941 - val_loss: 0.3780 - val_acc: 0.9066\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 178s 456ms/step - loss: 0.3853 - acc: 0.8971 - val_loss: 0.3778 - val_acc: 0.9066\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 178s 458ms/step - loss: 0.3878 - acc: 0.8958 - val_loss: 0.3792 - val_acc: 0.9076\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3873 - acc: 0.8956 - val_loss: 0.3781 - val_acc: 0.9085\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3876 - acc: 0.8948 - val_loss: 0.3769 - val_acc: 0.9078\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3837 - acc: 0.8973 - val_loss: 0.3768 - val_acc: 0.9075\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3876 - acc: 0.8946 - val_loss: 0.3755 - val_acc: 0.9085\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3926 - acc: 0.8941 - val_loss: 0.3756 - val_acc: 0.9087\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3839 - acc: 0.8957 - val_loss: 0.3782 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3867 - acc: 0.8934 - val_loss: 0.3760 - val_acc: 0.9079\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3884 - acc: 0.8948 - val_loss: 0.3788 - val_acc: 0.9064\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3857 - acc: 0.8943 - val_loss: 0.3773 - val_acc: 0.9082\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3882 - acc: 0.8937 - val_loss: 0.3750 - val_acc: 0.9078\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.3839 - acc: 0.8945 - val_loss: 0.3787 - val_acc: 0.9079\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = e_swish_2, \"e_swish_2\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training\n",
-    "batch_size = 128\n",
-    "epochs=200\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('e_swish_2_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Resume training (200-250)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 180s 462ms/step - loss: 0.3458 - acc: 0.9072 - val_loss: 0.3603 - val_acc: 0.9113\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 179s 459ms/step - loss: 0.3383 - acc: 0.9088 - val_loss: 0.3585 - val_acc: 0.9120\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3398 - acc: 0.9091 - val_loss: 0.3578 - val_acc: 0.9116\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3422 - acc: 0.9076 - val_loss: 0.3607 - val_acc: 0.9110\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3449 - acc: 0.9081 - val_loss: 0.3593 - val_acc: 0.9117\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.3465 - acc: 0.9064 - val_loss: 0.3588 - val_acc: 0.9120\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3460 - acc: 0.9078 - val_loss: 0.3580 - val_acc: 0.9135\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3442 - acc: 0.9075 - val_loss: 0.3579 - val_acc: 0.9118\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3450 - acc: 0.9065 - val_loss: 0.3580 - val_acc: 0.9122\n",
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3432 - acc: 0.9070 - val_loss: 0.3580 - val_acc: 0.9107\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3406 - acc: 0.9088 - val_loss: 0.3579 - val_acc: 0.9126\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3393 - acc: 0.9086 - val_loss: 0.3589 - val_acc: 0.9110\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3440 - acc: 0.9071 - val_loss: 0.3574 - val_acc: 0.9133\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3343 - acc: 0.9112 - val_loss: 0.3576 - val_acc: 0.9126\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3396 - acc: 0.9090 - val_loss: 0.3567 - val_acc: 0.9127\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3467 - acc: 0.9065 - val_loss: 0.3587 - val_acc: 0.9122\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3422 - acc: 0.9076 - val_loss: 0.3590 - val_acc: 0.9122\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3439 - acc: 0.9075 - val_loss: 0.3570 - val_acc: 0.9131\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3336 - acc: 0.9107 - val_loss: 0.3573 - val_acc: 0.9128\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3369 - acc: 0.9098 - val_loss: 0.3565 - val_acc: 0.9120\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3423 - acc: 0.9079 - val_loss: 0.3577 - val_acc: 0.9125\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3379 - acc: 0.9089 - val_loss: 0.3563 - val_acc: 0.9123\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3388 - acc: 0.9075 - val_loss: 0.3559 - val_acc: 0.9127\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3374 - acc: 0.9095 - val_loss: 0.3563 - val_acc: 0.9125\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3468 - acc: 0.9069 - val_loss: 0.3567 - val_acc: 0.9133\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3438 - acc: 0.9082 - val_loss: 0.3575 - val_acc: 0.9120\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3467 - acc: 0.9055 - val_loss: 0.3569 - val_acc: 0.9132\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3415 - acc: 0.9081 - val_loss: 0.3578 - val_acc: 0.9125\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3409 - acc: 0.9082 - val_loss: 0.3565 - val_acc: 0.9124\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3399 - acc: 0.9092 - val_loss: 0.3569 - val_acc: 0.9128\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3386 - acc: 0.9085 - val_loss: 0.3572 - val_acc: 0.9123\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3393 - acc: 0.9092 - val_loss: 0.3553 - val_acc: 0.9120\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3372 - acc: 0.9099 - val_loss: 0.3547 - val_acc: 0.9125\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3375 - acc: 0.9089 - val_loss: 0.3561 - val_acc: 0.9122\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3365 - acc: 0.9092 - val_loss: 0.3573 - val_acc: 0.9120\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3417 - acc: 0.9079 - val_loss: 0.3554 - val_acc: 0.9128\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3391 - acc: 0.9091 - val_loss: 0.3546 - val_acc: 0.9123\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3369 - acc: 0.9095 - val_loss: 0.3559 - val_acc: 0.9114\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3434 - acc: 0.9078 - val_loss: 0.3558 - val_acc: 0.9120\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3374 - acc: 0.9095 - val_loss: 0.3560 - val_acc: 0.9117\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3380 - acc: 0.9084 - val_loss: 0.3581 - val_acc: 0.9116\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3432 - acc: 0.9084 - val_loss: 0.3574 - val_acc: 0.9128\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3383 - acc: 0.9080 - val_loss: 0.3563 - val_acc: 0.9130\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3370 - acc: 0.9097 - val_loss: 0.3573 - val_acc: 0.9118\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3376 - acc: 0.9080 - val_loss: 0.3569 - val_acc: 0.9125\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3361 - acc: 0.9102 - val_loss: 0.3573 - val_acc: 0.9122\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3393 - acc: 0.9093 - val_loss: 0.3566 - val_acc: 0.9123\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3353 - acc: 0.9100 - val_loss: 0.3578 - val_acc: 0.9126\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3338 - acc: 0.9106 - val_loss: 0.3568 - val_acc: 0.9123\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3374 - acc: 0.9081 - val_loss: 0.3565 - val_acc: 0.9122\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "model = load_model('e_swish_2_at_2.h5', custom_objects={\"e_swish_2\": e_swish_2})\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000017,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=200)\n",
-    "# Save the model\n",
-    "model.save('e_swish_2_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3818 - acc: 0.8962 - val_loss: 0.3782 - val_acc: 0.9078\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3836 - acc: 0.8963 - val_loss: 0.3775 - val_acc: 0.9067\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3824 - acc: 0.8976 - val_loss: 0.3762 - val_acc: 0.9088\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3854 - acc: 0.8958 - val_loss: 0.3776 - val_acc: 0.9070\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3780 - acc: 0.8981 - val_loss: 0.3781 - val_acc: 0.9057\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3822 - acc: 0.8972 - val_loss: 0.3765 - val_acc: 0.9060\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3793 - acc: 0.8960 - val_loss: 0.3769 - val_acc: 0.9060\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3807 - acc: 0.8976 - val_loss: 0.3757 - val_acc: 0.9079\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3811 - acc: 0.8971 - val_loss: 0.3752 - val_acc: 0.9081\n",
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3829 - acc: 0.8961 - val_loss: 0.3751 - val_acc: 0.9073\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3807 - acc: 0.8955 - val_loss: 0.3756 - val_acc: 0.9077\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3787 - acc: 0.8972 - val_loss: 0.3758 - val_acc: 0.9076\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3797 - acc: 0.8960 - val_loss: 0.3757 - val_acc: 0.9071\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3789 - acc: 0.8955 - val_loss: 0.3749 - val_acc: 0.9074\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3822 - acc: 0.8958 - val_loss: 0.3747 - val_acc: 0.9068\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3793 - acc: 0.8958 - val_loss: 0.3744 - val_acc: 0.9071\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3826 - acc: 0.8951 - val_loss: 0.3751 - val_acc: 0.9073\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3817 - acc: 0.8962 - val_loss: 0.3744 - val_acc: 0.9071\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3800 - acc: 0.8978 - val_loss: 0.3750 - val_acc: 0.9069\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3760 - acc: 0.8967 - val_loss: 0.3757 - val_acc: 0.9066\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3808 - acc: 0.8956 - val_loss: 0.3742 - val_acc: 0.9072\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3775 - acc: 0.8975 - val_loss: 0.3752 - val_acc: 0.9076\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3772 - acc: 0.8976 - val_loss: 0.3729 - val_acc: 0.9085\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3767 - acc: 0.8978 - val_loss: 0.3731 - val_acc: 0.9084\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3789 - acc: 0.8964 - val_loss: 0.3752 - val_acc: 0.9071\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3784 - acc: 0.8970 - val_loss: 0.3739 - val_acc: 0.9076\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3806 - acc: 0.8957 - val_loss: 0.3744 - val_acc: 0.9080\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3729 - acc: 0.8995 - val_loss: 0.3753 - val_acc: 0.9083\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3720 - acc: 0.8988 - val_loss: 0.3749 - val_acc: 0.9082\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3792 - acc: 0.8967 - val_loss: 0.3751 - val_acc: 0.9079\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3762 - acc: 0.8984 - val_loss: 0.3752 - val_acc: 0.9078\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3787 - acc: 0.8963 - val_loss: 0.3746 - val_acc: 0.9076\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3801 - acc: 0.8978 - val_loss: 0.3756 - val_acc: 0.9074\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3752 - acc: 0.8976 - val_loss: 0.3743 - val_acc: 0.9081\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3780 - acc: 0.8964 - val_loss: 0.3734 - val_acc: 0.9078\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3717 - acc: 0.8989 - val_loss: 0.3750 - val_acc: 0.9082\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3747 - acc: 0.8973 - val_loss: 0.3745 - val_acc: 0.9070\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3735 - acc: 0.8971 - val_loss: 0.3725 - val_acc: 0.9089\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3738 - acc: 0.8988 - val_loss: 0.3742 - val_acc: 0.9077\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3733 - acc: 0.8977 - val_loss: 0.3734 - val_acc: 0.9077\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3769 - acc: 0.8974 - val_loss: 0.3738 - val_acc: 0.9073\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3726 - acc: 0.8982 - val_loss: 0.3740 - val_acc: 0.9077\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3763 - acc: 0.8980 - val_loss: 0.3723 - val_acc: 0.9083\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3717 - acc: 0.8986 - val_loss: 0.3733 - val_acc: 0.9078\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3760 - acc: 0.8976 - val_loss: 0.3726 - val_acc: 0.9084\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3791 - acc: 0.8974 - val_loss: 0.3723 - val_acc: 0.9093\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3760 - acc: 0.8976 - val_loss: 0.3731 - val_acc: 0.9081\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3766 - acc: 0.8962 - val_loss: 0.3728 - val_acc: 0.9077\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3777 - acc: 0.8967 - val_loss: 0.3731 - val_acc: 0.9083\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3720 - acc: 0.8983 - val_loss: 0.3729 - val_acc: 0.9082\n",
-      "{'val_acc': [0.90780000000000005, 0.90669999999999995, 0.90880000000000005, 0.90700000000000003, 0.90569999999999995, 0.90600000000000003, 0.90600000000000003, 0.90790000000000004, 0.90810000000000002, 0.9073, 0.90769999999999995, 0.90759999999999996, 0.90710000000000002, 0.90739999999999998, 0.90680000000000005, 0.90710000000000002, 0.9073, 0.90710000000000002, 0.90690000000000004, 0.90659999999999996, 0.90720000000000001, 0.90759999999999996, 0.90849999999999997, 0.90839999999999999, 0.90710000000000002, 0.90759999999999996, 0.90800000000000003, 0.9083, 0.90820000000000001, 0.90790000000000004, 0.90780000000000005, 0.90759999999999996, 0.90739999999999998, 0.90810000000000002, 0.90780000000000005, 0.90820000000000001, 0.90700000000000003, 0.90890000000000004, 0.90769999999999995, 0.90769999999999995, 0.9073, 0.90769999999999995, 0.9083, 0.90780000000000005, 0.90839999999999999, 0.9093, 0.90810000000000002, 0.90769999999999995, 0.9083, 0.90820000000000001], 'acc': [0.89619425729868463, 0.89627446266256316, 0.89753769650304782, 0.89581328198280252, 0.89805903108142149, 0.89715672122541057, 0.89601379529688652, 0.89767805578466175, 0.89711661858171621, 0.89607394934847906, 0.89551251201167636, 0.89713666983663476, 0.89597369265319216, 0.89557266604414654, 0.8957732371794872, 0.89577317937735301, 0.89509071933204876, 0.89625441129290984, 0.89785851778645986, 0.89671559193442563, 0.89563282005749412, 0.89755774781533382, 0.8975777991276197, 0.8978184151427655, 0.89633461661854352, 0.89697625918536761, 0.89569310897435894, 0.89948619131714236, 0.89876082775720545, 0.89673564326583399, 0.89839990371536438, 0.89629451397484905, 0.89787856915611308, 0.8976179018286814, 0.89645492458787146, 0.89888113568828854, 0.89727702917561614, 0.897056464587491, 0.89878087906949133, 0.89767628205128203, 0.89745905593410857, 0.89829964709656718, 0.89795673076923077, 0.89860308285163781, 0.89753769648392534, 0.89739733716406656, 0.89759785053551788, 0.89619391025641026, 0.89669637122671808, 0.89827959572691396], 'loss': [0.38192385609118573, 0.38366235368596768, 0.38243792514637337, 0.38529716774864309, 0.3780222623670258, 0.3821340719495479, 0.37929437002349364, 0.38054197024641262, 0.38106803024736857, 0.38285865186497614, 0.38067114429669691, 0.3789266852669736, 0.37971156418686841, 0.37872003302346219, 0.38215108131751035, 0.37933256552890204, 0.38256422186709399, 0.38166929811554762, 0.37994617678853693, 0.37597584788486443, 0.3808525275124699, 0.37747222928997792, 0.37706229256099716, 0.37657802916506294, 0.37898766002021694, 0.37830471665498955, 0.38057093031895467, 0.37296465769477127, 0.37208341142861245, 0.37913693226144829, 0.37625773273525537, 0.37866799462136069, 0.38012097520173493, 0.37506118954126749, 0.37796168323315832, 0.37171630813289919, 0.37480485195489432, 0.37357130567347502, 0.3739193721788221, 0.37334053279497686, 0.37675303092505125, 0.37235717508400484, 0.37632885819826373, 0.37153071385204756, 0.37608381087557413, 0.3790424772570215, 0.3759769953459855, 0.3766193736440096, 0.37768305890745435, 0.3720105104542788], 'val_loss': [0.37823536205291747, 0.37747736020088196, 0.37619412536621094, 0.37756411218643188, 0.37812863197326663, 0.37653512229919434, 0.37685041356086729, 0.37571484167575836, 0.37520423455238344, 0.37509226880073548, 0.37559298319816592, 0.37577983741760251, 0.37568331146240236, 0.37494024095535278, 0.3747356897354126, 0.37437291817665103, 0.37507482862472535, 0.37437629785537718, 0.37495786361694333, 0.37567552423477174, 0.37424509992599486, 0.37515580270290377, 0.37290649290084837, 0.37313869249820708, 0.37515520482063291, 0.37387374358177183, 0.37436483311653135, 0.3752700170278549, 0.37491791720390322, 0.37514262261390685, 0.37520979089736939, 0.37460635347366333, 0.37557938446998596, 0.37433548512458803, 0.37342010231018069, 0.37502991566658023, 0.37452910547256468, 0.37250451855659483, 0.37424582848548887, 0.37337181138992309, 0.37381111397743227, 0.37404450764656066, 0.37225306589603424, 0.37333880391120911, 0.37256061735153201, 0.37233733358383181, 0.37306021027565001, 0.37276746668815613, 0.37307930479049684, 0.3729367765903473]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "model = load_model('e_swish_2_at_3.h5', custom_objects={\"e_swish_2\": e_swish_2})\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000017,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=200)\n",
-    "# Save the model\n",
-    "model.save('e_swish_2_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9239\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(e_swish_2)\n",
-    "        model.load_weights('e_swish_2_at_'+str(i+1)+'.h5')\n",
-    "        model.save('e_swish_2_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('e_swish_2_at_'+str(i+1)+'.h5', custom_objects={\"e_swish_2\": e_swish_2})\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Deeper_CNN/cifar10_deeper_cnn_elu.ipynb b/cifar/Deeper_CNN/cifar10_deeper_cnn_elu.ipynb
deleted file mode 100644
index c0afd61..0000000
--- a/cifar/Deeper_CNN/cifar10_deeper_cnn_elu.ipynb
+++ /dev/null
@@ -1,1709 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "import keras.regularizers as regularizers\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 2.0441 - acc: 0.3842 - val_loss: 1.4719 - val_acc: 0.5211\n",
-      "Epoch 2/250\n",
-      "390/390 [==============================] - 145s 372ms/step - loss: 1.4238 - acc: 0.5356 - val_loss: 1.0515 - val_acc: 0.6488\n",
-      "Epoch 3/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 1.2189 - acc: 0.6031 - val_loss: 0.9504 - val_acc: 0.7042\n",
-      "Epoch 4/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 1.0990 - acc: 0.6478 - val_loss: 0.8618 - val_acc: 0.7261\n",
-      "Epoch 5/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 1.0263 - acc: 0.6726 - val_loss: 0.8084 - val_acc: 0.7480\n",
-      "Epoch 6/250\n",
-      "390/390 [==============================] - 145s 372ms/step - loss: 0.9630 - acc: 0.6924 - val_loss: 0.7860 - val_acc: 0.7603\n",
-      "Epoch 7/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.9235 - acc: 0.7081 - val_loss: 0.6973 - val_acc: 0.7842\n",
-      "Epoch 8/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8852 - acc: 0.7216 - val_loss: 0.6893 - val_acc: 0.7946\n",
-      "Epoch 9/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8515 - acc: 0.7357 - val_loss: 0.6928 - val_acc: 0.7892\n",
-      "Epoch 10/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8301 - acc: 0.7441 - val_loss: 0.6603 - val_acc: 0.8061\n",
-      "Epoch 11/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8091 - acc: 0.7520 - val_loss: 0.6253 - val_acc: 0.8187\n",
-      "Epoch 12/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7971 - acc: 0.7569 - val_loss: 0.6954 - val_acc: 0.8001\n",
-      "Epoch 13/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7799 - acc: 0.7634 - val_loss: 0.5979 - val_acc: 0.8261\n",
-      "Epoch 14/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7614 - acc: 0.7706 - val_loss: 0.6250 - val_acc: 0.8186\n",
-      "Epoch 15/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7527 - acc: 0.7751 - val_loss: 0.5733 - val_acc: 0.8386\n",
-      "Epoch 16/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7396 - acc: 0.7779 - val_loss: 0.5829 - val_acc: 0.8377\n",
-      "Epoch 17/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7265 - acc: 0.7854 - val_loss: 0.5548 - val_acc: 0.8456\n",
-      "Epoch 18/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.7186 - acc: 0.7886 - val_loss: 0.5842 - val_acc: 0.8381\n",
-      "Epoch 19/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7163 - acc: 0.7896 - val_loss: 0.5739 - val_acc: 0.8398\n",
-      "Epoch 20/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.7048 - acc: 0.7939 - val_loss: 0.6158 - val_acc: 0.8335\n",
-      "Epoch 21/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.6969 - acc: 0.7984 - val_loss: 0.5708 - val_acc: 0.8421\n",
-      "Epoch 22/250\n",
-      "390/390 [==============================] - 144s 371ms/step - loss: 0.6889 - acc: 0.8009 - val_loss: 0.5664 - val_acc: 0.8473\n",
-      "Epoch 23/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.6866 - acc: 0.8027 - val_loss: 0.5506 - val_acc: 0.8510\n",
-      "Epoch 24/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6791 - acc: 0.8056 - val_loss: 0.5621 - val_acc: 0.8458\n",
-      "Epoch 25/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6746 - acc: 0.8072 - val_loss: 0.5372 - val_acc: 0.8546\n",
-      "Epoch 26/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6681 - acc: 0.8083 - val_loss: 0.5762 - val_acc: 0.8479\n",
-      "Epoch 27/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6676 - acc: 0.8107 - val_loss: 0.5505 - val_acc: 0.8512\n",
-      "Epoch 28/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6618 - acc: 0.8104 - val_loss: 0.5256 - val_acc: 0.8609\n",
-      "Epoch 29/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.6612 - acc: 0.8126 - val_loss: 0.5718 - val_acc: 0.8452\n",
-      "Epoch 30/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6555 - acc: 0.8128 - val_loss: 0.5674 - val_acc: 0.8545\n",
-      "Epoch 31/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6514 - acc: 0.8168 - val_loss: 0.5625 - val_acc: 0.8516\n",
-      "Epoch 32/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6471 - acc: 0.8175 - val_loss: 0.5254 - val_acc: 0.8627\n",
-      "Epoch 33/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6470 - acc: 0.8197 - val_loss: 0.5403 - val_acc: 0.8556\n",
-      "Epoch 34/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6451 - acc: 0.8187 - val_loss: 0.5181 - val_acc: 0.8650\n",
-      "Epoch 35/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6431 - acc: 0.8182 - val_loss: 0.5238 - val_acc: 0.8636\n",
-      "Epoch 36/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6379 - acc: 0.8209 - val_loss: 0.5368 - val_acc: 0.8582\n",
-      "Epoch 37/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6329 - acc: 0.8231 - val_loss: 0.5277 - val_acc: 0.8629\n",
-      "Epoch 38/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6327 - acc: 0.8235 - val_loss: 0.5277 - val_acc: 0.8651\n",
-      "Epoch 39/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6304 - acc: 0.8246 - val_loss: 0.5318 - val_acc: 0.8627\n",
-      "Epoch 40/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6307 - acc: 0.8248 - val_loss: 0.5306 - val_acc: 0.8656\n",
-      "Epoch 41/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6274 - acc: 0.8278 - val_loss: 0.5323 - val_acc: 0.8643\n",
-      "Epoch 42/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6223 - acc: 0.8277 - val_loss: 0.5071 - val_acc: 0.8714\n",
-      "Epoch 43/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6218 - acc: 0.8302 - val_loss: 0.5173 - val_acc: 0.8649\n",
-      "Epoch 44/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6146 - acc: 0.8301 - val_loss: 0.5140 - val_acc: 0.8694\n",
-      "Epoch 45/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6198 - acc: 0.8296 - val_loss: 0.5175 - val_acc: 0.8683\n",
-      "Epoch 46/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6170 - acc: 0.8324 - val_loss: 0.4919 - val_acc: 0.8749\n",
-      "Epoch 47/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6108 - acc: 0.8323 - val_loss: 0.5256 - val_acc: 0.8659\n",
-      "Epoch 48/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6161 - acc: 0.8301 - val_loss: 0.5015 - val_acc: 0.8734\n",
-      "Epoch 49/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6109 - acc: 0.8328 - val_loss: 0.5102 - val_acc: 0.8717\n",
-      "Epoch 50/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6092 - acc: 0.8332 - val_loss: 0.5145 - val_acc: 0.8699\n",
-      "Epoch 51/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6080 - acc: 0.8348 - val_loss: 0.5603 - val_acc: 0.8636\n",
-      "Epoch 52/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6029 - acc: 0.8363 - val_loss: 0.5127 - val_acc: 0.8714\n",
-      "Epoch 53/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6085 - acc: 0.8351 - val_loss: 0.4978 - val_acc: 0.8766\n",
-      "Epoch 54/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5973 - acc: 0.8379 - val_loss: 0.5223 - val_acc: 0.8674\n",
-      "Epoch 55/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6008 - acc: 0.8366 - val_loss: 0.5106 - val_acc: 0.8746\n",
-      "Epoch 56/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6051 - acc: 0.8357 - val_loss: 0.5408 - val_acc: 0.8644\n",
-      "Epoch 57/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5980 - acc: 0.8393 - val_loss: 0.5141 - val_acc: 0.8680\n",
-      "Epoch 58/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5972 - acc: 0.8389 - val_loss: 0.5128 - val_acc: 0.8746\n",
-      "Epoch 59/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5945 - acc: 0.8382 - val_loss: 0.4964 - val_acc: 0.8768\n",
-      "Epoch 60/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5907 - acc: 0.8415 - val_loss: 0.5129 - val_acc: 0.8765\n",
-      "Epoch 61/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5982 - acc: 0.8372 - val_loss: 0.4905 - val_acc: 0.8772\n",
-      "Epoch 62/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5951 - acc: 0.8389 - val_loss: 0.5285 - val_acc: 0.8691\n",
-      "Epoch 63/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5974 - acc: 0.8370 - val_loss: 0.4888 - val_acc: 0.8815\n",
-      "Epoch 64/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5892 - acc: 0.8407 - val_loss: 0.5252 - val_acc: 0.8683\n",
-      "Epoch 65/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5923 - acc: 0.8400 - val_loss: 0.4876 - val_acc: 0.8810\n",
-      "Epoch 66/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5899 - acc: 0.8406 - val_loss: 0.5073 - val_acc: 0.8763\n",
-      "Epoch 67/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5891 - acc: 0.8426 - val_loss: 0.4959 - val_acc: 0.8780\n",
-      "Epoch 68/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5862 - acc: 0.8430 - val_loss: 0.5050 - val_acc: 0.8772\n",
-      "Epoch 69/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5914 - acc: 0.8410 - val_loss: 0.4895 - val_acc: 0.8812\n",
-      "Epoch 70/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5892 - acc: 0.8421 - val_loss: 0.4879 - val_acc: 0.8824\n",
-      "Epoch 71/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.5862 - acc: 0.8419 - val_loss: 0.5059 - val_acc: 0.8743\n",
-      "Epoch 72/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5842 - acc: 0.8434 - val_loss: 0.4915 - val_acc: 0.8783\n",
-      "Epoch 73/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5826 - acc: 0.8449 - val_loss: 0.5082 - val_acc: 0.8733\n",
-      "Epoch 74/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5813 - acc: 0.8451 - val_loss: 0.5218 - val_acc: 0.8700\n",
-      "Epoch 75/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5816 - acc: 0.8450 - val_loss: 0.4915 - val_acc: 0.8785\n",
-      "Epoch 76/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5421 - acc: 0.8578 - val_loss: 0.4608 - val_acc: 0.8905\n",
-      "Epoch 77/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5309 - acc: 0.8612 - val_loss: 0.4514 - val_acc: 0.8920\n",
-      "Epoch 78/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.5215 - acc: 0.8632 - val_loss: 0.4533 - val_acc: 0.8925\n",
-      "Epoch 79/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5188 - acc: 0.8634 - val_loss: 0.4519 - val_acc: 0.8911\n",
-      "Epoch 80/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5147 - acc: 0.8639 - val_loss: 0.4607 - val_acc: 0.8902\n",
-      "Epoch 81/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5144 - acc: 0.8652 - val_loss: 0.4347 - val_acc: 0.8941\n",
-      "Epoch 82/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5039 - acc: 0.8663 - val_loss: 0.4351 - val_acc: 0.8945\n",
-      "Epoch 83/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5048 - acc: 0.8671 - val_loss: 0.4459 - val_acc: 0.8901\n",
-      "Epoch 84/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5002 - acc: 0.8674 - val_loss: 0.4368 - val_acc: 0.8954\n",
-      "Epoch 85/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.5013 - acc: 0.8669 - val_loss: 0.4411 - val_acc: 0.8957\n",
-      "Epoch 86/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4961 - acc: 0.8680 - val_loss: 0.4401 - val_acc: 0.8938\n",
-      "Epoch 87/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4984 - acc: 0.8678 - val_loss: 0.4317 - val_acc: 0.8958\n",
-      "Epoch 88/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4963 - acc: 0.8674 - val_loss: 0.4217 - val_acc: 0.8972\n",
-      "Epoch 89/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4971 - acc: 0.8665 - val_loss: 0.4300 - val_acc: 0.8946\n",
-      "Epoch 90/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4877 - acc: 0.8701 - val_loss: 0.4226 - val_acc: 0.8973\n",
-      "Epoch 91/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4855 - acc: 0.8720 - val_loss: 0.4363 - val_acc: 0.8944\n",
-      "Epoch 92/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4901 - acc: 0.8698 - val_loss: 0.4319 - val_acc: 0.8958\n",
-      "Epoch 93/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4876 - acc: 0.8706 - val_loss: 0.4279 - val_acc: 0.8962\n",
-      "Epoch 94/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4809 - acc: 0.8711 - val_loss: 0.4216 - val_acc: 0.8980\n",
-      "Epoch 95/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4864 - acc: 0.8692 - val_loss: 0.4304 - val_acc: 0.8971\n",
-      "Epoch 96/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4841 - acc: 0.8692 - val_loss: 0.4368 - val_acc: 0.8938\n",
-      "Epoch 97/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4802 - acc: 0.8717 - val_loss: 0.4323 - val_acc: 0.8953\n",
-      "Epoch 98/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4823 - acc: 0.8695 - val_loss: 0.4223 - val_acc: 0.8978\n",
-      "Epoch 99/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4767 - acc: 0.8717 - val_loss: 0.4190 - val_acc: 0.8988\n",
-      "Epoch 100/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4779 - acc: 0.8706 - val_loss: 0.4294 - val_acc: 0.8920\n",
-      "Epoch 101/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4594 - acc: 0.8772 - val_loss: 0.4142 - val_acc: 0.8980\n",
-      "Epoch 102/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4542 - acc: 0.8781 - val_loss: 0.4034 - val_acc: 0.9022\n",
-      "Epoch 103/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4450 - acc: 0.8830 - val_loss: 0.4006 - val_acc: 0.9026\n",
-      "Epoch 104/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4517 - acc: 0.8792 - val_loss: 0.4006 - val_acc: 0.9025\n",
-      "Epoch 105/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4433 - acc: 0.8827 - val_loss: 0.4007 - val_acc: 0.9026\n",
-      "Epoch 106/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4448 - acc: 0.8809 - val_loss: 0.3926 - val_acc: 0.9059\n",
-      "Epoch 107/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4449 - acc: 0.8825 - val_loss: 0.4019 - val_acc: 0.9019\n",
-      "Epoch 108/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4400 - acc: 0.8811 - val_loss: 0.4080 - val_acc: 0.9015\n",
-      "Epoch 109/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4418 - acc: 0.8804 - val_loss: 0.3966 - val_acc: 0.9051\n",
-      "Epoch 110/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4382 - acc: 0.8818 - val_loss: 0.4004 - val_acc: 0.9008\n",
-      "Epoch 111/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4407 - acc: 0.8812 - val_loss: 0.3947 - val_acc: 0.9044\n",
-      "Epoch 112/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4398 - acc: 0.8814 - val_loss: 0.4007 - val_acc: 0.9036\n",
-      "Epoch 113/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4357 - acc: 0.8835 - val_loss: 0.4011 - val_acc: 0.9025\n",
-      "Epoch 114/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4339 - acc: 0.8834 - val_loss: 0.3874 - val_acc: 0.9038\n",
-      "Epoch 115/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4319 - acc: 0.8833 - val_loss: 0.3963 - val_acc: 0.9025\n",
-      "Epoch 116/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4266 - acc: 0.8861 - val_loss: 0.3837 - val_acc: 0.9057\n",
-      "Epoch 117/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4330 - acc: 0.8819 - val_loss: 0.3996 - val_acc: 0.9010\n",
-      "Epoch 118/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4281 - acc: 0.8832 - val_loss: 0.3858 - val_acc: 0.9062\n",
-      "Epoch 119/250\n",
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-      "Epoch 120/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4245 - acc: 0.8865 - val_loss: 0.4014 - val_acc: 0.9011\n",
-      "Epoch 121/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4242 - acc: 0.8857 - val_loss: 0.3973 - val_acc: 0.9031\n",
-      "Epoch 122/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4293 - acc: 0.8832 - val_loss: 0.3985 - val_acc: 0.9017\n",
-      "Epoch 123/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4222 - acc: 0.8854 - val_loss: 0.3964 - val_acc: 0.9017\n",
-      "Epoch 124/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4207 - acc: 0.8852 - val_loss: 0.3904 - val_acc: 0.9041\n",
-      "Epoch 125/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4229 - acc: 0.8852 - val_loss: 0.3934 - val_acc: 0.9033\n",
-      "Epoch 126/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4062 - acc: 0.8913 - val_loss: 0.3796 - val_acc: 0.9083\n",
-      "Epoch 127/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4117 - acc: 0.8895 - val_loss: 0.3836 - val_acc: 0.9078\n",
-      "Epoch 128/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4012 - acc: 0.8913 - val_loss: 0.3852 - val_acc: 0.9050\n",
-      "Epoch 129/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4029 - acc: 0.8912 - val_loss: 0.3786 - val_acc: 0.9083\n",
-      "Epoch 130/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4041 - acc: 0.8909 - val_loss: 0.3794 - val_acc: 0.9067\n",
-      "Epoch 131/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3971 - acc: 0.8930 - val_loss: 0.3796 - val_acc: 0.9054\n",
-      "Epoch 132/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3993 - acc: 0.8931 - val_loss: 0.3711 - val_acc: 0.9096\n",
-      "Epoch 133/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3976 - acc: 0.8927 - val_loss: 0.3741 - val_acc: 0.9085\n",
-      "Epoch 134/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3926 - acc: 0.8939 - val_loss: 0.3732 - val_acc: 0.9095\n",
-      "Epoch 135/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3918 - acc: 0.8939 - val_loss: 0.3751 - val_acc: 0.9085\n",
-      "Epoch 136/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4003 - acc: 0.8903 - val_loss: 0.3719 - val_acc: 0.9092\n",
-      "Epoch 137/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3933 - acc: 0.8928 - val_loss: 0.3764 - val_acc: 0.9086\n",
-      "Epoch 138/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3967 - acc: 0.8937 - val_loss: 0.3722 - val_acc: 0.9072\n",
-      "Epoch 139/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3876 - acc: 0.8957 - val_loss: 0.3736 - val_acc: 0.9068\n",
-      "Epoch 140/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3858 - acc: 0.8963 - val_loss: 0.3749 - val_acc: 0.9071\n",
-      "Epoch 141/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3851 - acc: 0.8974 - val_loss: 0.3740 - val_acc: 0.9064\n",
-      "Epoch 142/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3885 - acc: 0.8944 - val_loss: 0.3702 - val_acc: 0.9087\n",
-      "Epoch 143/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3848 - acc: 0.8981 - val_loss: 0.3763 - val_acc: 0.9074\n",
-      "Epoch 144/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3908 - acc: 0.8927 - val_loss: 0.3703 - val_acc: 0.9080\n",
-      "Epoch 145/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3864 - acc: 0.8963 - val_loss: 0.3789 - val_acc: 0.9059\n",
-      "Epoch 146/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3863 - acc: 0.8945 - val_loss: 0.3679 - val_acc: 0.9067\n",
-      "Epoch 147/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3830 - acc: 0.8970 - val_loss: 0.3714 - val_acc: 0.9078\n",
-      "Epoch 148/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3825 - acc: 0.8972 - val_loss: 0.3729 - val_acc: 0.9081\n",
-      "Epoch 149/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3864 - acc: 0.8962 - val_loss: 0.3684 - val_acc: 0.9077\n",
-      "Epoch 150/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3819 - acc: 0.8967 - val_loss: 0.3705 - val_acc: 0.9082\n",
-      "Epoch 151/250\n"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3798 - acc: 0.8975 - val_loss: 0.3664 - val_acc: 0.9089\n",
-      "Epoch 152/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3735 - acc: 0.8973 - val_loss: 0.3659 - val_acc: 0.9083\n",
-      "Epoch 153/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3717 - acc: 0.8992 - val_loss: 0.3662 - val_acc: 0.9100\n",
-      "Epoch 154/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3753 - acc: 0.8977 - val_loss: 0.3625 - val_acc: 0.9095\n",
-      "Epoch 155/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3735 - acc: 0.8987 - val_loss: 0.3633 - val_acc: 0.9116\n",
-      "Epoch 156/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3626 - acc: 0.9030 - val_loss: 0.3689 - val_acc: 0.9097\n",
-      "Epoch 157/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3709 - acc: 0.8987 - val_loss: 0.3671 - val_acc: 0.9090\n",
-      "Epoch 158/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3681 - acc: 0.8996 - val_loss: 0.3662 - val_acc: 0.9102\n",
-      "Epoch 159/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3658 - acc: 0.9003 - val_loss: 0.3619 - val_acc: 0.9114\n",
-      "Epoch 160/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3726 - acc: 0.8985 - val_loss: 0.3648 - val_acc: 0.9097\n",
-      "Epoch 161/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3644 - acc: 0.9008 - val_loss: 0.3606 - val_acc: 0.9104\n",
-      "Epoch 162/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3685 - acc: 0.8993 - val_loss: 0.3623 - val_acc: 0.9108\n",
-      "Epoch 163/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3685 - acc: 0.9008 - val_loss: 0.3645 - val_acc: 0.9113\n",
-      "Epoch 164/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3629 - acc: 0.9016 - val_loss: 0.3614 - val_acc: 0.9101\n",
-      "Epoch 165/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3651 - acc: 0.9024 - val_loss: 0.3647 - val_acc: 0.9084\n",
-      "Epoch 166/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3660 - acc: 0.9010 - val_loss: 0.3606 - val_acc: 0.9104\n",
-      "Epoch 167/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3651 - acc: 0.9004 - val_loss: 0.3647 - val_acc: 0.9093\n",
-      "Epoch 168/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3649 - acc: 0.9008 - val_loss: 0.3616 - val_acc: 0.9075\n",
-      "Epoch 169/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3630 - acc: 0.9018 - val_loss: 0.3633 - val_acc: 0.9100\n",
-      "Epoch 170/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3680 - acc: 0.9001 - val_loss: 0.3641 - val_acc: 0.9091\n",
-      "Epoch 171/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3610 - acc: 0.9028 - val_loss: 0.3647 - val_acc: 0.9101\n",
-      "Epoch 172/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3680 - acc: 0.8991 - val_loss: 0.3599 - val_acc: 0.9102\n",
-      "Epoch 173/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3671 - acc: 0.9002 - val_loss: 0.3618 - val_acc: 0.9108\n",
-      "Epoch 174/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3610 - acc: 0.9027 - val_loss: 0.3623 - val_acc: 0.9107\n",
-      "Epoch 175/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3593 - acc: 0.9033 - val_loss: 0.3603 - val_acc: 0.9114\n",
-      "Epoch 176/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3629 - acc: 0.8998 - val_loss: 0.3590 - val_acc: 0.9111\n",
-      "Epoch 177/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3536 - acc: 0.9047 - val_loss: 0.3570 - val_acc: 0.9115\n",
-      "Epoch 178/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3582 - acc: 0.9030 - val_loss: 0.3554 - val_acc: 0.9118\n",
-      "Epoch 179/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3546 - acc: 0.9038 - val_loss: 0.3578 - val_acc: 0.9123\n",
-      "Epoch 180/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3512 - acc: 0.9063 - val_loss: 0.3602 - val_acc: 0.9123\n",
-      "Epoch 181/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3535 - acc: 0.9036 - val_loss: 0.3587 - val_acc: 0.9122\n",
-      "Epoch 182/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3536 - acc: 0.9045 - val_loss: 0.3577 - val_acc: 0.9117\n",
-      "Epoch 183/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3526 - acc: 0.9051 - val_loss: 0.3570 - val_acc: 0.9120\n",
-      "Epoch 184/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3529 - acc: 0.9043 - val_loss: 0.3576 - val_acc: 0.9122\n",
-      "Epoch 185/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3532 - acc: 0.9040 - val_loss: 0.3559 - val_acc: 0.9122\n",
-      "Epoch 186/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3550 - acc: 0.9033 - val_loss: 0.3572 - val_acc: 0.9117\n",
-      "Epoch 187/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3556 - acc: 0.9025 - val_loss: 0.3555 - val_acc: 0.9121\n",
-      "Epoch 188/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3511 - acc: 0.9049 - val_loss: 0.3542 - val_acc: 0.9124\n",
-      "Epoch 189/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3535 - acc: 0.9050 - val_loss: 0.3540 - val_acc: 0.9118\n",
-      "Epoch 190/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3499 - acc: 0.9055 - val_loss: 0.3572 - val_acc: 0.9113\n",
-      "Epoch 191/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3486 - acc: 0.9057 - val_loss: 0.3572 - val_acc: 0.9124\n",
-      "Epoch 192/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3518 - acc: 0.9048 - val_loss: 0.3552 - val_acc: 0.9117\n",
-      "Epoch 193/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3482 - acc: 0.9055 - val_loss: 0.3564 - val_acc: 0.9126\n",
-      "Epoch 194/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3545 - acc: 0.9039 - val_loss: 0.3591 - val_acc: 0.9126\n",
-      "Epoch 195/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3492 - acc: 0.9067 - val_loss: 0.3602 - val_acc: 0.9124\n",
-      "Epoch 196/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3489 - acc: 0.9060 - val_loss: 0.3614 - val_acc: 0.9120\n",
-      "Epoch 197/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3529 - acc: 0.9040 - val_loss: 0.3562 - val_acc: 0.9123\n",
-      "Epoch 198/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3500 - acc: 0.9058 - val_loss: 0.3563 - val_acc: 0.9131\n",
-      "Epoch 199/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3472 - acc: 0.9052 - val_loss: 0.3557 - val_acc: 0.9138\n",
-      "Epoch 200/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3528 - acc: 0.9040 - val_loss: 0.3571 - val_acc: 0.9120\n",
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3497 - acc: 0.9047 - val_loss: 0.3574 - val_acc: 0.9128\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3499 - acc: 0.9052 - val_loss: 0.3576 - val_acc: 0.9130\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3486 - acc: 0.9059 - val_loss: 0.3552 - val_acc: 0.9125\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3488 - acc: 0.9051 - val_loss: 0.3553 - val_acc: 0.9134\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3490 - acc: 0.9058 - val_loss: 0.3567 - val_acc: 0.9129\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3453 - acc: 0.9063 - val_loss: 0.3544 - val_acc: 0.9121\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3421 - acc: 0.9072 - val_loss: 0.3544 - val_acc: 0.9129\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3450 - acc: 0.9070 - val_loss: 0.3546 - val_acc: 0.9129\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3394 - acc: 0.9089 - val_loss: 0.3529 - val_acc: 0.9130\n",
-      "Epoch 210/250\n"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3492 - acc: 0.9059 - val_loss: 0.3545 - val_acc: 0.9130\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3455 - acc: 0.9068 - val_loss: 0.3543 - val_acc: 0.9129\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3464 - acc: 0.9073 - val_loss: 0.3538 - val_acc: 0.9133\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3447 - acc: 0.9067 - val_loss: 0.3537 - val_acc: 0.9136\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3458 - acc: 0.9064 - val_loss: 0.3564 - val_acc: 0.9132\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3503 - acc: 0.9051 - val_loss: 0.3545 - val_acc: 0.9141\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3438 - acc: 0.9045 - val_loss: 0.3542 - val_acc: 0.9129\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3457 - acc: 0.9066 - val_loss: 0.3537 - val_acc: 0.9135\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3424 - acc: 0.9074 - val_loss: 0.3548 - val_acc: 0.9132\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3528 - acc: 0.9035 - val_loss: 0.3546 - val_acc: 0.9127\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3428 - acc: 0.9066 - val_loss: 0.3552 - val_acc: 0.9130\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3465 - acc: 0.9078 - val_loss: 0.3540 - val_acc: 0.9131\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3475 - acc: 0.9046 - val_loss: 0.3535 - val_acc: 0.9133\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3500 - acc: 0.9031 - val_loss: 0.3548 - val_acc: 0.9126\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3434 - acc: 0.9074 - val_loss: 0.3535 - val_acc: 0.9128\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3490 - acc: 0.9055 - val_loss: 0.3539 - val_acc: 0.9129\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3395 - acc: 0.9078 - val_loss: 0.3538 - val_acc: 0.9128\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3440 - acc: 0.9076 - val_loss: 0.3531 - val_acc: 0.9129\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3424 - acc: 0.9075 - val_loss: 0.3537 - val_acc: 0.9130\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3439 - acc: 0.9086 - val_loss: 0.3539 - val_acc: 0.9120\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3475 - acc: 0.9047 - val_loss: 0.3524 - val_acc: 0.9124\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3366 - acc: 0.9087 - val_loss: 0.3533 - val_acc: 0.9128\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3450 - acc: 0.9072 - val_loss: 0.3535 - val_acc: 0.9127\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3454 - acc: 0.9062 - val_loss: 0.3536 - val_acc: 0.9131\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3420 - acc: 0.9076 - val_loss: 0.3534 - val_acc: 0.9134\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3473 - acc: 0.9059 - val_loss: 0.3530 - val_acc: 0.9127\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3404 - acc: 0.9078 - val_loss: 0.3533 - val_acc: 0.9125\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3433 - acc: 0.9066 - val_loss: 0.3526 - val_acc: 0.9130\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3413 - acc: 0.9084 - val_loss: 0.3523 - val_acc: 0.9137\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3371 - acc: 0.9084 - val_loss: 0.3528 - val_acc: 0.9135\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3409 - acc: 0.9073 - val_loss: 0.3522 - val_acc: 0.9135\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3405 - acc: 0.9081 - val_loss: 0.3531 - val_acc: 0.9130\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3371 - acc: 0.9082 - val_loss: 0.3538 - val_acc: 0.9136\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3472 - acc: 0.9062 - val_loss: 0.3519 - val_acc: 0.9135\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3375 - acc: 0.9084 - val_loss: 0.3533 - val_acc: 0.9130\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3408 - acc: 0.9088 - val_loss: 0.3549 - val_acc: 0.9130\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3400 - acc: 0.9076 - val_loss: 0.3528 - val_acc: 0.9129\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3383 - acc: 0.9073 - val_loss: 0.3532 - val_acc: 0.9134\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3399 - acc: 0.9073 - val_loss: 0.3541 - val_acc: 0.9132\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3405 - acc: 0.9086 - val_loss: 0.3534 - val_acc: 0.9132\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3421 - acc: 0.9075 - val_loss: 0.3538 - val_acc: 0.9128\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"elu\", \"elu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('elu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 2.0713 - acc: 0.3743 - val_loss: 1.3665 - val_acc: 0.5438\n",
-      "Epoch 2/250\n",
-      "390/390 [==============================] - 146s 373ms/step - loss: 1.4644 - acc: 0.5267 - val_loss: 1.1220 - val_acc: 0.6277\n",
-      "Epoch 3/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 1.2595 - acc: 0.5950 - val_loss: 1.0474 - val_acc: 0.6793\n",
-      "Epoch 4/250\n",
-      "390/390 [==============================] - 146s 373ms/step - loss: 1.1375 - acc: 0.6351 - val_loss: 1.0178 - val_acc: 0.7092\n",
-      "Epoch 5/250\n",
-      "390/390 [==============================] - 146s 373ms/step - loss: 1.0704 - acc: 0.6603 - val_loss: 0.9037 - val_acc: 0.7183\n",
-      "Epoch 6/250\n",
-      "390/390 [==============================] - 146s 373ms/step - loss: 1.0201 - acc: 0.6796 - val_loss: 0.7980 - val_acc: 0.7538\n",
-      "Epoch 7/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.9697 - acc: 0.6959 - val_loss: 0.7565 - val_acc: 0.7676\n",
-      "Epoch 8/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.9409 - acc: 0.7094 - val_loss: 0.7361 - val_acc: 0.7758\n",
-      "Epoch 9/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.9201 - acc: 0.7202 - val_loss: 0.7476 - val_acc: 0.7760\n",
-      "Epoch 10/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8957 - acc: 0.7266 - val_loss: 0.7093 - val_acc: 0.7875\n",
-      "Epoch 11/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8655 - acc: 0.7398 - val_loss: 0.6546 - val_acc: 0.8139\n",
-      "Epoch 12/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8584 - acc: 0.7436 - val_loss: 0.7769 - val_acc: 0.7738\n",
-      "Epoch 13/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8437 - acc: 0.7494 - val_loss: 0.6671 - val_acc: 0.8121\n",
-      "Epoch 14/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8245 - acc: 0.7552 - val_loss: 0.6601 - val_acc: 0.8125\n",
-      "Epoch 15/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8124 - acc: 0.7605 - val_loss: 0.6404 - val_acc: 0.8205\n",
-      "Epoch 16/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.8108 - acc: 0.7632 - val_loss: 0.6932 - val_acc: 0.8109\n",
-      "Epoch 17/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7979 - acc: 0.7691 - val_loss: 0.5928 - val_acc: 0.8370\n",
-      "Epoch 18/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7831 - acc: 0.7722 - val_loss: 0.6426 - val_acc: 0.8202\n",
-      "Epoch 19/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7790 - acc: 0.7752 - val_loss: 0.6309 - val_acc: 0.8296\n",
-      "Epoch 20/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7737 - acc: 0.7772 - val_loss: 0.6037 - val_acc: 0.8343\n",
-      "Epoch 21/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7549 - acc: 0.7837 - val_loss: 0.5871 - val_acc: 0.8396\n",
-      "Epoch 22/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7569 - acc: 0.7849 - val_loss: 0.5851 - val_acc: 0.8379\n",
-      "Epoch 23/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7441 - acc: 0.7906 - val_loss: 0.5901 - val_acc: 0.8398\n",
-      "Epoch 24/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7422 - acc: 0.7885 - val_loss: 0.5734 - val_acc: 0.8507\n",
-      "Epoch 25/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7337 - acc: 0.7916 - val_loss: 0.5934 - val_acc: 0.8412\n",
-      "Epoch 26/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7314 - acc: 0.7934 - val_loss: 0.5657 - val_acc: 0.8439\n",
-      "Epoch 27/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7241 - acc: 0.7976 - val_loss: 0.6183 - val_acc: 0.8362\n",
-      "Epoch 28/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7165 - acc: 0.8000 - val_loss: 0.5909 - val_acc: 0.8399\n",
-      "Epoch 29/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7142 - acc: 0.7995 - val_loss: 0.5780 - val_acc: 0.8462\n",
-      "Epoch 30/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7094 - acc: 0.8004 - val_loss: 0.5803 - val_acc: 0.8481\n",
-      "Epoch 31/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.7009 - acc: 0.8041 - val_loss: 0.5563 - val_acc: 0.8554\n",
-      "Epoch 32/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6942 - acc: 0.8076 - val_loss: 0.5707 - val_acc: 0.8524\n",
-      "Epoch 33/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6939 - acc: 0.8073 - val_loss: 0.5522 - val_acc: 0.8538\n",
-      "Epoch 34/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6942 - acc: 0.8082 - val_loss: 0.6007 - val_acc: 0.8466\n",
-      "Epoch 35/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6885 - acc: 0.8087 - val_loss: 0.5460 - val_acc: 0.8588\n",
-      "Epoch 36/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6761 - acc: 0.8135 - val_loss: 0.5982 - val_acc: 0.8412\n",
-      "Epoch 37/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6801 - acc: 0.8119 - val_loss: 0.5409 - val_acc: 0.8624\n",
-      "Epoch 38/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6765 - acc: 0.8136 - val_loss: 0.5562 - val_acc: 0.8564\n",
-      "Epoch 39/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6674 - acc: 0.8166 - val_loss: 0.5540 - val_acc: 0.8592\n",
-      "Epoch 40/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6628 - acc: 0.8153 - val_loss: 0.5382 - val_acc: 0.8636\n",
-      "Epoch 41/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6617 - acc: 0.8165 - val_loss: 0.5627 - val_acc: 0.8525\n",
-      "Epoch 42/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6604 - acc: 0.8181 - val_loss: 0.5355 - val_acc: 0.8639\n",
-      "Epoch 43/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6538 - acc: 0.8206 - val_loss: 0.5390 - val_acc: 0.8635\n",
-      "Epoch 44/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6530 - acc: 0.8209 - val_loss: 0.5151 - val_acc: 0.8700\n",
-      "Epoch 45/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6463 - acc: 0.8217 - val_loss: 0.5557 - val_acc: 0.8569\n",
-      "Epoch 46/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6467 - acc: 0.8223 - val_loss: 0.5709 - val_acc: 0.8538\n",
-      "Epoch 47/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6443 - acc: 0.8226 - val_loss: 0.5340 - val_acc: 0.8641\n",
-      "Epoch 48/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.6388 - acc: 0.8244 - val_loss: 0.5511 - val_acc: 0.8582\n",
-      "Epoch 49/250\n",
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-      "Epoch 54/250\n",
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-      "Epoch 55/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.6212 - acc: 0.8293 - val_loss: 0.5197 - val_acc: 0.8672\n",
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-      "Epoch 77/250\n",
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-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5204 - acc: 0.8632 - val_loss: 0.4483 - val_acc: 0.8936\n",
-      "Epoch 80/250\n",
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-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5195 - acc: 0.8616 - val_loss: 0.4419 - val_acc: 0.8942\n",
-      "Epoch 82/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5119 - acc: 0.8638 - val_loss: 0.4458 - val_acc: 0.8925\n",
-      "Epoch 83/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5130 - acc: 0.8616 - val_loss: 0.4661 - val_acc: 0.8888\n",
-      "Epoch 84/250\n",
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-      "Epoch 85/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5071 - acc: 0.8647 - val_loss: 0.4468 - val_acc: 0.8916\n",
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-      "Epoch 87/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5037 - acc: 0.8653 - val_loss: 0.4354 - val_acc: 0.8952\n",
-      "Epoch 88/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5001 - acc: 0.8663 - val_loss: 0.4359 - val_acc: 0.8955\n",
-      "Epoch 89/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4987 - acc: 0.8664 - val_loss: 0.4557 - val_acc: 0.8903\n",
-      "Epoch 90/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4988 - acc: 0.8653 - val_loss: 0.4346 - val_acc: 0.8947\n",
-      "Epoch 91/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.5009 - acc: 0.8651 - val_loss: 0.4361 - val_acc: 0.8954\n",
-      "Epoch 92/250\n"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4973 - acc: 0.8655 - val_loss: 0.4344 - val_acc: 0.8935\n",
-      "Epoch 93/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4920 - acc: 0.8667 - val_loss: 0.4270 - val_acc: 0.8927\n",
-      "Epoch 94/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4936 - acc: 0.8673 - val_loss: 0.4292 - val_acc: 0.8939\n",
-      "Epoch 95/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4897 - acc: 0.8665 - val_loss: 0.4326 - val_acc: 0.8923\n",
-      "Epoch 96/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4885 - acc: 0.8687 - val_loss: 0.4247 - val_acc: 0.8961\n",
-      "Epoch 97/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4882 - acc: 0.8675 - val_loss: 0.4320 - val_acc: 0.8931\n",
-      "Epoch 98/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4845 - acc: 0.8683 - val_loss: 0.4332 - val_acc: 0.8926\n",
-      "Epoch 99/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4831 - acc: 0.8698 - val_loss: 0.4198 - val_acc: 0.8995\n",
-      "Epoch 100/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4844 - acc: 0.8681 - val_loss: 0.4265 - val_acc: 0.8958\n",
-      "Epoch 101/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4697 - acc: 0.8731 - val_loss: 0.4091 - val_acc: 0.9023\n",
-      "Epoch 102/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4583 - acc: 0.8788 - val_loss: 0.4109 - val_acc: 0.8999\n",
-      "Epoch 103/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4573 - acc: 0.8766 - val_loss: 0.4042 - val_acc: 0.8993\n",
-      "Epoch 104/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4504 - acc: 0.8795 - val_loss: 0.4055 - val_acc: 0.9011\n",
-      "Epoch 105/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4537 - acc: 0.8776 - val_loss: 0.4113 - val_acc: 0.8994\n",
-      "Epoch 106/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4485 - acc: 0.8799 - val_loss: 0.4009 - val_acc: 0.9028\n",
-      "Epoch 107/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4476 - acc: 0.8795 - val_loss: 0.4076 - val_acc: 0.9015\n",
-      "Epoch 108/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4477 - acc: 0.8794 - val_loss: 0.4030 - val_acc: 0.9007\n",
-      "Epoch 109/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4443 - acc: 0.8801 - val_loss: 0.4068 - val_acc: 0.9012\n",
-      "Epoch 110/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4503 - acc: 0.8791 - val_loss: 0.4007 - val_acc: 0.8994\n",
-      "Epoch 111/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4425 - acc: 0.8810 - val_loss: 0.3986 - val_acc: 0.9006\n",
-      "Epoch 112/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4436 - acc: 0.8801 - val_loss: 0.3975 - val_acc: 0.9029\n",
-      "Epoch 113/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4464 - acc: 0.8806 - val_loss: 0.3940 - val_acc: 0.9018\n",
-      "Epoch 114/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4398 - acc: 0.8804 - val_loss: 0.3999 - val_acc: 0.9006\n",
-      "Epoch 115/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4378 - acc: 0.8810 - val_loss: 0.3954 - val_acc: 0.9009\n",
-      "Epoch 116/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4398 - acc: 0.8800 - val_loss: 0.3916 - val_acc: 0.9023\n",
-      "Epoch 117/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4364 - acc: 0.8812 - val_loss: 0.3922 - val_acc: 0.9012\n",
-      "Epoch 118/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4354 - acc: 0.8820 - val_loss: 0.3967 - val_acc: 0.9001\n",
-      "Epoch 119/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4399 - acc: 0.8792 - val_loss: 0.3964 - val_acc: 0.9008\n",
-      "Epoch 120/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4323 - acc: 0.8816 - val_loss: 0.3943 - val_acc: 0.9025\n",
-      "Epoch 121/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4362 - acc: 0.8806 - val_loss: 0.3971 - val_acc: 0.9036\n",
-      "Epoch 122/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4335 - acc: 0.8823 - val_loss: 0.4065 - val_acc: 0.8976\n",
-      "Epoch 123/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4309 - acc: 0.8818 - val_loss: 0.3966 - val_acc: 0.9018\n",
-      "Epoch 124/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4271 - acc: 0.8840 - val_loss: 0.4024 - val_acc: 0.9000\n",
-      "Epoch 125/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4331 - acc: 0.8818 - val_loss: 0.3913 - val_acc: 0.9012\n",
-      "Epoch 126/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4127 - acc: 0.8884 - val_loss: 0.3890 - val_acc: 0.9051\n",
-      "Epoch 127/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4115 - acc: 0.8891 - val_loss: 0.3860 - val_acc: 0.9053\n",
-      "Epoch 128/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4110 - acc: 0.8893 - val_loss: 0.3850 - val_acc: 0.9064\n",
-      "Epoch 129/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4115 - acc: 0.8884 - val_loss: 0.3816 - val_acc: 0.9058\n",
-      "Epoch 130/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4050 - acc: 0.8894 - val_loss: 0.3803 - val_acc: 0.9076\n",
-      "Epoch 131/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4049 - acc: 0.8905 - val_loss: 0.3767 - val_acc: 0.9084\n",
-      "Epoch 132/250\n",
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-      "Epoch 133/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4005 - acc: 0.8927 - val_loss: 0.3824 - val_acc: 0.9077\n",
-      "Epoch 134/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4041 - acc: 0.8908 - val_loss: 0.3769 - val_acc: 0.9083\n",
-      "Epoch 135/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4028 - acc: 0.8896 - val_loss: 0.3764 - val_acc: 0.9079\n",
-      "Epoch 136/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3993 - acc: 0.8921 - val_loss: 0.3737 - val_acc: 0.9067\n",
-      "Epoch 137/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3984 - acc: 0.8918 - val_loss: 0.3796 - val_acc: 0.9059\n",
-      "Epoch 138/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3952 - acc: 0.8930 - val_loss: 0.3854 - val_acc: 0.9029\n",
-      "Epoch 139/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3977 - acc: 0.8926 - val_loss: 0.3759 - val_acc: 0.9083\n",
-      "Epoch 140/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3953 - acc: 0.8917 - val_loss: 0.3716 - val_acc: 0.9082\n",
-      "Epoch 141/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4000 - acc: 0.8915 - val_loss: 0.3755 - val_acc: 0.9099\n",
-      "Epoch 142/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3894 - acc: 0.8935 - val_loss: 0.3696 - val_acc: 0.9091\n",
-      "Epoch 143/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3969 - acc: 0.8932 - val_loss: 0.3716 - val_acc: 0.9076\n",
-      "Epoch 144/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3915 - acc: 0.8928 - val_loss: 0.3727 - val_acc: 0.9065\n",
-      "Epoch 145/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3907 - acc: 0.8944 - val_loss: 0.3804 - val_acc: 0.9055\n",
-      "Epoch 146/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3944 - acc: 0.8928 - val_loss: 0.3758 - val_acc: 0.9067\n",
-      "Epoch 147/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3889 - acc: 0.8944 - val_loss: 0.3814 - val_acc: 0.9051\n",
-      "Epoch 148/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3928 - acc: 0.8932 - val_loss: 0.3680 - val_acc: 0.9108\n",
-      "Epoch 149/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3918 - acc: 0.8928 - val_loss: 0.3772 - val_acc: 0.9079\n",
-      "Epoch 150/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3900 - acc: 0.8936 - val_loss: 0.3679 - val_acc: 0.9104\n",
-      "Epoch 151/250\n"
-     ]
-    },
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-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3848 - acc: 0.8945 - val_loss: 0.3683 - val_acc: 0.9088\n",
-      "Epoch 152/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3831 - acc: 0.8951 - val_loss: 0.3696 - val_acc: 0.9104\n",
-      "Epoch 153/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3771 - acc: 0.8980 - val_loss: 0.3699 - val_acc: 0.9090\n",
-      "Epoch 154/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3749 - acc: 0.8980 - val_loss: 0.3689 - val_acc: 0.9102\n",
-      "Epoch 155/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3764 - acc: 0.8982 - val_loss: 0.3678 - val_acc: 0.9103\n",
-      "Epoch 156/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3772 - acc: 0.8976 - val_loss: 0.3679 - val_acc: 0.9079\n",
-      "Epoch 157/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3767 - acc: 0.8966 - val_loss: 0.3681 - val_acc: 0.9092\n",
-      "Epoch 158/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3754 - acc: 0.8977 - val_loss: 0.3650 - val_acc: 0.9096\n",
-      "Epoch 159/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3793 - acc: 0.8974 - val_loss: 0.3661 - val_acc: 0.9103\n",
-      "Epoch 160/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3736 - acc: 0.8982 - val_loss: 0.3683 - val_acc: 0.9100\n",
-      "Epoch 161/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3757 - acc: 0.8987 - val_loss: 0.3640 - val_acc: 0.9104\n",
-      "Epoch 162/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3698 - acc: 0.9003 - val_loss: 0.3664 - val_acc: 0.9087\n",
-      "Epoch 163/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3759 - acc: 0.8977 - val_loss: 0.3639 - val_acc: 0.9091\n",
-      "Epoch 164/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3639 - acc: 0.9028 - val_loss: 0.3654 - val_acc: 0.9093\n",
-      "Epoch 165/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3721 - acc: 0.8990 - val_loss: 0.3675 - val_acc: 0.9109\n",
-      "Epoch 166/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3669 - acc: 0.9007 - val_loss: 0.3631 - val_acc: 0.9101\n",
-      "Epoch 167/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3706 - acc: 0.8973 - val_loss: 0.3662 - val_acc: 0.9088\n",
-      "Epoch 168/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3713 - acc: 0.8986 - val_loss: 0.3646 - val_acc: 0.9104\n",
-      "Epoch 169/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3687 - acc: 0.8983 - val_loss: 0.3636 - val_acc: 0.9108\n",
-      "Epoch 170/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3726 - acc: 0.8972 - val_loss: 0.3601 - val_acc: 0.9114\n",
-      "Epoch 171/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3671 - acc: 0.8983 - val_loss: 0.3598 - val_acc: 0.9104\n",
-      "Epoch 172/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3717 - acc: 0.8985 - val_loss: 0.3600 - val_acc: 0.9116\n",
-      "Epoch 173/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3690 - acc: 0.8993 - val_loss: 0.3680 - val_acc: 0.9089\n",
-      "Epoch 174/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3699 - acc: 0.8993 - val_loss: 0.3639 - val_acc: 0.9106\n",
-      "Epoch 175/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3667 - acc: 0.8995 - val_loss: 0.3602 - val_acc: 0.9118\n",
-      "Epoch 176/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3692 - acc: 0.9002 - val_loss: 0.3589 - val_acc: 0.9116\n",
-      "Epoch 177/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3632 - acc: 0.9017 - val_loss: 0.3602 - val_acc: 0.9107\n",
-      "Epoch 178/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3625 - acc: 0.9000 - val_loss: 0.3598 - val_acc: 0.9120\n",
-      "Epoch 179/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3609 - acc: 0.9023 - val_loss: 0.3619 - val_acc: 0.9114\n",
-      "Epoch 180/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3615 - acc: 0.9029 - val_loss: 0.3632 - val_acc: 0.9111\n",
-      "Epoch 181/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3609 - acc: 0.9014 - val_loss: 0.3618 - val_acc: 0.9115\n",
-      "Epoch 182/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3618 - acc: 0.9011 - val_loss: 0.3622 - val_acc: 0.9108\n",
-      "Epoch 183/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3610 - acc: 0.9024 - val_loss: 0.3608 - val_acc: 0.9122\n",
-      "Epoch 184/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3607 - acc: 0.9029 - val_loss: 0.3603 - val_acc: 0.9112\n",
-      "Epoch 185/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3601 - acc: 0.9017 - val_loss: 0.3625 - val_acc: 0.9113\n",
-      "Epoch 186/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3578 - acc: 0.9016 - val_loss: 0.3617 - val_acc: 0.9110\n",
-      "Epoch 187/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3560 - acc: 0.9034 - val_loss: 0.3583 - val_acc: 0.9128\n",
-      "Epoch 188/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3594 - acc: 0.9021 - val_loss: 0.3589 - val_acc: 0.9119\n",
-      "Epoch 189/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3597 - acc: 0.9030 - val_loss: 0.3578 - val_acc: 0.9124\n",
-      "Epoch 190/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3569 - acc: 0.9031 - val_loss: 0.3595 - val_acc: 0.9118\n",
-      "Epoch 191/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3566 - acc: 0.9026 - val_loss: 0.3573 - val_acc: 0.9121\n",
-      "Epoch 192/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3528 - acc: 0.9038 - val_loss: 0.3597 - val_acc: 0.9110\n",
-      "Epoch 193/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3622 - acc: 0.9013 - val_loss: 0.3592 - val_acc: 0.9120\n",
-      "Epoch 194/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3550 - acc: 0.9035 - val_loss: 0.3578 - val_acc: 0.9123\n",
-      "Epoch 195/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3533 - acc: 0.9034 - val_loss: 0.3607 - val_acc: 0.9114\n",
-      "Epoch 196/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3537 - acc: 0.9034 - val_loss: 0.3576 - val_acc: 0.9106\n",
-      "Epoch 197/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3573 - acc: 0.9011 - val_loss: 0.3593 - val_acc: 0.9119\n",
-      "Epoch 198/250\n",
-      "390/390 [==============================] - 146s 374ms/step - loss: 0.3474 - acc: 0.9065 - val_loss: 0.3578 - val_acc: 0.9114\n",
-      "Epoch 199/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 0.3580 - acc: 0.9029 - val_loss: 0.3573 - val_acc: 0.9106\n",
-      "Epoch 200/250\n",
-      "390/390 [==============================] - 146s 373ms/step - loss: 0.3546 - acc: 0.9035 - val_loss: 0.3612 - val_acc: 0.9105\n",
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 146s 373ms/step - loss: 0.3542 - acc: 0.9035 - val_loss: 0.3580 - val_acc: 0.9110\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3534 - acc: 0.9042 - val_loss: 0.3603 - val_acc: 0.9102\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3544 - acc: 0.9031 - val_loss: 0.3588 - val_acc: 0.9122\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3526 - acc: 0.9036 - val_loss: 0.3591 - val_acc: 0.9112\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3512 - acc: 0.9042 - val_loss: 0.3575 - val_acc: 0.9118\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3523 - acc: 0.9044 - val_loss: 0.3575 - val_acc: 0.9122\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3564 - acc: 0.9032 - val_loss: 0.3596 - val_acc: 0.9109\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3534 - acc: 0.9042 - val_loss: 0.3586 - val_acc: 0.9113\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3500 - acc: 0.9054 - val_loss: 0.3595 - val_acc: 0.9110\n",
-      "Epoch 210/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3489 - acc: 0.9048 - val_loss: 0.3579 - val_acc: 0.9111\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3524 - acc: 0.9053 - val_loss: 0.3575 - val_acc: 0.9117\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3512 - acc: 0.9045 - val_loss: 0.3577 - val_acc: 0.9121\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3512 - acc: 0.9053 - val_loss: 0.3575 - val_acc: 0.9115\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3524 - acc: 0.9027 - val_loss: 0.3568 - val_acc: 0.9124\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3500 - acc: 0.9046 - val_loss: 0.3566 - val_acc: 0.9111\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3514 - acc: 0.9043 - val_loss: 0.3580 - val_acc: 0.9131\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3508 - acc: 0.9047 - val_loss: 0.3582 - val_acc: 0.9122\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3516 - acc: 0.9050 - val_loss: 0.3573 - val_acc: 0.9118\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3526 - acc: 0.9044 - val_loss: 0.3576 - val_acc: 0.9126\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3524 - acc: 0.9045 - val_loss: 0.3583 - val_acc: 0.9118\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3537 - acc: 0.9025 - val_loss: 0.3569 - val_acc: 0.9122\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3496 - acc: 0.9051 - val_loss: 0.3568 - val_acc: 0.9123\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3487 - acc: 0.9039 - val_loss: 0.3572 - val_acc: 0.9126\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3503 - acc: 0.9048 - val_loss: 0.3575 - val_acc: 0.9123\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3516 - acc: 0.9041 - val_loss: 0.3563 - val_acc: 0.9124\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3505 - acc: 0.9059 - val_loss: 0.3576 - val_acc: 0.9123\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3489 - acc: 0.9057 - val_loss: 0.3561 - val_acc: 0.9121\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3523 - acc: 0.9037 - val_loss: 0.3570 - val_acc: 0.9116\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3506 - acc: 0.9031 - val_loss: 0.3575 - val_acc: 0.9120\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3516 - acc: 0.9031 - val_loss: 0.3567 - val_acc: 0.9118\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3454 - acc: 0.9060 - val_loss: 0.3569 - val_acc: 0.9123\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3473 - acc: 0.9060 - val_loss: 0.3569 - val_acc: 0.9120\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3532 - acc: 0.9035 - val_loss: 0.3560 - val_acc: 0.9118\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3532 - acc: 0.9046 - val_loss: 0.3565 - val_acc: 0.9128\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3428 - acc: 0.9077 - val_loss: 0.3555 - val_acc: 0.9119\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3481 - acc: 0.9045 - val_loss: 0.3557 - val_acc: 0.9121\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3502 - acc: 0.9045 - val_loss: 0.3559 - val_acc: 0.9123\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3516 - acc: 0.9044 - val_loss: 0.3553 - val_acc: 0.9120\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3515 - acc: 0.9046 - val_loss: 0.3560 - val_acc: 0.9115\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3431 - acc: 0.9051 - val_loss: 0.3557 - val_acc: 0.9122\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3486 - acc: 0.9038 - val_loss: 0.3548 - val_acc: 0.9109\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3472 - acc: 0.9058 - val_loss: 0.3557 - val_acc: 0.9108\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3517 - acc: 0.9043 - val_loss: 0.3573 - val_acc: 0.9126\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3551 - acc: 0.9027 - val_loss: 0.3556 - val_acc: 0.9121\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3451 - acc: 0.9051 - val_loss: 0.3566 - val_acc: 0.9121\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3441 - acc: 0.9058 - val_loss: 0.3564 - val_acc: 0.9116\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3470 - acc: 0.9055 - val_loss: 0.3562 - val_acc: 0.9126\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3511 - acc: 0.9049 - val_loss: 0.3557 - val_acc: 0.9120\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3484 - acc: 0.9040 - val_loss: 0.3549 - val_acc: 0.9135\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3473 - acc: 0.9055 - val_loss: 0.3553 - val_acc: 0.9121\n",
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-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"elu\", \"elu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('models/elu/elu_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9187\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(\"relu\")\n",
-    "        model.load_weights('relu_at_'+str(i+1)+'.h5')\n",
-    "        model.save('relu_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('relu_at_'+str(i+1)+'.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_19 (Conv2D)           (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_19 (Activation)   (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_19 (Batc (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_20 (Conv2D)           (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_20 (Activation)   (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_20 (Batc (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_10 (MaxPooling (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_10 (Dropout)         (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_21 (Conv2D)           (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_21 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_21 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_22 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_22 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_22 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_11 (MaxPooling (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_11 (Dropout)         (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_23 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_23 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_23 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_24 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_24 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_24 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_12 (MaxPooling (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_12 (Dropout)         (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_4 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Accuracy 1: 0.9105\n",
-      "Accuracy 2: 0.9128\n",
-      "Accuracy 3: 0.9121\n",
-      "0.9221\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(\"elu\")\n",
-    "        model.load_weights('models/elu/elu_at_'+str(i+1)+'.h5')\n",
-    "        model.save('models/elu/elu_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('models/elu/elu_at_'+str(i+1)+'.h5')\n",
-    "        \n",
-    "    pred = model.predict_proba(x_test)\n",
-    "    print(\"Accuracy \"+str(i+1)+\":\", accuracy(pred, y_test))\n",
-    "    ensembler += pred\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Deeper_CNN/cifar10_deeper_cnn_eswish.ipynb b/cifar/Deeper_CNN/cifar10_deeper_cnn_eswish.ipynb
deleted file mode 100644
index b377fcb..0000000
--- a/cifar/Deeper_CNN/cifar10_deeper_cnn_eswish.ipynb
+++ /dev/null
@@ -1,1713 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "from keras.models import load_model\n",
-    "import keras.regularizers as regularizers\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 181s 464ms/step - loss: 2.0738 - acc: 0.3787 - val_loss: 1.3396 - val_acc: 0.5477\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 1.4269 - acc: 0.5314 - val_loss: 1.0368 - val_acc: 0.6504\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.2076 - acc: 0.6036 - val_loss: 0.8844 - val_acc: 0.7165\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.0982 - acc: 0.6431 - val_loss: 0.9312 - val_acc: 0.7087\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.0170 - acc: 0.6697 - val_loss: 0.8638 - val_acc: 0.7204\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.9570 - acc: 0.6918 - val_loss: 0.7709 - val_acc: 0.7666\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9127 - acc: 0.7112 - val_loss: 0.7347 - val_acc: 0.7747\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8836 - acc: 0.7216 - val_loss: 0.6990 - val_acc: 0.7934\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.8563 - acc: 0.7334 - val_loss: 0.6695 - val_acc: 0.8014\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.8293 - acc: 0.7423 - val_loss: 0.6457 - val_acc: 0.8079\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.8102 - acc: 0.7501 - val_loss: 0.6209 - val_acc: 0.8144\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.7946 - acc: 0.7554 - val_loss: 0.6253 - val_acc: 0.8169\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.7806 - acc: 0.7630 - val_loss: 0.6238 - val_acc: 0.8160\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7676 - acc: 0.7677 - val_loss: 0.6359 - val_acc: 0.8151\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7511 - acc: 0.7760 - val_loss: 0.5995 - val_acc: 0.8298\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7422 - acc: 0.7781 - val_loss: 0.5971 - val_acc: 0.8316\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7293 - acc: 0.7822 - val_loss: 0.5970 - val_acc: 0.8326\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7216 - acc: 0.7869 - val_loss: 0.5847 - val_acc: 0.8387\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7170 - acc: 0.7887 - val_loss: 0.5724 - val_acc: 0.8416\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7001 - acc: 0.7967 - val_loss: 0.5934 - val_acc: 0.8340\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7073 - acc: 0.7944 - val_loss: 0.5727 - val_acc: 0.8420\n",
-      "Epoch 22/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6971 - acc: 0.7980 - val_loss: 0.5887 - val_acc: 0.8398\n",
-      "Epoch 23/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6881 - acc: 0.8010 - val_loss: 0.5795 - val_acc: 0.8377\n",
-      "Epoch 24/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6834 - acc: 0.8002 - val_loss: 0.5555 - val_acc: 0.8478\n",
-      "Epoch 25/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6780 - acc: 0.8056 - val_loss: 0.5808 - val_acc: 0.8450\n",
-      "Epoch 26/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6694 - acc: 0.8087 - val_loss: 0.5288 - val_acc: 0.8604\n",
-      "Epoch 27/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6664 - acc: 0.8102 - val_loss: 0.5656 - val_acc: 0.8519\n",
-      "Epoch 28/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6637 - acc: 0.8117 - val_loss: 0.5846 - val_acc: 0.8441\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6652 - acc: 0.8115 - val_loss: 0.5603 - val_acc: 0.8541\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.6602 - acc: 0.8138 - val_loss: 0.5834 - val_acc: 0.8460\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.6539 - acc: 0.8143 - val_loss: 0.5562 - val_acc: 0.8533\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.6479 - acc: 0.8185 - val_loss: 0.5478 - val_acc: 0.8554\n",
-      "Epoch 33/200\n",
-      "390/390 [==============================] - 176s 453ms/step - loss: 0.6480 - acc: 0.8174 - val_loss: 0.5290 - val_acc: 0.8623\n",
-      "Epoch 34/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.6456 - acc: 0.8186 - val_loss: 0.5237 - val_acc: 0.8662\n",
-      "Epoch 35/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.6409 - acc: 0.8200 - val_loss: 0.5190 - val_acc: 0.8657\n",
-      "Epoch 36/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.6417 - acc: 0.8206 - val_loss: 0.5265 - val_acc: 0.8661\n",
-      "Epoch 37/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6397 - acc: 0.8211 - val_loss: 0.5297 - val_acc: 0.8629\n",
-      "Epoch 38/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6375 - acc: 0.8255 - val_loss: 0.5341 - val_acc: 0.8622\n",
-      "Epoch 39/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6396 - acc: 0.8250 - val_loss: 0.5483 - val_acc: 0.8583\n",
-      "Epoch 40/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6333 - acc: 0.8252 - val_loss: 0.5127 - val_acc: 0.8709\n",
-      "Epoch 41/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6323 - acc: 0.8232 - val_loss: 0.5198 - val_acc: 0.8644\n",
-      "Epoch 42/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6191 - acc: 0.8302 - val_loss: 0.5101 - val_acc: 0.8725\n",
-      "Epoch 43/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6240 - acc: 0.8282 - val_loss: 0.5391 - val_acc: 0.8662\n",
-      "Epoch 44/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6241 - acc: 0.8276 - val_loss: 0.5139 - val_acc: 0.8679\n",
-      "Epoch 45/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6245 - acc: 0.8274 - val_loss: 0.5194 - val_acc: 0.8676\n",
-      "Epoch 46/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6174 - acc: 0.8309 - val_loss: 0.5196 - val_acc: 0.8658\n",
-      "Epoch 47/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6248 - acc: 0.8296 - val_loss: 0.5104 - val_acc: 0.8718\n",
-      "Epoch 48/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6160 - acc: 0.8336 - val_loss: 0.5210 - val_acc: 0.8695\n",
-      "Epoch 49/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6149 - acc: 0.8317 - val_loss: 0.5055 - val_acc: 0.8755\n",
-      "Epoch 50/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6095 - acc: 0.8353 - val_loss: 0.5209 - val_acc: 0.8665\n",
-      "Epoch 51/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6137 - acc: 0.8347 - val_loss: 0.5293 - val_acc: 0.8643\n",
-      "Epoch 52/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6149 - acc: 0.8335 - val_loss: 0.5048 - val_acc: 0.8716\n",
-      "Epoch 53/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6100 - acc: 0.8361 - val_loss: 0.5130 - val_acc: 0.8726\n",
-      "Epoch 54/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6075 - acc: 0.8358 - val_loss: 0.5101 - val_acc: 0.8688\n",
-      "Epoch 55/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6033 - acc: 0.8362 - val_loss: 0.5189 - val_acc: 0.8666\n",
-      "Epoch 56/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6120 - acc: 0.8348 - val_loss: 0.5211 - val_acc: 0.8709\n",
-      "Epoch 57/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5973 - acc: 0.8390 - val_loss: 0.5192 - val_acc: 0.8670\n",
-      "Epoch 58/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6051 - acc: 0.8386 - val_loss: 0.5078 - val_acc: 0.8772\n",
-      "Epoch 59/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5959 - acc: 0.8397 - val_loss: 0.5162 - val_acc: 0.8681\n",
-      "Epoch 60/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.6022 - acc: 0.8385 - val_loss: 0.5355 - val_acc: 0.8681\n",
-      "Epoch 61/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5930 - acc: 0.8403 - val_loss: 0.5117 - val_acc: 0.8745\n",
-      "Epoch 62/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5985 - acc: 0.8409 - val_loss: 0.5003 - val_acc: 0.8769\n",
-      "Epoch 63/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5990 - acc: 0.8394 - val_loss: 0.4987 - val_acc: 0.8739\n",
-      "Epoch 64/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5972 - acc: 0.8404 - val_loss: 0.5158 - val_acc: 0.8737\n",
-      "Epoch 65/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5946 - acc: 0.8408 - val_loss: 0.5010 - val_acc: 0.8760\n",
-      "Epoch 66/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5931 - acc: 0.8412 - val_loss: 0.5097 - val_acc: 0.8718\n",
-      "Epoch 67/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5990 - acc: 0.8399 - val_loss: 0.4921 - val_acc: 0.8805\n",
-      "Epoch 68/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5945 - acc: 0.8424 - val_loss: 0.4981 - val_acc: 0.8723\n",
-      "Epoch 69/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5967 - acc: 0.8404 - val_loss: 0.5170 - val_acc: 0.8749\n",
-      "Epoch 70/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5886 - acc: 0.8450 - val_loss: 0.4881 - val_acc: 0.8829\n",
-      "Epoch 71/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5883 - acc: 0.8448 - val_loss: 0.5076 - val_acc: 0.8767\n",
-      "Epoch 72/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5882 - acc: 0.8442 - val_loss: 0.4994 - val_acc: 0.8809\n",
-      "Epoch 73/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5904 - acc: 0.8448 - val_loss: 0.5110 - val_acc: 0.8738\n",
-      "Epoch 74/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5892 - acc: 0.8439 - val_loss: 0.5284 - val_acc: 0.8652\n",
-      "Epoch 75/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5921 - acc: 0.8422 - val_loss: 0.5199 - val_acc: 0.8696\n",
-      "Epoch 76/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5479 - acc: 0.8577 - val_loss: 0.4723 - val_acc: 0.8866\n",
-      "Epoch 77/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5350 - acc: 0.8624 - val_loss: 0.4661 - val_acc: 0.8872\n",
-      "Epoch 78/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5290 - acc: 0.8610 - val_loss: 0.4749 - val_acc: 0.8869\n",
-      "Epoch 79/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5201 - acc: 0.8647 - val_loss: 0.4587 - val_acc: 0.8902\n",
-      "Epoch 80/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5172 - acc: 0.8647 - val_loss: 0.4545 - val_acc: 0.8921\n",
-      "Epoch 81/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5120 - acc: 0.8671 - val_loss: 0.4536 - val_acc: 0.8896\n",
-      "Epoch 82/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5096 - acc: 0.8673 - val_loss: 0.4476 - val_acc: 0.8930\n",
-      "Epoch 83/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5096 - acc: 0.8676 - val_loss: 0.4417 - val_acc: 0.8924\n",
-      "Epoch 84/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5062 - acc: 0.8658 - val_loss: 0.4310 - val_acc: 0.8963\n",
-      "Epoch 85/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4995 - acc: 0.8675 - val_loss: 0.4582 - val_acc: 0.8878\n",
-      "Epoch 86/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5029 - acc: 0.8681 - val_loss: 0.4409 - val_acc: 0.8950\n",
-      "Epoch 87/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5016 - acc: 0.8660 - val_loss: 0.4393 - val_acc: 0.8952\n",
-      "Epoch 88/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4947 - acc: 0.8676 - val_loss: 0.4402 - val_acc: 0.8957\n",
-      "Epoch 89/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4988 - acc: 0.8681 - val_loss: 0.4408 - val_acc: 0.8913\n",
-      "Epoch 90/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4920 - acc: 0.8706 - val_loss: 0.4598 - val_acc: 0.8858\n",
-      "Epoch 91/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4922 - acc: 0.8701 - val_loss: 0.4346 - val_acc: 0.8926\n",
-      "Epoch 92/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4867 - acc: 0.8718 - val_loss: 0.4358 - val_acc: 0.8934\n",
-      "Epoch 93/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4882 - acc: 0.8698 - val_loss: 0.4363 - val_acc: 0.8902\n",
-      "Epoch 94/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4898 - acc: 0.8692 - val_loss: 0.4398 - val_acc: 0.8910\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4860 - acc: 0.8715 - val_loss: 0.4347 - val_acc: 0.8945\n",
-      "Epoch 96/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4859 - acc: 0.8704 - val_loss: 0.4294 - val_acc: 0.8947\n",
-      "Epoch 97/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4858 - acc: 0.8717 - val_loss: 0.4414 - val_acc: 0.8877\n",
-      "Epoch 98/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4723 - acc: 0.8761 - val_loss: 0.4346 - val_acc: 0.8959\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4806 - acc: 0.8716 - val_loss: 0.4317 - val_acc: 0.8894\n",
-      "Epoch 100/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4771 - acc: 0.8725 - val_loss: 0.4303 - val_acc: 0.8930\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4691 - acc: 0.8759 - val_loss: 0.4073 - val_acc: 0.9027\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4557 - acc: 0.8786 - val_loss: 0.4046 - val_acc: 0.9021\n",
-      "Epoch 103/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4461 - acc: 0.8828 - val_loss: 0.4059 - val_acc: 0.9004\n",
-      "Epoch 104/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4506 - acc: 0.8798 - val_loss: 0.4070 - val_acc: 0.9018\n",
-      "Epoch 105/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4484 - acc: 0.8827 - val_loss: 0.4061 - val_acc: 0.9037\n",
-      "Epoch 106/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4457 - acc: 0.8809 - val_loss: 0.3990 - val_acc: 0.9041\n",
-      "Epoch 107/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4471 - acc: 0.8810 - val_loss: 0.4061 - val_acc: 0.9027\n",
-      "Epoch 108/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4377 - acc: 0.8840 - val_loss: 0.3989 - val_acc: 0.9050\n",
-      "Epoch 109/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4420 - acc: 0.8824 - val_loss: 0.4043 - val_acc: 0.9026\n",
-      "Epoch 110/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4411 - acc: 0.8820 - val_loss: 0.3997 - val_acc: 0.9013\n",
-      "Epoch 111/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4390 - acc: 0.8825 - val_loss: 0.4076 - val_acc: 0.9025\n",
-      "Epoch 112/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4378 - acc: 0.8818 - val_loss: 0.4060 - val_acc: 0.9019\n",
-      "Epoch 113/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4362 - acc: 0.8830 - val_loss: 0.3946 - val_acc: 0.9047\n",
-      "Epoch 114/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4329 - acc: 0.8838 - val_loss: 0.3950 - val_acc: 0.9034\n",
-      "Epoch 115/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4353 - acc: 0.8835 - val_loss: 0.3903 - val_acc: 0.9013\n",
-      "Epoch 116/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4328 - acc: 0.8837 - val_loss: 0.4027 - val_acc: 0.9007\n",
-      "Epoch 117/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4329 - acc: 0.8836 - val_loss: 0.3977 - val_acc: 0.9026\n",
-      "Epoch 118/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4287 - acc: 0.8850 - val_loss: 0.3907 - val_acc: 0.9043\n",
-      "Epoch 119/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4311 - acc: 0.8829 - val_loss: 0.4071 - val_acc: 0.8989\n",
-      "Epoch 120/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4293 - acc: 0.8838 - val_loss: 0.4066 - val_acc: 0.8984\n",
-      "Epoch 121/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4245 - acc: 0.8864 - val_loss: 0.4008 - val_acc: 0.9011\n",
-      "Epoch 122/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4226 - acc: 0.8862 - val_loss: 0.3902 - val_acc: 0.9007\n",
-      "Epoch 123/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4295 - acc: 0.8836 - val_loss: 0.4070 - val_acc: 0.8995\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4223 - acc: 0.8860 - val_loss: 0.3952 - val_acc: 0.9024\n",
-      "Epoch 125/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4224 - acc: 0.8862 - val_loss: 0.3888 - val_acc: 0.9026\n",
-      "Epoch 126/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4120 - acc: 0.8894 - val_loss: 0.3801 - val_acc: 0.9060\n",
-      "Epoch 127/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4033 - acc: 0.8921 - val_loss: 0.3826 - val_acc: 0.9075\n",
-      "Epoch 128/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4016 - acc: 0.8919 - val_loss: 0.3783 - val_acc: 0.9093\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3960 - acc: 0.8961 - val_loss: 0.3833 - val_acc: 0.9066\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4076 - acc: 0.8898 - val_loss: 0.3781 - val_acc: 0.9081\n",
-      "Epoch 131/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3994 - acc: 0.8931 - val_loss: 0.3823 - val_acc: 0.9070\n",
-      "Epoch 132/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3933 - acc: 0.8942 - val_loss: 0.3825 - val_acc: 0.9071\n",
-      "Epoch 133/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3984 - acc: 0.8931 - val_loss: 0.3786 - val_acc: 0.9066\n",
-      "Epoch 134/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3906 - acc: 0.8955 - val_loss: 0.3804 - val_acc: 0.9079\n",
-      "Epoch 135/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3942 - acc: 0.8936 - val_loss: 0.3854 - val_acc: 0.9065\n",
-      "Epoch 136/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3917 - acc: 0.8940 - val_loss: 0.3771 - val_acc: 0.9087\n",
-      "Epoch 137/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3895 - acc: 0.8944 - val_loss: 0.3792 - val_acc: 0.9085\n",
-      "Epoch 138/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3935 - acc: 0.8935 - val_loss: 0.3757 - val_acc: 0.9068\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3915 - acc: 0.8926 - val_loss: 0.3780 - val_acc: 0.9084\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3897 - acc: 0.8946 - val_loss: 0.3734 - val_acc: 0.9093\n",
-      "Epoch 141/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3893 - acc: 0.8962 - val_loss: 0.3782 - val_acc: 0.9086\n",
-      "Epoch 142/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3918 - acc: 0.8945 - val_loss: 0.3733 - val_acc: 0.9096\n",
-      "Epoch 143/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3825 - acc: 0.8968 - val_loss: 0.3733 - val_acc: 0.9079\n",
-      "Epoch 144/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3880 - acc: 0.8968 - val_loss: 0.3754 - val_acc: 0.9064\n",
-      "Epoch 145/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3837 - acc: 0.8958 - val_loss: 0.3733 - val_acc: 0.9075\n",
-      "Epoch 146/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3863 - acc: 0.8965 - val_loss: 0.3781 - val_acc: 0.9082\n",
-      "Epoch 147/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3806 - acc: 0.8982 - val_loss: 0.3708 - val_acc: 0.9096\n",
-      "Epoch 148/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3798 - acc: 0.8983 - val_loss: 0.3827 - val_acc: 0.9062\n",
-      "Epoch 149/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3790 - acc: 0.8982 - val_loss: 0.3792 - val_acc: 0.9059\n",
-      "Epoch 150/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3845 - acc: 0.8958 - val_loss: 0.3769 - val_acc: 0.9065\n",
-      "Epoch 151/200\n"
-     ]
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3757 - acc: 0.8990 - val_loss: 0.3710 - val_acc: 0.9084\n",
-      "Epoch 152/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3753 - acc: 0.8992 - val_loss: 0.3667 - val_acc: 0.9089\n",
-      "Epoch 153/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3777 - acc: 0.8979 - val_loss: 0.3694 - val_acc: 0.9082\n",
-      "Epoch 154/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3701 - acc: 0.9016 - val_loss: 0.3693 - val_acc: 0.9076\n",
-      "Epoch 155/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3694 - acc: 0.9005 - val_loss: 0.3673 - val_acc: 0.9090\n",
-      "Epoch 156/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3658 - acc: 0.9016 - val_loss: 0.3685 - val_acc: 0.9088\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3661 - acc: 0.9015 - val_loss: 0.3656 - val_acc: 0.9093\n",
-      "Epoch 158/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3675 - acc: 0.9023 - val_loss: 0.3692 - val_acc: 0.9102\n",
-      "Epoch 159/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3704 - acc: 0.8999 - val_loss: 0.3653 - val_acc: 0.9095\n",
-      "Epoch 160/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3600 - acc: 0.9032 - val_loss: 0.3660 - val_acc: 0.9105\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3649 - acc: 0.9033 - val_loss: 0.3668 - val_acc: 0.9091\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3672 - acc: 0.9009 - val_loss: 0.3667 - val_acc: 0.9109\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3621 - acc: 0.9018 - val_loss: 0.3613 - val_acc: 0.9107\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3634 - acc: 0.9027 - val_loss: 0.3636 - val_acc: 0.9114\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3643 - acc: 0.9029 - val_loss: 0.3636 - val_acc: 0.9086\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3633 - acc: 0.9015 - val_loss: 0.3660 - val_acc: 0.9109\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3656 - acc: 0.9013 - val_loss: 0.3653 - val_acc: 0.9101\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3562 - acc: 0.9058 - val_loss: 0.3678 - val_acc: 0.9097\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3578 - acc: 0.9045 - val_loss: 0.3601 - val_acc: 0.9119\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3656 - acc: 0.9004 - val_loss: 0.3641 - val_acc: 0.9106\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3629 - acc: 0.9014 - val_loss: 0.3638 - val_acc: 0.9112\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3626 - acc: 0.9024 - val_loss: 0.3631 - val_acc: 0.9109\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3599 - acc: 0.9024 - val_loss: 0.3660 - val_acc: 0.9090\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3592 - acc: 0.9043 - val_loss: 0.3651 - val_acc: 0.9098\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3592 - acc: 0.9028 - val_loss: 0.3653 - val_acc: 0.9098\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3566 - acc: 0.9041 - val_loss: 0.3640 - val_acc: 0.9092\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3496 - acc: 0.9073 - val_loss: 0.3636 - val_acc: 0.9112\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3600 - acc: 0.9033 - val_loss: 0.3636 - val_acc: 0.9116\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3510 - acc: 0.9057 - val_loss: 0.3639 - val_acc: 0.9126\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3477 - acc: 0.9073 - val_loss: 0.3602 - val_acc: 0.9134\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3510 - acc: 0.9048 - val_loss: 0.3616 - val_acc: 0.9120\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3527 - acc: 0.9043 - val_loss: 0.3637 - val_acc: 0.9100\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3509 - acc: 0.9055 - val_loss: 0.3614 - val_acc: 0.9127\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3560 - acc: 0.9039 - val_loss: 0.3578 - val_acc: 0.9119\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3473 - acc: 0.9077 - val_loss: 0.3626 - val_acc: 0.9117\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3477 - acc: 0.9048 - val_loss: 0.3616 - val_acc: 0.9120\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3479 - acc: 0.9064 - val_loss: 0.3593 - val_acc: 0.9135\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3538 - acc: 0.9041 - val_loss: 0.3615 - val_acc: 0.9104\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3488 - acc: 0.9061 - val_loss: 0.3621 - val_acc: 0.9110\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3521 - acc: 0.9066 - val_loss: 0.3612 - val_acc: 0.9110\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3498 - acc: 0.9060 - val_loss: 0.3584 - val_acc: 0.9119\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3525 - acc: 0.9046 - val_loss: 0.3589 - val_acc: 0.9116\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3438 - acc: 0.9087 - val_loss: 0.3593 - val_acc: 0.9112\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3505 - acc: 0.9053 - val_loss: 0.3601 - val_acc: 0.9108\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3490 - acc: 0.9054 - val_loss: 0.3583 - val_acc: 0.9119\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3462 - acc: 0.9072 - val_loss: 0.3578 - val_acc: 0.9129\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3523 - acc: 0.9055 - val_loss: 0.3582 - val_acc: 0.9116\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3487 - acc: 0.9055 - val_loss: 0.3586 - val_acc: 0.9110\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3506 - acc: 0.9060 - val_loss: 0.3575 - val_acc: 0.9119\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3483 - acc: 0.9067 - val_loss: 0.3584 - val_acc: 0.9115\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = e_swish_2, \"e_swish_2\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training\n",
-    "batch_size = 128\n",
-    "epochs=200\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('e_swish_2_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 2.0201 - acc: 0.3906 - val_loss: 1.3828 - val_acc: 0.5563\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.4731 - acc: 0.5308 - val_loss: 1.1149 - val_acc: 0.6337\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.2980 - acc: 0.5922 - val_loss: 1.0801 - val_acc: 0.6678\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 1.2151 - acc: 0.6298 - val_loss: 0.9754 - val_acc: 0.6856\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1390 - acc: 0.6510 - val_loss: 1.5834 - val_acc: 0.6358\n",
-      "Epoch 6/200\n",
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-      "Epoch 7/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0241 - acc: 0.6928 - val_loss: 0.7856 - val_acc: 0.7628\n",
-      "Epoch 8/200\n",
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-      "Epoch 20/200\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8591 - acc: 0.7642 - val_loss: 0.6625 - val_acc: 0.8172\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8197 - acc: 0.7769 - val_loss: 0.6442 - val_acc: 0.8197\n",
-      "Epoch 25/200\n",
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-      "Epoch 26/200\n",
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-      "Epoch 27/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8053 - acc: 0.7823 - val_loss: 0.6519 - val_acc: 0.8270\n",
-      "Epoch 28/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8434 - acc: 0.7808 - val_loss: 0.6330 - val_acc: 0.8318\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7910 - acc: 0.7879 - val_loss: 0.6119 - val_acc: 0.8378\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7811 - acc: 0.7891 - val_loss: 0.5965 - val_acc: 0.8451\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7707 - acc: 0.7902 - val_loss: 0.6395 - val_acc: 0.8271\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7675 - acc: 0.7932 - val_loss: 0.5810 - val_acc: 0.8464\n",
-      "Epoch 33/200\n",
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-      "Epoch 34/200\n",
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-      "Epoch 35/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7599 - acc: 0.7993 - val_loss: 0.5962 - val_acc: 0.8421\n",
-      "Epoch 36/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7571 - acc: 0.7990 - val_loss: 0.6151 - val_acc: 0.8365\n",
-      "Epoch 37/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7565 - acc: 0.7981 - val_loss: 0.5864 - val_acc: 0.8464\n",
-      "Epoch 38/200\n",
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-      "Epoch 39/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7530 - acc: 0.7973 - val_loss: 0.5760 - val_acc: 0.8520\n",
-      "Epoch 40/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7489 - acc: 0.8030 - val_loss: 0.6164 - val_acc: 0.8407\n",
-      "Epoch 41/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7573 - acc: 0.8010 - val_loss: 0.6014 - val_acc: 0.8493\n",
-      "Epoch 42/200\n",
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-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7395 - acc: 0.8055 - val_loss: 0.5853 - val_acc: 0.8527\n",
-      "Epoch 44/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7227 - acc: 0.8074 - val_loss: 0.5628 - val_acc: 0.8596\n",
-      "Epoch 45/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7235 - acc: 0.8086 - val_loss: 0.5740 - val_acc: 0.8546\n",
-      "Epoch 46/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7244 - acc: 0.8098 - val_loss: 0.5851 - val_acc: 0.8470\n",
-      "Epoch 47/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7256 - acc: 0.8081 - val_loss: 0.5751 - val_acc: 0.8535\n",
-      "Epoch 48/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7238 - acc: 0.8093 - val_loss: 0.5957 - val_acc: 0.8468\n",
-      "Epoch 49/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7146 - acc: 0.8112 - val_loss: 0.5766 - val_acc: 0.8543\n",
-      "Epoch 50/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7047 - acc: 0.8129 - val_loss: 0.5858 - val_acc: 0.8507\n",
-      "Epoch 51/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7186 - acc: 0.8132 - val_loss: 0.5443 - val_acc: 0.8671\n",
-      "Epoch 52/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7213 - acc: 0.8124 - val_loss: 0.5546 - val_acc: 0.8629\n",
-      "Epoch 53/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7064 - acc: 0.8124 - val_loss: 0.5631 - val_acc: 0.8606\n",
-      "Epoch 54/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7087 - acc: 0.8195 - val_loss: 0.5347 - val_acc: 0.8653\n",
-      "Epoch 55/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7108 - acc: 0.8152 - val_loss: 0.5700 - val_acc: 0.8593\n",
-      "Epoch 56/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7082 - acc: 0.8148 - val_loss: 0.5746 - val_acc: 0.8537\n",
-      "Epoch 57/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6987 - acc: 0.8188 - val_loss: 0.5549 - val_acc: 0.8612\n",
-      "Epoch 58/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6983 - acc: 0.8193 - val_loss: 0.5453 - val_acc: 0.8599\n",
-      "Epoch 59/200\n",
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-      "Epoch 60/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6905 - acc: 0.8199 - val_loss: 0.5770 - val_acc: 0.8546\n",
-      "Epoch 61/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6847 - acc: 0.8224 - val_loss: 0.5566 - val_acc: 0.8653\n",
-      "Epoch 62/200\n",
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-      "Epoch 63/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6949 - acc: 0.8193 - val_loss: 0.5522 - val_acc: 0.8611\n",
-      "Epoch 64/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6857 - acc: 0.8212 - val_loss: 0.5427 - val_acc: 0.8663\n",
-      "Epoch 65/200\n",
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-      "Epoch 66/200\n",
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-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6796 - acc: 0.8253 - val_loss: 0.5433 - val_acc: 0.8695\n",
-      "Epoch 71/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6776 - acc: 0.8265 - val_loss: 0.5272 - val_acc: 0.8718\n",
-      "Epoch 72/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6764 - acc: 0.8266 - val_loss: 0.5555 - val_acc: 0.8612\n",
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-      "Epoch 77/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6095 - acc: 0.8447 - val_loss: 0.4853 - val_acc: 0.8847\n",
-      "Epoch 78/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5955 - acc: 0.8486 - val_loss: 0.4940 - val_acc: 0.8829\n",
-      "Epoch 79/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5962 - acc: 0.8474 - val_loss: 0.4920 - val_acc: 0.8826\n",
-      "Epoch 80/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5897 - acc: 0.8493 - val_loss: 0.4860 - val_acc: 0.8858\n",
-      "Epoch 81/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5869 - acc: 0.8470 - val_loss: 0.4761 - val_acc: 0.8886\n",
-      "Epoch 82/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5713 - acc: 0.8523 - val_loss: 0.4877 - val_acc: 0.8820\n",
-      "Epoch 83/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5799 - acc: 0.8501 - val_loss: 0.4911 - val_acc: 0.8843\n",
-      "Epoch 84/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5708 - acc: 0.8521 - val_loss: 0.4748 - val_acc: 0.8885\n",
-      "Epoch 85/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5667 - acc: 0.8513 - val_loss: 0.4805 - val_acc: 0.8842\n",
-      "Epoch 86/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5677 - acc: 0.8520 - val_loss: 0.4675 - val_acc: 0.8858\n",
-      "Epoch 87/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5537 - acc: 0.8549 - val_loss: 0.4713 - val_acc: 0.8847\n",
-      "Epoch 88/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5667 - acc: 0.8520 - val_loss: 0.4694 - val_acc: 0.8878\n",
-      "Epoch 89/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5627 - acc: 0.8526 - val_loss: 0.4810 - val_acc: 0.8814\n",
-      "Epoch 90/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5565 - acc: 0.8538 - val_loss: 0.4680 - val_acc: 0.8862\n",
-      "Epoch 91/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5535 - acc: 0.8546 - val_loss: 0.4712 - val_acc: 0.8847\n",
-      "Epoch 92/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5423 - acc: 0.8576 - val_loss: 0.4586 - val_acc: 0.8902\n",
-      "Epoch 93/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5474 - acc: 0.8566 - val_loss: 0.4638 - val_acc: 0.8858\n",
-      "Epoch 94/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5499 - acc: 0.8535 - val_loss: 0.4542 - val_acc: 0.8877\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5362 - acc: 0.8586 - val_loss: 0.4561 - val_acc: 0.8869\n",
-      "Epoch 96/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5433 - acc: 0.8543 - val_loss: 0.4426 - val_acc: 0.8915\n",
-      "Epoch 97/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5360 - acc: 0.8568 - val_loss: 0.4597 - val_acc: 0.8866\n",
-      "Epoch 98/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5389 - acc: 0.8566 - val_loss: 0.4537 - val_acc: 0.8865\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5435 - acc: 0.8535 - val_loss: 0.4490 - val_acc: 0.8913\n",
-      "Epoch 100/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5295 - acc: 0.8574 - val_loss: 0.4588 - val_acc: 0.8837\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5126 - acc: 0.8649 - val_loss: 0.4349 - val_acc: 0.8947\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5037 - acc: 0.8662 - val_loss: 0.4344 - val_acc: 0.8927\n",
-      "Epoch 103/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5049 - acc: 0.8671 - val_loss: 0.4230 - val_acc: 0.8981\n",
-      "Epoch 104/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4946 - acc: 0.8680 - val_loss: 0.4370 - val_acc: 0.8915\n",
-      "Epoch 105/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4985 - acc: 0.8676 - val_loss: 0.4267 - val_acc: 0.8978\n",
-      "Epoch 106/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4964 - acc: 0.8688 - val_loss: 0.4269 - val_acc: 0.8958\n",
-      "Epoch 107/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4917 - acc: 0.8703 - val_loss: 0.4228 - val_acc: 0.8984\n",
-      "Epoch 108/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4929 - acc: 0.8693 - val_loss: 0.4239 - val_acc: 0.8957\n",
-      "Epoch 109/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4857 - acc: 0.8693 - val_loss: 0.4249 - val_acc: 0.8974\n",
-      "Epoch 110/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4896 - acc: 0.8700 - val_loss: 0.4235 - val_acc: 0.8971\n",
-      "Epoch 111/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4832 - acc: 0.8697 - val_loss: 0.4233 - val_acc: 0.8974\n",
-      "Epoch 112/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.4821 - acc: 0.8717 - val_loss: 0.4369 - val_acc: 0.8953\n",
-      "Epoch 113/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4878 - acc: 0.8698 - val_loss: 0.4254 - val_acc: 0.8970\n",
-      "Epoch 114/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4785 - acc: 0.8715 - val_loss: 0.4166 - val_acc: 0.8970\n",
-      "Epoch 115/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4844 - acc: 0.8698 - val_loss: 0.4253 - val_acc: 0.8967\n",
-      "Epoch 116/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4799 - acc: 0.8711 - val_loss: 0.4380 - val_acc: 0.8919\n",
-      "Epoch 117/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4786 - acc: 0.8706 - val_loss: 0.4211 - val_acc: 0.8948\n",
-      "Epoch 118/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4725 - acc: 0.8729 - val_loss: 0.4296 - val_acc: 0.8936\n",
-      "Epoch 119/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4752 - acc: 0.8712 - val_loss: 0.4253 - val_acc: 0.8922\n",
-      "Epoch 120/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4740 - acc: 0.8720 - val_loss: 0.4342 - val_acc: 0.8906\n",
-      "Epoch 121/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4714 - acc: 0.8740 - val_loss: 0.4373 - val_acc: 0.8936\n",
-      "Epoch 122/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4708 - acc: 0.8729 - val_loss: 0.4101 - val_acc: 0.8962\n",
-      "Epoch 123/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4622 - acc: 0.8761 - val_loss: 0.4203 - val_acc: 0.8945\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4684 - acc: 0.8729 - val_loss: 0.4186 - val_acc: 0.8958\n",
-      "Epoch 125/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4613 - acc: 0.8746 - val_loss: 0.4081 - val_acc: 0.8997\n",
-      "Epoch 126/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4521 - acc: 0.8776 - val_loss: 0.4075 - val_acc: 0.8980\n",
-      "Epoch 127/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4516 - acc: 0.8782 - val_loss: 0.4002 - val_acc: 0.8997\n",
-      "Epoch 128/200\n",
-      "390/390 [==============================] - 181s 464ms/step - loss: 0.4431 - acc: 0.8817 - val_loss: 0.4046 - val_acc: 0.9000\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4360 - acc: 0.8839 - val_loss: 0.3950 - val_acc: 0.9007\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4390 - acc: 0.8811 - val_loss: 0.3987 - val_acc: 0.9011\n",
-      "Epoch 131/200\n",
-      "390/390 [==============================] - 178s 457ms/step - loss: 0.4398 - acc: 0.8813 - val_loss: 0.3989 - val_acc: 0.8995\n",
-      "Epoch 132/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4388 - acc: 0.8825 - val_loss: 0.4080 - val_acc: 0.8963\n",
-      "Epoch 133/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4423 - acc: 0.8800 - val_loss: 0.4072 - val_acc: 0.8986\n",
-      "Epoch 134/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4373 - acc: 0.8807 - val_loss: 0.4090 - val_acc: 0.8962\n",
-      "Epoch 135/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4330 - acc: 0.8823 - val_loss: 0.4040 - val_acc: 0.8990\n",
-      "Epoch 136/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.4277 - acc: 0.8842 - val_loss: 0.3929 - val_acc: 0.9014\n",
-      "Epoch 137/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4341 - acc: 0.8825 - val_loss: 0.4060 - val_acc: 0.9006\n",
-      "Epoch 138/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4285 - acc: 0.8839 - val_loss: 0.3915 - val_acc: 0.9013\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4296 - acc: 0.8833 - val_loss: 0.4029 - val_acc: 0.9015\n",
-      "Epoch 140/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4305 - acc: 0.8839 - val_loss: 0.4011 - val_acc: 0.9009\n",
-      "Epoch 141/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4266 - acc: 0.8849 - val_loss: 0.3881 - val_acc: 0.9016\n",
-      "Epoch 142/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4307 - acc: 0.8837 - val_loss: 0.3954 - val_acc: 0.9019\n",
-      "Epoch 143/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4255 - acc: 0.8869 - val_loss: 0.3977 - val_acc: 0.9002\n",
-      "Epoch 144/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4284 - acc: 0.8838 - val_loss: 0.4010 - val_acc: 0.9011\n",
-      "Epoch 145/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4236 - acc: 0.8841 - val_loss: 0.3886 - val_acc: 0.9028\n",
-      "Epoch 146/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4223 - acc: 0.8849 - val_loss: 0.3961 - val_acc: 0.9010\n",
-      "Epoch 147/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4214 - acc: 0.8852 - val_loss: 0.3977 - val_acc: 0.9002\n",
-      "Epoch 148/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4208 - acc: 0.8858 - val_loss: 0.3911 - val_acc: 0.9024\n",
-      "Epoch 149/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4228 - acc: 0.8851 - val_loss: 0.3914 - val_acc: 0.9003\n",
-      "Epoch 150/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4217 - acc: 0.8861 - val_loss: 0.3957 - val_acc: 0.9004\n",
-      "Epoch 151/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4115 - acc: 0.8871 - val_loss: 0.3882 - val_acc: 0.9029\n",
-      "Epoch 152/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4149 - acc: 0.8877 - val_loss: 0.3919 - val_acc: 0.9037\n",
-      "Epoch 153/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4113 - acc: 0.8888 - val_loss: 0.3920 - val_acc: 0.9013\n",
-      "Epoch 154/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4110 - acc: 0.8882 - val_loss: 0.3838 - val_acc: 0.9035\n",
-      "Epoch 155/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4095 - acc: 0.8899 - val_loss: 0.3948 - val_acc: 0.9024\n",
-      "Epoch 156/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4084 - acc: 0.8893 - val_loss: 0.3909 - val_acc: 0.9042\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4045 - acc: 0.8903 - val_loss: 0.3851 - val_acc: 0.9046\n",
-      "Epoch 158/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4040 - acc: 0.8916 - val_loss: 0.3865 - val_acc: 0.9032\n",
-      "Epoch 159/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4052 - acc: 0.8896 - val_loss: 0.3857 - val_acc: 0.9046\n",
-      "Epoch 160/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.4019 - acc: 0.8913 - val_loss: 0.3871 - val_acc: 0.9056\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4055 - acc: 0.8913 - val_loss: 0.3808 - val_acc: 0.9062\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4025 - acc: 0.8923 - val_loss: 0.3901 - val_acc: 0.9044\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8910 - val_loss: 0.3853 - val_acc: 0.9044\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4027 - acc: 0.8910 - val_loss: 0.3823 - val_acc: 0.9061\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3982 - acc: 0.8928 - val_loss: 0.3831 - val_acc: 0.9062\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8903 - val_loss: 0.3881 - val_acc: 0.9056\n",
-      "Epoch 167/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3985 - acc: 0.8912 - val_loss: 0.3836 - val_acc: 0.9070\n",
-      "Epoch 168/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3989 - acc: 0.8925 - val_loss: 0.3874 - val_acc: 0.9035\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4048 - acc: 0.8895 - val_loss: 0.3850 - val_acc: 0.9034\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3951 - acc: 0.8937 - val_loss: 0.3849 - val_acc: 0.9045\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3978 - acc: 0.8920 - val_loss: 0.3835 - val_acc: 0.9052\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3997 - acc: 0.8901 - val_loss: 0.3865 - val_acc: 0.9044\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3989 - acc: 0.8913 - val_loss: 0.3794 - val_acc: 0.9049\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3992 - acc: 0.8915 - val_loss: 0.3835 - val_acc: 0.9040\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3927 - acc: 0.8924 - val_loss: 0.3854 - val_acc: 0.9039\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3928 - acc: 0.8938 - val_loss: 0.3804 - val_acc: 0.9056\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3919 - acc: 0.8931 - val_loss: 0.3817 - val_acc: 0.9057\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3892 - acc: 0.8952 - val_loss: 0.3801 - val_acc: 0.9065\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3919 - acc: 0.8941 - val_loss: 0.3808 - val_acc: 0.9056\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3892 - acc: 0.8936 - val_loss: 0.3816 - val_acc: 0.9055\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.3855 - acc: 0.8959 - val_loss: 0.3810 - val_acc: 0.9069\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3895 - acc: 0.8943 - val_loss: 0.3812 - val_acc: 0.9051\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3922 - acc: 0.8943 - val_loss: 0.3776 - val_acc: 0.9068\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3883 - acc: 0.8951 - val_loss: 0.3796 - val_acc: 0.9074\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 180s 461ms/step - loss: 0.3942 - acc: 0.8928 - val_loss: 0.3794 - val_acc: 0.9056\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 179s 459ms/step - loss: 0.3870 - acc: 0.8960 - val_loss: 0.3777 - val_acc: 0.9072\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3899 - acc: 0.8941 - val_loss: 0.3780 - val_acc: 0.9066\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 178s 456ms/step - loss: 0.3853 - acc: 0.8971 - val_loss: 0.3778 - val_acc: 0.9066\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 178s 458ms/step - loss: 0.3878 - acc: 0.8958 - val_loss: 0.3792 - val_acc: 0.9076\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3873 - acc: 0.8956 - val_loss: 0.3781 - val_acc: 0.9085\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3876 - acc: 0.8948 - val_loss: 0.3769 - val_acc: 0.9078\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3837 - acc: 0.8973 - val_loss: 0.3768 - val_acc: 0.9075\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3876 - acc: 0.8946 - val_loss: 0.3755 - val_acc: 0.9085\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3926 - acc: 0.8941 - val_loss: 0.3756 - val_acc: 0.9087\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3839 - acc: 0.8957 - val_loss: 0.3782 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3867 - acc: 0.8934 - val_loss: 0.3760 - val_acc: 0.9079\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3884 - acc: 0.8948 - val_loss: 0.3788 - val_acc: 0.9064\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3857 - acc: 0.8943 - val_loss: 0.3773 - val_acc: 0.9082\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3882 - acc: 0.8937 - val_loss: 0.3750 - val_acc: 0.9078\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.3839 - acc: 0.8945 - val_loss: 0.3787 - val_acc: 0.9079\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = e_swish_2, \"e_swish_2\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training\n",
-    "batch_size = 128\n",
-    "epochs=200\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('e_swish_2_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Resume training (200-250)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 180s 462ms/step - loss: 0.3458 - acc: 0.9072 - val_loss: 0.3603 - val_acc: 0.9113\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 179s 459ms/step - loss: 0.3383 - acc: 0.9088 - val_loss: 0.3585 - val_acc: 0.9120\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3398 - acc: 0.9091 - val_loss: 0.3578 - val_acc: 0.9116\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3422 - acc: 0.9076 - val_loss: 0.3607 - val_acc: 0.9110\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3449 - acc: 0.9081 - val_loss: 0.3593 - val_acc: 0.9117\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.3465 - acc: 0.9064 - val_loss: 0.3588 - val_acc: 0.9120\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3460 - acc: 0.9078 - val_loss: 0.3580 - val_acc: 0.9135\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3442 - acc: 0.9075 - val_loss: 0.3579 - val_acc: 0.9118\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3450 - acc: 0.9065 - val_loss: 0.3580 - val_acc: 0.9122\n",
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3432 - acc: 0.9070 - val_loss: 0.3580 - val_acc: 0.9107\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3406 - acc: 0.9088 - val_loss: 0.3579 - val_acc: 0.9126\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3393 - acc: 0.9086 - val_loss: 0.3589 - val_acc: 0.9110\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3440 - acc: 0.9071 - val_loss: 0.3574 - val_acc: 0.9133\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3343 - acc: 0.9112 - val_loss: 0.3576 - val_acc: 0.9126\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3396 - acc: 0.9090 - val_loss: 0.3567 - val_acc: 0.9127\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3467 - acc: 0.9065 - val_loss: 0.3587 - val_acc: 0.9122\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3422 - acc: 0.9076 - val_loss: 0.3590 - val_acc: 0.9122\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3439 - acc: 0.9075 - val_loss: 0.3570 - val_acc: 0.9131\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3336 - acc: 0.9107 - val_loss: 0.3573 - val_acc: 0.9128\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3369 - acc: 0.9098 - val_loss: 0.3565 - val_acc: 0.9120\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3423 - acc: 0.9079 - val_loss: 0.3577 - val_acc: 0.9125\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3379 - acc: 0.9089 - val_loss: 0.3563 - val_acc: 0.9123\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3388 - acc: 0.9075 - val_loss: 0.3559 - val_acc: 0.9127\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3374 - acc: 0.9095 - val_loss: 0.3563 - val_acc: 0.9125\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3468 - acc: 0.9069 - val_loss: 0.3567 - val_acc: 0.9133\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3438 - acc: 0.9082 - val_loss: 0.3575 - val_acc: 0.9120\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3467 - acc: 0.9055 - val_loss: 0.3569 - val_acc: 0.9132\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3415 - acc: 0.9081 - val_loss: 0.3578 - val_acc: 0.9125\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3409 - acc: 0.9082 - val_loss: 0.3565 - val_acc: 0.9124\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3399 - acc: 0.9092 - val_loss: 0.3569 - val_acc: 0.9128\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3386 - acc: 0.9085 - val_loss: 0.3572 - val_acc: 0.9123\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3393 - acc: 0.9092 - val_loss: 0.3553 - val_acc: 0.9120\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3372 - acc: 0.9099 - val_loss: 0.3547 - val_acc: 0.9125\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3375 - acc: 0.9089 - val_loss: 0.3561 - val_acc: 0.9122\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3365 - acc: 0.9092 - val_loss: 0.3573 - val_acc: 0.9120\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3417 - acc: 0.9079 - val_loss: 0.3554 - val_acc: 0.9128\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3391 - acc: 0.9091 - val_loss: 0.3546 - val_acc: 0.9123\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3369 - acc: 0.9095 - val_loss: 0.3559 - val_acc: 0.9114\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3434 - acc: 0.9078 - val_loss: 0.3558 - val_acc: 0.9120\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3374 - acc: 0.9095 - val_loss: 0.3560 - val_acc: 0.9117\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3380 - acc: 0.9084 - val_loss: 0.3581 - val_acc: 0.9116\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3432 - acc: 0.9084 - val_loss: 0.3574 - val_acc: 0.9128\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3383 - acc: 0.9080 - val_loss: 0.3563 - val_acc: 0.9130\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3370 - acc: 0.9097 - val_loss: 0.3573 - val_acc: 0.9118\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3376 - acc: 0.9080 - val_loss: 0.3569 - val_acc: 0.9125\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3361 - acc: 0.9102 - val_loss: 0.3573 - val_acc: 0.9122\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3393 - acc: 0.9093 - val_loss: 0.3566 - val_acc: 0.9123\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3353 - acc: 0.9100 - val_loss: 0.3578 - val_acc: 0.9126\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3338 - acc: 0.9106 - val_loss: 0.3568 - val_acc: 0.9123\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3374 - acc: 0.9081 - val_loss: 0.3565 - val_acc: 0.9122\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "model = load_model('e_swish_2_at_2.h5', custom_objects={\"e_swish_2\": e_swish_2})\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000017,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=200)\n",
-    "# Save the model\n",
-    "model.save('e_swish_2_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3818 - acc: 0.8962 - val_loss: 0.3782 - val_acc: 0.9078\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3836 - acc: 0.8963 - val_loss: 0.3775 - val_acc: 0.9067\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3824 - acc: 0.8976 - val_loss: 0.3762 - val_acc: 0.9088\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3854 - acc: 0.8958 - val_loss: 0.3776 - val_acc: 0.9070\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3780 - acc: 0.8981 - val_loss: 0.3781 - val_acc: 0.9057\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3822 - acc: 0.8972 - val_loss: 0.3765 - val_acc: 0.9060\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3793 - acc: 0.8960 - val_loss: 0.3769 - val_acc: 0.9060\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3807 - acc: 0.8976 - val_loss: 0.3757 - val_acc: 0.9079\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3811 - acc: 0.8971 - val_loss: 0.3752 - val_acc: 0.9081\n",
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3829 - acc: 0.8961 - val_loss: 0.3751 - val_acc: 0.9073\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3807 - acc: 0.8955 - val_loss: 0.3756 - val_acc: 0.9077\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3787 - acc: 0.8972 - val_loss: 0.3758 - val_acc: 0.9076\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3797 - acc: 0.8960 - val_loss: 0.3757 - val_acc: 0.9071\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3789 - acc: 0.8955 - val_loss: 0.3749 - val_acc: 0.9074\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3822 - acc: 0.8958 - val_loss: 0.3747 - val_acc: 0.9068\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3793 - acc: 0.8958 - val_loss: 0.3744 - val_acc: 0.9071\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3826 - acc: 0.8951 - val_loss: 0.3751 - val_acc: 0.9073\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3817 - acc: 0.8962 - val_loss: 0.3744 - val_acc: 0.9071\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3800 - acc: 0.8978 - val_loss: 0.3750 - val_acc: 0.9069\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3760 - acc: 0.8967 - val_loss: 0.3757 - val_acc: 0.9066\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3808 - acc: 0.8956 - val_loss: 0.3742 - val_acc: 0.9072\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3775 - acc: 0.8975 - val_loss: 0.3752 - val_acc: 0.9076\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3772 - acc: 0.8976 - val_loss: 0.3729 - val_acc: 0.9085\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3767 - acc: 0.8978 - val_loss: 0.3731 - val_acc: 0.9084\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3789 - acc: 0.8964 - val_loss: 0.3752 - val_acc: 0.9071\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3784 - acc: 0.8970 - val_loss: 0.3739 - val_acc: 0.9076\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3806 - acc: 0.8957 - val_loss: 0.3744 - val_acc: 0.9080\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3729 - acc: 0.8995 - val_loss: 0.3753 - val_acc: 0.9083\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3720 - acc: 0.8988 - val_loss: 0.3749 - val_acc: 0.9082\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3792 - acc: 0.8967 - val_loss: 0.3751 - val_acc: 0.9079\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3762 - acc: 0.8984 - val_loss: 0.3752 - val_acc: 0.9078\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3787 - acc: 0.8963 - val_loss: 0.3746 - val_acc: 0.9076\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3801 - acc: 0.8978 - val_loss: 0.3756 - val_acc: 0.9074\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3752 - acc: 0.8976 - val_loss: 0.3743 - val_acc: 0.9081\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3780 - acc: 0.8964 - val_loss: 0.3734 - val_acc: 0.9078\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3717 - acc: 0.8989 - val_loss: 0.3750 - val_acc: 0.9082\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3747 - acc: 0.8973 - val_loss: 0.3745 - val_acc: 0.9070\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3735 - acc: 0.8971 - val_loss: 0.3725 - val_acc: 0.9089\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3738 - acc: 0.8988 - val_loss: 0.3742 - val_acc: 0.9077\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3733 - acc: 0.8977 - val_loss: 0.3734 - val_acc: 0.9077\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3769 - acc: 0.8974 - val_loss: 0.3738 - val_acc: 0.9073\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3726 - acc: 0.8982 - val_loss: 0.3740 - val_acc: 0.9077\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3763 - acc: 0.8980 - val_loss: 0.3723 - val_acc: 0.9083\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3717 - acc: 0.8986 - val_loss: 0.3733 - val_acc: 0.9078\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3760 - acc: 0.8976 - val_loss: 0.3726 - val_acc: 0.9084\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3791 - acc: 0.8974 - val_loss: 0.3723 - val_acc: 0.9093\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3760 - acc: 0.8976 - val_loss: 0.3731 - val_acc: 0.9081\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3766 - acc: 0.8962 - val_loss: 0.3728 - val_acc: 0.9077\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3777 - acc: 0.8967 - val_loss: 0.3731 - val_acc: 0.9083\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.3720 - acc: 0.8983 - val_loss: 0.3729 - val_acc: 0.9082\n",
-      "{'val_acc': [0.90780000000000005, 0.90669999999999995, 0.90880000000000005, 0.90700000000000003, 0.90569999999999995, 0.90600000000000003, 0.90600000000000003, 0.90790000000000004, 0.90810000000000002, 0.9073, 0.90769999999999995, 0.90759999999999996, 0.90710000000000002, 0.90739999999999998, 0.90680000000000005, 0.90710000000000002, 0.9073, 0.90710000000000002, 0.90690000000000004, 0.90659999999999996, 0.90720000000000001, 0.90759999999999996, 0.90849999999999997, 0.90839999999999999, 0.90710000000000002, 0.90759999999999996, 0.90800000000000003, 0.9083, 0.90820000000000001, 0.90790000000000004, 0.90780000000000005, 0.90759999999999996, 0.90739999999999998, 0.90810000000000002, 0.90780000000000005, 0.90820000000000001, 0.90700000000000003, 0.90890000000000004, 0.90769999999999995, 0.90769999999999995, 0.9073, 0.90769999999999995, 0.9083, 0.90780000000000005, 0.90839999999999999, 0.9093, 0.90810000000000002, 0.90769999999999995, 0.9083, 0.90820000000000001], 'acc': [0.89619425729868463, 0.89627446266256316, 0.89753769650304782, 0.89581328198280252, 0.89805903108142149, 0.89715672122541057, 0.89601379529688652, 0.89767805578466175, 0.89711661858171621, 0.89607394934847906, 0.89551251201167636, 0.89713666983663476, 0.89597369265319216, 0.89557266604414654, 0.8957732371794872, 0.89577317937735301, 0.89509071933204876, 0.89625441129290984, 0.89785851778645986, 0.89671559193442563, 0.89563282005749412, 0.89755774781533382, 0.8975777991276197, 0.8978184151427655, 0.89633461661854352, 0.89697625918536761, 0.89569310897435894, 0.89948619131714236, 0.89876082775720545, 0.89673564326583399, 0.89839990371536438, 0.89629451397484905, 0.89787856915611308, 0.8976179018286814, 0.89645492458787146, 0.89888113568828854, 0.89727702917561614, 0.897056464587491, 0.89878087906949133, 0.89767628205128203, 0.89745905593410857, 0.89829964709656718, 0.89795673076923077, 0.89860308285163781, 0.89753769648392534, 0.89739733716406656, 0.89759785053551788, 0.89619391025641026, 0.89669637122671808, 0.89827959572691396], 'loss': [0.38192385609118573, 0.38366235368596768, 0.38243792514637337, 0.38529716774864309, 0.3780222623670258, 0.3821340719495479, 0.37929437002349364, 0.38054197024641262, 0.38106803024736857, 0.38285865186497614, 0.38067114429669691, 0.3789266852669736, 0.37971156418686841, 0.37872003302346219, 0.38215108131751035, 0.37933256552890204, 0.38256422186709399, 0.38166929811554762, 0.37994617678853693, 0.37597584788486443, 0.3808525275124699, 0.37747222928997792, 0.37706229256099716, 0.37657802916506294, 0.37898766002021694, 0.37830471665498955, 0.38057093031895467, 0.37296465769477127, 0.37208341142861245, 0.37913693226144829, 0.37625773273525537, 0.37866799462136069, 0.38012097520173493, 0.37506118954126749, 0.37796168323315832, 0.37171630813289919, 0.37480485195489432, 0.37357130567347502, 0.3739193721788221, 0.37334053279497686, 0.37675303092505125, 0.37235717508400484, 0.37632885819826373, 0.37153071385204756, 0.37608381087557413, 0.3790424772570215, 0.3759769953459855, 0.3766193736440096, 0.37768305890745435, 0.3720105104542788], 'val_loss': [0.37823536205291747, 0.37747736020088196, 0.37619412536621094, 0.37756411218643188, 0.37812863197326663, 0.37653512229919434, 0.37685041356086729, 0.37571484167575836, 0.37520423455238344, 0.37509226880073548, 0.37559298319816592, 0.37577983741760251, 0.37568331146240236, 0.37494024095535278, 0.3747356897354126, 0.37437291817665103, 0.37507482862472535, 0.37437629785537718, 0.37495786361694333, 0.37567552423477174, 0.37424509992599486, 0.37515580270290377, 0.37290649290084837, 0.37313869249820708, 0.37515520482063291, 0.37387374358177183, 0.37436483311653135, 0.3752700170278549, 0.37491791720390322, 0.37514262261390685, 0.37520979089736939, 0.37460635347366333, 0.37557938446998596, 0.37433548512458803, 0.37342010231018069, 0.37502991566658023, 0.37452910547256468, 0.37250451855659483, 0.37424582848548887, 0.37337181138992309, 0.37381111397743227, 0.37404450764656066, 0.37225306589603424, 0.37333880391120911, 0.37256061735153201, 0.37233733358383181, 0.37306021027565001, 0.37276746668815613, 0.37307930479049684, 0.3729367765903473]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "model = load_model('e_swish_2_at_3.h5', custom_objects={\"e_swish_2\": e_swish_2})\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000017,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=200)\n",
-    "# Save the model\n",
-    "model.save('e_swish_2_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9239\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(e_swish_2)\n",
-    "        model.load_weights('e_swish_2_at_'+str(i+1)+'.h5')\n",
-    "        model.save('e_swish_2_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('e_swish_2_at_'+str(i+1)+'.h5', custom_objects={\"e_swish_2\": e_swish_2})\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Deeper_CNN/cifar10_deeper_cnn_leaky.ipynb b/cifar/Deeper_CNN/cifar10_deeper_cnn_leaky.ipynb
deleted file mode 100644
index 5e91459..0000000
--- a/cifar/Deeper_CNN/cifar10_deeper_cnn_leaky.ipynb
+++ /dev/null
@@ -1,1534 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "import keras.regularizers as regularizers\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def leaky(x):\n",
-    "    return K.maximum(0.3*x, x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "390/390 [==============================] - 160s 411ms/step - loss: 2.0497 - acc: 0.3803 - val_loss: 1.5434 - val_acc: 0.5072\n",
-      "Epoch 2/250\n",
-      "390/390 [==============================] - 156s 400ms/step - loss: 1.4977 - acc: 0.5185 - val_loss: 1.1258 - val_acc: 0.6378\n",
-      "Epoch 3/250\n",
-      "390/390 [==============================] - 159s 407ms/step - loss: 1.3323 - acc: 0.5770 - val_loss: 1.0530 - val_acc: 0.6606\n",
-      "Epoch 4/250\n",
-      "390/390 [==============================] - 157s 401ms/step - loss: 1.2426 - acc: 0.6093 - val_loss: 0.9503 - val_acc: 0.6990\n",
-      "Epoch 5/250\n",
-      "390/390 [==============================] - 158s 406ms/step - loss: 1.1912 - acc: 0.6337 - val_loss: 0.9501 - val_acc: 0.7043\n",
-      "Epoch 6/250\n",
-      "390/390 [==============================] - 158s 404ms/step - loss: 1.1404 - acc: 0.6505 - val_loss: 0.9167 - val_acc: 0.7123\n",
-      "Epoch 7/250\n",
-      "390/390 [==============================] - 156s 399ms/step - loss: 1.1165 - acc: 0.6575 - val_loss: 0.8119 - val_acc: 0.7536\n",
-      "Epoch 8/250\n",
-      "390/390 [==============================] - 156s 399ms/step - loss: 1.1051 - acc: 0.6693 - val_loss: 1.0560 - val_acc: 0.6871\n",
-      "Epoch 9/250\n",
-      "390/390 [==============================] - 156s 399ms/step - loss: 1.0505 - acc: 0.6839 - val_loss: 0.7961 - val_acc: 0.7609\n",
-      "Epoch 10/250\n",
-      "390/390 [==============================] - 156s 399ms/step - loss: 1.0288 - acc: 0.6873 - val_loss: 0.7886 - val_acc: 0.7653\n",
-      "Epoch 11/250\n",
-      "390/390 [==============================] - 156s 399ms/step - loss: 1.0052 - acc: 0.6981 - val_loss: 0.8151 - val_acc: 0.7677\n",
-      "Epoch 12/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 0.9966 - acc: 0.7026 - val_loss: 0.7218 - val_acc: 0.7849\n",
-      "Epoch 13/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.9767 - acc: 0.7073 - val_loss: 0.7375 - val_acc: 0.7873\n",
-      "Epoch 14/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.9539 - acc: 0.7103 - val_loss: 0.7404 - val_acc: 0.7859\n",
-      "Epoch 15/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.9500 - acc: 0.7160 - val_loss: 0.7225 - val_acc: 0.7840\n",
-      "Epoch 16/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.9283 - acc: 0.7214 - val_loss: 0.7956 - val_acc: 0.7753\n",
-      "Epoch 17/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.9043 - acc: 0.7260 - val_loss: 0.7144 - val_acc: 0.8003\n",
-      "Epoch 18/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.8811 - acc: 0.7322 - val_loss: 0.7141 - val_acc: 0.7914\n",
-      "Epoch 19/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.8693 - acc: 0.7347 - val_loss: 0.6629 - val_acc: 0.8068\n",
-      "Epoch 20/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.8460 - acc: 0.7404 - val_loss: 0.6518 - val_acc: 0.8093\n",
-      "Epoch 21/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.8437 - acc: 0.7434 - val_loss: 0.6353 - val_acc: 0.8138\n",
-      "Epoch 22/250\n",
-      "390/390 [==============================] - 155s 396ms/step - loss: 0.8181 - acc: 0.7485 - val_loss: 0.6353 - val_acc: 0.8155\n",
-      "Epoch 23/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.8200 - acc: 0.7481 - val_loss: 0.6167 - val_acc: 0.8229\n",
-      "Epoch 24/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.8064 - acc: 0.7531 - val_loss: 0.6624 - val_acc: 0.8098\n",
-      "Epoch 25/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7993 - acc: 0.7546 - val_loss: 0.6195 - val_acc: 0.8232\n",
-      "Epoch 26/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7899 - acc: 0.7600 - val_loss: 0.6012 - val_acc: 0.8244\n",
-      "Epoch 27/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7837 - acc: 0.7631 - val_loss: 0.6281 - val_acc: 0.8170\n",
-      "Epoch 28/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7763 - acc: 0.7627 - val_loss: 0.6182 - val_acc: 0.8204\n",
-      "Epoch 29/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7733 - acc: 0.7653 - val_loss: 0.5992 - val_acc: 0.8281\n",
-      "Epoch 30/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7661 - acc: 0.7680 - val_loss: 0.6715 - val_acc: 0.8065\n",
-      "Epoch 31/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7620 - acc: 0.7694 - val_loss: 0.6001 - val_acc: 0.8287\n",
-      "Epoch 32/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.7569 - acc: 0.7742 - val_loss: 0.5742 - val_acc: 0.8391\n",
-      "Epoch 33/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7549 - acc: 0.7730 - val_loss: 0.6329 - val_acc: 0.8161\n",
-      "Epoch 34/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7490 - acc: 0.7738 - val_loss: 0.6141 - val_acc: 0.8216\n",
-      "Epoch 35/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7473 - acc: 0.7754 - val_loss: 0.5810 - val_acc: 0.8386\n",
-      "Epoch 36/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7389 - acc: 0.7775 - val_loss: 0.5655 - val_acc: 0.8410\n",
-      "Epoch 37/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7412 - acc: 0.7783 - val_loss: 0.5700 - val_acc: 0.8421\n",
-      "Epoch 38/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7299 - acc: 0.7807 - val_loss: 0.5777 - val_acc: 0.8365\n",
-      "Epoch 39/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7374 - acc: 0.7797 - val_loss: 0.5976 - val_acc: 0.8302\n",
-      "Epoch 40/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7291 - acc: 0.7819 - val_loss: 0.5783 - val_acc: 0.8350\n",
-      "Epoch 41/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7284 - acc: 0.7834 - val_loss: 0.5807 - val_acc: 0.8346\n",
-      "Epoch 42/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7224 - acc: 0.7850 - val_loss: 0.5847 - val_acc: 0.8347\n",
-      "Epoch 43/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7256 - acc: 0.7839 - val_loss: 0.5669 - val_acc: 0.8382\n",
-      "Epoch 44/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7215 - acc: 0.7846 - val_loss: 0.5972 - val_acc: 0.8302\n",
-      "Epoch 45/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7183 - acc: 0.7882 - val_loss: 0.5575 - val_acc: 0.8439\n",
-      "Epoch 46/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7098 - acc: 0.7892 - val_loss: 0.6131 - val_acc: 0.8286\n",
-      "Epoch 47/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7129 - acc: 0.7903 - val_loss: 0.5823 - val_acc: 0.8370\n",
-      "Epoch 48/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7086 - acc: 0.7903 - val_loss: 0.5668 - val_acc: 0.8442\n",
-      "Epoch 49/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7084 - acc: 0.7891 - val_loss: 0.6080 - val_acc: 0.8273\n",
-      "Epoch 50/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7088 - acc: 0.7912 - val_loss: 0.5531 - val_acc: 0.8502\n",
-      "Epoch 51/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7033 - acc: 0.7914 - val_loss: 0.5383 - val_acc: 0.8464\n",
-      "Epoch 52/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7031 - acc: 0.7916 - val_loss: 0.5640 - val_acc: 0.8417\n",
-      "Epoch 53/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6999 - acc: 0.7948 - val_loss: 0.5853 - val_acc: 0.8342\n",
-      "Epoch 54/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6932 - acc: 0.7948 - val_loss: 0.5610 - val_acc: 0.8452\n",
-      "Epoch 55/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6993 - acc: 0.7955 - val_loss: 0.5654 - val_acc: 0.8394\n",
-      "Epoch 56/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6965 - acc: 0.7970 - val_loss: 0.5507 - val_acc: 0.8524\n",
-      "Epoch 57/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6930 - acc: 0.7963 - val_loss: 0.5974 - val_acc: 0.8336\n",
-      "Epoch 58/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6976 - acc: 0.7943 - val_loss: 0.5457 - val_acc: 0.8537\n",
-      "Epoch 59/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6928 - acc: 0.7951 - val_loss: 0.5417 - val_acc: 0.8511\n",
-      "Epoch 60/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6883 - acc: 0.8005 - val_loss: 0.5428 - val_acc: 0.8525\n",
-      "Epoch 61/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6916 - acc: 0.7971 - val_loss: 0.6210 - val_acc: 0.8244\n",
-      "Epoch 62/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6878 - acc: 0.7999 - val_loss: 0.5533 - val_acc: 0.8542\n",
-      "Epoch 63/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6809 - acc: 0.7998 - val_loss: 0.5672 - val_acc: 0.8448\n",
-      "Epoch 64/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6853 - acc: 0.7992 - val_loss: 0.5813 - val_acc: 0.8398\n",
-      "Epoch 65/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6840 - acc: 0.7992 - val_loss: 0.5424 - val_acc: 0.8512\n",
-      "Epoch 66/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6845 - acc: 0.8018 - val_loss: 0.5472 - val_acc: 0.8493\n",
-      "Epoch 67/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6814 - acc: 0.7999 - val_loss: 0.5660 - val_acc: 0.8428\n",
-      "Epoch 68/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6805 - acc: 0.8004 - val_loss: 0.5491 - val_acc: 0.8481\n",
-      "Epoch 69/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.6799 - acc: 0.8017 - val_loss: 0.5508 - val_acc: 0.8496\n",
-      "Epoch 70/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6801 - acc: 0.8011 - val_loss: 0.5613 - val_acc: 0.8442\n",
-      "Epoch 71/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6752 - acc: 0.8051 - val_loss: 0.5512 - val_acc: 0.8497\n",
-      "Epoch 72/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.6702 - acc: 0.8039 - val_loss: 0.5882 - val_acc: 0.8393\n",
-      "Epoch 73/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6729 - acc: 0.8048 - val_loss: 0.5638 - val_acc: 0.8449\n",
-      "Epoch 74/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6745 - acc: 0.8022 - val_loss: 0.5602 - val_acc: 0.8477\n",
-      "Epoch 75/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6733 - acc: 0.8029 - val_loss: 0.5571 - val_acc: 0.8484\n",
-      "Epoch 76/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6218 - acc: 0.8219 - val_loss: 0.4882 - val_acc: 0.8699\n",
-      "Epoch 77/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6121 - acc: 0.8259 - val_loss: 0.4837 - val_acc: 0.8694\n",
-      "Epoch 78/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6038 - acc: 0.8279 - val_loss: 0.4912 - val_acc: 0.8667\n",
-      "Epoch 79/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.6038 - acc: 0.8259 - val_loss: 0.4843 - val_acc: 0.8693\n",
-      "Epoch 80/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.5962 - acc: 0.8274 - val_loss: 0.4969 - val_acc: 0.8651\n",
-      "Epoch 81/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5985 - acc: 0.8256 - val_loss: 0.4701 - val_acc: 0.8724\n",
-      "Epoch 82/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5913 - acc: 0.8292 - val_loss: 0.4709 - val_acc: 0.8696\n",
-      "Epoch 83/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5895 - acc: 0.8286 - val_loss: 0.4852 - val_acc: 0.8689\n",
-      "Epoch 84/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5889 - acc: 0.8296 - val_loss: 0.4597 - val_acc: 0.8748\n",
-      "Epoch 85/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5828 - acc: 0.8312 - val_loss: 0.4769 - val_acc: 0.8712\n",
-      "Epoch 86/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5860 - acc: 0.8296 - val_loss: 0.4577 - val_acc: 0.8788\n",
-      "Epoch 87/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5836 - acc: 0.8305 - val_loss: 0.4806 - val_acc: 0.8697\n",
-      "Epoch 88/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.5811 - acc: 0.8310 - val_loss: 0.4644 - val_acc: 0.8717\n",
-      "Epoch 89/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5802 - acc: 0.8324 - val_loss: 0.4677 - val_acc: 0.8730\n",
-      "Epoch 90/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5731 - acc: 0.8338 - val_loss: 0.4575 - val_acc: 0.8725\n",
-      "Epoch 91/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5746 - acc: 0.8327 - val_loss: 0.4578 - val_acc: 0.8738\n",
-      "Epoch 92/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5747 - acc: 0.8312 - val_loss: 0.4679 - val_acc: 0.8708\n",
-      "Epoch 93/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.5703 - acc: 0.8350 - val_loss: 0.4547 - val_acc: 0.8726\n",
-      "Epoch 94/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5770 - acc: 0.8316 - val_loss: 0.4657 - val_acc: 0.8716\n",
-      "Epoch 95/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5697 - acc: 0.8338 - val_loss: 0.4771 - val_acc: 0.8665\n",
-      "Epoch 96/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5691 - acc: 0.8330 - val_loss: 0.4679 - val_acc: 0.8702\n",
-      "Epoch 97/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5677 - acc: 0.8340 - val_loss: 0.4673 - val_acc: 0.8727\n",
-      "Epoch 98/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5696 - acc: 0.8352 - val_loss: 0.4712 - val_acc: 0.8705\n",
-      "Epoch 99/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5665 - acc: 0.8351 - val_loss: 0.4713 - val_acc: 0.8710\n",
-      "Epoch 100/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5718 - acc: 0.8312 - val_loss: 0.4595 - val_acc: 0.8722\n",
-      "Epoch 101/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5464 - acc: 0.8428 - val_loss: 0.4349 - val_acc: 0.8823\n",
-      "Epoch 102/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5345 - acc: 0.8466 - val_loss: 0.4318 - val_acc: 0.8816\n",
-      "Epoch 103/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.5333 - acc: 0.8465 - val_loss: 0.4305 - val_acc: 0.8823\n",
-      "Epoch 104/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5373 - acc: 0.8433 - val_loss: 0.4250 - val_acc: 0.8850\n",
-      "Epoch 105/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5330 - acc: 0.8454 - val_loss: 0.4216 - val_acc: 0.8864\n",
-      "Epoch 106/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5291 - acc: 0.8457 - val_loss: 0.4333 - val_acc: 0.8831\n",
-      "Epoch 107/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5250 - acc: 0.8482 - val_loss: 0.4312 - val_acc: 0.8807\n",
-      "Epoch 108/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5216 - acc: 0.8506 - val_loss: 0.4475 - val_acc: 0.8761\n",
-      "Epoch 109/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.5268 - acc: 0.8458 - val_loss: 0.4136 - val_acc: 0.8825\n",
-      "Epoch 110/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5212 - acc: 0.8499 - val_loss: 0.4281 - val_acc: 0.8834\n",
-      "Epoch 111/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5211 - acc: 0.8485 - val_loss: 0.4310 - val_acc: 0.8790\n",
-      "Epoch 112/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.5206 - acc: 0.8482 - val_loss: 0.4194 - val_acc: 0.8857\n",
-      "Epoch 113/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5214 - acc: 0.8498 - val_loss: 0.4262 - val_acc: 0.8829\n",
-      "Epoch 114/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5144 - acc: 0.8496 - val_loss: 0.4336 - val_acc: 0.8832\n",
-      "Epoch 115/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5250 - acc: 0.8467 - val_loss: 0.4232 - val_acc: 0.8832\n",
-      "Epoch 116/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.5100 - acc: 0.8510 - val_loss: 0.4285 - val_acc: 0.8799\n",
-      "Epoch 117/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5151 - acc: 0.8500 - val_loss: 0.4093 - val_acc: 0.8880\n",
-      "Epoch 118/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5130 - acc: 0.8497 - val_loss: 0.4226 - val_acc: 0.8857\n",
-      "Epoch 119/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5168 - acc: 0.8482 - val_loss: 0.4183 - val_acc: 0.8842\n",
-      "Epoch 120/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.5160 - acc: 0.8490 - val_loss: 0.4122 - val_acc: 0.8845\n",
-      "Epoch 121/250\n",
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-      "Epoch 142/250\n",
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-      "Epoch 144/250\n",
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-      "Epoch 146/250\n",
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-      "Epoch 147/250\n",
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-      "Epoch 148/250\n",
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-      "Epoch 150/250\n",
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-      "Epoch 151/250\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4586 - acc: 0.8655 - val_loss: 0.3817 - val_acc: 0.8969\n",
-      "Epoch 152/250\n",
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-      "Epoch 153/250\n",
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-      "Epoch 154/250\n",
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-      "Epoch 155/250\n",
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-      "Epoch 156/250\n",
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-      "Epoch 157/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4478 - acc: 0.8706 - val_loss: 0.3755 - val_acc: 0.8989\n",
-      "Epoch 158/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4446 - acc: 0.8705 - val_loss: 0.3769 - val_acc: 0.8982\n",
-      "Epoch 159/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4482 - acc: 0.8706 - val_loss: 0.3737 - val_acc: 0.8981\n",
-      "Epoch 160/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4476 - acc: 0.8693 - val_loss: 0.3771 - val_acc: 0.8990\n",
-      "Epoch 161/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4427 - acc: 0.8704 - val_loss: 0.3721 - val_acc: 0.8991\n",
-      "Epoch 162/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4460 - acc: 0.8706 - val_loss: 0.3780 - val_acc: 0.8976\n",
-      "Epoch 163/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4436 - acc: 0.8711 - val_loss: 0.3802 - val_acc: 0.8969\n",
-      "Epoch 164/250\n",
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-      "Epoch 165/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4442 - acc: 0.8696 - val_loss: 0.3760 - val_acc: 0.8965\n",
-      "Epoch 166/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4437 - acc: 0.8700 - val_loss: 0.3773 - val_acc: 0.8981\n",
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-      "Epoch 170/250\n",
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-      "Epoch 171/250\n",
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-      "Epoch 172/250\n",
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-      "Epoch 173/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4438 - acc: 0.8710 - val_loss: 0.3753 - val_acc: 0.8989\n",
-      "Epoch 174/250\n",
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-      "Epoch 181/250\n",
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-      "Epoch 182/250\n",
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-      "Epoch 183/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4260 - acc: 0.8765 - val_loss: 0.3721 - val_acc: 0.8990\n",
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-      "Epoch 185/250\n",
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-      "Epoch 186/250\n",
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-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4346 - acc: 0.8747 - val_loss: 0.3759 - val_acc: 0.8986\n",
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-      "Epoch 189/250\n",
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-      "Epoch 194/250\n",
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-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4294 - acc: 0.8746 - val_loss: 0.3681 - val_acc: 0.9011\n",
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-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4211 - acc: 0.8762 - val_loss: 0.3708 - val_acc: 0.9003\n",
-      "Epoch 198/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4310 - acc: 0.8728 - val_loss: 0.3678 - val_acc: 0.9014\n",
-      "Epoch 199/250\n",
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-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4273 - acc: 0.8751 - val_loss: 0.3668 - val_acc: 0.9025\n",
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-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4240 - acc: 0.8749 - val_loss: 0.3681 - val_acc: 0.9005\n",
-      "Epoch 202/250\n",
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-      "390/390 [==============================] - 155s 397ms/step - loss: 0.4158 - acc: 0.8796 - val_loss: 0.3684 - val_acc: 0.9012\n",
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-      "390/390 [==============================] - 157s 401ms/step - loss: 0.4217 - acc: 0.8764 - val_loss: 0.3680 - val_acc: 0.9023\n",
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-      "390/390 [==============================] - 156s 400ms/step - loss: 0.4236 - acc: 0.8761 - val_loss: 0.3667 - val_acc: 0.9018\n",
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-      "390/390 [==============================] - 156s 400ms/step - loss: 0.4199 - acc: 0.8769 - val_loss: 0.3662 - val_acc: 0.9010\n",
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-      "390/390 [==============================] - 156s 399ms/step - loss: 0.4253 - acc: 0.8764 - val_loss: 0.3664 - val_acc: 0.9013\n",
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-      "Epoch 210/250\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4194 - acc: 0.8785 - val_loss: 0.3655 - val_acc: 0.9033\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4193 - acc: 0.8779 - val_loss: 0.3629 - val_acc: 0.9019\n",
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-      "Epoch 213/250\n",
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-      "Epoch 214/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4190 - acc: 0.8764 - val_loss: 0.3657 - val_acc: 0.9009\n",
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-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4227 - acc: 0.8781 - val_loss: 0.3676 - val_acc: 0.9025\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4229 - acc: 0.8768 - val_loss: 0.3646 - val_acc: 0.9020\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4231 - acc: 0.8765 - val_loss: 0.3667 - val_acc: 0.9016\n",
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-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4169 - acc: 0.8784 - val_loss: 0.3659 - val_acc: 0.9037\n",
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-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4213 - acc: 0.8785 - val_loss: 0.3651 - val_acc: 0.9020\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4195 - acc: 0.8782 - val_loss: 0.3643 - val_acc: 0.9025\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4208 - acc: 0.8762 - val_loss: 0.3650 - val_acc: 0.9012\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4188 - acc: 0.8785 - val_loss: 0.3650 - val_acc: 0.9010\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4206 - acc: 0.8769 - val_loss: 0.3642 - val_acc: 0.9022\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4205 - acc: 0.8748 - val_loss: 0.3668 - val_acc: 0.9015\n",
-      "Epoch 226/250\n",
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-      "Epoch 227/250\n",
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-      "Epoch 228/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4146 - acc: 0.8783 - val_loss: 0.3676 - val_acc: 0.9019\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4170 - acc: 0.8766 - val_loss: 0.3669 - val_acc: 0.9019\n",
-      "Epoch 230/250\n",
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-      "Epoch 231/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4146 - acc: 0.8785 - val_loss: 0.3658 - val_acc: 0.9014\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4188 - acc: 0.8797 - val_loss: 0.3658 - val_acc: 0.9026\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4184 - acc: 0.8780 - val_loss: 0.3656 - val_acc: 0.9027\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4215 - acc: 0.8773 - val_loss: 0.3656 - val_acc: 0.9011\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4186 - acc: 0.8783 - val_loss: 0.3663 - val_acc: 0.9013\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4128 - acc: 0.8797 - val_loss: 0.3658 - val_acc: 0.9013\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4186 - acc: 0.8783 - val_loss: 0.3660 - val_acc: 0.9012\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4155 - acc: 0.8792 - val_loss: 0.3656 - val_acc: 0.9015\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4203 - acc: 0.8767 - val_loss: 0.3668 - val_acc: 0.9011\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4194 - acc: 0.8783 - val_loss: 0.3657 - val_acc: 0.9011\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4211 - acc: 0.8770 - val_loss: 0.3655 - val_acc: 0.9018\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4116 - acc: 0.8804 - val_loss: 0.3649 - val_acc: 0.9025\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4201 - acc: 0.8775 - val_loss: 0.3655 - val_acc: 0.9025\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4179 - acc: 0.8777 - val_loss: 0.3649 - val_acc: 0.9017\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4219 - acc: 0.8759 - val_loss: 0.3649 - val_acc: 0.9017\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4117 - acc: 0.8801 - val_loss: 0.3654 - val_acc: 0.9023\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4194 - acc: 0.8772 - val_loss: 0.3641 - val_acc: 0.9034\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4160 - acc: 0.8783 - val_loss: 0.3630 - val_acc: 0.9038\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4231 - acc: 0.8750 - val_loss: 0.3637 - val_acc: 0.9022\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4183 - acc: 0.8786 - val_loss: 0.3645 - val_acc: 0.9013\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = leaky, \"leaky\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('models/leaky_relu/leaky_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "390/390 [==============================] - 158s 405ms/step - loss: 2.0187 - acc: 0.3877 - val_loss: 1.6984 - val_acc: 0.4242\n",
-      "Epoch 2/250\n",
-      "390/390 [==============================] - 155s 398ms/step - loss: 1.4728 - acc: 0.5273 - val_loss: 1.1026 - val_acc: 0.6552\n",
-      "Epoch 3/250\n",
-      "390/390 [==============================] - 155s 398ms/step - loss: 1.3274 - acc: 0.5788 - val_loss: 1.0162 - val_acc: 0.6786\n",
-      "Epoch 4/250\n",
-      "390/390 [==============================] - 156s 399ms/step - loss: 1.2513 - acc: 0.6113 - val_loss: 0.9912 - val_acc: 0.6954\n",
-      "Epoch 5/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 1.1921 - acc: 0.6330 - val_loss: 0.9877 - val_acc: 0.7016\n",
-      "Epoch 6/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 1.1351 - acc: 0.6514 - val_loss: 0.9224 - val_acc: 0.7261\n",
-      "Epoch 7/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 1.1040 - acc: 0.6649 - val_loss: 0.8577 - val_acc: 0.7413\n",
-      "Epoch 8/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 1.0846 - acc: 0.6734 - val_loss: 0.8276 - val_acc: 0.7465\n",
-      "Epoch 9/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 1.0717 - acc: 0.6793 - val_loss: 0.7919 - val_acc: 0.7667\n",
-      "Epoch 10/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 1.0318 - acc: 0.6882 - val_loss: 0.8241 - val_acc: 0.7489\n",
-      "Epoch 11/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 1.0091 - acc: 0.6976 - val_loss: 0.7637 - val_acc: 0.7735\n",
-      "Epoch 12/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 1.0045 - acc: 0.7013 - val_loss: 0.7834 - val_acc: 0.7789\n",
-      "Epoch 13/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9775 - acc: 0.7078 - val_loss: 0.7633 - val_acc: 0.7755\n",
-      "Epoch 14/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9667 - acc: 0.7118 - val_loss: 0.7498 - val_acc: 0.7761\n",
-      "Epoch 15/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9531 - acc: 0.7171 - val_loss: 0.7334 - val_acc: 0.7829\n",
-      "Epoch 16/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9309 - acc: 0.7235 - val_loss: 0.7671 - val_acc: 0.7907\n",
-      "Epoch 17/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9220 - acc: 0.7249 - val_loss: 0.7211 - val_acc: 0.7929\n",
-      "Epoch 18/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9041 - acc: 0.7303 - val_loss: 0.7120 - val_acc: 0.7940\n",
-      "Epoch 19/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.9014 - acc: 0.7297 - val_loss: 0.7206 - val_acc: 0.7921\n",
-      "Epoch 20/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8709 - acc: 0.7373 - val_loss: 0.6885 - val_acc: 0.8060\n",
-      "Epoch 21/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8559 - acc: 0.7386 - val_loss: 0.6510 - val_acc: 0.8108\n",
-      "Epoch 22/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8403 - acc: 0.7442 - val_loss: 0.6884 - val_acc: 0.8009\n",
-      "Epoch 23/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8309 - acc: 0.7474 - val_loss: 0.6261 - val_acc: 0.8188\n",
-      "Epoch 24/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8249 - acc: 0.7464 - val_loss: 0.6359 - val_acc: 0.8186\n",
-      "Epoch 25/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.8077 - acc: 0.7553 - val_loss: 0.6508 - val_acc: 0.8112\n",
-      "Epoch 26/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7998 - acc: 0.7557 - val_loss: 0.5940 - val_acc: 0.8323\n",
-      "Epoch 27/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7990 - acc: 0.7580 - val_loss: 0.6385 - val_acc: 0.8179\n",
-      "Epoch 28/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7833 - acc: 0.7616 - val_loss: 0.6023 - val_acc: 0.8304\n",
-      "Epoch 29/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7806 - acc: 0.7629 - val_loss: 0.6287 - val_acc: 0.8172\n",
-      "Epoch 30/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7735 - acc: 0.7659 - val_loss: 0.6216 - val_acc: 0.8211\n",
-      "Epoch 31/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7695 - acc: 0.7668 - val_loss: 0.6119 - val_acc: 0.8238\n",
-      "Epoch 32/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7598 - acc: 0.7697 - val_loss: 0.5801 - val_acc: 0.8383\n",
-      "Epoch 33/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7559 - acc: 0.7708 - val_loss: 0.6157 - val_acc: 0.8208\n",
-      "Epoch 34/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7564 - acc: 0.7719 - val_loss: 0.5777 - val_acc: 0.8366\n",
-      "Epoch 35/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7415 - acc: 0.7770 - val_loss: 0.5707 - val_acc: 0.8409\n",
-      "Epoch 36/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7410 - acc: 0.7778 - val_loss: 0.5970 - val_acc: 0.8287\n",
-      "Epoch 37/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7365 - acc: 0.7777 - val_loss: 0.6265 - val_acc: 0.8236\n",
-      "Epoch 38/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7386 - acc: 0.7800 - val_loss: 0.6287 - val_acc: 0.8206\n",
-      "Epoch 39/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7345 - acc: 0.7794 - val_loss: 0.6056 - val_acc: 0.8251\n",
-      "Epoch 40/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7334 - acc: 0.7794 - val_loss: 0.5592 - val_acc: 0.8403\n",
-      "Epoch 41/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7323 - acc: 0.7794 - val_loss: 0.6101 - val_acc: 0.8220\n",
-      "Epoch 42/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7238 - acc: 0.7826 - val_loss: 0.5687 - val_acc: 0.8396\n",
-      "Epoch 43/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7235 - acc: 0.7849 - val_loss: 0.5998 - val_acc: 0.8275\n",
-      "Epoch 44/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7207 - acc: 0.7847 - val_loss: 0.5663 - val_acc: 0.8386\n",
-      "Epoch 45/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7185 - acc: 0.7866 - val_loss: 0.5845 - val_acc: 0.8363\n",
-      "Epoch 46/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7163 - acc: 0.7861 - val_loss: 0.5689 - val_acc: 0.8395\n",
-      "Epoch 47/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.7154 - acc: 0.7882 - val_loss: 0.5389 - val_acc: 0.8545\n",
-      "Epoch 48/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.7077 - acc: 0.7898 - val_loss: 0.5888 - val_acc: 0.8328\n",
-      "Epoch 49/250\n",
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-      "Epoch 89/250\n",
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-      "Epoch 91/250\n",
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-      "Epoch 92/250\n"
-     ]
-    },
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-     "text": [
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-      "Epoch 96/250\n",
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-      "Epoch 98/250\n",
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-      "Epoch 99/250\n",
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-      "Epoch 101/250\n",
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-      "Epoch 102/250\n",
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-      "Epoch 103/250\n",
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-      "Epoch 104/250\n",
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-      "Epoch 107/250\n",
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-      "Epoch 111/250\n",
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-      "Epoch 113/250\n",
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-      "Epoch 114/250\n",
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-      "Epoch 139/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4691 - acc: 0.8638 - val_loss: 0.3870 - val_acc: 0.8955\n",
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-      "Epoch 151/250\n"
-     ]
-    },
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-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4508 - acc: 0.8674 - val_loss: 0.3747 - val_acc: 0.8983\n",
-      "Epoch 152/250\n",
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-      "Epoch 154/250\n",
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-      "Epoch 155/250\n",
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-      "Epoch 162/250\n",
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-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4446 - acc: 0.8696 - val_loss: 0.3705 - val_acc: 0.8993\n",
-      "Epoch 170/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4371 - acc: 0.8723 - val_loss: 0.3727 - val_acc: 0.8996\n",
-      "Epoch 171/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4399 - acc: 0.8705 - val_loss: 0.3696 - val_acc: 0.8983\n",
-      "Epoch 172/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4431 - acc: 0.8718 - val_loss: 0.3706 - val_acc: 0.8995\n",
-      "Epoch 173/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4406 - acc: 0.8699 - val_loss: 0.3718 - val_acc: 0.9002\n",
-      "Epoch 174/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4363 - acc: 0.8748 - val_loss: 0.3708 - val_acc: 0.8987\n",
-      "Epoch 175/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4431 - acc: 0.8698 - val_loss: 0.3684 - val_acc: 0.9018\n",
-      "Epoch 176/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4340 - acc: 0.8736 - val_loss: 0.3629 - val_acc: 0.9028\n",
-      "Epoch 177/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4333 - acc: 0.8743 - val_loss: 0.3659 - val_acc: 0.9018\n",
-      "Epoch 178/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4313 - acc: 0.8731 - val_loss: 0.3638 - val_acc: 0.9013\n",
-      "Epoch 179/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4285 - acc: 0.8745 - val_loss: 0.3638 - val_acc: 0.9016\n",
-      "Epoch 180/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4359 - acc: 0.8729 - val_loss: 0.3668 - val_acc: 0.8998\n",
-      "Epoch 181/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4301 - acc: 0.8775 - val_loss: 0.3630 - val_acc: 0.9008\n",
-      "Epoch 182/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4323 - acc: 0.8723 - val_loss: 0.3651 - val_acc: 0.9016\n",
-      "Epoch 183/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4235 - acc: 0.8757 - val_loss: 0.3654 - val_acc: 0.9017\n",
-      "Epoch 184/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4366 - acc: 0.8735 - val_loss: 0.3641 - val_acc: 0.9015\n",
-      "Epoch 185/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4233 - acc: 0.8770 - val_loss: 0.3638 - val_acc: 0.9016\n",
-      "Epoch 186/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4293 - acc: 0.8755 - val_loss: 0.3637 - val_acc: 0.9006\n",
-      "Epoch 187/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4299 - acc: 0.8730 - val_loss: 0.3661 - val_acc: 0.9003\n",
-      "Epoch 188/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4300 - acc: 0.8730 - val_loss: 0.3629 - val_acc: 0.9020\n",
-      "Epoch 189/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4273 - acc: 0.8756 - val_loss: 0.3606 - val_acc: 0.9022\n",
-      "Epoch 190/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4270 - acc: 0.8752 - val_loss: 0.3617 - val_acc: 0.9025\n",
-      "Epoch 191/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4269 - acc: 0.8765 - val_loss: 0.3629 - val_acc: 0.9008\n",
-      "Epoch 192/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4243 - acc: 0.8764 - val_loss: 0.3652 - val_acc: 0.9006\n",
-      "Epoch 193/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4301 - acc: 0.8764 - val_loss: 0.3647 - val_acc: 0.9016\n",
-      "Epoch 194/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4267 - acc: 0.8763 - val_loss: 0.3645 - val_acc: 0.9022\n",
-      "Epoch 195/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4189 - acc: 0.8788 - val_loss: 0.3646 - val_acc: 0.9010\n",
-      "Epoch 196/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4214 - acc: 0.8784 - val_loss: 0.3616 - val_acc: 0.9032\n",
-      "Epoch 197/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4298 - acc: 0.8745 - val_loss: 0.3649 - val_acc: 0.9007\n",
-      "Epoch 198/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4275 - acc: 0.8737 - val_loss: 0.3647 - val_acc: 0.9019\n",
-      "Epoch 199/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4266 - acc: 0.8759 - val_loss: 0.3628 - val_acc: 0.9020\n",
-      "Epoch 200/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4250 - acc: 0.8770 - val_loss: 0.3624 - val_acc: 0.9003\n",
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4267 - acc: 0.8755 - val_loss: 0.3625 - val_acc: 0.9021\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4228 - acc: 0.8771 - val_loss: 0.3623 - val_acc: 0.9031\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4228 - acc: 0.8753 - val_loss: 0.3599 - val_acc: 0.9021\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4209 - acc: 0.8764 - val_loss: 0.3619 - val_acc: 0.9029\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4260 - acc: 0.8760 - val_loss: 0.3597 - val_acc: 0.9030\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4255 - acc: 0.8763 - val_loss: 0.3605 - val_acc: 0.9030\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4246 - acc: 0.8737 - val_loss: 0.3609 - val_acc: 0.9034\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4227 - acc: 0.8758 - val_loss: 0.3623 - val_acc: 0.9029\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4202 - acc: 0.8783 - val_loss: 0.3602 - val_acc: 0.9028\n",
-      "Epoch 210/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4192 - acc: 0.8769 - val_loss: 0.3605 - val_acc: 0.9026\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4259 - acc: 0.8754 - val_loss: 0.3601 - val_acc: 0.9043\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4221 - acc: 0.8769 - val_loss: 0.3610 - val_acc: 0.9035\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4216 - acc: 0.8767 - val_loss: 0.3593 - val_acc: 0.9041\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4192 - acc: 0.8782 - val_loss: 0.3621 - val_acc: 0.9037\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4226 - acc: 0.8766 - val_loss: 0.3615 - val_acc: 0.9028\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4167 - acc: 0.8776 - val_loss: 0.3610 - val_acc: 0.9020\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4214 - acc: 0.8763 - val_loss: 0.3619 - val_acc: 0.9017\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4186 - acc: 0.8772 - val_loss: 0.3593 - val_acc: 0.9037\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4206 - acc: 0.8757 - val_loss: 0.3622 - val_acc: 0.9026\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4214 - acc: 0.8755 - val_loss: 0.3602 - val_acc: 0.9019\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4148 - acc: 0.8795 - val_loss: 0.3605 - val_acc: 0.9033\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4223 - acc: 0.8751 - val_loss: 0.3587 - val_acc: 0.9031\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4173 - acc: 0.8779 - val_loss: 0.3598 - val_acc: 0.9030\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4185 - acc: 0.8767 - val_loss: 0.3573 - val_acc: 0.9047\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4214 - acc: 0.8768 - val_loss: 0.3585 - val_acc: 0.9035\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4181 - acc: 0.8769 - val_loss: 0.3583 - val_acc: 0.9034\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4141 - acc: 0.8804 - val_loss: 0.3583 - val_acc: 0.9021\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4136 - acc: 0.8809 - val_loss: 0.3588 - val_acc: 0.9035\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4133 - acc: 0.8782 - val_loss: 0.3595 - val_acc: 0.9032\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4223 - acc: 0.8776 - val_loss: 0.3595 - val_acc: 0.9036\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 153s 394ms/step - loss: 0.4159 - acc: 0.8796 - val_loss: 0.3593 - val_acc: 0.9020\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4213 - acc: 0.8769 - val_loss: 0.3590 - val_acc: 0.9032\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4140 - acc: 0.8785 - val_loss: 0.3591 - val_acc: 0.9030\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4160 - acc: 0.8787 - val_loss: 0.3588 - val_acc: 0.9033\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4162 - acc: 0.8797 - val_loss: 0.3582 - val_acc: 0.9028\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4176 - acc: 0.8759 - val_loss: 0.3595 - val_acc: 0.9023\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4183 - acc: 0.8766 - val_loss: 0.3578 - val_acc: 0.9032\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4218 - acc: 0.8763 - val_loss: 0.3598 - val_acc: 0.9023\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4122 - acc: 0.8801 - val_loss: 0.3581 - val_acc: 0.9029\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 0.4158 - acc: 0.8780 - val_loss: 0.3574 - val_acc: 0.9036\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 0.4185 - acc: 0.8769 - val_loss: 0.3578 - val_acc: 0.9017\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 0.4185 - acc: 0.8777 - val_loss: 0.3583 - val_acc: 0.9038\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 155s 396ms/step - loss: 0.4181 - acc: 0.8785 - val_loss: 0.3591 - val_acc: 0.9035\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4131 - acc: 0.8802 - val_loss: 0.3593 - val_acc: 0.9032\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4169 - acc: 0.8781 - val_loss: 0.3590 - val_acc: 0.9036\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4229 - acc: 0.8756 - val_loss: 0.3575 - val_acc: 0.9037\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4151 - acc: 0.8804 - val_loss: 0.3577 - val_acc: 0.9033\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4190 - acc: 0.8774 - val_loss: 0.3573 - val_acc: 0.9033\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4117 - acc: 0.8800 - val_loss: 0.3579 - val_acc: 0.9040\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4163 - acc: 0.8794 - val_loss: 0.3575 - val_acc: 0.9041\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = leaky, \"leaky\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('models/leaky_relu/leaky_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_25 (Conv2D)           (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_25 (Activation)   (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_25 (Batc (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_26 (Conv2D)           (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_26 (Activation)   (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_26 (Batc (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_13 (MaxPooling (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_13 (Dropout)         (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_27 (Conv2D)           (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_27 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_27 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_28 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_28 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_28 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_14 (MaxPooling (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_14 (Dropout)         (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_29 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_29 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_29 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_30 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_30 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_30 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_15 (MaxPooling (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_15 (Dropout)         (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_5 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_5 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9138\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(leaky)\n",
-    "        model.load_weights('models/leaky_relu/leaky_at_'+str(i+1)+'.h5')\n",
-    "        model.save('models/leaky_relu/leaky_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('models/leaky_relu/leaky_at_'+str(i+1)+'.h5', custom_objects={\"leaky\": leaky})\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Deeper_CNN/cifar10_deeper_cnn_relu.ipynb b/cifar/Deeper_CNN/cifar10_deeper_cnn_relu.ipynb
deleted file mode 100644
index c22d83c..0000000
--- a/cifar/Deeper_CNN/cifar10_deeper_cnn_relu.ipynb
+++ /dev/null
@@ -1,2206 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "import keras.regularizers as regularizers\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/125\n",
-      "390/390 [==============================] - 146s 375ms/step - loss: 1.9621 - acc: 0.4016 - val_loss: 1.4939 - val_acc: 0.4811\n",
-      "Epoch 2/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 1.5067 - acc: 0.5318 - val_loss: 1.1457 - val_acc: 0.6151\n",
-      "Epoch 3/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 1.3314 - acc: 0.5865 - val_loss: 0.9872 - val_acc: 0.6775\n",
-      "Epoch 4/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 1.2071 - acc: 0.6284 - val_loss: 1.2618 - val_acc: 0.6857\n",
-      "Epoch 5/125\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 1.1495 - acc: 0.6520 - val_loss: 0.8920 - val_acc: 0.7103\n",
-      "Epoch 6/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1091 - acc: 0.6689 - val_loss: 0.8913 - val_acc: 0.7200\n",
-      "Epoch 7/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0341 - acc: 0.6907 - val_loss: 0.7908 - val_acc: 0.7604\n",
-      "Epoch 8/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0400 - acc: 0.6987 - val_loss: 0.8802 - val_acc: 0.7294\n",
-      "Epoch 9/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9719 - acc: 0.7125 - val_loss: 0.7907 - val_acc: 0.7680\n",
-      "Epoch 10/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9527 - acc: 0.7223 - val_loss: 0.7163 - val_acc: 0.7884\n",
-      "Epoch 11/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9404 - acc: 0.7334 - val_loss: 0.6833 - val_acc: 0.7984\n",
-      "Epoch 12/125\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.9198 - acc: 0.7346 - val_loss: 0.6708 - val_acc: 0.8057\n",
-      "Epoch 13/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.8923 - acc: 0.7455 - val_loss: 0.6519 - val_acc: 0.8101\n",
-      "Epoch 14/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.8747 - acc: 0.7513 - val_loss: 0.6905 - val_acc: 0.8073\n",
-      "Epoch 15/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.8910 - acc: 0.7484 - val_loss: 0.6858 - val_acc: 0.8061\n",
-      "Epoch 16/125\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.9040 - acc: 0.7514 - val_loss: 0.6874 - val_acc: 0.8148\n",
-      "Epoch 17/125\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.8576 - acc: 0.7604 - val_loss: 0.6712 - val_acc: 0.8145\n",
-      "Epoch 18/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8816 - acc: 0.7589 - val_loss: 0.6655 - val_acc: 0.8074\n",
-      "Epoch 19/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8236 - acc: 0.7680 - val_loss: 0.6724 - val_acc: 0.8163\n",
-      "Epoch 20/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8232 - acc: 0.7696 - val_loss: 0.6804 - val_acc: 0.8120\n",
-      "Epoch 21/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8158 - acc: 0.7723 - val_loss: 0.6547 - val_acc: 0.8204\n",
-      "Epoch 22/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8126 - acc: 0.7763 - val_loss: 0.6153 - val_acc: 0.8299\n",
-      "Epoch 23/125\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.8032 - acc: 0.7786 - val_loss: 0.6098 - val_acc: 0.8344\n",
-      "Epoch 24/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7968 - acc: 0.7807 - val_loss: 0.6179 - val_acc: 0.8299\n",
-      "Epoch 25/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8056 - acc: 0.7837 - val_loss: 0.6230 - val_acc: 0.8284\n",
-      "Epoch 26/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8150 - acc: 0.7807 - val_loss: 0.5978 - val_acc: 0.8442\n",
-      "Epoch 27/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7985 - acc: 0.7839 - val_loss: 0.6175 - val_acc: 0.8330\n",
-      "Epoch 28/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7816 - acc: 0.7865 - val_loss: 0.6203 - val_acc: 0.8445\n",
-      "Epoch 29/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7693 - acc: 0.7911 - val_loss: 0.6177 - val_acc: 0.8387\n",
-      "Epoch 30/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8150 - acc: 0.7875 - val_loss: 0.5981 - val_acc: 0.8404\n",
-      "Epoch 31/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8255 - acc: 0.7864 - val_loss: 0.6219 - val_acc: 0.8306\n",
-      "Epoch 32/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7817 - acc: 0.7916 - val_loss: 0.5718 - val_acc: 0.8497\n",
-      "Epoch 33/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7612 - acc: 0.7958 - val_loss: 0.6599 - val_acc: 0.8408\n",
-      "Epoch 34/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8247 - acc: 0.7897 - val_loss: 0.6348 - val_acc: 0.8374\n",
-      "Epoch 35/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7594 - acc: 0.7983 - val_loss: 0.6220 - val_acc: 0.8367\n",
-      "Epoch 36/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7541 - acc: 0.7980 - val_loss: 0.5908 - val_acc: 0.8478\n",
-      "Epoch 37/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7514 - acc: 0.8012 - val_loss: 0.5862 - val_acc: 0.8510\n",
-      "Epoch 38/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7468 - acc: 0.8026 - val_loss: 0.6233 - val_acc: 0.8364\n",
-      "Epoch 39/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7378 - acc: 0.8034 - val_loss: 0.5990 - val_acc: 0.8416\n",
-      "Epoch 40/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7554 - acc: 0.8037 - val_loss: 0.5806 - val_acc: 0.8475\n",
-      "Epoch 41/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7492 - acc: 0.8021 - val_loss: 0.6153 - val_acc: 0.8409\n",
-      "Epoch 42/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7543 - acc: 0.8030 - val_loss: 0.5969 - val_acc: 0.8475\n",
-      "Epoch 43/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7350 - acc: 0.8066 - val_loss: 0.5890 - val_acc: 0.8553\n",
-      "Epoch 44/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7237 - acc: 0.8072 - val_loss: 0.6020 - val_acc: 0.8447\n",
-      "Epoch 45/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7277 - acc: 0.8085 - val_loss: 0.5714 - val_acc: 0.8580\n",
-      "Epoch 46/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7123 - acc: 0.8115 - val_loss: 0.5915 - val_acc: 0.8435\n",
-      "Epoch 47/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7434 - acc: 0.8069 - val_loss: 0.5516 - val_acc: 0.8622\n",
-      "Epoch 48/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7106 - acc: 0.8124 - val_loss: 0.5625 - val_acc: 0.8581\n",
-      "Epoch 49/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7221 - acc: 0.8119 - val_loss: 0.5998 - val_acc: 0.8554\n",
-      "Epoch 50/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7249 - acc: 0.8101 - val_loss: 0.5772 - val_acc: 0.8547\n",
-      "Epoch 51/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7213 - acc: 0.8130 - val_loss: 0.5706 - val_acc: 0.8511\n",
-      "Epoch 52/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7211 - acc: 0.8145 - val_loss: 0.5483 - val_acc: 0.8580\n",
-      "Epoch 53/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7152 - acc: 0.8146 - val_loss: 0.5521 - val_acc: 0.8583\n",
-      "Epoch 54/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7081 - acc: 0.8170 - val_loss: 0.5966 - val_acc: 0.8452\n",
-      "Epoch 55/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7176 - acc: 0.8147 - val_loss: 0.6625 - val_acc: 0.8547\n",
-      "Epoch 56/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7040 - acc: 0.8169 - val_loss: 0.5779 - val_acc: 0.8493\n",
-      "Epoch 57/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7076 - acc: 0.8171 - val_loss: 0.5624 - val_acc: 0.8556\n",
-      "Epoch 58/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.6955 - acc: 0.8202 - val_loss: 0.5591 - val_acc: 0.8575\n",
-      "Epoch 59/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7046 - acc: 0.8191 - val_loss: 0.5517 - val_acc: 0.8609\n",
-      "Epoch 60/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7191 - acc: 0.8194 - val_loss: 0.5530 - val_acc: 0.8630\n",
-      "Epoch 61/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7056 - acc: 0.8189 - val_loss: 0.6082 - val_acc: 0.8457\n",
-      "Epoch 62/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7118 - acc: 0.8174 - val_loss: 0.5708 - val_acc: 0.8565\n",
-      "Epoch 63/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7015 - acc: 0.8229 - val_loss: 0.5680 - val_acc: 0.8549\n",
-      "Epoch 64/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7110 - acc: 0.8192 - val_loss: 0.5639 - val_acc: 0.8560\n",
-      "Epoch 65/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6944 - acc: 0.8225 - val_loss: 0.5434 - val_acc: 0.8657\n",
-      "Epoch 66/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.7065 - acc: 0.8209 - val_loss: 0.5615 - val_acc: 0.8599\n",
-      "Epoch 67/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7077 - acc: 0.8198 - val_loss: 0.6333 - val_acc: 0.8404\n",
-      "Epoch 68/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7029 - acc: 0.8212 - val_loss: 0.5489 - val_acc: 0.8644\n",
-      "Epoch 69/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.6911 - acc: 0.8249 - val_loss: 0.5461 - val_acc: 0.8615\n",
-      "Epoch 70/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6913 - acc: 0.8224 - val_loss: 0.5886 - val_acc: 0.8538\n",
-      "Epoch 71/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6985 - acc: 0.8230 - val_loss: 0.5514 - val_acc: 0.8669\n",
-      "Epoch 72/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6773 - acc: 0.8290 - val_loss: 0.5306 - val_acc: 0.8692\n",
-      "Epoch 73/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6831 - acc: 0.8243 - val_loss: 0.5528 - val_acc: 0.8715\n",
-      "Epoch 74/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.6717 - acc: 0.8278 - val_loss: 0.5222 - val_acc: 0.8700\n",
-      "Epoch 75/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6835 - acc: 0.8249 - val_loss: 0.5600 - val_acc: 0.8611\n",
-      "Epoch 76/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6396 - acc: 0.8380 - val_loss: 0.5075 - val_acc: 0.8776\n",
-      "Epoch 77/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6297 - acc: 0.8446 - val_loss: 0.4977 - val_acc: 0.8806\n",
-      "Epoch 78/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6111 - acc: 0.8483 - val_loss: 0.4977 - val_acc: 0.8845\n",
-      "Epoch 79/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6137 - acc: 0.8453 - val_loss: 0.5036 - val_acc: 0.8797\n",
-      "Epoch 80/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6007 - acc: 0.8488 - val_loss: 0.4972 - val_acc: 0.8782\n",
-      "Epoch 81/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5993 - acc: 0.8480 - val_loss: 0.4925 - val_acc: 0.8806\n",
-      "Epoch 82/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6030 - acc: 0.8474 - val_loss: 0.4848 - val_acc: 0.8846\n",
-      "Epoch 83/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5981 - acc: 0.8483 - val_loss: 0.4782 - val_acc: 0.8828\n",
-      "Epoch 84/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5964 - acc: 0.8504 - val_loss: 0.4847 - val_acc: 0.8840\n",
-      "Epoch 85/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5902 - acc: 0.8484 - val_loss: 0.4850 - val_acc: 0.8813\n",
-      "Epoch 86/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6004 - acc: 0.8488 - val_loss: 0.5161 - val_acc: 0.8711\n",
-      "Epoch 87/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5769 - acc: 0.8524 - val_loss: 0.4756 - val_acc: 0.8833\n",
-      "Epoch 88/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5900 - acc: 0.8519 - val_loss: 0.4779 - val_acc: 0.8847\n",
-      "Epoch 89/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5801 - acc: 0.8538 - val_loss: 0.4774 - val_acc: 0.8826\n",
-      "Epoch 90/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5851 - acc: 0.8502 - val_loss: 0.4810 - val_acc: 0.8806\n",
-      "Epoch 91/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5725 - acc: 0.8546 - val_loss: 0.4734 - val_acc: 0.8839\n",
-      "Epoch 92/125\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5766 - acc: 0.8513 - val_loss: 0.4702 - val_acc: 0.8843\n",
-      "Epoch 93/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5672 - acc: 0.8561 - val_loss: 0.4829 - val_acc: 0.8812\n",
-      "Epoch 94/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5708 - acc: 0.8523 - val_loss: 0.4743 - val_acc: 0.8816\n",
-      "Epoch 95/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5769 - acc: 0.8535 - val_loss: 0.4854 - val_acc: 0.8795\n",
-      "Epoch 96/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5685 - acc: 0.8564 - val_loss: 0.4765 - val_acc: 0.8840\n",
-      "Epoch 97/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5668 - acc: 0.8556 - val_loss: 0.4792 - val_acc: 0.8811\n",
-      "Epoch 98/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5623 - acc: 0.8547 - val_loss: 0.4645 - val_acc: 0.8857\n",
-      "Epoch 99/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5675 - acc: 0.8545 - val_loss: 0.4627 - val_acc: 0.8856\n",
-      "Epoch 100/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5555 - acc: 0.8561 - val_loss: 0.4856 - val_acc: 0.8813\n",
-      "Epoch 101/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5419 - acc: 0.8616 - val_loss: 0.4488 - val_acc: 0.8868\n",
-      "Epoch 102/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5284 - acc: 0.8620 - val_loss: 0.4432 - val_acc: 0.8904\n",
-      "Epoch 103/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5261 - acc: 0.8658 - val_loss: 0.4591 - val_acc: 0.8864\n",
-      "Epoch 104/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5330 - acc: 0.8643 - val_loss: 0.4532 - val_acc: 0.8870\n",
-      "Epoch 105/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5299 - acc: 0.8644 - val_loss: 0.4517 - val_acc: 0.8890\n",
-      "Epoch 106/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5213 - acc: 0.8674 - val_loss: 0.4434 - val_acc: 0.8907\n",
-      "Epoch 107/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5239 - acc: 0.8646 - val_loss: 0.4397 - val_acc: 0.8933\n",
-      "Epoch 108/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5194 - acc: 0.8656 - val_loss: 0.4463 - val_acc: 0.8870\n",
-      "Epoch 109/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5232 - acc: 0.8671 - val_loss: 0.4365 - val_acc: 0.8936\n",
-      "Epoch 110/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5229 - acc: 0.8658 - val_loss: 0.4443 - val_acc: 0.8900\n",
-      "Epoch 111/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5215 - acc: 0.8660 - val_loss: 0.4420 - val_acc: 0.8920\n",
-      "Epoch 112/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5095 - acc: 0.8711 - val_loss: 0.4391 - val_acc: 0.8916\n",
-      "Epoch 113/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5077 - acc: 0.8692 - val_loss: 0.4451 - val_acc: 0.8884\n",
-      "Epoch 114/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5166 - acc: 0.8676 - val_loss: 0.4258 - val_acc: 0.8937\n",
-      "Epoch 115/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5121 - acc: 0.8689 - val_loss: 0.4405 - val_acc: 0.8921\n",
-      "Epoch 116/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5200 - acc: 0.8675 - val_loss: 0.4283 - val_acc: 0.8937\n",
-      "Epoch 117/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5042 - acc: 0.8695 - val_loss: 0.4554 - val_acc: 0.8851\n",
-      "Epoch 118/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5047 - acc: 0.8706 - val_loss: 0.4282 - val_acc: 0.8930\n",
-      "Epoch 119/125\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.5128 - acc: 0.8673 - val_loss: 0.4449 - val_acc: 0.8920\n",
-      "Epoch 120/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4991 - acc: 0.8706 - val_loss: 0.4303 - val_acc: 0.8950\n",
-      "Epoch 121/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5151 - acc: 0.8684 - val_loss: 0.4401 - val_acc: 0.8922\n",
-      "Epoch 122/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5105 - acc: 0.8699 - val_loss: 0.4391 - val_acc: 0.8890\n",
-      "Epoch 123/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5075 - acc: 0.8694 - val_loss: 0.4322 - val_acc: 0.8912\n",
-      "Epoch 124/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5027 - acc: 0.8698 - val_loss: 0.4223 - val_acc: 0.8909\n",
-      "Epoch 125/125\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5075 - acc: 0.8699 - val_loss: 0.4211 - val_acc: 0.8933\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"relu\", \"relu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=125\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 126/150\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4875 - acc: 0.8745 - val_loss: 0.4247 - val_acc: 0.8959\n",
-      "Epoch 127/150\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4853 - acc: 0.8769 - val_loss: 0.4142 - val_acc: 0.8995\n",
-      "Epoch 128/150\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4909 - acc: 0.8755 - val_loss: 0.4278 - val_acc: 0.8981\n",
-      "Epoch 129/150\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4765 - acc: 0.8781 - val_loss: 0.4252 - val_acc: 0.8962\n",
-      "Epoch 130/150\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4871 - acc: 0.8743 - val_loss: 0.4140 - val_acc: 0.8983\n",
-      "Epoch 131/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4754 - acc: 0.8785 - val_loss: 0.4117 - val_acc: 0.9006\n",
-      "Epoch 132/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4788 - acc: 0.8788 - val_loss: 0.4254 - val_acc: 0.8969\n",
-      "Epoch 133/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4734 - acc: 0.8776 - val_loss: 0.4189 - val_acc: 0.8973\n",
-      "Epoch 134/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4772 - acc: 0.8777 - val_loss: 0.4208 - val_acc: 0.8977\n",
-      "Epoch 135/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4681 - acc: 0.8801 - val_loss: 0.4138 - val_acc: 0.8959\n",
-      "Epoch 136/150\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4753 - acc: 0.8788 - val_loss: 0.4117 - val_acc: 0.8989\n",
-      "Epoch 137/150\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4707 - acc: 0.8794 - val_loss: 0.4113 - val_acc: 0.8995\n",
-      "Epoch 138/150\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4698 - acc: 0.8785 - val_loss: 0.4103 - val_acc: 0.8987\n",
-      "Epoch 139/150\n",
-      "390/390 [==============================] - 144s 368ms/step - loss: 0.4758 - acc: 0.8794 - val_loss: 0.4211 - val_acc: 0.8972\n",
-      "Epoch 140/150\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4689 - acc: 0.8800 - val_loss: 0.4116 - val_acc: 0.9001\n",
-      "Epoch 141/150\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4636 - acc: 0.8819 - val_loss: 0.4126 - val_acc: 0.8972\n",
-      "Epoch 142/150\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4676 - acc: 0.8795 - val_loss: 0.4149 - val_acc: 0.8976\n",
-      "Epoch 143/150\n",
-      "390/390 [==============================] - 144s 368ms/step - loss: 0.4665 - acc: 0.8799 - val_loss: 0.4090 - val_acc: 0.8995\n",
-      "Epoch 144/150\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4640 - acc: 0.8793 - val_loss: 0.4077 - val_acc: 0.8991\n",
-      "Epoch 145/150\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.4636 - acc: 0.8807 - val_loss: 0.4054 - val_acc: 0.8982\n",
-      "Epoch 146/150\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4657 - acc: 0.8805 - val_loss: 0.4114 - val_acc: 0.9003\n",
-      "Epoch 147/150\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.4612 - acc: 0.8816 - val_loss: 0.4021 - val_acc: 0.9011\n",
-      "Epoch 148/150\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.4566 - acc: 0.8812 - val_loss: 0.3988 - val_acc: 0.8991\n",
-      "Epoch 149/150\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.4568 - acc: 0.8809 - val_loss: 0.4044 - val_acc: 0.8998\n",
-      "Epoch 150/150\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.4591 - acc: 0.8818 - val_loss: 0.4018 - val_acc: 0.8999\n",
-      "{'acc': [0.874478665364259, 0.87690487646467608, 0.87552133465486337, 0.87806785370548601, 0.87431825469386937, 0.8784488290404906, 0.87884985564953633, 0.87758662178992919, 0.87772698107154312, 0.88021334616618541, 0.87870949631055506, 0.87935113887737915, 0.87852903430875695, 0.8794513956109109, 0.88005293551491814, 0.88189765796624664, 0.87957170352287162, 0.87997273022752942, 0.87929098494052127, 0.8806544754189255, 0.88047401349361709, 0.88163699071530466, 0.881229967948718, 0.88094091208683967, 0.88185755537991961], 'val_acc': [0.89590000000000003, 0.89949999999999997, 0.89810000000000001, 0.8962, 0.89829999999999999, 0.90059999999999996, 0.89690000000000003, 0.89729999999999999, 0.89770000000000005, 0.89590000000000003, 0.89890000000000003, 0.89949999999999997, 0.89870000000000005, 0.8972, 0.90010000000000001, 0.8972, 0.89759999999999995, 0.89949999999999997, 0.89910000000000001, 0.8982, 0.90029999999999999, 0.90110000000000001, 0.89910000000000001, 0.89980000000000004, 0.89990000000000003], 'loss': [0.48735690415192384, 0.48509300074441181, 0.49076752481760411, 0.4762809316164261, 0.48713528641455062, 0.47547251031036974, 0.47876259538276017, 0.47331453350671104, 0.47724341407279613, 0.46761857245187938, 0.47525001708077513, 0.47088334544713889, 0.46990926435141367, 0.47536514581378009, 0.46891183380060253, 0.46364587774864174, 0.46747687480982808, 0.46656295007951065, 0.4640342264765151, 0.46359020543282026, 0.46583449418895856, 0.46073419578495339, 0.45663233292408478, 0.45680606752806319, 0.45876933880482573], 'val_loss': [0.42471205363273623, 0.41424565310478212, 0.4277682622909546, 0.42521119308471678, 0.4140267830848694, 0.41170915389060975, 0.42535561351776124, 0.41889373297691346, 0.42084854388236997, 0.41376996846199038, 0.41165704236030581, 0.41126591663360595, 0.41026577069759368, 0.4210781241416931, 0.41164072477817537, 0.41259382781982423, 0.41487700324058535, 0.40902580733299254, 0.4077197432041168, 0.40536285705566405, 0.4114024616718292, 0.40205375580787661, 0.39884623870849611, 0.40435921936035157, 0.40175050497055054]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Fit the model\n",
-    "epochs = 150\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=125)\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 151/250\n",
-      "390/390 [==============================] - 148s 379ms/step - loss: 0.4485 - acc: 0.8838 - val_loss: 0.4056 - val_acc: 0.9003\n",
-      "Epoch 152/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.4527 - acc: 0.8834 - val_loss: 0.4075 - val_acc: 0.8992\n",
-      "Epoch 153/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.4425 - acc: 0.8855 - val_loss: 0.4035 - val_acc: 0.9007\n",
-      "Epoch 154/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.4423 - acc: 0.8861 - val_loss: 0.4013 - val_acc: 0.9009\n",
-      "Epoch 155/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4474 - acc: 0.8845 - val_loss: 0.4014 - val_acc: 0.9007\n",
-      "Epoch 156/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4438 - acc: 0.8841 - val_loss: 0.4067 - val_acc: 0.8999\n",
-      "Epoch 157/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4473 - acc: 0.8848 - val_loss: 0.4016 - val_acc: 0.9004\n",
-      "Epoch 158/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4472 - acc: 0.8844 - val_loss: 0.4033 - val_acc: 0.8999\n",
-      "Epoch 159/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4436 - acc: 0.8845 - val_loss: 0.4004 - val_acc: 0.9011\n",
-      "Epoch 160/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4413 - acc: 0.8849 - val_loss: 0.3993 - val_acc: 0.9011\n",
-      "Epoch 161/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4426 - acc: 0.8856 - val_loss: 0.4032 - val_acc: 0.8993\n",
-      "Epoch 162/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4472 - acc: 0.8853 - val_loss: 0.4002 - val_acc: 0.9005\n",
-      "Epoch 163/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4429 - acc: 0.8856 - val_loss: 0.3979 - val_acc: 0.9004\n",
-      "Epoch 164/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4376 - acc: 0.8861 - val_loss: 0.3984 - val_acc: 0.9009\n",
-      "Epoch 165/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4389 - acc: 0.8856 - val_loss: 0.3996 - val_acc: 0.9016\n",
-      "Epoch 166/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4424 - acc: 0.8859 - val_loss: 0.3930 - val_acc: 0.9013\n",
-      "Epoch 167/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4371 - acc: 0.8868 - val_loss: 0.3988 - val_acc: 0.9005\n",
-      "Epoch 168/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4465 - acc: 0.8863 - val_loss: 0.3958 - val_acc: 0.9003\n",
-      "Epoch 169/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4351 - acc: 0.8864 - val_loss: 0.3986 - val_acc: 0.9007\n",
-      "Epoch 170/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4361 - acc: 0.8872 - val_loss: 0.3977 - val_acc: 0.8997\n",
-      "Epoch 171/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4305 - acc: 0.8881 - val_loss: 0.3993 - val_acc: 0.8996\n",
-      "Epoch 172/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4433 - acc: 0.8861 - val_loss: 0.3993 - val_acc: 0.9005\n",
-      "Epoch 173/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4368 - acc: 0.8875 - val_loss: 0.3972 - val_acc: 0.8991\n",
-      "Epoch 174/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4306 - acc: 0.8895 - val_loss: 0.4012 - val_acc: 0.8998\n",
-      "Epoch 175/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4388 - acc: 0.8865 - val_loss: 0.3993 - val_acc: 0.9003\n",
-      "Epoch 176/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4399 - acc: 0.8867 - val_loss: 0.3964 - val_acc: 0.9009\n",
-      "Epoch 177/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4331 - acc: 0.8889 - val_loss: 0.3961 - val_acc: 0.8996\n",
-      "Epoch 178/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4315 - acc: 0.8885 - val_loss: 0.3941 - val_acc: 0.8998\n",
-      "Epoch 179/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4270 - acc: 0.8909 - val_loss: 0.3971 - val_acc: 0.9006\n",
-      "Epoch 180/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4333 - acc: 0.8880 - val_loss: 0.3954 - val_acc: 0.9013\n",
-      "Epoch 181/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4233 - acc: 0.8912 - val_loss: 0.3964 - val_acc: 0.9019\n",
-      "Epoch 182/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4299 - acc: 0.8890 - val_loss: 0.3920 - val_acc: 0.9006\n",
-      "Epoch 183/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4305 - acc: 0.8889 - val_loss: 0.3953 - val_acc: 0.9002\n",
-      "Epoch 184/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4213 - acc: 0.8898 - val_loss: 0.3989 - val_acc: 0.9014\n",
-      "Epoch 185/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4272 - acc: 0.8904 - val_loss: 0.3964 - val_acc: 0.9012\n",
-      "Epoch 186/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4317 - acc: 0.8894 - val_loss: 0.3967 - val_acc: 0.9005\n",
-      "Epoch 187/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4252 - acc: 0.8892 - val_loss: 0.3950 - val_acc: 0.9005\n",
-      "Epoch 188/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4224 - acc: 0.8907 - val_loss: 0.3905 - val_acc: 0.9015\n",
-      "Epoch 189/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4210 - acc: 0.8917 - val_loss: 0.3939 - val_acc: 0.9008\n",
-      "Epoch 190/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4223 - acc: 0.8897 - val_loss: 0.3949 - val_acc: 0.9022\n",
-      "Epoch 191/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4334 - acc: 0.8894 - val_loss: 0.3893 - val_acc: 0.9031\n",
-      "Epoch 192/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4160 - acc: 0.8921 - val_loss: 0.3944 - val_acc: 0.9002\n",
-      "Epoch 193/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4240 - acc: 0.8892 - val_loss: 0.3939 - val_acc: 0.9011\n",
-      "Epoch 194/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4234 - acc: 0.8898 - val_loss: 0.3930 - val_acc: 0.9008\n",
-      "Epoch 195/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4216 - acc: 0.8897 - val_loss: 0.3916 - val_acc: 0.9025\n",
-      "Epoch 196/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4191 - acc: 0.8914 - val_loss: 0.3937 - val_acc: 0.9025\n",
-      "Epoch 197/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4267 - acc: 0.8890 - val_loss: 0.3936 - val_acc: 0.9020\n",
-      "Epoch 198/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4223 - acc: 0.8908 - val_loss: 0.3945 - val_acc: 0.9008\n",
-      "Epoch 199/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4211 - acc: 0.8895 - val_loss: 0.3941 - val_acc: 0.9017\n",
-      "Epoch 200/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4188 - acc: 0.8904 - val_loss: 0.3949 - val_acc: 0.9020\n",
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4152 - acc: 0.8913 - val_loss: 0.3943 - val_acc: 0.9018\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4366 - acc: 0.8885 - val_loss: 0.3964 - val_acc: 0.9012\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 143s 365ms/step - loss: 0.4158 - acc: 0.8921 - val_loss: 0.3944 - val_acc: 0.9017\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 143s 365ms/step - loss: 0.4203 - acc: 0.8907 - val_loss: 0.3913 - val_acc: 0.9015\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4202 - acc: 0.8901 - val_loss: 0.3911 - val_acc: 0.9031\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4139 - acc: 0.8913 - val_loss: 0.3923 - val_acc: 0.9020\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.4225 - acc: 0.8912 - val_loss: 0.3918 - val_acc: 0.9012\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 143s 365ms/step - loss: 0.4239 - acc: 0.8915 - val_loss: 0.3914 - val_acc: 0.9025\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 143s 365ms/step - loss: 0.4144 - acc: 0.8928 - val_loss: 0.3926 - val_acc: 0.9031\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4161 - acc: 0.8918 - val_loss: 0.3911 - val_acc: 0.9026\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4184 - acc: 0.8928 - val_loss: 0.3927 - val_acc: 0.9019\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4162 - acc: 0.8927 - val_loss: 0.3938 - val_acc: 0.9017\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4182 - acc: 0.8925 - val_loss: 0.3945 - val_acc: 0.9012\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4164 - acc: 0.8923 - val_loss: 0.3919 - val_acc: 0.9011\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4135 - acc: 0.8941 - val_loss: 0.3926 - val_acc: 0.9018\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4133 - acc: 0.8925 - val_loss: 0.3919 - val_acc: 0.9022\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.4222 - acc: 0.8918 - val_loss: 0.3933 - val_acc: 0.9025\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4183 - acc: 0.8932 - val_loss: 0.3925 - val_acc: 0.9027\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4179 - acc: 0.8919 - val_loss: 0.3903 - val_acc: 0.9029\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4146 - acc: 0.8940 - val_loss: 0.3935 - val_acc: 0.9021\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4196 - acc: 0.8922 - val_loss: 0.3914 - val_acc: 0.9031\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4195 - acc: 0.8945 - val_loss: 0.3883 - val_acc: 0.9044\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4175 - acc: 0.8938 - val_loss: 0.3898 - val_acc: 0.9035\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4134 - acc: 0.8944 - val_loss: 0.3878 - val_acc: 0.9043\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4131 - acc: 0.8938 - val_loss: 0.3913 - val_acc: 0.9034\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.4121 - acc: 0.8920 - val_loss: 0.3891 - val_acc: 0.9039\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4117 - acc: 0.8931 - val_loss: 0.3903 - val_acc: 0.9030\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4135 - acc: 0.8925 - val_loss: 0.3929 - val_acc: 0.9028\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4124 - acc: 0.8939 - val_loss: 0.3906 - val_acc: 0.9017\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4171 - acc: 0.8927 - val_loss: 0.3904 - val_acc: 0.9031\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4168 - acc: 0.8929 - val_loss: 0.3905 - val_acc: 0.9017\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4110 - acc: 0.8929 - val_loss: 0.3920 - val_acc: 0.9031\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.4106 - acc: 0.8943 - val_loss: 0.3924 - val_acc: 0.9028\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4088 - acc: 0.8927 - val_loss: 0.3904 - val_acc: 0.9038\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4094 - acc: 0.8938 - val_loss: 0.3920 - val_acc: 0.9027\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4165 - acc: 0.8916 - val_loss: 0.3915 - val_acc: 0.9017\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4056 - acc: 0.8941 - val_loss: 0.3889 - val_acc: 0.9022\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4192 - acc: 0.8911 - val_loss: 0.3890 - val_acc: 0.9036\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4153 - acc: 0.8929 - val_loss: 0.3914 - val_acc: 0.9027\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.4154 - acc: 0.8930 - val_loss: 0.3886 - val_acc: 0.9024\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4149 - acc: 0.8942 - val_loss: 0.3916 - val_acc: 0.9015\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4144 - acc: 0.8921 - val_loss: 0.3902 - val_acc: 0.9014\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4111 - acc: 0.8924 - val_loss: 0.3901 - val_acc: 0.9022\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4209 - acc: 0.8922 - val_loss: 0.3894 - val_acc: 0.9041\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4128 - acc: 0.8932 - val_loss: 0.3899 - val_acc: 0.9032\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4119 - acc: 0.8933 - val_loss: 0.3884 - val_acc: 0.9034\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 142s 363ms/step - loss: 0.4146 - acc: 0.8910 - val_loss: 0.3898 - val_acc: 0.9032\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4113 - acc: 0.8941 - val_loss: 0.3904 - val_acc: 0.9026\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4105 - acc: 0.8930 - val_loss: 0.3909 - val_acc: 0.9028\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4130 - acc: 0.8939 - val_loss: 0.3892 - val_acc: 0.9030\n",
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-     ]
-    }
-   ],
-   "source": [
-    "model = load_model('relu_at_2.h5')\n",
-    "# Fit the model\n",
-    "batch_size = 128\n",
-    "epochs = 250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "#Compile the model again\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000075,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=150)\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 2.0326 - acc: 0.3921 - val_loss: 1.4817 - val_acc: 0.4821\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.4682 - acc: 0.5383 - val_loss: 1.1022 - val_acc: 0.6379\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.2809 - acc: 0.6011 - val_loss: 1.0062 - val_acc: 0.6743\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.2080 - acc: 0.6357 - val_loss: 0.9366 - val_acc: 0.7097\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1744 - acc: 0.6544 - val_loss: 0.8131 - val_acc: 0.7433\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.1130 - acc: 0.6762 - val_loss: 0.8670 - val_acc: 0.7193\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0570 - acc: 0.6917 - val_loss: 0.8936 - val_acc: 0.7333\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0653 - acc: 0.6989 - val_loss: 0.7705 - val_acc: 0.7695\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 1.0051 - acc: 0.7101 - val_loss: 0.8274 - val_acc: 0.7668\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9963 - acc: 0.7193 - val_loss: 0.7916 - val_acc: 0.7715\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9746 - acc: 0.7258 - val_loss: 0.6929 - val_acc: 0.7960\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9489 - acc: 0.7309 - val_loss: 0.6966 - val_acc: 0.7995\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.9129 - acc: 0.7412 - val_loss: 0.7122 - val_acc: 0.7965\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8949 - acc: 0.7477 - val_loss: 0.8933 - val_acc: 0.7692\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8833 - acc: 0.7503 - val_loss: 0.6525 - val_acc: 0.8149\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8926 - acc: 0.7532 - val_loss: 0.7042 - val_acc: 0.8006\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8922 - acc: 0.7571 - val_loss: 0.6713 - val_acc: 0.8192\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8520 - acc: 0.7604 - val_loss: 0.6556 - val_acc: 0.8152\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8443 - acc: 0.7646 - val_loss: 0.6444 - val_acc: 0.8202\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8402 - acc: 0.7689 - val_loss: 0.6360 - val_acc: 0.8229\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8367 - acc: 0.7703 - val_loss: 0.6278 - val_acc: 0.8332\n",
-      "Epoch 22/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8195 - acc: 0.7762 - val_loss: 0.6078 - val_acc: 0.8331\n",
-      "Epoch 23/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8249 - acc: 0.7757 - val_loss: 0.6614 - val_acc: 0.8244\n",
-      "Epoch 24/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8130 - acc: 0.7782 - val_loss: 0.6456 - val_acc: 0.8283\n",
-      "Epoch 25/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8167 - acc: 0.7785 - val_loss: 0.6081 - val_acc: 0.8375\n",
-      "Epoch 26/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7999 - acc: 0.7823 - val_loss: 0.6737 - val_acc: 0.8130\n",
-      "Epoch 27/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8070 - acc: 0.7832 - val_loss: 0.6138 - val_acc: 0.8360\n",
-      "Epoch 28/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.8108 - acc: 0.7843 - val_loss: 0.7208 - val_acc: 0.8022\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7996 - acc: 0.7860 - val_loss: 0.6415 - val_acc: 0.8277\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7843 - acc: 0.7877 - val_loss: 0.6660 - val_acc: 0.8384\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7805 - acc: 0.7894 - val_loss: 0.6038 - val_acc: 0.8442\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7730 - acc: 0.7919 - val_loss: 0.5988 - val_acc: 0.8400\n",
-      "Epoch 33/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7532 - acc: 0.7979 - val_loss: 0.6017 - val_acc: 0.8417\n",
-      "Epoch 34/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7705 - acc: 0.7946 - val_loss: 0.6732 - val_acc: 0.8328\n",
-      "Epoch 35/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7517 - acc: 0.7981 - val_loss: 0.6077 - val_acc: 0.8391\n",
-      "Epoch 36/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7458 - acc: 0.7984 - val_loss: 0.5767 - val_acc: 0.8505\n",
-      "Epoch 37/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7471 - acc: 0.7993 - val_loss: 0.6064 - val_acc: 0.8429\n",
-      "Epoch 38/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7408 - acc: 0.8023 - val_loss: 0.6010 - val_acc: 0.8422\n",
-      "Epoch 39/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7437 - acc: 0.8013 - val_loss: 0.5859 - val_acc: 0.8505\n",
-      "Epoch 40/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7425 - acc: 0.8042 - val_loss: 0.6082 - val_acc: 0.8435\n",
-      "Epoch 41/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7278 - acc: 0.8071 - val_loss: 0.5815 - val_acc: 0.8548\n",
-      "Epoch 42/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7393 - acc: 0.8070 - val_loss: 0.5913 - val_acc: 0.8477\n",
-      "Epoch 43/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7344 - acc: 0.8041 - val_loss: 0.5693 - val_acc: 0.8586\n",
-      "Epoch 44/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7297 - acc: 0.8091 - val_loss: 0.5748 - val_acc: 0.8528\n",
-      "Epoch 45/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7233 - acc: 0.8078 - val_loss: 0.5769 - val_acc: 0.8545\n",
-      "Epoch 46/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7283 - acc: 0.8070 - val_loss: 0.5843 - val_acc: 0.8478\n",
-      "Epoch 47/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7184 - acc: 0.8080 - val_loss: 0.5578 - val_acc: 0.8565\n",
-      "Epoch 48/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7086 - acc: 0.8104 - val_loss: 0.5498 - val_acc: 0.8616\n",
-      "Epoch 49/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7126 - acc: 0.8135 - val_loss: 0.6244 - val_acc: 0.8465\n",
-      "Epoch 50/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7045 - acc: 0.8147 - val_loss: 0.5479 - val_acc: 0.8629\n",
-      "Epoch 51/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7065 - acc: 0.8150 - val_loss: 0.5739 - val_acc: 0.8522\n",
-      "Epoch 52/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6940 - acc: 0.8170 - val_loss: 0.5590 - val_acc: 0.8596\n",
-      "Epoch 53/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7054 - acc: 0.8132 - val_loss: 0.6061 - val_acc: 0.8495\n",
-      "Epoch 54/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.7009 - acc: 0.8155 - val_loss: 0.5657 - val_acc: 0.8578\n",
-      "Epoch 55/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.6990 - acc: 0.8145 - val_loss: 0.5317 - val_acc: 0.8656\n",
-      "Epoch 56/200\n",
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-      "Epoch 85/200\n",
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-      "Epoch 88/200\n",
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-      "Epoch 89/200\n",
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-      "Epoch 91/200\n",
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-      "Epoch 92/200\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5442 - acc: 0.8552 - val_loss: 0.4543 - val_acc: 0.8901\n",
-      "Epoch 93/200\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5400 - acc: 0.8552 - val_loss: 0.4722 - val_acc: 0.8852\n",
-      "Epoch 95/200\n",
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-      "Epoch 96/200\n",
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-      "Epoch 98/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5306 - acc: 0.8582 - val_loss: 0.4627 - val_acc: 0.8852\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5301 - acc: 0.8599 - val_loss: 0.4680 - val_acc: 0.8808\n",
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-      "Epoch 101/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.5085 - acc: 0.8656 - val_loss: 0.4352 - val_acc: 0.8921\n",
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-      "Epoch 103/200\n",
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-      "Epoch 105/200\n",
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-      "Epoch 107/200\n",
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-      "Epoch 109/200\n",
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-      "Epoch 111/200\n",
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-      "Epoch 112/200\n",
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-      "Epoch 113/200\n",
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-      "Epoch 114/200\n",
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-      "390/390 [==============================] - 142s 365ms/step - loss: 0.4421 - acc: 0.8825 - val_loss: 0.4006 - val_acc: 0.9012\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4330 - acc: 0.8829 - val_loss: 0.3998 - val_acc: 0.9018\n",
-      "Epoch 139/200\n",
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-      "Epoch 140/200\n",
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-      "Epoch 142/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4345 - acc: 0.8837 - val_loss: 0.3999 - val_acc: 0.8991\n",
-      "Epoch 143/200\n",
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-      "Epoch 144/200\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4269 - acc: 0.8861 - val_loss: 0.3979 - val_acc: 0.9015\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4287 - acc: 0.8847 - val_loss: 0.3994 - val_acc: 0.8991\n",
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-      "Epoch 151/200\n"
-     ]
-    },
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4195 - acc: 0.8864 - val_loss: 0.3925 - val_acc: 0.9012\n",
-      "Epoch 152/200\n",
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-      "Epoch 154/200\n",
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-      "Epoch 155/200\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4097 - acc: 0.8905 - val_loss: 0.3883 - val_acc: 0.9034\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4152 - acc: 0.8880 - val_loss: 0.3860 - val_acc: 0.9038\n",
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-      "Epoch 162/200\n",
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-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4065 - acc: 0.8905 - val_loss: 0.3847 - val_acc: 0.9048\n",
-      "Epoch 166/200\n",
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-      "Epoch 171/200\n",
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-      "Epoch 172/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4022 - acc: 0.8913 - val_loss: 0.3841 - val_acc: 0.9045\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.4016 - acc: 0.8918 - val_loss: 0.3855 - val_acc: 0.9035\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3982 - acc: 0.8930 - val_loss: 0.3838 - val_acc: 0.9043\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3952 - acc: 0.8930 - val_loss: 0.3787 - val_acc: 0.9067\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3981 - acc: 0.8924 - val_loss: 0.3826 - val_acc: 0.9041\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3961 - acc: 0.8943 - val_loss: 0.3800 - val_acc: 0.9064\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3946 - acc: 0.8919 - val_loss: 0.3803 - val_acc: 0.9063\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3942 - acc: 0.8933 - val_loss: 0.3813 - val_acc: 0.9050\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3902 - acc: 0.8968 - val_loss: 0.3807 - val_acc: 0.9065\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3958 - acc: 0.8948 - val_loss: 0.3791 - val_acc: 0.9063\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3918 - acc: 0.8952 - val_loss: 0.3788 - val_acc: 0.9065\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3884 - acc: 0.8958 - val_loss: 0.3794 - val_acc: 0.9065\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3927 - acc: 0.8942 - val_loss: 0.3800 - val_acc: 0.9054\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3904 - acc: 0.8956 - val_loss: 0.3766 - val_acc: 0.9073\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3896 - acc: 0.8947 - val_loss: 0.3790 - val_acc: 0.9069\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3942 - acc: 0.8931 - val_loss: 0.3800 - val_acc: 0.9056\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3892 - acc: 0.8950 - val_loss: 0.3794 - val_acc: 0.9046\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3877 - acc: 0.8960 - val_loss: 0.3780 - val_acc: 0.9055\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3900 - acc: 0.8948 - val_loss: 0.3801 - val_acc: 0.9051\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3933 - acc: 0.8944 - val_loss: 0.3789 - val_acc: 0.9054\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3880 - acc: 0.8963 - val_loss: 0.3795 - val_acc: 0.9067\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3876 - acc: 0.8956 - val_loss: 0.3770 - val_acc: 0.9068\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3868 - acc: 0.8958 - val_loss: 0.3809 - val_acc: 0.9061\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3873 - acc: 0.8954 - val_loss: 0.3792 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3908 - acc: 0.8949 - val_loss: 0.3801 - val_acc: 0.9061\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3882 - acc: 0.8946 - val_loss: 0.3801 - val_acc: 0.9055\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3909 - acc: 0.8946 - val_loss: 0.3779 - val_acc: 0.9064\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3852 - acc: 0.8948 - val_loss: 0.3775 - val_acc: 0.9056\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3839 - acc: 0.8976 - val_loss: 0.3770 - val_acc: 0.9053\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"relu\", \"relu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=200\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('relu_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3826 - acc: 0.8967 - val_loss: 0.3779 - val_acc: 0.9063\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3823 - acc: 0.8968 - val_loss: 0.3773 - val_acc: 0.9072\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3856 - acc: 0.8974 - val_loss: 0.3766 - val_acc: 0.9069\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3803 - acc: 0.8979 - val_loss: 0.3789 - val_acc: 0.9064\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3843 - acc: 0.8954 - val_loss: 0.3786 - val_acc: 0.9070\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3842 - acc: 0.8968 - val_loss: 0.3771 - val_acc: 0.9068\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3845 - acc: 0.8976 - val_loss: 0.3755 - val_acc: 0.9065\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3851 - acc: 0.8966 - val_loss: 0.3754 - val_acc: 0.9078\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3873 - acc: 0.8942 - val_loss: 0.3758 - val_acc: 0.9072\n",
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3837 - acc: 0.8959 - val_loss: 0.3763 - val_acc: 0.9069\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3820 - acc: 0.8957 - val_loss: 0.3774 - val_acc: 0.9067\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3842 - acc: 0.8968 - val_loss: 0.3767 - val_acc: 0.9061\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3859 - acc: 0.8962 - val_loss: 0.3766 - val_acc: 0.9065\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3843 - acc: 0.8959 - val_loss: 0.3776 - val_acc: 0.9067\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3846 - acc: 0.8970 - val_loss: 0.3769 - val_acc: 0.9072\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3800 - acc: 0.8991 - val_loss: 0.3770 - val_acc: 0.9064\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3789 - acc: 0.8969 - val_loss: 0.3756 - val_acc: 0.9069\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3825 - acc: 0.8966 - val_loss: 0.3777 - val_acc: 0.9066\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3831 - acc: 0.8972 - val_loss: 0.3775 - val_acc: 0.9072\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3818 - acc: 0.8947 - val_loss: 0.3779 - val_acc: 0.9064\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3799 - acc: 0.8964 - val_loss: 0.3785 - val_acc: 0.9076\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3795 - acc: 0.8969 - val_loss: 0.3765 - val_acc: 0.9073\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3875 - acc: 0.8962 - val_loss: 0.3764 - val_acc: 0.9063\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3825 - acc: 0.8974 - val_loss: 0.3755 - val_acc: 0.9063\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3804 - acc: 0.8966 - val_loss: 0.3768 - val_acc: 0.9060\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3770 - acc: 0.8976 - val_loss: 0.3766 - val_acc: 0.9070\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 142s 365ms/step - loss: 0.3802 - acc: 0.8958 - val_loss: 0.3753 - val_acc: 0.9071\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3870 - acc: 0.8952 - val_loss: 0.3754 - val_acc: 0.9071\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3774 - acc: 0.8971 - val_loss: 0.3733 - val_acc: 0.9075\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 142s 364ms/step - loss: 0.3805 - acc: 0.8970 - val_loss: 0.3756 - val_acc: 0.9073\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 143s 366ms/step - loss: 0.3788 - acc: 0.8975 - val_loss: 0.3744 - val_acc: 0.9075\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 148s 380ms/step - loss: 0.3777 - acc: 0.8977 - val_loss: 0.3760 - val_acc: 0.9066\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 148s 381ms/step - loss: 0.3792 - acc: 0.8988 - val_loss: 0.3735 - val_acc: 0.9073\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.3801 - acc: 0.8968 - val_loss: 0.3741 - val_acc: 0.9080\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 150s 384ms/step - loss: 0.3789 - acc: 0.8979 - val_loss: 0.3754 - val_acc: 0.9071\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 146s 375ms/step - loss: 0.3765 - acc: 0.8977 - val_loss: 0.3757 - val_acc: 0.9076\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.3844 - acc: 0.8950 - val_loss: 0.3754 - val_acc: 0.9070\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 147s 376ms/step - loss: 0.3815 - acc: 0.8966 - val_loss: 0.3750 - val_acc: 0.9068\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3807 - acc: 0.8982 - val_loss: 0.3771 - val_acc: 0.9076\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 147s 376ms/step - loss: 0.3778 - acc: 0.9002 - val_loss: 0.3751 - val_acc: 0.9069\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 145s 372ms/step - loss: 0.3801 - acc: 0.8978 - val_loss: 0.3748 - val_acc: 0.9075\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 143s 367ms/step - loss: 0.3804 - acc: 0.8981 - val_loss: 0.3731 - val_acc: 0.9084\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 144s 368ms/step - loss: 0.3800 - acc: 0.8987 - val_loss: 0.3758 - val_acc: 0.9079\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3797 - acc: 0.8984 - val_loss: 0.3763 - val_acc: 0.9081\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 144s 370ms/step - loss: 0.3752 - acc: 0.8986 - val_loss: 0.3758 - val_acc: 0.9079\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 143s 368ms/step - loss: 0.3762 - acc: 0.8980 - val_loss: 0.3746 - val_acc: 0.9073\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 145s 371ms/step - loss: 0.3797 - acc: 0.8976 - val_loss: 0.3741 - val_acc: 0.9079\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3765 - acc: 0.8988 - val_loss: 0.3761 - val_acc: 0.9071\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 144s 369ms/step - loss: 0.3791 - acc: 0.8964 - val_loss: 0.3758 - val_acc: 0.9065\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 145s 373ms/step - loss: 0.3796 - acc: 0.8989 - val_loss: 0.3770 - val_acc: 0.9069\n",
-      "{'val_loss': [0.37785097351074221, 0.37731364712715149, 0.37661438734531405, 0.37886159641742706, 0.37856462571620941, 0.37713949706554412, 0.37550454359054564, 0.37540296201705931, 0.37580586643218994, 0.37626878676414488, 0.3773591064929962, 0.37666828496456145, 0.37660351555347443, 0.37759391674995424, 0.37693574063777924, 0.37701914649009705, 0.37559629876613615, 0.37769697990417478, 0.37752313163280488, 0.37790378603935243, 0.3785025860309601, 0.37651138377189636, 0.37637851953506468, 0.3754533318758011, 0.37683316841125486, 0.37656428048610685, 0.37534701485633848, 0.37539958257675171, 0.37330716643333434, 0.375648184299469, 0.37437661690711976, 0.37600070507526395, 0.37349254970550538, 0.37409095671176912, 0.37544303872585294, 0.37571356678009032, 0.37537829804420469, 0.37495623900890351, 0.37714893469810484, 0.3750675146341324, 0.37481870002746581, 0.37313218743801119, 0.37582568845748904, 0.37627374584674833, 0.37577459394931795, 0.37462998654842378, 0.37410167636871339, 0.37613337566852567, 0.37578551383018494, 0.37696433467864993], 'val_acc': [0.90629999999999999, 0.90720000000000001, 0.90690000000000004, 0.90639999999999998, 0.90700000000000003, 0.90680000000000005, 0.90649999999999997, 0.90780000000000005, 0.90720000000000001, 0.90690000000000004, 0.90669999999999995, 0.90610000000000002, 0.90649999999999997, 0.90669999999999995, 0.90720000000000001, 0.90639999999999998, 0.90690000000000004, 0.90659999999999996, 0.90720000000000001, 0.90639999999999998, 0.90759999999999996, 0.9073, 0.90629999999999999, 0.90629999999999999, 0.90600000000000003, 0.90700000000000003, 0.90710000000000002, 0.90710000000000002, 0.90749999999999997, 0.9073, 0.90749999999999997, 0.90659999999999996, 0.9073, 0.90800000000000003, 0.90710000000000002, 0.90759999999999996, 0.90700000000000003, 0.90680000000000005, 0.90759999999999996, 0.90690000000000004, 0.90749999999999997, 0.90839999999999999, 0.90790000000000004, 0.90810000000000002, 0.90790000000000004, 0.9073, 0.90790000000000004, 0.90710000000000002, 0.90649999999999997, 0.90690000000000004], 'acc': [0.89669554058389478, 0.89681584861059005, 0.89745749117741414, 0.89785851782470472, 0.89541225539287927, 0.89679579722181435, 0.89753769650304782, 0.89659455128205123, 0.89422768143866405, 0.89585338464561926, 0.8957330766954138, 0.89681584859146768, 0.89619425731780711, 0.89601379527776415, 0.89697625926185731, 0.89908164900237264, 0.89693615652255076, 0.89655518128315836, 0.89719682390734978, 0.89475056139903453, 0.8963747192431154, 0.89679579720269187, 0.89621394230769236, 0.89743898522800258, 0.89663538662791442, 0.89751764515251697, 0.89583333333333337, 0.8951910725563248, 0.89703641323696015, 0.89703641317959282, 0.89751764513339449, 0.89765800445325339, 0.89880093042002218, 0.89685595121603956, 0.89791867181892993, 0.8977181585239683, 0.8949911774333027, 0.89649502723156582, 0.89813923644529992, 0.90028472894424427, 0.89781841516188787, 0.89809695512820509, 0.89864322414900455, 0.89837985240307838, 0.89858036577452982, 0.89797882579403276, 0.89763795312184491, 0.89882098173230818, 0.89641426282051284, 0.89886108435688017], 'loss': [0.38247765058082078, 0.38237306306405749, 0.38536368729711001, 0.38022890837099982, 0.38418068871843841, 0.38421753206656917, 0.38468616945566869, 0.38509517396107701, 0.38704997230280497, 0.38390190780334915, 0.38193131318297341, 0.38425878466686303, 0.38578086314118748, 0.38412407446947855, 0.38460791421732077, 0.37987503716300841, 0.378891275389515, 0.38247182903130694, 0.38302359059524721, 0.38172237312598684, 0.38006335567655491, 0.37968259891581757, 0.38745315521955492, 0.38236816986371253, 0.38026291069825335, 0.37708598499616292, 0.38020494740742905, 0.38690472353553651, 0.37743982981518748, 0.38049827865541536, 0.37883050631245324, 0.37772402394877286, 0.3792561863644367, 0.38013887082226278, 0.3788666189347929, 0.37660440887669017, 0.38446455074305103, 0.38157797130323734, 0.38074120329403133, 0.37764427879509116, 0.38006463212809349, 0.38043843278518091, 0.38019017297140512, 0.37979160633116227, 0.37523847440706448, 0.3762182190435836, 0.37966022781733716, 0.37660038402381141, 0.37906198035448024, 0.37974316005068864]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "epochs = 250\n",
-    "model = load_model('relu_at_3.h5')\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000017,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=200)\n",
-    "# Save the model\n",
-    "model.save('relu_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9187\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(\"relu\")\n",
-    "        model.load_weights('relu_at_'+str(i+1)+'.h5')\n",
-    "        model.save('relu_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('relu_at_'+str(i+1)+'.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 2.0201 - acc: 0.3906 - val_loss: 1.3828 - val_acc: 0.5563\n",
-      "Epoch 2/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.4731 - acc: 0.5308 - val_loss: 1.1149 - val_acc: 0.6337\n",
-      "Epoch 3/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 1.2980 - acc: 0.5922 - val_loss: 1.0801 - val_acc: 0.6678\n",
-      "Epoch 4/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 1.2151 - acc: 0.6298 - val_loss: 0.9754 - val_acc: 0.6856\n",
-      "Epoch 5/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1390 - acc: 0.6510 - val_loss: 1.5834 - val_acc: 0.6358\n",
-      "Epoch 6/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.1029 - acc: 0.6706 - val_loss: 0.7973 - val_acc: 0.7567\n",
-      "Epoch 7/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0241 - acc: 0.6928 - val_loss: 0.7856 - val_acc: 0.7628\n",
-      "Epoch 8/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9956 - acc: 0.7041 - val_loss: 1.2215 - val_acc: 0.7283\n",
-      "Epoch 9/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0520 - acc: 0.7052 - val_loss: 1.6160 - val_acc: 0.7280\n",
-      "Epoch 10/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0199 - acc: 0.7127 - val_loss: 0.7939 - val_acc: 0.7845\n",
-      "Epoch 11/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 1.0037 - acc: 0.7170 - val_loss: 0.7445 - val_acc: 0.7803\n",
-      "Epoch 12/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9561 - acc: 0.7259 - val_loss: 0.7351 - val_acc: 0.7896\n",
-      "Epoch 13/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9214 - acc: 0.7366 - val_loss: 0.6897 - val_acc: 0.8018\n",
-      "Epoch 14/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9240 - acc: 0.7409 - val_loss: 0.7333 - val_acc: 0.7836\n",
-      "Epoch 15/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9455 - acc: 0.7394 - val_loss: 0.6761 - val_acc: 0.8085\n",
-      "Epoch 16/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9299 - acc: 0.7462 - val_loss: 0.6588 - val_acc: 0.8135\n",
-      "Epoch 17/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9478 - acc: 0.7482 - val_loss: 0.7012 - val_acc: 0.8151\n",
-      "Epoch 18/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9236 - acc: 0.7535 - val_loss: 0.6909 - val_acc: 0.8094\n",
-      "Epoch 19/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.9234 - acc: 0.7549 - val_loss: 0.6782 - val_acc: 0.8189\n",
-      "Epoch 20/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8722 - acc: 0.7605 - val_loss: 0.6582 - val_acc: 0.8214\n",
-      "Epoch 21/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8591 - acc: 0.7642 - val_loss: 0.6625 - val_acc: 0.8172\n",
-      "Epoch 22/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8269 - acc: 0.7725 - val_loss: 0.6077 - val_acc: 0.8343\n",
-      "Epoch 23/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8260 - acc: 0.7745 - val_loss: 0.6269 - val_acc: 0.8280\n",
-      "Epoch 24/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8197 - acc: 0.7769 - val_loss: 0.6442 - val_acc: 0.8197\n",
-      "Epoch 25/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8047 - acc: 0.7790 - val_loss: 0.6241 - val_acc: 0.8293\n",
-      "Epoch 26/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8188 - acc: 0.7806 - val_loss: 0.6076 - val_acc: 0.8352\n",
-      "Epoch 27/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8053 - acc: 0.7823 - val_loss: 0.6519 - val_acc: 0.8270\n",
-      "Epoch 28/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.8434 - acc: 0.7808 - val_loss: 0.6330 - val_acc: 0.8318\n",
-      "Epoch 29/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7910 - acc: 0.7879 - val_loss: 0.6119 - val_acc: 0.8378\n",
-      "Epoch 30/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7811 - acc: 0.7891 - val_loss: 0.5965 - val_acc: 0.8451\n",
-      "Epoch 31/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7707 - acc: 0.7902 - val_loss: 0.6395 - val_acc: 0.8271\n",
-      "Epoch 32/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7675 - acc: 0.7932 - val_loss: 0.5810 - val_acc: 0.8464\n",
-      "Epoch 33/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7828 - acc: 0.7923 - val_loss: 0.6336 - val_acc: 0.8310\n",
-      "Epoch 34/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7781 - acc: 0.7948 - val_loss: 0.5732 - val_acc: 0.8480\n",
-      "Epoch 35/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7599 - acc: 0.7993 - val_loss: 0.5962 - val_acc: 0.8421\n",
-      "Epoch 36/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7571 - acc: 0.7990 - val_loss: 0.6151 - val_acc: 0.8365\n",
-      "Epoch 37/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7565 - acc: 0.7981 - val_loss: 0.5864 - val_acc: 0.8464\n",
-      "Epoch 38/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7765 - acc: 0.7984 - val_loss: 0.6086 - val_acc: 0.8371\n",
-      "Epoch 39/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7530 - acc: 0.7973 - val_loss: 0.5760 - val_acc: 0.8520\n",
-      "Epoch 40/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7489 - acc: 0.8030 - val_loss: 0.6164 - val_acc: 0.8407\n",
-      "Epoch 41/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7573 - acc: 0.8010 - val_loss: 0.6014 - val_acc: 0.8493\n",
-      "Epoch 42/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7443 - acc: 0.8053 - val_loss: 0.5915 - val_acc: 0.8531\n",
-      "Epoch 43/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7395 - acc: 0.8055 - val_loss: 0.5853 - val_acc: 0.8527\n",
-      "Epoch 44/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7227 - acc: 0.8074 - val_loss: 0.5628 - val_acc: 0.8596\n",
-      "Epoch 45/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7235 - acc: 0.8086 - val_loss: 0.5740 - val_acc: 0.8546\n",
-      "Epoch 46/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7244 - acc: 0.8098 - val_loss: 0.5851 - val_acc: 0.8470\n",
-      "Epoch 47/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7256 - acc: 0.8081 - val_loss: 0.5751 - val_acc: 0.8535\n",
-      "Epoch 48/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7238 - acc: 0.8093 - val_loss: 0.5957 - val_acc: 0.8468\n",
-      "Epoch 49/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7146 - acc: 0.8112 - val_loss: 0.5766 - val_acc: 0.8543\n",
-      "Epoch 50/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7047 - acc: 0.8129 - val_loss: 0.5858 - val_acc: 0.8507\n",
-      "Epoch 51/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7186 - acc: 0.8132 - val_loss: 0.5443 - val_acc: 0.8671\n",
-      "Epoch 52/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7213 - acc: 0.8124 - val_loss: 0.5546 - val_acc: 0.8629\n",
-      "Epoch 53/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7064 - acc: 0.8124 - val_loss: 0.5631 - val_acc: 0.8606\n",
-      "Epoch 54/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7087 - acc: 0.8195 - val_loss: 0.5347 - val_acc: 0.8653\n",
-      "Epoch 55/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.7108 - acc: 0.8152 - val_loss: 0.5700 - val_acc: 0.8593\n",
-      "Epoch 56/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7082 - acc: 0.8148 - val_loss: 0.5746 - val_acc: 0.8537\n",
-      "Epoch 57/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6987 - acc: 0.8188 - val_loss: 0.5549 - val_acc: 0.8612\n",
-      "Epoch 58/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6983 - acc: 0.8193 - val_loss: 0.5453 - val_acc: 0.8599\n",
-      "Epoch 59/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7052 - acc: 0.8172 - val_loss: 0.5706 - val_acc: 0.8608\n",
-      "Epoch 60/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6905 - acc: 0.8199 - val_loss: 0.5770 - val_acc: 0.8546\n",
-      "Epoch 61/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6847 - acc: 0.8224 - val_loss: 0.5566 - val_acc: 0.8653\n",
-      "Epoch 62/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.7043 - acc: 0.8194 - val_loss: 0.5629 - val_acc: 0.8581\n",
-      "Epoch 63/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6949 - acc: 0.8193 - val_loss: 0.5522 - val_acc: 0.8611\n",
-      "Epoch 64/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6857 - acc: 0.8212 - val_loss: 0.5427 - val_acc: 0.8663\n",
-      "Epoch 65/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6768 - acc: 0.8262 - val_loss: 0.5607 - val_acc: 0.8605\n",
-      "Epoch 66/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6842 - acc: 0.8230 - val_loss: 0.5553 - val_acc: 0.8618\n",
-      "Epoch 67/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6684 - acc: 0.8280 - val_loss: 0.5316 - val_acc: 0.8725\n",
-      "Epoch 68/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6789 - acc: 0.8240 - val_loss: 0.5659 - val_acc: 0.8556\n",
-      "Epoch 69/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6752 - acc: 0.8236 - val_loss: 0.5545 - val_acc: 0.8627\n",
-      "Epoch 70/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6796 - acc: 0.8253 - val_loss: 0.5433 - val_acc: 0.8695\n",
-      "Epoch 71/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.6776 - acc: 0.8265 - val_loss: 0.5272 - val_acc: 0.8718\n",
-      "Epoch 72/200\n",
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-      "Epoch 80/200\n",
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-      "Epoch 85/200\n",
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-      "Epoch 87/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5537 - acc: 0.8549 - val_loss: 0.4713 - val_acc: 0.8847\n",
-      "Epoch 88/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5667 - acc: 0.8520 - val_loss: 0.4694 - val_acc: 0.8878\n",
-      "Epoch 89/200\n",
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-      "Epoch 90/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5565 - acc: 0.8538 - val_loss: 0.4680 - val_acc: 0.8862\n",
-      "Epoch 91/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5535 - acc: 0.8546 - val_loss: 0.4712 - val_acc: 0.8847\n",
-      "Epoch 92/200\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5423 - acc: 0.8576 - val_loss: 0.4586 - val_acc: 0.8902\n",
-      "Epoch 93/200\n",
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-      "Epoch 94/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5499 - acc: 0.8535 - val_loss: 0.4542 - val_acc: 0.8877\n",
-      "Epoch 95/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5362 - acc: 0.8586 - val_loss: 0.4561 - val_acc: 0.8869\n",
-      "Epoch 96/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5433 - acc: 0.8543 - val_loss: 0.4426 - val_acc: 0.8915\n",
-      "Epoch 97/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5360 - acc: 0.8568 - val_loss: 0.4597 - val_acc: 0.8866\n",
-      "Epoch 98/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5389 - acc: 0.8566 - val_loss: 0.4537 - val_acc: 0.8865\n",
-      "Epoch 99/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.5435 - acc: 0.8535 - val_loss: 0.4490 - val_acc: 0.8913\n",
-      "Epoch 100/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5295 - acc: 0.8574 - val_loss: 0.4588 - val_acc: 0.8837\n",
-      "Epoch 101/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5126 - acc: 0.8649 - val_loss: 0.4349 - val_acc: 0.8947\n",
-      "Epoch 102/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5037 - acc: 0.8662 - val_loss: 0.4344 - val_acc: 0.8927\n",
-      "Epoch 103/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.5049 - acc: 0.8671 - val_loss: 0.4230 - val_acc: 0.8981\n",
-      "Epoch 104/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4946 - acc: 0.8680 - val_loss: 0.4370 - val_acc: 0.8915\n",
-      "Epoch 105/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4985 - acc: 0.8676 - val_loss: 0.4267 - val_acc: 0.8978\n",
-      "Epoch 106/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4964 - acc: 0.8688 - val_loss: 0.4269 - val_acc: 0.8958\n",
-      "Epoch 107/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4917 - acc: 0.8703 - val_loss: 0.4228 - val_acc: 0.8984\n",
-      "Epoch 108/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4929 - acc: 0.8693 - val_loss: 0.4239 - val_acc: 0.8957\n",
-      "Epoch 109/200\n",
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-      "Epoch 110/200\n",
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-      "Epoch 111/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4832 - acc: 0.8697 - val_loss: 0.4233 - val_acc: 0.8974\n",
-      "Epoch 112/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.4821 - acc: 0.8717 - val_loss: 0.4369 - val_acc: 0.8953\n",
-      "Epoch 113/200\n",
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-      "Epoch 114/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4785 - acc: 0.8715 - val_loss: 0.4166 - val_acc: 0.8970\n",
-      "Epoch 115/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4844 - acc: 0.8698 - val_loss: 0.4253 - val_acc: 0.8967\n",
-      "Epoch 116/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4799 - acc: 0.8711 - val_loss: 0.4380 - val_acc: 0.8919\n",
-      "Epoch 117/200\n",
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-      "Epoch 118/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4725 - acc: 0.8729 - val_loss: 0.4296 - val_acc: 0.8936\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4740 - acc: 0.8720 - val_loss: 0.4342 - val_acc: 0.8906\n",
-      "Epoch 121/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4714 - acc: 0.8740 - val_loss: 0.4373 - val_acc: 0.8936\n",
-      "Epoch 122/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4708 - acc: 0.8729 - val_loss: 0.4101 - val_acc: 0.8962\n",
-      "Epoch 123/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4622 - acc: 0.8761 - val_loss: 0.4203 - val_acc: 0.8945\n",
-      "Epoch 124/200\n",
-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4684 - acc: 0.8729 - val_loss: 0.4186 - val_acc: 0.8958\n",
-      "Epoch 125/200\n",
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-      "Epoch 126/200\n",
-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4521 - acc: 0.8776 - val_loss: 0.4075 - val_acc: 0.8980\n",
-      "Epoch 127/200\n",
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-      "Epoch 128/200\n",
-      "390/390 [==============================] - 181s 464ms/step - loss: 0.4431 - acc: 0.8817 - val_loss: 0.4046 - val_acc: 0.9000\n",
-      "Epoch 129/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4360 - acc: 0.8839 - val_loss: 0.3950 - val_acc: 0.9007\n",
-      "Epoch 130/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.4390 - acc: 0.8811 - val_loss: 0.3987 - val_acc: 0.9011\n",
-      "Epoch 131/200\n",
-      "390/390 [==============================] - 178s 457ms/step - loss: 0.4398 - acc: 0.8813 - val_loss: 0.3989 - val_acc: 0.8995\n",
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-      "390/390 [==============================] - 177s 455ms/step - loss: 0.4388 - acc: 0.8825 - val_loss: 0.4080 - val_acc: 0.8963\n",
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-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4423 - acc: 0.8800 - val_loss: 0.4072 - val_acc: 0.8986\n",
-      "Epoch 134/200\n",
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-      "Epoch 135/200\n",
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-      "Epoch 136/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.4277 - acc: 0.8842 - val_loss: 0.3929 - val_acc: 0.9014\n",
-      "Epoch 137/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4341 - acc: 0.8825 - val_loss: 0.4060 - val_acc: 0.9006\n",
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-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4285 - acc: 0.8839 - val_loss: 0.3915 - val_acc: 0.9013\n",
-      "Epoch 139/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.4296 - acc: 0.8833 - val_loss: 0.4029 - val_acc: 0.9015\n",
-      "Epoch 140/200\n",
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-      "Epoch 143/200\n",
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-      "Epoch 144/200\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4236 - acc: 0.8841 - val_loss: 0.3886 - val_acc: 0.9028\n",
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-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4223 - acc: 0.8849 - val_loss: 0.3961 - val_acc: 0.9010\n",
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-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4214 - acc: 0.8852 - val_loss: 0.3977 - val_acc: 0.9002\n",
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-      "390/390 [==============================] - 175s 447ms/step - loss: 0.4228 - acc: 0.8851 - val_loss: 0.3914 - val_acc: 0.9003\n",
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-      "Epoch 151/200\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4115 - acc: 0.8871 - val_loss: 0.3882 - val_acc: 0.9029\n",
-      "Epoch 152/200\n",
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-      "Epoch 153/200\n",
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-      "Epoch 154/200\n",
-      "390/390 [==============================] - 174s 447ms/step - loss: 0.4110 - acc: 0.8882 - val_loss: 0.3838 - val_acc: 0.9035\n",
-      "Epoch 155/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4095 - acc: 0.8899 - val_loss: 0.3948 - val_acc: 0.9024\n",
-      "Epoch 156/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4084 - acc: 0.8893 - val_loss: 0.3909 - val_acc: 0.9042\n",
-      "Epoch 157/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.4045 - acc: 0.8903 - val_loss: 0.3851 - val_acc: 0.9046\n",
-      "Epoch 158/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.4040 - acc: 0.8916 - val_loss: 0.3865 - val_acc: 0.9032\n",
-      "Epoch 159/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4052 - acc: 0.8896 - val_loss: 0.3857 - val_acc: 0.9046\n",
-      "Epoch 160/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.4019 - acc: 0.8913 - val_loss: 0.3871 - val_acc: 0.9056\n",
-      "Epoch 161/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.4055 - acc: 0.8913 - val_loss: 0.3808 - val_acc: 0.9062\n",
-      "Epoch 162/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4025 - acc: 0.8923 - val_loss: 0.3901 - val_acc: 0.9044\n",
-      "Epoch 163/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8910 - val_loss: 0.3853 - val_acc: 0.9044\n",
-      "Epoch 164/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4027 - acc: 0.8910 - val_loss: 0.3823 - val_acc: 0.9061\n",
-      "Epoch 165/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3982 - acc: 0.8928 - val_loss: 0.3831 - val_acc: 0.9062\n",
-      "Epoch 166/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.4029 - acc: 0.8903 - val_loss: 0.3881 - val_acc: 0.9056\n",
-      "Epoch 167/200\n",
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-      "Epoch 168/200\n",
-      "390/390 [==============================] - 174s 446ms/step - loss: 0.3989 - acc: 0.8925 - val_loss: 0.3874 - val_acc: 0.9035\n",
-      "Epoch 169/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.4048 - acc: 0.8895 - val_loss: 0.3850 - val_acc: 0.9034\n",
-      "Epoch 170/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3951 - acc: 0.8937 - val_loss: 0.3849 - val_acc: 0.9045\n",
-      "Epoch 171/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3978 - acc: 0.8920 - val_loss: 0.3835 - val_acc: 0.9052\n",
-      "Epoch 172/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3997 - acc: 0.8901 - val_loss: 0.3865 - val_acc: 0.9044\n",
-      "Epoch 173/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3989 - acc: 0.8913 - val_loss: 0.3794 - val_acc: 0.9049\n",
-      "Epoch 174/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3992 - acc: 0.8915 - val_loss: 0.3835 - val_acc: 0.9040\n",
-      "Epoch 175/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3927 - acc: 0.8924 - val_loss: 0.3854 - val_acc: 0.9039\n",
-      "Epoch 176/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3928 - acc: 0.8938 - val_loss: 0.3804 - val_acc: 0.9056\n",
-      "Epoch 177/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3919 - acc: 0.8931 - val_loss: 0.3817 - val_acc: 0.9057\n",
-      "Epoch 178/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3892 - acc: 0.8952 - val_loss: 0.3801 - val_acc: 0.9065\n",
-      "Epoch 179/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3919 - acc: 0.8941 - val_loss: 0.3808 - val_acc: 0.9056\n",
-      "Epoch 180/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3892 - acc: 0.8936 - val_loss: 0.3816 - val_acc: 0.9055\n",
-      "Epoch 181/200\n",
-      "390/390 [==============================] - 176s 452ms/step - loss: 0.3855 - acc: 0.8959 - val_loss: 0.3810 - val_acc: 0.9069\n",
-      "Epoch 182/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3895 - acc: 0.8943 - val_loss: 0.3812 - val_acc: 0.9051\n",
-      "Epoch 183/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3922 - acc: 0.8943 - val_loss: 0.3776 - val_acc: 0.9068\n",
-      "Epoch 184/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3883 - acc: 0.8951 - val_loss: 0.3796 - val_acc: 0.9074\n",
-      "Epoch 185/200\n",
-      "390/390 [==============================] - 180s 461ms/step - loss: 0.3942 - acc: 0.8928 - val_loss: 0.3794 - val_acc: 0.9056\n",
-      "Epoch 186/200\n",
-      "390/390 [==============================] - 179s 459ms/step - loss: 0.3870 - acc: 0.8960 - val_loss: 0.3777 - val_acc: 0.9072\n",
-      "Epoch 187/200\n",
-      "390/390 [==============================] - 177s 454ms/step - loss: 0.3899 - acc: 0.8941 - val_loss: 0.3780 - val_acc: 0.9066\n",
-      "Epoch 188/200\n",
-      "390/390 [==============================] - 178s 456ms/step - loss: 0.3853 - acc: 0.8971 - val_loss: 0.3778 - val_acc: 0.9066\n",
-      "Epoch 189/200\n",
-      "390/390 [==============================] - 178s 458ms/step - loss: 0.3878 - acc: 0.8958 - val_loss: 0.3792 - val_acc: 0.9076\n",
-      "Epoch 190/200\n",
-      "390/390 [==============================] - 177s 453ms/step - loss: 0.3873 - acc: 0.8956 - val_loss: 0.3781 - val_acc: 0.9085\n",
-      "Epoch 191/200\n",
-      "390/390 [==============================] - 176s 450ms/step - loss: 0.3876 - acc: 0.8948 - val_loss: 0.3769 - val_acc: 0.9078\n",
-      "Epoch 192/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3837 - acc: 0.8973 - val_loss: 0.3768 - val_acc: 0.9075\n",
-      "Epoch 193/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3876 - acc: 0.8946 - val_loss: 0.3755 - val_acc: 0.9085\n",
-      "Epoch 194/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3926 - acc: 0.8941 - val_loss: 0.3756 - val_acc: 0.9087\n",
-      "Epoch 195/200\n",
-      "390/390 [==============================] - 175s 448ms/step - loss: 0.3839 - acc: 0.8957 - val_loss: 0.3782 - val_acc: 0.9072\n",
-      "Epoch 196/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3867 - acc: 0.8934 - val_loss: 0.3760 - val_acc: 0.9079\n",
-      "Epoch 197/200\n",
-      "390/390 [==============================] - 175s 450ms/step - loss: 0.3884 - acc: 0.8948 - val_loss: 0.3788 - val_acc: 0.9064\n",
-      "Epoch 198/200\n",
-      "390/390 [==============================] - 175s 449ms/step - loss: 0.3857 - acc: 0.8943 - val_loss: 0.3773 - val_acc: 0.9082\n",
-      "Epoch 199/200\n",
-      "390/390 [==============================] - 176s 451ms/step - loss: 0.3882 - acc: 0.8937 - val_loss: 0.3750 - val_acc: 0.9078\n",
-      "Epoch 200/200\n",
-      "390/390 [==============================] - 179s 458ms/step - loss: 0.3839 - acc: 0.8945 - val_loss: 0.3787 - val_acc: 0.9079\n",
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0.89125722545266683, 0.89230429900545394, 0.89100096244478522, 0.89108116782778612, 0.89276547962784725, 0.89037937115200216, 0.89132178378556448, 0.89246470963759872, 0.8894369586331744, 0.89372996794871795, 0.89201991004521219, 0.8900841346153846, 0.89127729615877282, 0.89152644230769229, 0.89234104044328688, 0.89383012820512819, 0.89312640363144347, 0.89525128454065661, 0.89409054487179485, 0.89360549132947975, 0.89591346153846152, 0.89434953480911128, 0.89426932944523285, 0.89509143403297742, 0.89268224145176323, 0.89601362179487176, 0.89410725750828213, 0.89719682384998245, 0.89575312796945483, 0.8955929487179487, 0.89474951832357241, 0.89725560897435896, 0.89458895307668296, 0.89405048076923077, 0.89571290942209225, 0.89348732759679472, 0.89475160256410258, 0.89432948347770291, 0.89376804621739003, 0.89452874109235114]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = \"relu\", \"relu\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - \n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('relu_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    model = load_model('relu_at_+'str(i)'+.h5')\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Deeper_CNN/cifar10_deeper_cnn_swish.ipynb b/cifar/Deeper_CNN/cifar10_deeper_cnn_swish.ipynb
deleted file mode 100644
index f92e65f..0000000
--- a/cifar/Deeper_CNN/cifar10_deeper_cnn_swish.ipynb
+++ /dev/null
@@ -1,1649 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Performing a large-scale experiment on cifar10 \"\"\"\n",
-    "\n",
-    "import keras\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "from keras.utils import np_utils\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
-    "from keras.layers import Conv2D, MaxPooling2D\n",
-    "from keras.datasets import cifar10\n",
-    "import keras.regularizers as regularizers\n",
-    "from keras.models import load_model\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    baseMapNum = 32\n",
-    "    weight_decay = 1e-4\n",
-    "\n",
-    "    model = Sequential()\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay), input_shape=x_train.shape[1:]))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.2))\n",
-    "\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(2*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.3))\n",
-    "\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(Conv2D(4*baseMapNum, (3,3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))\n",
-    "    model.add(Activation(act))\n",
-    "    model.add(BatchNormalization())\n",
-    "    model.add(MaxPooling2D(pool_size=(2,2)))\n",
-    "    model.add(Dropout(0.4))\n",
-    "\n",
-    "    model.add(Flatten())\n",
-    "    model.add(Dense(num_classes, activation='softmax'))\n",
-    "\n",
-    "    model.summary()\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "#data augmentation\n",
-    "datagen = ImageDataGenerator(\n",
-    "    featurewise_center=False,\n",
-    "    samplewise_center=False,\n",
-    "    featurewise_std_normalization=False,\n",
-    "    samplewise_std_normalization=False,\n",
-    "    zca_whitening=False,\n",
-    "    rotation_range=15,\n",
-    "    width_shift_range=0.15,\n",
-    "    height_shift_range=0.15,\n",
-    "    horizontal_flip=True,\n",
-    "    vertical_flip=False\n",
-    "    )\n",
-    "datagen.fit(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def schedule(x):\n",
-    "    if x < 75: \n",
-    "        return 0.001\n",
-    "    elif x < 100:\n",
-    "        return 0.0005\n",
-    "    elif x < 125:\n",
-    "        return 0.0003\n",
-    "    elif x<150:\n",
-    "        return 0.00015\n",
-    "    elif x<175:\n",
-    "        return 0.000075\n",
-    "    elif x<200: \n",
-    "        return 0.000035\n",
-    "    elif x<225:\n",
-    "        return 0.000017\n",
-    "    else:\n",
-    "        return 0.000012"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_2 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_4 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_5 (Conv2D)            (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_6 (Conv2D)            (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_6 (Batch (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "390/390 [==============================] - 159s 407ms/step - loss: 1.9607 - acc: 0.4037 - val_loss: 1.5353 - val_acc: 0.4661\n",
-      "Epoch 2/250\n",
-      "390/390 [==============================] - 152s 389ms/step - loss: 1.4286 - acc: 0.5464 - val_loss: 1.2511 - val_acc: 0.6299\n",
-      "Epoch 3/250\n",
-      "390/390 [==============================] - 152s 389ms/step - loss: 1.2857 - acc: 0.5990 - val_loss: 1.0288 - val_acc: 0.6810\n",
-      "Epoch 4/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 1.2402 - acc: 0.6301 - val_loss: 0.9638 - val_acc: 0.7194\n",
-      "Epoch 5/250\n",
-      "390/390 [==============================] - 152s 389ms/step - loss: 1.2152 - acc: 0.6521 - val_loss: 0.9194 - val_acc: 0.7308\n",
-      "Epoch 6/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 1.0922 - acc: 0.6738 - val_loss: 0.7698 - val_acc: 0.7634\n",
-      "Epoch 7/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 1.0202 - acc: 0.6946 - val_loss: 0.8365 - val_acc: 0.7496\n",
-      "Epoch 8/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9961 - acc: 0.7088 - val_loss: 0.7827 - val_acc: 0.7660\n",
-      "Epoch 9/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9665 - acc: 0.7173 - val_loss: 0.7273 - val_acc: 0.7776\n",
-      "Epoch 10/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9594 - acc: 0.7254 - val_loss: 0.7126 - val_acc: 0.7974\n",
-      "Epoch 11/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9199 - acc: 0.7341 - val_loss: 0.7007 - val_acc: 0.7997\n",
-      "Epoch 12/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9265 - acc: 0.7388 - val_loss: 0.6689 - val_acc: 0.8065\n",
-      "Epoch 13/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.8972 - acc: 0.7449 - val_loss: 0.6445 - val_acc: 0.8118\n",
-      "Epoch 14/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9353 - acc: 0.7466 - val_loss: 0.7344 - val_acc: 0.8036\n",
-      "Epoch 15/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9597 - acc: 0.7479 - val_loss: 0.6855 - val_acc: 0.8020\n",
-      "Epoch 16/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.8871 - acc: 0.7552 - val_loss: 0.7053 - val_acc: 0.8134\n",
-      "Epoch 17/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.8633 - acc: 0.7630 - val_loss: 0.6666 - val_acc: 0.8233\n",
-      "Epoch 18/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.8575 - acc: 0.7643 - val_loss: 0.6169 - val_acc: 0.8243\n",
-      "Epoch 19/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.8306 - acc: 0.7709 - val_loss: 0.6180 - val_acc: 0.8285\n",
-      "Epoch 20/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.8410 - acc: 0.7733 - val_loss: 0.6410 - val_acc: 0.8217\n",
-      "Epoch 21/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.8234 - acc: 0.7738 - val_loss: 0.6827 - val_acc: 0.8170\n",
-      "Epoch 22/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.8191 - acc: 0.7768 - val_loss: 0.6031 - val_acc: 0.8374\n",
-      "Epoch 23/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.7964 - acc: 0.7798 - val_loss: 0.5974 - val_acc: 0.8337\n",
-      "Epoch 24/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.7923 - acc: 0.7820 - val_loss: 0.6062 - val_acc: 0.8327\n",
-      "Epoch 25/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.8048 - acc: 0.7842 - val_loss: 0.5988 - val_acc: 0.8347\n",
-      "Epoch 26/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.7899 - acc: 0.7871 - val_loss: 0.5919 - val_acc: 0.8415\n",
-      "Epoch 27/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.7810 - acc: 0.7915 - val_loss: 0.6094 - val_acc: 0.8382\n",
-      "Epoch 28/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.7917 - acc: 0.7895 - val_loss: 0.5819 - val_acc: 0.8405\n",
-      "Epoch 29/250\n",
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-      "Epoch 30/250\n",
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-      "Epoch 31/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.7697 - acc: 0.7936 - val_loss: 0.5832 - val_acc: 0.8498\n",
-      "Epoch 32/250\n",
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-      "Epoch 33/250\n"
-     ]
-    },
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-      "Epoch 34/250\n",
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-      "Epoch 36/250\n",
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-      "Epoch 37/250\n",
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-      "Epoch 39/250\n",
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-      "Epoch 40/250\n",
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-      "Epoch 42/250\n",
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-      "Epoch 44/250\n",
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-      "Epoch 45/250\n",
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-      "Epoch 46/250\n",
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-      "Epoch 47/250\n",
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-      "Epoch 48/250\n",
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-      "Epoch 49/250\n",
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-      "Epoch 50/250\n",
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-      "Epoch 52/250\n",
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-      "Epoch 53/250\n",
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-      "Epoch 54/250\n",
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-      "Epoch 55/250\n",
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-      "Epoch 56/250\n",
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-      "Epoch 92/250\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5776 - acc: 0.8518 - val_loss: 0.4753 - val_acc: 0.8864\n",
-      "Epoch 93/250\n",
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-      "Epoch 95/250\n",
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-      "Epoch 96/250\n",
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-      "Epoch 99/250\n",
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-      "Epoch 103/250\n",
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-      "Epoch 108/250\n",
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-      "Epoch 111/250\n",
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-      "Epoch 112/250\n",
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-      "Epoch 113/250\n",
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-      "Epoch 114/250\n",
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-      "Epoch 115/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5140 - acc: 0.8671 - val_loss: 0.4354 - val_acc: 0.8931\n",
-      "Epoch 116/250\n",
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-      "Epoch 117/250\n",
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-      "Epoch 118/250\n",
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-      "Epoch 119/250\n",
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-      "Epoch 120/250\n",
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-      "Epoch 121/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4933 - acc: 0.8713 - val_loss: 0.4266 - val_acc: 0.8937\n",
-      "Epoch 122/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5012 - acc: 0.8699 - val_loss: 0.4199 - val_acc: 0.8949\n",
-      "Epoch 123/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4957 - acc: 0.8701 - val_loss: 0.4270 - val_acc: 0.8952\n",
-      "Epoch 124/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5054 - acc: 0.8707 - val_loss: 0.4258 - val_acc: 0.8947\n",
-      "Epoch 125/250\n",
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-      "Epoch 126/250\n",
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-      "Epoch 127/250\n",
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-      "Epoch 128/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4743 - acc: 0.8754 - val_loss: 0.4138 - val_acc: 0.8989\n",
-      "Epoch 129/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4728 - acc: 0.8772 - val_loss: 0.4148 - val_acc: 0.8999\n",
-      "Epoch 130/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4729 - acc: 0.8771 - val_loss: 0.4100 - val_acc: 0.8996\n",
-      "Epoch 131/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4806 - acc: 0.8756 - val_loss: 0.4102 - val_acc: 0.9013\n",
-      "Epoch 132/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4702 - acc: 0.8756 - val_loss: 0.4066 - val_acc: 0.9009\n",
-      "Epoch 133/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4665 - acc: 0.8785 - val_loss: 0.4116 - val_acc: 0.9013\n",
-      "Epoch 134/250\n",
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-      "Epoch 135/250\n",
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-      "Epoch 136/250\n",
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-      "Epoch 137/250\n",
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-      "Epoch 138/250\n",
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-      "Epoch 139/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4730 - acc: 0.8768 - val_loss: 0.4038 - val_acc: 0.9009\n",
-      "Epoch 140/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4615 - acc: 0.8786 - val_loss: 0.4090 - val_acc: 0.8993\n",
-      "Epoch 141/250\n",
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-      "Epoch 142/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4594 - acc: 0.8804 - val_loss: 0.4079 - val_acc: 0.8994\n",
-      "Epoch 143/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4672 - acc: 0.8786 - val_loss: 0.4062 - val_acc: 0.9019\n",
-      "Epoch 144/250\n",
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-      "Epoch 145/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4558 - acc: 0.8806 - val_loss: 0.4125 - val_acc: 0.9000\n",
-      "Epoch 146/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4571 - acc: 0.8800 - val_loss: 0.4090 - val_acc: 0.8989\n",
-      "Epoch 147/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4587 - acc: 0.8794 - val_loss: 0.4107 - val_acc: 0.9012\n",
-      "Epoch 148/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4529 - acc: 0.8810 - val_loss: 0.4158 - val_acc: 0.9004\n",
-      "Epoch 149/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4613 - acc: 0.8802 - val_loss: 0.4089 - val_acc: 0.9009\n",
-      "Epoch 150/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4523 - acc: 0.8833 - val_loss: 0.4183 - val_acc: 0.9005\n",
-      "Epoch 151/250\n"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4558 - acc: 0.8821 - val_loss: 0.4013 - val_acc: 0.9032\n",
-      "Epoch 152/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4455 - acc: 0.8815 - val_loss: 0.4038 - val_acc: 0.9020\n",
-      "Epoch 153/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4510 - acc: 0.8824 - val_loss: 0.4017 - val_acc: 0.9032\n",
-      "Epoch 154/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4404 - acc: 0.8848 - val_loss: 0.4056 - val_acc: 0.9021\n",
-      "Epoch 155/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4498 - acc: 0.8828 - val_loss: 0.4026 - val_acc: 0.9015\n",
-      "Epoch 156/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4371 - acc: 0.8846 - val_loss: 0.4042 - val_acc: 0.9017\n",
-      "Epoch 157/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4392 - acc: 0.8833 - val_loss: 0.4030 - val_acc: 0.9032\n",
-      "Epoch 158/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4372 - acc: 0.8873 - val_loss: 0.3990 - val_acc: 0.9021\n",
-      "Epoch 159/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4281 - acc: 0.8863 - val_loss: 0.3996 - val_acc: 0.9034\n",
-      "Epoch 160/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4400 - acc: 0.8828 - val_loss: 0.4030 - val_acc: 0.9022\n",
-      "Epoch 161/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4468 - acc: 0.8848 - val_loss: 0.3962 - val_acc: 0.9044\n",
-      "Epoch 162/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4409 - acc: 0.8843 - val_loss: 0.3986 - val_acc: 0.9038\n",
-      "Epoch 163/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4314 - acc: 0.8875 - val_loss: 0.4032 - val_acc: 0.9022\n",
-      "Epoch 164/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4289 - acc: 0.8863 - val_loss: 0.4030 - val_acc: 0.9028\n",
-      "Epoch 165/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4336 - acc: 0.8859 - val_loss: 0.3985 - val_acc: 0.9046\n",
-      "Epoch 166/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4401 - acc: 0.8848 - val_loss: 0.3937 - val_acc: 0.9040\n",
-      "Epoch 167/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4365 - acc: 0.8860 - val_loss: 0.3976 - val_acc: 0.9039\n",
-      "Epoch 168/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4307 - acc: 0.8868 - val_loss: 0.3939 - val_acc: 0.9063\n",
-      "Epoch 169/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4343 - acc: 0.8838 - val_loss: 0.3913 - val_acc: 0.9066\n",
-      "Epoch 170/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4370 - acc: 0.8870 - val_loss: 0.4016 - val_acc: 0.9034\n",
-      "Epoch 171/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4343 - acc: 0.8866 - val_loss: 0.3982 - val_acc: 0.9031\n",
-      "Epoch 172/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4341 - acc: 0.8883 - val_loss: 0.3960 - val_acc: 0.9031\n",
-      "Epoch 173/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4298 - acc: 0.8879 - val_loss: 0.3942 - val_acc: 0.9046\n",
-      "Epoch 174/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4281 - acc: 0.8878 - val_loss: 0.3977 - val_acc: 0.9043\n",
-      "Epoch 175/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4319 - acc: 0.8869 - val_loss: 0.3896 - val_acc: 0.9036\n",
-      "Epoch 176/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4295 - acc: 0.8861 - val_loss: 0.3899 - val_acc: 0.9058\n",
-      "Epoch 177/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4249 - acc: 0.8886 - val_loss: 0.3915 - val_acc: 0.9049\n",
-      "Epoch 178/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4228 - acc: 0.8904 - val_loss: 0.3927 - val_acc: 0.9047\n",
-      "Epoch 179/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4314 - acc: 0.8859 - val_loss: 0.3882 - val_acc: 0.9059\n",
-      "Epoch 180/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4262 - acc: 0.8881 - val_loss: 0.3908 - val_acc: 0.9055\n",
-      "Epoch 181/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4155 - acc: 0.8924 - val_loss: 0.3944 - val_acc: 0.9046\n",
-      "Epoch 182/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4219 - acc: 0.8912 - val_loss: 0.3904 - val_acc: 0.9056\n",
-      "Epoch 183/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4236 - acc: 0.8887 - val_loss: 0.3917 - val_acc: 0.9049\n",
-      "Epoch 184/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4249 - acc: 0.8869 - val_loss: 0.3892 - val_acc: 0.9054\n",
-      "Epoch 185/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4277 - acc: 0.8900 - val_loss: 0.3896 - val_acc: 0.9061\n",
-      "Epoch 186/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4183 - acc: 0.8894 - val_loss: 0.3899 - val_acc: 0.9052\n",
-      "Epoch 187/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4275 - acc: 0.8881 - val_loss: 0.3913 - val_acc: 0.9051\n",
-      "Epoch 188/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4256 - acc: 0.8872 - val_loss: 0.3913 - val_acc: 0.9052\n",
-      "Epoch 189/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4203 - acc: 0.8910 - val_loss: 0.3971 - val_acc: 0.9049\n",
-      "Epoch 190/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4239 - acc: 0.8902 - val_loss: 0.3947 - val_acc: 0.9044\n",
-      "Epoch 191/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4304 - acc: 0.8884 - val_loss: 0.3913 - val_acc: 0.9062\n",
-      "Epoch 192/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4125 - acc: 0.8910 - val_loss: 0.3908 - val_acc: 0.9062\n",
-      "Epoch 193/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4260 - acc: 0.8880 - val_loss: 0.3904 - val_acc: 0.9035\n",
-      "Epoch 194/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4234 - acc: 0.8903 - val_loss: 0.3897 - val_acc: 0.9055\n",
-      "Epoch 195/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4208 - acc: 0.8907 - val_loss: 0.3863 - val_acc: 0.9071\n",
-      "Epoch 196/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.4245 - acc: 0.8871 - val_loss: 0.3886 - val_acc: 0.9054\n",
-      "Epoch 197/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4171 - acc: 0.8908 - val_loss: 0.3893 - val_acc: 0.9042\n",
-      "Epoch 198/250\n",
-      "390/390 [==============================] - 152s 389ms/step - loss: 0.4272 - acc: 0.8884 - val_loss: 0.3888 - val_acc: 0.9050\n",
-      "Epoch 199/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4164 - acc: 0.8895 - val_loss: 0.3919 - val_acc: 0.9048\n",
-      "Epoch 200/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4216 - acc: 0.8897 - val_loss: 0.3838 - val_acc: 0.9076\n",
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.4196 - acc: 0.8889 - val_loss: 0.3863 - val_acc: 0.9055\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.4119 - acc: 0.8917 - val_loss: 0.3888 - val_acc: 0.9069\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.4101 - acc: 0.8901 - val_loss: 0.3889 - val_acc: 0.9062\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4184 - acc: 0.8902 - val_loss: 0.3859 - val_acc: 0.9061\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.4185 - acc: 0.8907 - val_loss: 0.3884 - val_acc: 0.9062\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.4217 - acc: 0.8918 - val_loss: 0.3884 - val_acc: 0.9067\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4095 - acc: 0.8930 - val_loss: 0.3885 - val_acc: 0.9067\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.4205 - acc: 0.8919 - val_loss: 0.3890 - val_acc: 0.9060\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.4157 - acc: 0.8920 - val_loss: 0.3872 - val_acc: 0.9060\n",
-      "Epoch 210/250\n"
-     ]
-    },
-    {
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-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4151 - acc: 0.8919 - val_loss: 0.3867 - val_acc: 0.9061\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4108 - acc: 0.8911 - val_loss: 0.3858 - val_acc: 0.9067\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4157 - acc: 0.8901 - val_loss: 0.3851 - val_acc: 0.9062\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4156 - acc: 0.8897 - val_loss: 0.3867 - val_acc: 0.9059\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4125 - acc: 0.8933 - val_loss: 0.3861 - val_acc: 0.9056\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4115 - acc: 0.8920 - val_loss: 0.3853 - val_acc: 0.9065\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4139 - acc: 0.8915 - val_loss: 0.3849 - val_acc: 0.9062\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4056 - acc: 0.8913 - val_loss: 0.3864 - val_acc: 0.9068\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4063 - acc: 0.8922 - val_loss: 0.3835 - val_acc: 0.9058\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4164 - acc: 0.8909 - val_loss: 0.3859 - val_acc: 0.9067\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4095 - acc: 0.8928 - val_loss: 0.3866 - val_acc: 0.9063\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4103 - acc: 0.8922 - val_loss: 0.3841 - val_acc: 0.9071\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4123 - acc: 0.8927 - val_loss: 0.3858 - val_acc: 0.9056\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4169 - acc: 0.8909 - val_loss: 0.3826 - val_acc: 0.9066\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4040 - acc: 0.8927 - val_loss: 0.3835 - val_acc: 0.9059\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4157 - acc: 0.8910 - val_loss: 0.3838 - val_acc: 0.9061\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4099 - acc: 0.8905 - val_loss: 0.3833 - val_acc: 0.9069\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4170 - acc: 0.8902 - val_loss: 0.3850 - val_acc: 0.9073\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4120 - acc: 0.8931 - val_loss: 0.3851 - val_acc: 0.9061\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4103 - acc: 0.8920 - val_loss: 0.3846 - val_acc: 0.9076\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4180 - acc: 0.8912 - val_loss: 0.3862 - val_acc: 0.9060\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4036 - acc: 0.8934 - val_loss: 0.3844 - val_acc: 0.9063\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4111 - acc: 0.8932 - val_loss: 0.3829 - val_acc: 0.9067\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4167 - acc: 0.8905 - val_loss: 0.3864 - val_acc: 0.9072\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4123 - acc: 0.8926 - val_loss: 0.3833 - val_acc: 0.9065\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4074 - acc: 0.8920 - val_loss: 0.3856 - val_acc: 0.9066\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4161 - acc: 0.8911 - val_loss: 0.3874 - val_acc: 0.9062\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4083 - acc: 0.8933 - val_loss: 0.3882 - val_acc: 0.9066\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4122 - acc: 0.8926 - val_loss: 0.3864 - val_acc: 0.9056\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4134 - acc: 0.8913 - val_loss: 0.3865 - val_acc: 0.9072\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4080 - acc: 0.8921 - val_loss: 0.3838 - val_acc: 0.9061\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4032 - acc: 0.8934 - val_loss: 0.3836 - val_acc: 0.9077\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4087 - acc: 0.8922 - val_loss: 0.3833 - val_acc: 0.9073\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4023 - acc: 0.8933 - val_loss: 0.3835 - val_acc: 0.9076\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4170 - acc: 0.8902 - val_loss: 0.3845 - val_acc: 0.9077\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4165 - acc: 0.8920 - val_loss: 0.3871 - val_acc: 0.9067\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4125 - acc: 0.8923 - val_loss: 0.3866 - val_acc: 0.9072\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4044 - acc: 0.8944 - val_loss: 0.3838 - val_acc: 0.9070\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4118 - acc: 0.8921 - val_loss: 0.3868 - val_acc: 0.9072\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4136 - acc: 0.8919 - val_loss: 0.3869 - val_acc: 0.9066\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4125 - acc: 0.8923 - val_loss: 0.3830 - val_acc: 0.9080\n",
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-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = swish, \"swish\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('swish_at_2.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_7 (Conv2D)            (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_7 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_8 (Conv2D)            (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_8 (Batch (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_4 (MaxPooling2 (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_9 (Conv2D)            (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_9 (Activation)    (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_9 (Batch (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_10 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_10 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_10 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_5 (MaxPooling2 (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_11 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_11 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_11 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_12 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_12 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_12 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_6 (MaxPooling2 (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "Epoch 1/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 1.9464 - acc: 0.4039 - val_loss: 1.4471 - val_acc: 0.5117\n",
-      "Epoch 2/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 1.4337 - acc: 0.5465 - val_loss: 1.1592 - val_acc: 0.6386\n",
-      "Epoch 3/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 1.2999 - acc: 0.5991 - val_loss: 1.0106 - val_acc: 0.6816\n",
-      "Epoch 4/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 1.1703 - acc: 0.6432 - val_loss: 0.8829 - val_acc: 0.7202\n",
-      "Epoch 5/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 1.2003 - acc: 0.6546 - val_loss: 0.8272 - val_acc: 0.7360\n",
-      "Epoch 6/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 1.1354 - acc: 0.6746 - val_loss: 0.8357 - val_acc: 0.7412\n",
-      "Epoch 7/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 1.0873 - acc: 0.6887 - val_loss: 1.0684 - val_acc: 0.7244\n",
-      "Epoch 8/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 1.0980 - acc: 0.6948 - val_loss: 0.8153 - val_acc: 0.7709\n",
-      "Epoch 9/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 1.0361 - acc: 0.7049 - val_loss: 0.7569 - val_acc: 0.7854\n",
-      "Epoch 10/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 1.0472 - acc: 0.7118 - val_loss: 0.7660 - val_acc: 0.7759\n",
-      "Epoch 11/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 1.0057 - acc: 0.7235 - val_loss: 0.7519 - val_acc: 0.7772\n",
-      "Epoch 12/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 1.0136 - acc: 0.7210 - val_loss: 0.6889 - val_acc: 0.8023\n",
-      "Epoch 13/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.9482 - acc: 0.7350 - val_loss: 0.7510 - val_acc: 0.7869\n",
-      "Epoch 14/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9440 - acc: 0.7386 - val_loss: 0.7215 - val_acc: 0.8071\n",
-      "Epoch 15/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9716 - acc: 0.7392 - val_loss: 0.7619 - val_acc: 0.8019\n",
-      "Epoch 16/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9585 - acc: 0.7439 - val_loss: 0.7187 - val_acc: 0.7997\n",
-      "Epoch 17/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9323 - acc: 0.7487 - val_loss: 0.7043 - val_acc: 0.7924\n",
-      "Epoch 18/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.9302 - acc: 0.7521 - val_loss: 0.7805 - val_acc: 0.7990\n",
-      "Epoch 19/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9052 - acc: 0.7570 - val_loss: 0.6689 - val_acc: 0.8126\n",
-      "Epoch 20/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.9075 - acc: 0.7567 - val_loss: 0.6928 - val_acc: 0.8091\n",
-      "Epoch 21/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.9016 - acc: 0.7617 - val_loss: 0.6281 - val_acc: 0.8279\n",
-      "Epoch 22/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.8688 - acc: 0.7662 - val_loss: 0.6257 - val_acc: 0.8270\n",
-      "Epoch 23/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.8663 - acc: 0.7690 - val_loss: 0.6697 - val_acc: 0.8151\n",
-      "Epoch 24/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.8672 - acc: 0.7700 - val_loss: 0.6610 - val_acc: 0.8273\n",
-      "Epoch 25/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.8482 - acc: 0.7727 - val_loss: 0.6466 - val_acc: 0.8325\n",
-      "Epoch 26/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.8230 - acc: 0.7750 - val_loss: 0.6025 - val_acc: 0.8389\n",
-      "Epoch 27/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.8188 - acc: 0.7814 - val_loss: 0.6174 - val_acc: 0.8387\n",
-      "Epoch 28/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.8251 - acc: 0.7799 - val_loss: 0.6985 - val_acc: 0.8273\n",
-      "Epoch 29/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.8033 - acc: 0.7831 - val_loss: 0.6251 - val_acc: 0.8335\n",
-      "Epoch 30/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.8084 - acc: 0.7843 - val_loss: 0.5876 - val_acc: 0.8404\n",
-      "Epoch 31/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7992 - acc: 0.7869 - val_loss: 0.5997 - val_acc: 0.8415\n",
-      "Epoch 32/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.8021 - acc: 0.7874 - val_loss: 0.6602 - val_acc: 0.8239\n",
-      "Epoch 33/250\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7993 - acc: 0.7884 - val_loss: 0.5978 - val_acc: 0.8431\n",
-      "Epoch 34/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7899 - acc: 0.7920 - val_loss: 0.5976 - val_acc: 0.8474\n",
-      "Epoch 35/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7935 - acc: 0.7945 - val_loss: 0.6001 - val_acc: 0.8441\n",
-      "Epoch 36/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7906 - acc: 0.7925 - val_loss: 0.6192 - val_acc: 0.8378\n",
-      "Epoch 37/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.8034 - acc: 0.7949 - val_loss: 0.6145 - val_acc: 0.8352\n",
-      "Epoch 38/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7828 - acc: 0.7953 - val_loss: 0.6108 - val_acc: 0.8417\n",
-      "Epoch 39/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7778 - acc: 0.7965 - val_loss: 0.6288 - val_acc: 0.8310\n",
-      "Epoch 40/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7822 - acc: 0.7974 - val_loss: 0.5776 - val_acc: 0.8559\n",
-      "Epoch 41/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7711 - acc: 0.8010 - val_loss: 0.5688 - val_acc: 0.8552\n",
-      "Epoch 42/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7722 - acc: 0.7981 - val_loss: 0.5938 - val_acc: 0.8451\n",
-      "Epoch 43/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7633 - acc: 0.8023 - val_loss: 0.6009 - val_acc: 0.8437\n",
-      "Epoch 44/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7612 - acc: 0.8019 - val_loss: 0.6213 - val_acc: 0.8505\n",
-      "Epoch 45/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7631 - acc: 0.8023 - val_loss: 0.5930 - val_acc: 0.8543\n",
-      "Epoch 46/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7547 - acc: 0.8043 - val_loss: 0.5747 - val_acc: 0.8528\n",
-      "Epoch 47/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7512 - acc: 0.8050 - val_loss: 0.5841 - val_acc: 0.8511\n",
-      "Epoch 48/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.7598 - acc: 0.8057 - val_loss: 0.6557 - val_acc: 0.8234\n",
-      "Epoch 49/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.7417 - acc: 0.8073 - val_loss: 0.5825 - val_acc: 0.8526\n",
-      "Epoch 50/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.7461 - acc: 0.8085 - val_loss: 0.5713 - val_acc: 0.8596\n",
-      "Epoch 51/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.7468 - acc: 0.8090 - val_loss: 0.5633 - val_acc: 0.8588\n",
-      "Epoch 52/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.7465 - acc: 0.8100 - val_loss: 0.5579 - val_acc: 0.8593\n",
-      "Epoch 53/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.7274 - acc: 0.8114 - val_loss: 0.6038 - val_acc: 0.8449\n",
-      "Epoch 54/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7677 - acc: 0.8063 - val_loss: 0.6142 - val_acc: 0.8436\n",
-      "Epoch 55/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7397 - acc: 0.8116 - val_loss: 0.5617 - val_acc: 0.8570\n",
-      "Epoch 56/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7410 - acc: 0.8100 - val_loss: 0.5782 - val_acc: 0.8564\n",
-      "Epoch 57/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7385 - acc: 0.8117 - val_loss: 0.5911 - val_acc: 0.8505\n",
-      "Epoch 58/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7209 - acc: 0.8128 - val_loss: 0.5822 - val_acc: 0.8581\n",
-      "Epoch 59/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7334 - acc: 0.8138 - val_loss: 0.5526 - val_acc: 0.8603\n",
-      "Epoch 60/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7242 - acc: 0.8142 - val_loss: 0.5849 - val_acc: 0.8519\n",
-      "Epoch 61/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7310 - acc: 0.8127 - val_loss: 0.5771 - val_acc: 0.8517\n",
-      "Epoch 62/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7271 - acc: 0.8140 - val_loss: 0.5835 - val_acc: 0.8573\n",
-      "Epoch 63/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.7344 - acc: 0.8147 - val_loss: 0.5623 - val_acc: 0.8591\n",
-      "Epoch 64/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.7208 - acc: 0.8131 - val_loss: 0.6005 - val_acc: 0.8474\n",
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-      "Epoch 80/250\n",
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-      "Epoch 83/250\n",
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-      "Epoch 84/250\n",
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-      "Epoch 85/250\n",
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-      "Epoch 87/250\n",
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-      "Epoch 88/250\n",
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-      "Epoch 89/250\n",
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-      "Epoch 90/250\n",
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-      "Epoch 91/250\n",
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-      "Epoch 92/250\n"
-     ]
-    },
-    {
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-     "text": [
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5906 - acc: 0.8489 - val_loss: 0.4795 - val_acc: 0.8803\n",
-      "Epoch 93/250\n",
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-      "Epoch 94/250\n",
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-      "Epoch 95/250\n",
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-      "Epoch 96/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5817 - acc: 0.8498 - val_loss: 0.4829 - val_acc: 0.8783\n",
-      "Epoch 97/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.5853 - acc: 0.8501 - val_loss: 0.4820 - val_acc: 0.8802\n",
-      "Epoch 98/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5754 - acc: 0.8505 - val_loss: 0.4649 - val_acc: 0.8855\n",
-      "Epoch 99/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.5756 - acc: 0.8507 - val_loss: 0.4841 - val_acc: 0.8822\n",
-      "Epoch 100/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5713 - acc: 0.8512 - val_loss: 0.4757 - val_acc: 0.8854\n",
-      "Epoch 101/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.5601 - acc: 0.8549 - val_loss: 0.4440 - val_acc: 0.8912\n",
-      "Epoch 102/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5436 - acc: 0.8610 - val_loss: 0.4473 - val_acc: 0.8918\n",
-      "Epoch 103/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.5513 - acc: 0.8566 - val_loss: 0.4710 - val_acc: 0.8861\n",
-      "Epoch 104/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5385 - acc: 0.8635 - val_loss: 0.4469 - val_acc: 0.8905\n",
-      "Epoch 105/250\n",
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-      "Epoch 106/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5331 - acc: 0.8618 - val_loss: 0.4478 - val_acc: 0.8921\n",
-      "Epoch 107/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5450 - acc: 0.8615 - val_loss: 0.4447 - val_acc: 0.8909\n",
-      "Epoch 108/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5324 - acc: 0.8619 - val_loss: 0.4516 - val_acc: 0.8861\n",
-      "Epoch 109/250\n",
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-      "Epoch 110/250\n",
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-      "Epoch 111/250\n",
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-      "Epoch 112/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.5271 - acc: 0.8634 - val_loss: 0.4486 - val_acc: 0.8881\n",
-      "Epoch 113/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.5326 - acc: 0.8624 - val_loss: 0.4426 - val_acc: 0.8921\n",
-      "Epoch 114/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.5271 - acc: 0.8639 - val_loss: 0.4402 - val_acc: 0.8913\n",
-      "Epoch 115/250\n",
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-      "Epoch 116/250\n",
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-      "Epoch 117/250\n",
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-      "Epoch 118/250\n",
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-      "Epoch 119/250\n",
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-      "Epoch 121/250\n",
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-      "Epoch 122/250\n",
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-      "Epoch 123/250\n",
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-      "Epoch 124/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.5093 - acc: 0.8678 - val_loss: 0.4241 - val_acc: 0.8952\n",
-      "Epoch 125/250\n",
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-      "Epoch 126/250\n",
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-      "Epoch 127/250\n",
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-      "Epoch 128/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4929 - acc: 0.8717 - val_loss: 0.4085 - val_acc: 0.9005\n",
-      "Epoch 129/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4832 - acc: 0.8735 - val_loss: 0.4064 - val_acc: 0.8992\n",
-      "Epoch 130/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4796 - acc: 0.8728 - val_loss: 0.4111 - val_acc: 0.8991\n",
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-      "Epoch 135/250\n",
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-      "Epoch 136/250\n",
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-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4680 - acc: 0.8774 - val_loss: 0.4085 - val_acc: 0.8989\n",
-      "Epoch 139/250\n",
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-      "Epoch 140/250\n",
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-      "Epoch 141/250\n",
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-      "Epoch 144/250\n",
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-      "Epoch 147/250\n",
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-      "Epoch 148/250\n",
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-      "Epoch 149/250\n",
-      "390/390 [==============================] - 150s 386ms/step - loss: 0.4552 - acc: 0.8775 - val_loss: 0.4030 - val_acc: 0.9004\n",
-      "Epoch 150/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4580 - acc: 0.8793 - val_loss: 0.4005 - val_acc: 0.9003\n",
-      "Epoch 151/250\n"
-     ]
-    },
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-     "text": [
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.4607 - acc: 0.8783 - val_loss: 0.4059 - val_acc: 0.8984\n",
-      "Epoch 152/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4492 - acc: 0.8814 - val_loss: 0.3894 - val_acc: 0.9032\n",
-      "Epoch 153/250\n",
-      "390/390 [==============================] - 152s 389ms/step - loss: 0.4503 - acc: 0.8802 - val_loss: 0.3909 - val_acc: 0.9024\n",
-      "Epoch 154/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.4454 - acc: 0.8822 - val_loss: 0.3923 - val_acc: 0.9018\n",
-      "Epoch 155/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.4506 - acc: 0.8808 - val_loss: 0.3891 - val_acc: 0.9003\n",
-      "Epoch 156/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.4438 - acc: 0.8824 - val_loss: 0.3917 - val_acc: 0.9016\n",
-      "Epoch 157/250\n",
-      "390/390 [==============================] - 151s 386ms/step - loss: 0.4510 - acc: 0.8817 - val_loss: 0.3913 - val_acc: 0.9025\n",
-      "Epoch 158/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4477 - acc: 0.8821 - val_loss: 0.3866 - val_acc: 0.9041\n",
-      "Epoch 159/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.4458 - acc: 0.8828 - val_loss: 0.3881 - val_acc: 0.9037\n",
-      "Epoch 160/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.4471 - acc: 0.8826 - val_loss: 0.3872 - val_acc: 0.9028\n",
-      "Epoch 161/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.4432 - acc: 0.8817 - val_loss: 0.3897 - val_acc: 0.9027\n",
-      "Epoch 162/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.4501 - acc: 0.8809 - val_loss: 0.3891 - val_acc: 0.9017\n",
-      "Epoch 163/250\n",
-      "390/390 [==============================] - 163s 418ms/step - loss: 0.4477 - acc: 0.8837 - val_loss: 0.3925 - val_acc: 0.9000\n",
-      "Epoch 164/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4352 - acc: 0.8840 - val_loss: 0.3939 - val_acc: 0.9003\n",
-      "Epoch 165/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4457 - acc: 0.8846 - val_loss: 0.3868 - val_acc: 0.9054\n",
-      "Epoch 166/250\n",
-      "390/390 [==============================] - 157s 403ms/step - loss: 0.4419 - acc: 0.8840 - val_loss: 0.3854 - val_acc: 0.9026\n",
-      "Epoch 167/250\n",
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-      "Epoch 168/250\n",
-      "390/390 [==============================] - 152s 389ms/step - loss: 0.4362 - acc: 0.8854 - val_loss: 0.3852 - val_acc: 0.9029\n",
-      "Epoch 169/250\n",
-      "390/390 [==============================] - 153s 391ms/step - loss: 0.4415 - acc: 0.8821 - val_loss: 0.3849 - val_acc: 0.9041\n",
-      "Epoch 170/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4369 - acc: 0.8830 - val_loss: 0.3851 - val_acc: 0.9034\n",
-      "Epoch 171/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4394 - acc: 0.8829 - val_loss: 0.3862 - val_acc: 0.9031\n",
-      "Epoch 172/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4356 - acc: 0.8847 - val_loss: 0.3832 - val_acc: 0.9039\n",
-      "Epoch 173/250\n",
-      "390/390 [==============================] - 153s 391ms/step - loss: 0.4382 - acc: 0.8844 - val_loss: 0.3853 - val_acc: 0.9028\n",
-      "Epoch 174/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4344 - acc: 0.8861 - val_loss: 0.3901 - val_acc: 0.9039\n",
-      "Epoch 175/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4374 - acc: 0.8860 - val_loss: 0.3843 - val_acc: 0.9034\n",
-      "Epoch 176/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4265 - acc: 0.8886 - val_loss: 0.3860 - val_acc: 0.9032\n",
-      "Epoch 177/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4369 - acc: 0.8857 - val_loss: 0.3814 - val_acc: 0.9030\n",
-      "Epoch 178/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4316 - acc: 0.8864 - val_loss: 0.3828 - val_acc: 0.9027\n",
-      "Epoch 179/250\n",
-      "390/390 [==============================] - 153s 393ms/step - loss: 0.4217 - acc: 0.8892 - val_loss: 0.3823 - val_acc: 0.9026\n",
-      "Epoch 180/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4346 - acc: 0.8844 - val_loss: 0.3807 - val_acc: 0.9029\n",
-      "Epoch 181/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4319 - acc: 0.8852 - val_loss: 0.3838 - val_acc: 0.9027\n",
-      "Epoch 182/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4275 - acc: 0.8876 - val_loss: 0.3810 - val_acc: 0.9041\n",
-      "Epoch 183/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4293 - acc: 0.8875 - val_loss: 0.3820 - val_acc: 0.9043\n",
-      "Epoch 184/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.4264 - acc: 0.8870 - val_loss: 0.3802 - val_acc: 0.9032\n",
-      "Epoch 185/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.4263 - acc: 0.8865 - val_loss: 0.3815 - val_acc: 0.9031\n",
-      "Epoch 186/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.4337 - acc: 0.8851 - val_loss: 0.3805 - val_acc: 0.9024\n",
-      "Epoch 187/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.4205 - acc: 0.8881 - val_loss: 0.3809 - val_acc: 0.9030\n",
-      "Epoch 188/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4230 - acc: 0.8878 - val_loss: 0.3808 - val_acc: 0.9033\n",
-      "Epoch 189/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4274 - acc: 0.8885 - val_loss: 0.3825 - val_acc: 0.9036\n",
-      "Epoch 190/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4264 - acc: 0.8882 - val_loss: 0.3806 - val_acc: 0.9029\n",
-      "Epoch 191/250\n",
-      "390/390 [==============================] - 150s 385ms/step - loss: 0.4297 - acc: 0.8857 - val_loss: 0.3772 - val_acc: 0.9037\n",
-      "Epoch 192/250\n",
-      "390/390 [==============================] - 153s 393ms/step - loss: 0.4217 - acc: 0.8888 - val_loss: 0.3802 - val_acc: 0.9037\n",
-      "Epoch 193/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4214 - acc: 0.8875 - val_loss: 0.3770 - val_acc: 0.9033\n",
-      "Epoch 194/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 0.4186 - acc: 0.8887 - val_loss: 0.3815 - val_acc: 0.9022\n",
-      "Epoch 195/250\n",
-      "101/390 [======>.......................] - ETA: 1:49 - loss: 0.4259 - acc: 0.8858"
-     ]
-    },
-    {
-     "ename": "StopIteration",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\keras\\utils\\data_utils.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    550\u001b[0m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 551\u001b[1;33m                 \u001b[0minputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    552\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtask_done\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    607\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 608\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mworker\u001b[1;34m(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)\u001b[0m\n\u001b[0;32m    118\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    120\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\keras\\utils\\data_utils.py\u001b[0m in \u001b[0;36mget_index\u001b[1;34m(uid, i)\u001b[0m\n\u001b[0;32m    390\u001b[0m     \u001b[1;32mglobal\u001b[0m \u001b[0m_SHARED_SEQUENCES\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 391\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_SHARED_SEQUENCES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0muid\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    392\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\keras\\preprocessing\\image.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, idx)\u001b[0m\n\u001b[0;32m    760\u001b[0m                                        self.batch_size * (idx + 1)]\n\u001b[1;32m--> 761\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_batches_of_transformed_samples\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    762\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\keras\\preprocessing\\image.py\u001b[0m in \u001b[0;36m_get_batches_of_transformed_samples\u001b[1;34m(self, index_array)\u001b[0m\n\u001b[0;32m    870\u001b[0m         batch_x = np.zeros(tuple([len(index_array)] + list(self.x.shape)[1:]),\n\u001b[1;32m--> 871\u001b[1;33m                            dtype=K.floatx())\n\u001b[0m\u001b[0;32m    872\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mMemoryError\u001b[0m: ",
-      "\nThe above exception was the direct cause of the following exception:\n",
-      "\u001b[1;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-7-6435fc5d5a97>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;31m# Fit the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n\u001b[1;32m---> 15\u001b[1;33m                     epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;31m# Save the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'swish_at_3.h5'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\keras\\legacy\\interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     85\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[0;32m     86\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[1;32m---> 87\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     88\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\keras\\models.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m   1225\u001b[0m                                         \u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1226\u001b[0m                                         \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1227\u001b[1;33m                                         initial_epoch=initial_epoch)\n\u001b[0m\u001b[0;32m   1228\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1229\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\keras\\legacy\\interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     85\u001b[0m                 warnings.warn('Update your `' + object_name +\n\u001b[0;32m     86\u001b[0m                               '` call to the Keras 2 API: ' + signature, stacklevel=2)\n\u001b[1;32m---> 87\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     88\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     89\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m   2113\u001b[0m                 \u001b[0mbatch_index\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2114\u001b[0m                 \u001b[1;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2115\u001b[1;33m                     \u001b[0mgenerator_output\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_generator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2116\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2117\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'__len__'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\keras\\utils\\data_utils.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    555\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    556\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 557\u001b[1;33m             \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    558\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    559\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_send_sequence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mc:\\users\\eric\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\six.py\u001b[0m in \u001b[0;36mraise_from\u001b[1;34m(value, from_value)\u001b[0m\n",
-      "\u001b[1;31mStopIteration\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = swish, \"swish\"\n",
-    "model = create(act)\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.001,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1])\n",
-    "# Save the model\n",
-    "model.save('swish_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 195/250\n",
-      "390/390 [==============================] - 155s 396ms/step - loss: 0.4274 - acc: 0.8867 - val_loss: 0.3809 - val_acc: 0.9027\n",
-      "Epoch 196/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4235 - acc: 0.8865 - val_loss: 0.3774 - val_acc: 0.9031\n",
-      "Epoch 197/250\n",
-      "390/390 [==============================] - 153s 391ms/step - loss: 0.4232 - acc: 0.8900 - val_loss: 0.3790 - val_acc: 0.9041\n",
-      "Epoch 198/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4214 - acc: 0.8881 - val_loss: 0.3795 - val_acc: 0.9042\n",
-      "Epoch 199/250\n",
-      "390/390 [==============================] - 154s 396ms/step - loss: 0.4217 - acc: 0.8890 - val_loss: 0.3781 - val_acc: 0.9037\n",
-      "Epoch 200/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.4260 - acc: 0.8870 - val_loss: 0.3800 - val_acc: 0.9041\n",
-      "Epoch 201/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4196 - acc: 0.8889 - val_loss: 0.3769 - val_acc: 0.9046\n",
-      "Epoch 202/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4254 - acc: 0.8864 - val_loss: 0.3769 - val_acc: 0.9042\n",
-      "Epoch 203/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4228 - acc: 0.8877 - val_loss: 0.3789 - val_acc: 0.9046\n",
-      "Epoch 204/250\n",
-      "390/390 [==============================] - 156s 401ms/step - loss: 0.4245 - acc: 0.8876 - val_loss: 0.3789 - val_acc: 0.9039\n",
-      "Epoch 205/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4176 - acc: 0.8877 - val_loss: 0.3784 - val_acc: 0.9034\n",
-      "Epoch 206/250\n",
-      "390/390 [==============================] - 153s 393ms/step - loss: 0.4190 - acc: 0.8886 - val_loss: 0.3772 - val_acc: 0.9044\n",
-      "Epoch 207/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 0.4167 - acc: 0.8885 - val_loss: 0.3787 - val_acc: 0.9044\n",
-      "Epoch 208/250\n",
-      "390/390 [==============================] - 153s 393ms/step - loss: 0.4230 - acc: 0.8861 - val_loss: 0.3787 - val_acc: 0.9047\n",
-      "Epoch 209/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4198 - acc: 0.8888 - val_loss: 0.3768 - val_acc: 0.9032\n",
-      "Epoch 210/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4179 - acc: 0.8890 - val_loss: 0.3802 - val_acc: 0.9048\n",
-      "Epoch 211/250\n",
-      "390/390 [==============================] - 180s 462ms/step - loss: 0.4177 - acc: 0.8913 - val_loss: 0.3791 - val_acc: 0.9044\n",
-      "Epoch 212/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4149 - acc: 0.8887 - val_loss: 0.3782 - val_acc: 0.9053\n",
-      "Epoch 213/250\n",
-      "390/390 [==============================] - 153s 391ms/step - loss: 0.4192 - acc: 0.8881 - val_loss: 0.3769 - val_acc: 0.9053\n",
-      "Epoch 214/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4174 - acc: 0.8885 - val_loss: 0.3773 - val_acc: 0.9044\n",
-      "Epoch 215/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4217 - acc: 0.8880 - val_loss: 0.3770 - val_acc: 0.9035\n",
-      "Epoch 216/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4172 - acc: 0.8909 - val_loss: 0.3761 - val_acc: 0.9050\n",
-      "Epoch 217/250\n",
-      "390/390 [==============================] - 153s 394ms/step - loss: 0.4163 - acc: 0.8887 - val_loss: 0.3762 - val_acc: 0.9047\n",
-      "Epoch 218/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4229 - acc: 0.8880 - val_loss: 0.3808 - val_acc: 0.9040\n",
-      "Epoch 219/250\n",
-      "390/390 [==============================] - 153s 394ms/step - loss: 0.4227 - acc: 0.8872 - val_loss: 0.3775 - val_acc: 0.9039\n",
-      "Epoch 220/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4204 - acc: 0.8892 - val_loss: 0.3767 - val_acc: 0.9031\n",
-      "Epoch 221/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4183 - acc: 0.8887 - val_loss: 0.3776 - val_acc: 0.9033\n",
-      "Epoch 222/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4151 - acc: 0.8895 - val_loss: 0.3769 - val_acc: 0.9029\n",
-      "Epoch 223/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4096 - acc: 0.8894 - val_loss: 0.3775 - val_acc: 0.9038\n",
-      "Epoch 224/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4236 - acc: 0.8862 - val_loss: 0.3784 - val_acc: 0.9032\n",
-      "Epoch 225/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4150 - acc: 0.8903 - val_loss: 0.3776 - val_acc: 0.9037\n",
-      "Epoch 226/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4156 - acc: 0.8905 - val_loss: 0.3743 - val_acc: 0.9048\n",
-      "Epoch 227/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4156 - acc: 0.8879 - val_loss: 0.3746 - val_acc: 0.9051\n",
-      "Epoch 228/250\n",
-      "390/390 [==============================] - 153s 393ms/step - loss: 0.4133 - acc: 0.8908 - val_loss: 0.3765 - val_acc: 0.9049\n",
-      "Epoch 229/250\n",
-      "390/390 [==============================] - 152s 390ms/step - loss: 0.4146 - acc: 0.8908 - val_loss: 0.3769 - val_acc: 0.9040\n",
-      "Epoch 230/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4145 - acc: 0.8910 - val_loss: 0.3768 - val_acc: 0.9041\n",
-      "Epoch 231/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4158 - acc: 0.8897 - val_loss: 0.3776 - val_acc: 0.9046\n",
-      "Epoch 232/250\n",
-      "390/390 [==============================] - 153s 392ms/step - loss: 0.4158 - acc: 0.8882 - val_loss: 0.3761 - val_acc: 0.9033\n",
-      "Epoch 233/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4128 - acc: 0.8890 - val_loss: 0.3767 - val_acc: 0.9052\n",
-      "Epoch 234/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.4170 - acc: 0.8887 - val_loss: 0.3762 - val_acc: 0.9046\n",
-      "Epoch 235/250\n",
-      "390/390 [==============================] - 151s 388ms/step - loss: 0.4162 - acc: 0.8914 - val_loss: 0.3770 - val_acc: 0.9042\n",
-      "Epoch 236/250\n",
-      "390/390 [==============================] - 151s 387ms/step - loss: 0.4121 - acc: 0.8912 - val_loss: 0.3768 - val_acc: 0.9045\n",
-      "Epoch 237/250\n",
-      "390/390 [==============================] - 153s 393ms/step - loss: 0.4196 - acc: 0.8874 - val_loss: 0.3745 - val_acc: 0.9046\n",
-      "Epoch 238/250\n",
-      "390/390 [==============================] - 153s 393ms/step - loss: 0.4158 - acc: 0.8895 - val_loss: 0.3744 - val_acc: 0.9051\n",
-      "Epoch 239/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4136 - acc: 0.8900 - val_loss: 0.3757 - val_acc: 0.9050\n",
-      "Epoch 240/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4102 - acc: 0.8907 - val_loss: 0.3749 - val_acc: 0.9052\n",
-      "Epoch 241/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4164 - acc: 0.8908 - val_loss: 0.3775 - val_acc: 0.9049\n",
-      "Epoch 242/250\n",
-      "390/390 [==============================] - 153s 393ms/step - loss: 0.4113 - acc: 0.8910 - val_loss: 0.3759 - val_acc: 0.9040\n",
-      "Epoch 243/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4134 - acc: 0.8884 - val_loss: 0.3764 - val_acc: 0.9042\n",
-      "Epoch 244/250\n",
-      "390/390 [==============================] - 153s 391ms/step - loss: 0.4140 - acc: 0.8896 - val_loss: 0.3752 - val_acc: 0.9045\n",
-      "Epoch 245/250\n",
-      "390/390 [==============================] - 153s 393ms/step - loss: 0.4085 - acc: 0.8919 - val_loss: 0.3754 - val_acc: 0.9046\n",
-      "Epoch 246/250\n",
-      "390/390 [==============================] - 155s 397ms/step - loss: 0.4109 - acc: 0.8895 - val_loss: 0.3764 - val_acc: 0.9035\n",
-      "Epoch 247/250\n",
-      "390/390 [==============================] - 152s 391ms/step - loss: 0.4145 - acc: 0.8910 - val_loss: 0.3773 - val_acc: 0.9046\n",
-      "Epoch 248/250\n",
-      "390/390 [==============================] - 154s 395ms/step - loss: 0.4094 - acc: 0.8910 - val_loss: 0.3773 - val_acc: 0.9052\n",
-      "Epoch 249/250\n",
-      "390/390 [==============================] - 155s 396ms/step - loss: 0.4137 - acc: 0.8919 - val_loss: 0.3756 - val_acc: 0.9052\n",
-      "Epoch 250/250\n",
-      "390/390 [==============================] - 154s 394ms/step - loss: 0.4158 - acc: 0.8898 - val_loss: 0.3764 - val_acc: 0.9043\n",
-      "{'loss': [0.42736591107576827, 0.42361747660161136, 0.42311377273104611, 0.42147118119922705, 0.42160200728488956, 0.42600957449330784, 0.41950118040709605, 0.42541198789557089, 0.42289348014050432, 0.42439042618345824, 0.41743921531788003, 0.41912914580705268, 0.41670897996001111, 0.4229592672525308, 0.41957258734093617, 0.41791497504133651, 0.41768184573863765, 0.41487685794397389, 0.41893954698879593, 0.41731131751834866, 0.42182604434553689, 0.41729834920556108, 0.41634731345120096, 0.42287347672036874, 0.42270558933856817, 0.42027598470306027, 0.41838503157362023, 0.41512466962803318, 0.40978968724307241, 0.42374543432130773, 0.41501511246644807, 0.41573981847524416, 0.41557714002691554, 0.41327204276353885, 0.41474434646299885, 0.41451695096733709, 0.41589793780884915, 0.41588689298403353, 0.41275330283223416, 0.41703742060823473, 0.41626410563480865, 0.41218234296980139, 0.41952734816560083, 0.4157894280858529, 0.41354787993171027, 0.40997884972361931, 0.41638916751915422, 0.41125042024697483, 0.41346816515402418, 0.4140684687731776, 0.40846215314590012, 0.41093260214272931, 0.41440584079416881, 0.40943719666089085, 0.41378215953521252, 0.41565658036890724], 'val_loss': [0.38088713307380678, 0.37742783207893371, 0.37902091193199156, 0.3794846113204956, 0.37810357868671418, 0.38000050787925721, 0.37688323235511778, 0.37691866998672485, 0.37894276747703554, 0.37893698806762693, 0.378410378074646, 0.37720713467597961, 0.37869062676429749, 0.37869433007240294, 0.37681407442092896, 0.38017564320564268, 0.37905069117546081, 0.37821787242889404, 0.37690585675239563, 0.37730798220634459, 0.37704683861732485, 0.37614070692062379, 0.3762400686264038, 0.38075507221221921, 0.37752712478637696, 0.37666785120964053, 0.37764133720397947, 0.37692554392814637, 0.37747814779281619, 0.37837951045036317, 0.37764669251441957, 0.37425794653892519, 0.37462101230621336, 0.37646334600448611, 0.37690650424957273, 0.37678988690376281, 0.37755104074478152, 0.37612463812828062, 0.3766683038711548, 0.37622128429412843, 0.37702558937072755, 0.37682935872077944, 0.37450999126434326, 0.3744369225025177, 0.37574220390319824, 0.37493446455001833, 0.37754197945594786, 0.37594537429809571, 0.37638072562217711, 0.37520519165992738, 0.37540770578384397, 0.37641409044265745, 0.37725325574874879, 0.37729198694229127, 0.37560435285568239, 0.37644505438804626], 'acc': [0.88666987487969207, 0.88648941285877148, 0.89005854984946764, 0.88805341670862714, 0.88897577801078109, 0.88693054220712375, 0.88891562403567836, 0.88638915620172942, 0.88771254407468425, 0.88767244148835722, 0.88773259548258243, 0.888554700070327, 0.88855469999383729, 0.88607772435897436, 0.88884874761066301, 0.88893567534796425, 0.89128168112274775, 0.88865495667000172, 0.88817372475444489, 0.88851459735014293, 0.88799326271440193, 0.89082050048123196, 0.88865495665087924, 0.88801331408405515, 0.88715110685261622, 0.88923644526172307, 0.88863490539596068, 0.88951716393968561, 0.88933670197613235, 0.88616859161360428, 0.89027911457144993, 0.89041947385306386, 0.88787295474507388, 0.89078525641025641, 0.89071531793821601, 0.89098091117074407, 0.88971767729201456, 0.8881536734421589, 0.88907603468694552, 0.88871511070247178, 0.89136188646750381, 0.89120147581623654, 0.88735162010933288, 0.88950320512820513, 0.88999839593173224, 0.89075545921644184, 0.8907803978375376, 0.89098091111337674, 0.888354186756243, 0.88953721530933894, 0.89194711538461535, 0.88945700992633792, 0.89101637764932562, 0.89098091109425437, 0.89188322108412232, 0.88977783128623977], 'val_acc': [0.90269999999999995, 0.90310000000000001, 0.90410000000000001, 0.9042, 0.90370000028610231, 0.90410000000000001, 0.90459999999999996, 0.9042, 0.90459999999999996, 0.90390000000000004, 0.90339999999999998, 0.90439999999999998, 0.90439999999999998, 0.90469999999999995, 0.9032, 0.90480000000000005, 0.90439999999999998, 0.90529999999999999, 0.90529999999999999, 0.90439999999999998, 0.90349999999999997, 0.90500000000000003, 0.90469999999999995, 0.90400000000000003, 0.90390000000000004, 0.90310000000000001, 0.90329999999999999, 0.90290000000000004, 0.90380000000000005, 0.9032, 0.90369999999999995, 0.90480000000000005, 0.90510000000000002, 0.90490000000000004, 0.90400000000000003, 0.90410000000000001, 0.90459999999999996, 0.90329999999999999, 0.9052, 0.90459999999999996, 0.9042, 0.90449999999999997, 0.90459999999999996, 0.90510000000000002, 0.90500000000000003, 0.9052, 0.90490000000000004, 0.90400000000000003, 0.9042, 0.90449999999999997, 0.90459999999999996, 0.90349999999999997, 0.90459999999999996, 0.9052, 0.9052, 0.90429999999999999]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# model.save(\"swish_at_3.h5\")\n",
-    "# Create model and get ready - reproducible cell\n",
-    "act, act_name = swish, \"swish\"\n",
-    "model = load_model(\"swish_at_3.h5\", custom_objects={\"swish\": swish})\n",
-    "opt_rms = keras.optimizers.rmsprop(lr=0.000035,decay=1e-6)\n",
-    "model.compile(loss='categorical_crossentropy', optimizer=opt_rms, metrics=['accuracy'])\n",
-    "\n",
-    "# Training - only for 125 eppochs\n",
-    "batch_size = 128\n",
-    "epochs=250\n",
-    "# lr scheduler\n",
-    "lr_1 = keras.callbacks.LearningRateScheduler(schedule)\n",
-    "\n",
-    "# Fit the model\n",
-    "his = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),steps_per_epoch=x_train.shape[0] // batch_size,\n",
-    "                    epochs=epochs,verbose=1,validation_data=(x_test,y_test), callbacks=[lr_1], initial_epoch=194)\n",
-    "# Save the model\n",
-    "model.save('swish_at_3.h5')\n",
-    "# Display logs\n",
-    "print(his.history)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_25 (Conv2D)           (None, 32, 32, 32)        896       \n",
-      "_________________________________________________________________\n",
-      "activation_25 (Activation)   (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_25 (Batc (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "conv2d_26 (Conv2D)           (None, 32, 32, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_26 (Activation)   (None, 32, 32, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_26 (Batc (None, 32, 32, 32)        128       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_13 (MaxPooling (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_13 (Dropout)         (None, 16, 16, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_27 (Conv2D)           (None, 16, 16, 64)        18496     \n",
-      "_________________________________________________________________\n",
-      "activation_27 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_27 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "conv2d_28 (Conv2D)           (None, 16, 16, 64)        36928     \n",
-      "_________________________________________________________________\n",
-      "activation_28 (Activation)   (None, 16, 16, 64)        0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_28 (Batc (None, 16, 16, 64)        256       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_14 (MaxPooling (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "dropout_14 (Dropout)         (None, 8, 8, 64)          0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_29 (Conv2D)           (None, 8, 8, 128)         73856     \n",
-      "_________________________________________________________________\n",
-      "activation_29 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_29 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "conv2d_30 (Conv2D)           (None, 8, 8, 128)         147584    \n",
-      "_________________________________________________________________\n",
-      "activation_30 (Activation)   (None, 8, 8, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "batch_normalization_30 (Batc (None, 8, 8, 128)         512       \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_15 (MaxPooling (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "dropout_15 (Dropout)         (None, 4, 4, 128)         0         \n",
-      "_________________________________________________________________\n",
-      "flatten_5 (Flatten)          (None, 2048)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_5 (Dense)              (None, 10)                20490     \n",
-      "=================================================================\n",
-      "Total params: 309,290\n",
-      "Trainable params: 308,394\n",
-      "Non-trainable params: 896\n",
-      "_________________________________________________________________\n",
-      "0.9173\n"
-     ]
-    }
-   ],
-   "source": [
-    "import keras.regularizers as regularizers\n",
-    "ensembler = 0\n",
-    "for i in range(3):\n",
-    "    if i == 0:\n",
-    "        model = create(swish)\n",
-    "        model.load_weights('swish_at_'+str(i+1)+'.h5')\n",
-    "        model.save('swish_at_'+str(i+1)+'.h5')\n",
-    "    else:\n",
-    "        model = load_model('swish_at_'+str(i+1)+'.h5', custom_objects={\"swish\": swish})\n",
-    "    ensembler += model.predict_proba(x_test)\n",
-    "    \n",
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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diff --git a/cifar/Simple_CNN/cifar10_simple_cnn_3_log.ipynb b/cifar/Simple_CNN/cifar10_simple_cnn_3_log.ipynb
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--- a/cifar/Simple_CNN/cifar10_simple_cnn_3_log.ipynb
+++ /dev/null
@@ -1,802 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" First attempt to build a ConvNet in Keras\n",
-    "\tfor CV (Computer Vision), more specifically for \n",
-    "\tImage Recognition (classification problem).\n",
-    "\"\"\"\n",
-    "\n",
-    "# Import libraries\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import keras.backend as K \n",
-    "from keras.utils import np_utils\n",
-    "from keras.datasets import cifar10\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
-    "from keras.layers.convolutional import Conv2D\n",
-    "from keras.layers.pooling import MaxPooling2D\n",
-    "\n",
-    "# For reproducibility\n",
-    "np.random.seed(42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cifar 10 data retrieved and processed\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"Retrieve the CIFAR dataset and process the data.\"\"\"\n",
-    "\n",
-    "nb_classes = 10\n",
-    "input_dim = 32*32*3\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "x_train = x_train.astype('float32')\n",
-    "x_test = x_test.astype('float32')\n",
-    "\n",
-    "#z-score\n",
-    "mean = np.mean(x_train,axis=(0,1,2,3))\n",
-    "std = np.std(x_train,axis=(0,1,2,3))\n",
-    "x_train = (x_train-mean)/(std+1e-7)\n",
-    "x_test = (x_test-mean)/(std+1e-7)\n",
-    "\n",
-    "num_classes = 10\n",
-    "y_train = np_utils.to_categorical(y_train,num_classes)\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)\n",
-    "\n",
-    "print(\"Cifar 10 data retrieved and processed\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def leaky(x):\n",
-    "    return K.maximum(0.3*x, x)\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['e_swish_2', 'swish', 'relu', 'elu', 'leaky']\n"
-     ]
-    }
-   ],
-   "source": [
-    "activations = [e_swish_2, swish, \"relu\", \"elu\", leaky]\n",
-    "names = activations[:]\n",
-    "for i,a in enumerate(names):\n",
-    "    if not isinstance(a, str):\n",
-    "        names[i] = a.__name__\n",
-    "print(names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    \n",
-    "    model = Sequential()\n",
-    "    # First Convolutional Block\n",
-    "    model.add(Conv2D(filters=32, kernel_size=(3,3), padding='same', activation=act, input_shape=x_train.shape[1:]))\n",
-    "    model.add(MaxPooling2D(pool_size = 2))\n",
-    "    model.add(Dropout(0.15))\n",
-    "    model.add(Conv2D(filters=64, kernel_size=(3,3), padding='same', activation=act))\n",
-    "    model.add(MaxPooling2D(pool_size = 2))\n",
-    "    model.add(Dropout(0.15))\n",
-    "    # Second Convolutional Block\n",
-    "    model.add(Conv2D(filters=64, kernel_size=(3,3), padding='same', activation=act))\n",
-    "    model.add(Dropout(0.15))\n",
-    "    model.add(Conv2D(filters=128, kernel_size=(3,3), padding='same', activation=act))\n",
-    "    model.add(MaxPooling2D(pool_size = 2))\n",
-    "    model.add(Dropout(0.15))\n",
-    "    # Last Convolutional Block\n",
-    "    model.add(Conv2D(filters=128, kernel_size=(3,3), padding='same', activation=act))\n",
-    "    model.add(MaxPooling2D(pool_size = 2))\n",
-    "    model.add(Dropout(0.15))\n",
-    "    # Flatten and pass trough fully connected layers\n",
-    "    model.add(Flatten())\n",
-    "    # Classification task\n",
-    "    model.add(Dense(64, activation=act))\n",
-    "    model.add(Dense(10, activation='softmax'))\n",
-    "\n",
-    "    # Compile model\n",
-    "    model.compile(loss='categorical_crossentropy', optimizer='adamax', metrics=['accuracy'])\n",
-    "        \n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"cifar10_simple_cnn_3_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 77s 2ms/step - loss: 1.5750 - acc: 0.4259 - val_loss: 1.4423 - val_acc: 0.4861\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 1.1439 - acc: 0.5893 - val_loss: 1.1077 - val_acc: 0.6065\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.9756 - acc: 0.6537 - val_loss: 1.0490 - val_acc: 0.6359\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.8594 - acc: 0.6958 - val_loss: 0.8763 - val_acc: 0.6917\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.7729 - acc: 0.7284 - val_loss: 0.7720 - val_acc: 0.7306\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.6971 - acc: 0.7549 - val_loss: 0.7178 - val_acc: 0.7501\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.6416 - acc: 0.7736 - val_loss: 0.6996 - val_acc: 0.7612\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.5929 - acc: 0.7923 - val_loss: 0.7005 - val_acc: 0.7626\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.5590 - acc: 0.8046 - val_loss: 0.6418 - val_acc: 0.7793\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.5269 - acc: 0.8141 - val_loss: 0.6354 - val_acc: 0.7830\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.4958 - acc: 0.8249 - val_loss: 0.6349 - val_acc: 0.7882\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.4725 - acc: 0.8324 - val_loss: 0.6193 - val_acc: 0.7912\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.4572 - acc: 0.8386 - val_loss: 0.6101 - val_acc: 0.8012\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 75s 2ms/step - loss: 0.4295 - acc: 0.8487 - val_loss: 0.6002 - val_acc: 0.8050\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.4154 - acc: 0.8535 - val_loss: 0.6043 - val_acc: 0.8064\n",
-      "10000/10000 [==============================] - 5s 469us/step\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 75s 2ms/step - loss: 1.5431 - acc: 0.4364 - val_loss: 1.3195 - val_acc: 0.5250\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 1.1038 - acc: 0.6036 - val_loss: 1.0599 - val_acc: 0.6258\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.9155 - acc: 0.6747 - val_loss: 0.8703 - val_acc: 0.6938\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.7972 - acc: 0.7191 - val_loss: 0.7642 - val_acc: 0.7341\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.7218 - acc: 0.7471 - val_loss: 0.6929 - val_acc: 0.7605\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.6553 - acc: 0.7709 - val_loss: 0.6812 - val_acc: 0.7631\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.6074 - acc: 0.7854 - val_loss: 0.6459 - val_acc: 0.7813\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.5639 - acc: 0.8006 - val_loss: 0.6909 - val_acc: 0.7696\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.5328 - acc: 0.8115 - val_loss: 0.6465 - val_acc: 0.7823\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.4995 - acc: 0.8237 - val_loss: 0.6214 - val_acc: 0.7886\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.4680 - acc: 0.8355 - val_loss: 0.5974 - val_acc: 0.8019\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.4491 - acc: 0.8399 - val_loss: 0.6416 - val_acc: 0.7882\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.4298 - acc: 0.8501 - val_loss: 0.6042 - val_acc: 0.8044\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 73s 1ms/step - loss: 0.4091 - acc: 0.8561 - val_loss: 0.6056 - val_acc: 0.8041\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 73s 1ms/step - loss: 0.3958 - acc: 0.8600 - val_loss: 0.6025 - val_acc: 0.8037\n",
-      "10000/10000 [==============================] - 5s 455us/step\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 1.5301 - acc: 0.4403 - val_loss: 1.1651 - val_acc: 0.5801\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 73s 1ms/step - loss: 1.1107 - acc: 0.5994 - val_loss: 1.0316 - val_acc: 0.6380\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 73s 1ms/step - loss: 0.9511 - acc: 0.6621 - val_loss: 0.9024 - val_acc: 0.6835\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 73s 1ms/step - loss: 0.8406 - acc: 0.7039 - val_loss: 0.8044 - val_acc: 0.7233\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.7524 - acc: 0.7322 - val_loss: 0.7840 - val_acc: 0.7308\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.6904 - acc: 0.7541 - val_loss: 0.7321 - val_acc: 0.7476\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 73s 1ms/step - loss: 0.6410 - acc: 0.7732 - val_loss: 0.6825 - val_acc: 0.7685\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.5951 - acc: 0.7904 - val_loss: 0.6704 - val_acc: 0.7749\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 75s 2ms/step - loss: 0.5564 - acc: 0.8041 - val_loss: 0.6579 - val_acc: 0.7778\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 75s 2ms/step - loss: 0.5286 - acc: 0.8136 - val_loss: 0.6382 - val_acc: 0.7831\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.4950 - acc: 0.8262 - val_loss: 0.6424 - val_acc: 0.7845\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.4752 - acc: 0.8324 - val_loss: 0.6357 - val_acc: 0.7940\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.4516 - acc: 0.8390 - val_loss: 0.6240 - val_acc: 0.7948\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.4271 - acc: 0.8473 - val_loss: 0.6141 - val_acc: 0.8000\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 75s 1ms/step - loss: 0.4188 - acc: 0.8518 - val_loss: 0.6193 - val_acc: 0.8005\n",
-      "10000/10000 [==============================] - 5s 462us/step\n",
-      "0.8335\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 1.5900 - acc: 0.4195 - val_loss: 1.3267 - val_acc: 0.5174\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 1.1912 - acc: 0.5711 - val_loss: 1.0368 - val_acc: 0.6324\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 1.0025 - acc: 0.6436 - val_loss: 0.9266 - val_acc: 0.6681\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.8810 - acc: 0.6887 - val_loss: 0.8201 - val_acc: 0.7111\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.7853 - acc: 0.7231 - val_loss: 0.7848 - val_acc: 0.7304\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.7156 - acc: 0.7457 - val_loss: 0.7484 - val_acc: 0.7400\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.6481 - acc: 0.7686 - val_loss: 0.7175 - val_acc: 0.7515\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.5986 - acc: 0.7896 - val_loss: 0.6974 - val_acc: 0.7568\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.5577 - acc: 0.8040 - val_loss: 0.6659 - val_acc: 0.7764\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.5227 - acc: 0.8147 - val_loss: 0.6548 - val_acc: 0.7788\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.4890 - acc: 0.8275 - val_loss: 0.6408 - val_acc: 0.7843\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.4601 - acc: 0.8360 - val_loss: 0.6375 - val_acc: 0.7870\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.4323 - acc: 0.8456 - val_loss: 0.6488 - val_acc: 0.7833\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.4087 - acc: 0.8547 - val_loss: 0.6260 - val_acc: 0.7943\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.3808 - acc: 0.8638 - val_loss: 0.6350 - val_acc: 0.7954\n",
-      "10000/10000 [==============================] - 4s 405us/step\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 1.5651 - acc: 0.4306 - val_loss: 1.3009 - val_acc: 0.5248\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 1.1786 - acc: 0.5781 - val_loss: 1.0869 - val_acc: 0.6149\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 1.0087 - acc: 0.6406 - val_loss: 0.9599 - val_acc: 0.6606\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 61s 1ms/step - loss: 0.8843 - acc: 0.6865 - val_loss: 0.8917 - val_acc: 0.6848\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.7887 - acc: 0.7210 - val_loss: 0.8214 - val_acc: 0.7135\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.7163 - acc: 0.7465 - val_loss: 0.7429 - val_acc: 0.7432\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.6483 - acc: 0.7731 - val_loss: 0.6986 - val_acc: 0.7583\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.6003 - acc: 0.7879 - val_loss: 0.7087 - val_acc: 0.7550\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.5593 - acc: 0.8042 - val_loss: 0.6776 - val_acc: 0.7689\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.5176 - acc: 0.8164 - val_loss: 0.6565 - val_acc: 0.7775\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.4883 - acc: 0.8265 - val_loss: 0.6451 - val_acc: 0.7793\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.4548 - acc: 0.8374 - val_loss: 0.6620 - val_acc: 0.7804\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.4321 - acc: 0.8455 - val_loss: 0.6564 - val_acc: 0.7865\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.4056 - acc: 0.8550 - val_loss: 0.6370 - val_acc: 0.7907\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.3830 - acc: 0.8634 - val_loss: 0.6570 - val_acc: 0.7898\n",
-      "10000/10000 [==============================] - 4s 388us/step\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 1.5848 - acc: 0.4202 - val_loss: 1.3208 - val_acc: 0.5226\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 1.2000 - acc: 0.5704 - val_loss: 1.0714 - val_acc: 0.6149\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 1.0166 - acc: 0.6397 - val_loss: 0.9442 - val_acc: 0.6659\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.8904 - acc: 0.6854 - val_loss: 0.8489 - val_acc: 0.6975\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.7918 - acc: 0.7217 - val_loss: 0.8019 - val_acc: 0.7154\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.7170 - acc: 0.7467 - val_loss: 0.7304 - val_acc: 0.7466\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.6556 - acc: 0.7681 - val_loss: 0.6994 - val_acc: 0.7534\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.6056 - acc: 0.7868 - val_loss: 0.7014 - val_acc: 0.7590\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.5612 - acc: 0.8012 - val_loss: 0.6750 - val_acc: 0.7682\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.5253 - acc: 0.8133 - val_loss: 0.6639 - val_acc: 0.7741\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.4918 - acc: 0.8250 - val_loss: 0.6491 - val_acc: 0.7791\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.4626 - acc: 0.8363 - val_loss: 0.6619 - val_acc: 0.7812\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.4417 - acc: 0.8425 - val_loss: 0.6668 - val_acc: 0.7769\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 0.4092 - acc: 0.8528 - val_loss: 0.6651 - val_acc: 0.7823\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.3939 - acc: 0.8592 - val_loss: 0.6625 - val_acc: 0.7836\n",
-      "10000/10000 [==============================] - 4s 409us/step\n",
-      "0.8188\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 59s 1ms/step - loss: 1.6636 - acc: 0.3837 - val_loss: 1.3238 - val_acc: 0.5271\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 1.2836 - acc: 0.5368 - val_loss: 1.1330 - val_acc: 0.5973\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 1.1029 - acc: 0.6077 - val_loss: 0.9883 - val_acc: 0.6548\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.9746 - acc: 0.6540 - val_loss: 0.8744 - val_acc: 0.6939\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.8830 - acc: 0.6851 - val_loss: 0.8101 - val_acc: 0.7182\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.8066 - acc: 0.7153 - val_loss: 0.7440 - val_acc: 0.7386\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.7456 - acc: 0.7373 - val_loss: 0.7101 - val_acc: 0.7547\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.6970 - acc: 0.7534 - val_loss: 0.6965 - val_acc: 0.7568\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.6590 - acc: 0.7680 - val_loss: 0.6680 - val_acc: 0.7640\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.6219 - acc: 0.7817 - val_loss: 0.6631 - val_acc: 0.7729\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.5961 - acc: 0.7889 - val_loss: 0.6376 - val_acc: 0.7814\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.5640 - acc: 0.8021 - val_loss: 0.6069 - val_acc: 0.7921\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.5383 - acc: 0.8106 - val_loss: 0.5934 - val_acc: 0.7947\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5166 - acc: 0.8168 - val_loss: 0.5954 - val_acc: 0.7954\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.4950 - acc: 0.8246 - val_loss: 0.5768 - val_acc: 0.8027\n",
-      "10000/10000 [==============================] - 4s 371us/step\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 58s 1ms/step - loss: 1.6792 - acc: 0.3747 - val_loss: 1.3411 - val_acc: 0.5148\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 1.2795 - acc: 0.5375 - val_loss: 1.1214 - val_acc: 0.6041\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 1.0964 - acc: 0.6068 - val_loss: 1.0048 - val_acc: 0.6475\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.9817 - acc: 0.6510 - val_loss: 0.8982 - val_acc: 0.6925\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.8928 - acc: 0.6842 - val_loss: 0.8496 - val_acc: 0.7015\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.8158 - acc: 0.7120 - val_loss: 0.7453 - val_acc: 0.7389\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.7571 - acc: 0.7319 - val_loss: 0.7048 - val_acc: 0.7536\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.7127 - acc: 0.7506 - val_loss: 0.7056 - val_acc: 0.7535\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.6694 - acc: 0.7631 - val_loss: 0.6718 - val_acc: 0.7671\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.6348 - acc: 0.7775 - val_loss: 0.6623 - val_acc: 0.7724\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.6039 - acc: 0.7879 - val_loss: 0.6514 - val_acc: 0.7736\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.5747 - acc: 0.7967 - val_loss: 0.6180 - val_acc: 0.7871\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5536 - acc: 0.8044 - val_loss: 0.6179 - val_acc: 0.7869\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5304 - acc: 0.8126 - val_loss: 0.6145 - val_acc: 0.7881\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5065 - acc: 0.8211 - val_loss: 0.6067 - val_acc: 0.7937\n",
-      "10000/10000 [==============================] - 4s 362us/step\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 59s 1ms/step - loss: 1.6333 - acc: 0.3965 - val_loss: 1.3221 - val_acc: 0.5229\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 1.2548 - acc: 0.5455 - val_loss: 1.0691 - val_acc: 0.6258\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 1.0739 - acc: 0.6168 - val_loss: 0.9565 - val_acc: 0.6629\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.9487 - acc: 0.6628 - val_loss: 0.8584 - val_acc: 0.6996\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.8581 - acc: 0.6966 - val_loss: 0.7917 - val_acc: 0.7270\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.7948 - acc: 0.7193 - val_loss: 0.7337 - val_acc: 0.7468\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.7351 - acc: 0.7405 - val_loss: 0.6972 - val_acc: 0.7523\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 55s 1ms/step - loss: 0.6886 - acc: 0.7591 - val_loss: 0.6832 - val_acc: 0.7600\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.6456 - acc: 0.7731 - val_loss: 0.6535 - val_acc: 0.7721\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.6105 - acc: 0.7839 - val_loss: 0.6358 - val_acc: 0.7786\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5811 - acc: 0.7942 - val_loss: 0.6296 - val_acc: 0.7806\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5527 - acc: 0.8064 - val_loss: 0.5966 - val_acc: 0.7901\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5350 - acc: 0.8115 - val_loss: 0.6030 - val_acc: 0.7899\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5101 - acc: 0.8189 - val_loss: 0.5903 - val_acc: 0.7954\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.4931 - acc: 0.8261 - val_loss: 0.5845 - val_acc: 0.7935\n",
-      "10000/10000 [==============================] - 4s 364us/step\n",
-      "0.8206\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 1.4019 - acc: 0.4973 - val_loss: 1.1037 - val_acc: 0.6061\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 1.0398 - acc: 0.6305 - val_loss: 0.8931 - val_acc: 0.6869\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.8930 - acc: 0.6849 - val_loss: 0.8742 - val_acc: 0.6922\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.7988 - acc: 0.7201 - val_loss: 0.7762 - val_acc: 0.7318\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 58s 1ms/step - loss: 0.7407 - acc: 0.7420 - val_loss: 0.7609 - val_acc: 0.7395\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.6765 - acc: 0.7628 - val_loss: 0.7172 - val_acc: 0.7579\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.6320 - acc: 0.7788 - val_loss: 0.6698 - val_acc: 0.7689\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.5980 - acc: 0.7898 - val_loss: 0.6829 - val_acc: 0.7691\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.5589 - acc: 0.8014 - val_loss: 0.6696 - val_acc: 0.7774\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.5298 - acc: 0.8115 - val_loss: 0.6565 - val_acc: 0.7810\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.4961 - acc: 0.8254 - val_loss: 0.6644 - val_acc: 0.7869\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.4740 - acc: 0.8312 - val_loss: 0.6824 - val_acc: 0.7819\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.4557 - acc: 0.8376 - val_loss: 0.6412 - val_acc: 0.7923\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.4298 - acc: 0.8466 - val_loss: 0.6901 - val_acc: 0.7851\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.4143 - acc: 0.8521 - val_loss: 0.6714 - val_acc: 0.7886\n",
-      "10000/10000 [==============================] - 4s 375us/step\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 1.4058 - acc: 0.4942 - val_loss: 1.1373 - val_acc: 0.5927\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 1.0303 - acc: 0.6357 - val_loss: 0.9098 - val_acc: 0.6794\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.8871 - acc: 0.6871 - val_loss: 0.8283 - val_acc: 0.7117\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.7978 - acc: 0.7201 - val_loss: 0.7772 - val_acc: 0.7328\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.7331 - acc: 0.7433 - val_loss: 0.8226 - val_acc: 0.7203\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.6760 - acc: 0.7606 - val_loss: 0.7064 - val_acc: 0.7600\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.6354 - acc: 0.7768 - val_loss: 0.7148 - val_acc: 0.7628\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5991 - acc: 0.7910 - val_loss: 0.6875 - val_acc: 0.7672\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5594 - acc: 0.7996 - val_loss: 0.6853 - val_acc: 0.7721\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5296 - acc: 0.8119 - val_loss: 0.6604 - val_acc: 0.7801\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5027 - acc: 0.8195 - val_loss: 0.7039 - val_acc: 0.7737\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.4773 - acc: 0.8295 - val_loss: 0.6624 - val_acc: 0.7842\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.4573 - acc: 0.8394 - val_loss: 0.6606 - val_acc: 0.7887\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.4356 - acc: 0.8453 - val_loss: 0.6503 - val_acc: 0.7944\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.4180 - acc: 0.8496 - val_loss: 0.6818 - val_acc: 0.7871\n",
-      "10000/10000 [==============================] - 4s 383us/step\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 60s 1ms/step - loss: 1.4161 - acc: 0.4889 - val_loss: 1.1666 - val_acc: 0.5840\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 1.0489 - acc: 0.6304 - val_loss: 0.9219 - val_acc: 0.6760\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.9010 - acc: 0.6836 - val_loss: 0.8576 - val_acc: 0.6996\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.7971 - acc: 0.7206 - val_loss: 0.7423 - val_acc: 0.7429\n",
-      "Epoch 5/15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.7349 - acc: 0.7413 - val_loss: 0.7919 - val_acc: 0.7334\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.6760 - acc: 0.7620 - val_loss: 0.7213 - val_acc: 0.7525\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.6284 - acc: 0.7783 - val_loss: 0.6854 - val_acc: 0.7686\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.5876 - acc: 0.7937 - val_loss: 0.6820 - val_acc: 0.7689\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.5474 - acc: 0.8070 - val_loss: 0.6769 - val_acc: 0.7746\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.5230 - acc: 0.8160 - val_loss: 0.6633 - val_acc: 0.7821\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.4966 - acc: 0.8257 - val_loss: 0.6709 - val_acc: 0.7768\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 56s 1ms/step - loss: 0.4740 - acc: 0.8328 - val_loss: 0.6596 - val_acc: 0.7828\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.4545 - acc: 0.8385 - val_loss: 0.6465 - val_acc: 0.7870\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 58s 1ms/step - loss: 0.4280 - acc: 0.8467 - val_loss: 0.6486 - val_acc: 0.7899\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 57s 1ms/step - loss: 0.4094 - acc: 0.8548 - val_loss: 0.6838 - val_acc: 0.7834\n",
-      "10000/10000 [==============================] - 4s 387us/step\n",
-      "0.8181\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gc\n",
-    "for i,act in enumerate(activations):\n",
-    "    logs.info(\"\\n \\n Starting training for \"+names[i])\n",
-    "    ensembler = 0\n",
-    "    logger = [names[i]]\n",
-    "    for i in range(3):\n",
-    "        gc.collect()\n",
-    "        model = create(act)\n",
-    "        his = model.fit(x_train, y_train, epochs=15, batch_size=64*2,  verbose=1, validation_data=(x_test, y_test))\n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(x_test, y_test)[1])\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(x_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, his\n",
-    "        \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, y_test)\n",
-    "    print(ensembled)\n",
-    "    logs.info(str(logger))\n",
-    "    logs.info(str(ensembled))\n",
-    "    del ensembler, ensembled"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 69s 1ms/step - loss: 1.5201 - acc: 0.4460 - val_loss: 1.2909 - val_acc: 0.5379\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 1.1186 - acc: 0.6027 - val_loss: 1.1061 - val_acc: 0.6123\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 0.9513 - acc: 0.6653 - val_loss: 1.0620 - val_acc: 0.6333\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 0.8434 - acc: 0.7046 - val_loss: 0.8630 - val_acc: 0.6982\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 0.7667 - acc: 0.7334 - val_loss: 0.7600 - val_acc: 0.7361\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.6992 - acc: 0.7562 - val_loss: 0.7310 - val_acc: 0.7442\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 0.6507 - acc: 0.7731 - val_loss: 0.7168 - val_acc: 0.7585\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 0.6080 - acc: 0.7876 - val_loss: 0.7100 - val_acc: 0.7641\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 0.5769 - acc: 0.7970 - val_loss: 0.6760 - val_acc: 0.7739\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.5477 - acc: 0.8073 - val_loss: 0.6431 - val_acc: 0.7852\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.5207 - acc: 0.8174 - val_loss: 0.6411 - val_acc: 0.7836\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.5026 - acc: 0.8256 - val_loss: 0.6286 - val_acc: 0.7927\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.4742 - acc: 0.8314 - val_loss: 0.6326 - val_acc: 0.7912\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.4604 - acc: 0.8380 - val_loss: 0.6134 - val_acc: 0.7985\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.4423 - acc: 0.8426 - val_loss: 0.6752 - val_acc: 0.7865\n",
-      "10000/10000 [==============================] - 4s 392us/step\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 1.5206 - acc: 0.4458 - val_loss: 1.2738 - val_acc: 0.5342\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 1.1170 - acc: 0.6040 - val_loss: 1.0628 - val_acc: 0.6191\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.9401 - acc: 0.6670 - val_loss: 0.9113 - val_acc: 0.6805\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.8319 - acc: 0.7081 - val_loss: 0.8069 - val_acc: 0.7184\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.7503 - acc: 0.7369 - val_loss: 0.7309 - val_acc: 0.7482\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.6898 - acc: 0.7588 - val_loss: 0.7012 - val_acc: 0.7566\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.6410 - acc: 0.7752 - val_loss: 0.7060 - val_acc: 0.7541\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.6019 - acc: 0.7883 - val_loss: 0.6597 - val_acc: 0.7748\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.5752 - acc: 0.7977 - val_loss: 0.6599 - val_acc: 0.7802\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.5367 - acc: 0.8114 - val_loss: 0.6304 - val_acc: 0.7853\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.5126 - acc: 0.8211 - val_loss: 0.6216 - val_acc: 0.7934\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.4911 - acc: 0.8270 - val_loss: 0.6424 - val_acc: 0.7908\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.4683 - acc: 0.8351 - val_loss: 0.6273 - val_acc: 0.7949\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.4500 - acc: 0.8400 - val_loss: 0.6488 - val_acc: 0.7881\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.4300 - acc: 0.8477 - val_loss: 0.6137 - val_acc: 0.8003\n",
-      "10000/10000 [==============================] - 4s 388us/step\n",
-      "Train on 50000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 1.5143 - acc: 0.4506 - val_loss: 1.1858 - val_acc: 0.5828\n",
-      "Epoch 2/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 1.1120 - acc: 0.6037 - val_loss: 1.0873 - val_acc: 0.6162\n",
-      "Epoch 3/15\n",
-      "50000/50000 [==============================] - 62s 1ms/step - loss: 0.9545 - acc: 0.6643 - val_loss: 0.9628 - val_acc: 0.6642\n",
-      "Epoch 4/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 0.8509 - acc: 0.7029 - val_loss: 0.7927 - val_acc: 0.7258\n",
-      "Epoch 5/15\n",
-      "50000/50000 [==============================] - 65s 1ms/step - loss: 0.7751 - acc: 0.7285 - val_loss: 0.8018 - val_acc: 0.7255\n",
-      "Epoch 6/15\n",
-      "50000/50000 [==============================] - 64s 1ms/step - loss: 0.7144 - acc: 0.7497 - val_loss: 0.7237 - val_acc: 0.7496\n",
-      "Epoch 7/15\n",
-      "50000/50000 [==============================] - 64s 1ms/step - loss: 0.6670 - acc: 0.7652 - val_loss: 0.6915 - val_acc: 0.7602\n",
-      "Epoch 8/15\n",
-      "50000/50000 [==============================] - 68s 1ms/step - loss: 0.6228 - acc: 0.7823 - val_loss: 0.7212 - val_acc: 0.7580\n",
-      "Epoch 9/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.5963 - acc: 0.7926 - val_loss: 0.6474 - val_acc: 0.7777\n",
-      "Epoch 10/15\n",
-      "50000/50000 [==============================] - 74s 1ms/step - loss: 0.5604 - acc: 0.8037 - val_loss: 0.6659 - val_acc: 0.7739\n",
-      "Epoch 11/15\n",
-      "50000/50000 [==============================] - 67s 1ms/step - loss: 0.5392 - acc: 0.8119 - val_loss: 0.6341 - val_acc: 0.7825\n",
-      "Epoch 12/15\n",
-      "50000/50000 [==============================] - 64s 1ms/step - loss: 0.5118 - acc: 0.8214 - val_loss: 0.6352 - val_acc: 0.7877\n",
-      "Epoch 13/15\n",
-      "50000/50000 [==============================] - 66s 1ms/step - loss: 0.4878 - acc: 0.8292 - val_loss: 0.6192 - val_acc: 0.7967\n",
-      "Epoch 14/15\n",
-      "50000/50000 [==============================] - 63s 1ms/step - loss: 0.4670 - acc: 0.8359 - val_loss: 0.6207 - val_acc: 0.7931\n",
-      "Epoch 15/15\n",
-      "50000/50000 [==============================] - 66s 1ms/step - loss: 0.4505 - acc: 0.8405 - val_loss: 0.6435 - val_acc: 0.7911\n",
-      "10000/10000 [==============================] - 4s 429us/step\n",
-      "0.8222\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gc\n",
-    "for i,act in enumerate(activations):\n",
-    "    if i < 4: pass\n",
-    "    else:\n",
-    "        logs.info(\"\\n \\n Starting training for \"+names[i])\n",
-    "        ensembler = 0\n",
-    "        logger = [names[i]]\n",
-    "        for i in range(3):\n",
-    "            gc.collect()\n",
-    "            model = create(act)\n",
-    "            his = model.fit(x_train, y_train, epochs=15, batch_size=64*2,  verbose=1, validation_data=(x_test, y_test))\n",
-    "            # Record accuracy of each model and save it\n",
-    "            logger.append(model.evaluate(x_test, y_test)[1])\n",
-    "            # Calculate probabilities of test data and sum them toghether\n",
-    "            ensembler += model.predict_proba(x_test)\n",
-    "            # Clear session (GPU MEMORY)\n",
-    "            K.get_session().close()\n",
-    "            K.set_session(K.tf.Session())\n",
-    "            del model, his\n",
-    "\n",
-    "        # Calculate the median accuracy\n",
-    "        ensembled = accuracy(ensembler, y_test)\n",
-    "        print(ensembled)\n",
-    "        logs.info(str(logger))\n",
-    "        logs.info(str(ensembled))\n",
-    "        del ensembler, ensembled"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# LOGS CAN BE FOUND AT THE cifar_10_small_3_logs.txt FILE"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Simple_CNN/cifar10_simple_cnn_3_log.txt b/cifar/Simple_CNN/cifar10_simple_cnn_3_log.txt
deleted file mode 100644
index 86a040d..0000000
--- a/cifar/Simple_CNN/cifar10_simple_cnn_3_log.txt
+++ /dev/null
@@ -1,25 +0,0 @@
-INFO 2017-12-16 19:34:52,911 - 
- 
- Starting training for e_swish_2
-INFO 2017-12-16 20:31:08,467 - ['e_swish_2', 0.80640000000000001, 0.80369999999999997, 0.80049999999999999]
-INFO 2017-12-16 20:31:08,467 - 0.8335
-INFO 2017-12-16 20:31:08,467 - 
- 
- Starting training for swish
-INFO 2017-12-16 21:16:57,079 - ['swish', 0.7954, 0.78979999999999995, 0.78359999999999996]
-INFO 2017-12-16 21:16:57,079 - 0.8188
-INFO 2017-12-16 21:16:57,079 - 
- 
- Starting training for relu
-INFO 2017-12-16 21:59:36,397 - ['relu', 0.80269999999999997, 0.79369999999999996, 0.79349999999999998]
-INFO 2017-12-16 21:59:36,403 - 0.8206
-INFO 2017-12-16 21:59:36,403 - 
- 
- Starting training for elu
-INFO 2017-12-16 22:42:49,610 - ['elu', 0.78859999999999997, 0.78710000000000002, 0.78339999999999999]
-INFO 2017-12-16 22:42:49,610 - 0.8181
-INFO 2017-12-16 22:42:49,610 - 
- 
- Starting training for leaky
-INFO 2017-12-17 22:01:48,509 - ['leaky', 0.78649999999999998, 0.80030000000000001, 0.79110000000000003]
-INFO 2017-12-17 22:01:48,510 - 0.8222
diff --git a/cifar/Wide_ResNets/16_4/eval.py b/cifar/Wide_ResNets/16_4/eval.py
deleted file mode 100644
index e69de29..0000000
diff --git a/cifar/Wide_ResNets/16_4/graphs/.ipynb_checkpoints/pytorch_ensembler-checkpoint.ipynb b/cifar/Wide_ResNets/16_4/graphs/.ipynb_checkpoints/pytorch_ensembler-checkpoint.ipynb
deleted file mode 100644
index 98de0cf..0000000
--- a/cifar/Wide_ResNets/16_4/graphs/.ipynb_checkpoints/pytorch_ensembler-checkpoint.ipynb
+++ /dev/null
@@ -1,742 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n",
-      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
-      "  return f(*args, **kwds)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from keras.datasets import cifar10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def softmax(x):\n",
-    "    e_x = np.exp(x) - np.amax(x)\n",
-    "    return e_x / e_x.sum(axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "(10000, 10)\n",
-      "[[ -4.90083042e-06  -4.49831680e-06  -4.81777193e-06 ...,  -4.91496108e-06\n",
-      "   -4.86961810e-06  -4.92445519e-06]\n",
-      " [ -4.88604739e-08   8.74079187e-07  -2.49583755e-07 ...,  -2.49658926e-07\n",
-      "    1.00000092e+00  -2.47317021e-07]\n",
-      " [ -4.98662597e-05   8.86949022e-03  -9.17242644e-05 ...,  -9.12299545e-05\n",
-      "    9.91811430e-01  -8.20444685e-05]\n",
-      " ..., \n",
-      " [ -7.29081978e-05  -7.25324876e-05  -1.25106549e-05 ...,  -6.97059817e-05\n",
-      "   -7.22895147e-05  -7.24782733e-05]\n",
-      " [ -9.70507635e-06   1.00008639e+00  -8.69566493e-06 ...,  -9.81943587e-06\n",
-      "   -9.91263143e-06  -9.94954674e-06]\n",
-      " [ -2.94160565e-05  -2.87808958e-05  -2.93322451e-05 ...,   1.00026233e+00\n",
-      "   -2.90999918e-05  -2.96926985e-05]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open(\"cifar_10_logs_1.txt\") as f:\n",
-    "    content = f.readlines()\n",
-    "    \n",
-    "new_content = []\n",
-    "for i, l in enumerate(content):\n",
-    "    # Merge lines\n",
-    "    if l[:4] == \"INFO\":\n",
-    "        try:\n",
-    "            l = content[i]\n",
-    "            l = l[33:-2]\n",
-    "            if i+2 < len(content):\n",
-    "                if content[i+2][:4] == \"INFO\":\n",
-    "    #             print(\"hey\")\n",
-    "                    l += content[i+1][:-2]\n",
-    "                else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "                    l += content[i+2][:-2]\n",
-    "            else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "\n",
-    "            # Clean unwanted characters\n",
-    "            for char in [\"[\", \"]\", \",\"]:\n",
-    "    #             print(\"replacing: \"+ char, l)\n",
-    "                l.replace(char, \" \")\n",
-    "    #             print(l)\n",
-    "\n",
-    "            parts = l.split(\" \")\n",
-    "    #         print(parts)\n",
-    "            # Convert to array\n",
-    "            lol = []\n",
-    "            for p in parts:\n",
-    "                try:\n",
-    "                    lol.append(float(p))\n",
-    "                except:pass\n",
-    "    #         print(lol)\n",
-    "            hello = np.array(lol)\n",
-    "    #         print(hello, hello.shape)\n",
-    "            if hello.shape[0] != 10:\n",
-    "                print(\"LOL\")\n",
-    "                break\n",
-    "            new_content.append(softmax(hello))\n",
-    "            \n",
-    "        except:pass\n",
-    "            \n",
-    "    else: pass\n",
-    "    \n",
-    "    if i%1000 == 0: print(\"1000 more\")\n",
-    "        \n",
-    "print(np.array(new_content).shape)\n",
-    "cleaned = np.array(new_content)\n",
-    "print(cleaned)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler += cleaned"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "(10000, 10)\n",
-      "[[ -6.88311285e-09  -6.82021809e-09  -6.71568857e-09 ...,  -6.90769316e-09\n",
-      "   -6.89457826e-09  -6.91148780e-09]\n",
-      " [ -4.51994275e-08   2.82880900e-07  -6.16410693e-08 ...,  -6.16259045e-08\n",
-      "    1.00000019e+00  -6.14057739e-08]\n",
-      " [ -5.23434323e-04   1.54378808e-02  -6.01702816e-04 ...,  -5.83987867e-04\n",
-      "    9.89013143e-01  -3.33328514e-04]\n",
-      " ..., \n",
-      " [ -6.93338813e-06  -6.80913658e-06  -6.74814784e-06 ...,   1.72600785e-03\n",
-      "   -6.88697996e-06  -6.84799566e-06]\n",
-      " [ -6.54411366e-06   1.00004902e+00   3.78909490e-06 ...,  -6.53696053e-06\n",
-      "   -6.65006164e-06  -6.64101143e-06]\n",
-      " [ -1.03540179e-05  -1.01708944e-05  -1.03501255e-05 ...,   1.00008875e+00\n",
-      "   -1.03910003e-05  -9.84605584e-06]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open(\"cifar_10_logs_2.txt\") as f:\n",
-    "    content = f.readlines()\n",
-    "    \n",
-    "new_content_2 = []\n",
-    "for i, l in enumerate(content):\n",
-    "    # Merge lines\n",
-    "    if l[:4] == \"INFO\":\n",
-    "        try:\n",
-    "            l = content[i]\n",
-    "            l = l[33:-2]\n",
-    "            if i+2 < len(content):\n",
-    "                if content[i+2][:4] == \"INFO\":\n",
-    "    #             print(\"hey\")\n",
-    "                    l += content[i+1][:-2]\n",
-    "                else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "                    l += content[i+2][:-2]\n",
-    "            else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "\n",
-    "            # Clean unwanted characters\n",
-    "            for char in [\"[\", \"]\", \",\"]:\n",
-    "    #             print(\"replacing: \"+ char, l)\n",
-    "                l.replace(char, \" \")\n",
-    "    #             print(l)\n",
-    "\n",
-    "            parts = l.split(\" \")\n",
-    "    #         print(parts)\n",
-    "            # Convert to array\n",
-    "            lol = []\n",
-    "            for p in parts:\n",
-    "                try:\n",
-    "                    lol.append(float(p))\n",
-    "                except:pass\n",
-    "    #         print(lol)\n",
-    "            hello = np.array(lol)\n",
-    "    #         print(hello, hello.shape)\n",
-    "            if hello.shape[0] != 10:\n",
-    "                print(\"LOL\")\n",
-    "                break\n",
-    "            new_content_2.append(softmax(hello))\n",
-    "            \n",
-    "        except:pass\n",
-    "            \n",
-    "    else: pass\n",
-    "    \n",
-    "    if i%1000 == 0: print(\"1000 more\")\n",
-    "        \n",
-    "print(np.array(new_content_2).shape)\n",
-    "cleaned_2 = np.array(new_content_2)\n",
-    "print(cleaned_2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler += cleaned_2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "(10000, 10)\n",
-      "[[ -1.57441159e-06  -1.57772741e-06  -1.45891873e-06 ...,  -1.59082624e-06\n",
-      "   -1.58431145e-06  -1.59152312e-06]\n",
-      " [  3.35313626e-07   2.62089756e-05  -1.70342628e-06 ...,  -1.70323368e-06\n",
-      "    9.99985350e-01  -1.69302179e-06]\n",
-      " [  6.57110095e-04   6.19226593e-03  -3.55112923e-04 ...,  -3.51745563e-04\n",
-      "    9.95548429e-01  -2.72781988e-04]\n",
-      " ..., \n",
-      " [ -6.46500489e-06  -6.44906528e-06  -6.08788953e-06 ...,  -5.89226992e-06\n",
-      "   -6.45000146e-06  -6.44374483e-06]\n",
-      " [ -4.90078489e-05   1.00040277e+00  -1.11118866e-05 ...,  -4.76586889e-05\n",
-      "   -4.91237558e-05  -4.93641643e-05]\n",
-      " [ -2.99193856e-06  -2.99422431e-06  -2.96956578e-06 ...,   1.00002660e+00\n",
-      "   -3.02093184e-06  -2.95644880e-06]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open(\"cifar_10_logs_3.txt\") as f:\n",
-    "    content = f.readlines()\n",
-    "    \n",
-    "new_content_3 = []\n",
-    "for i, l in enumerate(content):\n",
-    "    # Merge lines\n",
-    "    if l[:4] == \"INFO\":\n",
-    "        try:\n",
-    "            l = content[i]\n",
-    "            l = l[33:-2]\n",
-    "            if i+2 < len(content):\n",
-    "                if content[i+2][:4] == \"INFO\":\n",
-    "    #             print(\"hey\")\n",
-    "                    l += content[i+1][:-2]\n",
-    "                else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "                    l += content[i+2][:-2]\n",
-    "            else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "\n",
-    "            # Clean unwanted characters\n",
-    "            for char in [\"[\", \"]\", \",\"]:\n",
-    "    #             print(\"replacing: \"+ char, l)\n",
-    "                l.replace(char, \" \")\n",
-    "    #             print(l)\n",
-    "\n",
-    "            parts = l.split(\" \")\n",
-    "    #         print(parts)\n",
-    "            # Convert to array\n",
-    "            lol = []\n",
-    "            for p in parts:\n",
-    "                try:\n",
-    "                    lol.append(float(p))\n",
-    "                except:pass\n",
-    "    #         print(lol)\n",
-    "            hello = np.array(lol)\n",
-    "    #         print(hello, hello.shape)\n",
-    "            if hello.shape[0] != 10:\n",
-    "                print(\"LOL\")\n",
-    "                break\n",
-    "            new_content_3.append(softmax(hello))\n",
-    "            \n",
-    "        except:pass\n",
-    "            \n",
-    "    else: pass\n",
-    "    \n",
-    "    if i%1000 == 0: print(\"1000 more\")\n",
-    "        \n",
-    "print(np.array(new_content_3).shape)\n",
-    "cleaned_3 = np.array(new_content_3)\n",
-    "print(cleaned_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler += cleaned_3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "(10000, 10)\n",
-      "[[ -1.61031776e-06  -1.57999949e-06  -1.47885537e-06 ...,  -1.61145076e-06\n",
-      "   -1.59693400e-06  -1.61415272e-06]\n",
-      " [ -8.93514509e-07   9.21749650e-06  -9.55381190e-07 ...,  -9.55941850e-07\n",
-      "    9.99998325e-01  -9.44531811e-07]\n",
-      " [ -1.30832186e-05   1.39098117e-04  -1.39199934e-05 ...,  -1.38905197e-05\n",
-      "    9.99969839e-01  -1.23690552e-05]\n",
-      " ..., \n",
-      " [ -7.42248002e-06  -7.37042783e-06  -6.69785813e-06 ...,  -6.63722754e-06\n",
-      "   -7.38086397e-06  -7.37749568e-06]\n",
-      " [ -1.82307294e-04   1.00139259e+00   1.60950997e-04 ...,  -2.00064307e-04\n",
-      "   -1.99131945e-04  -1.93583899e-04]\n",
-      " [ -2.66235687e-06  -2.64394052e-06  -2.68672693e-06 ...,   1.00002362e+00\n",
-      "   -2.70357056e-06  -2.61416855e-06]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open(\"cifar_10_logs_4.txt\") as f:\n",
-    "    content = f.readlines()\n",
-    "    \n",
-    "new_content_4 = []\n",
-    "for i, l in enumerate(content):\n",
-    "    # Merge lines\n",
-    "    if l[:4] == \"INFO\":\n",
-    "        try:\n",
-    "            l = content[i]\n",
-    "            l = l[33:-2]\n",
-    "            if i+2 < len(content):\n",
-    "                if content[i+2][:4] == \"INFO\":\n",
-    "    #             print(\"hey\")\n",
-    "                    l += content[i+1][:-2]\n",
-    "                else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "                    l += content[i+2][:-2]\n",
-    "            else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "\n",
-    "            # Clean unwanted characters\n",
-    "            for char in [\"[\", \"]\", \",\"]:\n",
-    "    #             print(\"replacing: \"+ char, l)\n",
-    "                l.replace(char, \" \")\n",
-    "    #             print(l)\n",
-    "\n",
-    "            parts = l.split(\" \")\n",
-    "    #         print(parts)\n",
-    "            # Convert to array\n",
-    "            lol = []\n",
-    "            for p in parts:\n",
-    "                try:\n",
-    "                    lol.append(float(p))\n",
-    "                except:pass\n",
-    "    #         print(lol)\n",
-    "            hello = np.array(lol)\n",
-    "    #         print(hello, hello.shape)\n",
-    "            if hello.shape[0] != 10:\n",
-    "                print(\"LOL\")\n",
-    "                break\n",
-    "            new_content_4.append(softmax(hello))\n",
-    "            \n",
-    "        except:pass\n",
-    "            \n",
-    "    else: pass\n",
-    "    \n",
-    "    if i%1000 == 0: print(\"1000 more\")\n",
-    "        \n",
-    "print(np.array(new_content_4).shape)\n",
-    "cleaned_4 = np.array(new_content_4)\n",
-    "print(cleaned_4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler += cleaned_4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "with open(\"cifar_10_logs_5.txt\") as f:\n",
-    "    content = f.readlines()\n",
-    "    \n",
-    "new_content_5 = []\n",
-    "for i, l in enumerate(content):\n",
-    "    # Merge lines\n",
-    "    if l[:4] == \"INFO\":\n",
-    "        try:\n",
-    "            l = content[i]\n",
-    "            l = l[33:-2]\n",
-    "            if i+2 < len(content):\n",
-    "                if content[i+2][:4] == \"INFO\":\n",
-    "    #             print(\"hey\")\n",
-    "                    l += content[i+1][:-2]\n",
-    "                else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "                    l += content[i+2][:-2]\n",
-    "            else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "\n",
-    "            # Clean unwanted characters\n",
-    "            for char in [\"[\", \"]\", \",\"]:\n",
-    "    #             print(\"replacing: \"+ char, l)\n",
-    "                l.replace(char, \" \")\n",
-    "    #             print(l)\n",
-    "\n",
-    "            parts = l.split(\" \")\n",
-    "    #         print(parts)\n",
-    "            # Convert to array\n",
-    "            lol = []\n",
-    "            for p in parts:\n",
-    "                try:\n",
-    "                    lol.append(float(p))\n",
-    "                except:pass\n",
-    "    #         print(lol)\n",
-    "            hello = np.array(lol)\n",
-    "    #         print(hello, hello.shape)\n",
-    "            if hello.shape[0] != 10:\n",
-    "                print(\"LOL\")\n",
-    "                break\n",
-    "            new_content_5.append(softmax(hello))\n",
-    "            \n",
-    "        except:pass\n",
-    "            \n",
-    "    else: pass\n",
-    "    \n",
-    "    if i%1000 == 0: print(\"1000 more\")\n",
-    "        \n",
-    "print(np.array(new_content_5).shape)\n",
-    "cleaned_5 = np.array(new_content_5)\n",
-    "print(cleaned_5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler += cleaned_5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(ensembled, y_test):\n",
-    "    y_hat = np.argmax(ensembled/3, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from keras.utils import np_utils\n",
-    "num_classes = 10\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9505\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9483\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler-cleaned, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9482\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler-cleaned_2, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9486\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler-cleaned_3, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9496\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler-cleaned_4, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "print(accuracy(ensembler-cleaned_5, y_test))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
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diff --git a/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_1.txt b/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_1.txt
deleted file mode 100644
index d76e43a..0000000
--- a/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_1.txt
+++ /dev/null
@@ -1,20237 +0,0 @@
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-  -4.31191015  -2.09565473  -1.32466304  11.91450214   3.17630482]
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-  -0.93714774   0.43620378  -1.42719138  -5.31350756  -4.4272871 ]
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-  -1.62705684  -4.07858181  -1.32859957  -0.27990851  14.07187653]
-INFO 2017-12-17 16:44:30,155 - [ -2.65054417  -4.39077568   0.82339936  -1.9340955   14.01040459
-   0.06490445  -1.64380503   0.05682716  -2.65568972  -1.68124497]
-INFO 2017-12-17 16:44:30,156 - [ 14.79026794  -2.75761795   0.24235511  -2.23852563  -1.84495795
-  -1.38797998  -3.18643165  -0.65064996  -1.13285506  -1.83421099]
-INFO 2017-12-17 16:44:30,156 - [ -2.34497666  12.98167992  -2.23349786   0.36825565  -1.21339083
-  -2.49246311  -3.18708587  -1.61098373  -1.96556711   1.6971513 ]
-INFO 2017-12-17 16:44:30,156 - [  0.2924642   -1.07128847  -1.13550019  -1.48120558  -3.05379224
-   0.97756523  -3.19322515  15.14716339  -3.61880183  -2.86411095]
-INFO 2017-12-17 16:44:30,156 - [ -6.02795839  -1.75571012   2.93165588   0.1043029   -1.69046962
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- -2.44838166 -1.13612354 -5.17076302 -0.84708691]
-INFO 2017-12-17 16:44:30,157 - [-3.75830078 -2.59117007 -0.1308507  -0.69627142  4.69537926 -1.7294749
- -3.13949299  9.66871738 -2.20621133 -0.11284382]
-INFO 2017-12-17 16:44:30,158 - [ -0.35260504  -3.11504245  -2.91388106  16.38150406  -0.36817515
-   0.34483221  -3.77704382  -1.28118086  -3.25085115  -1.66822147]
-INFO 2017-12-17 16:44:30,158 - [ 14.24232674  -2.45668721   1.77215302  -0.22494651  -2.89026618
-  -2.72332644  -2.14938021  -2.89442778  -0.65855438  -2.01754117]
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-   0.64766359  -0.38771001   4.49838638  -2.64174104  -2.87548947]
-INFO 2017-12-17 16:44:30,159 - [ -2.93912935  -1.55959535   3.78293109  -1.68202996   0.63835824
-   0.35094848  10.85271835  -1.69056427  -4.3532443   -3.40100646]
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- -3.21396518 -0.4941197  -2.21342111 -2.09926844]
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-   0.68309158  -4.00466347  13.59942245  -3.63399386  -1.84380841]
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-INFO 2017-12-17 16:44:30,247 - [  3.84152818  -1.35446763  -2.32507324   0.02025607  -1.37204289
-  -3.35944891  -2.94886923  -1.9507817   11.22957897  -1.78119171]
-INFO 2017-12-17 16:44:30,247 - [ 11.61470795  -2.39957261  -0.73674911  -2.72369838  -1.07962799
-  -3.17775345  -0.10896853  -2.37255478   1.42148042  -0.43784592]
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-  -2.6428082   -2.16302681  -3.92609572  17.12390137   0.20982298]
-INFO 2017-12-17 16:44:30,248 - [ -3.63671112  -1.56612003   3.01072907  -2.2385869   11.19643688
-  -0.41264775  -3.29474378   3.61687136  -4.23944139  -2.43634963]
-INFO 2017-12-17 16:44:30,248 - [ -1.32158232  -2.02848554  -1.40014124   0.01308069  -3.38147497
-  -1.36941302  -3.14018703  10.69715691   1.3396107    0.59065324]
-INFO 2017-12-17 16:44:30,249 - [ 13.01502705   3.71426511  -0.92367554  -1.70103037  -4.55812645
-  -2.49140286  -3.38696671  -0.92344767  -1.27184677  -1.47347951]
-INFO 2017-12-17 16:44:30,249 - [ -1.12940717  -1.71967161  -2.46708083  15.44543839  -0.40563655
-   1.91197503  -1.88429272  -2.70783806  -4.29475832  -2.74941802]
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-  15.34566402  -4.07694244   1.94403183  -1.12924016  -1.56532896]
-INFO 2017-12-17 16:44:30,250 - [ -1.27369583  -2.86969256  -0.94465959  17.94788933  -1.94139314
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-INFO 2017-12-17 16:44:30,250 - [  2.49857545   0.65175539  -5.17463112  -0.94472492  -2.9631846
-  -2.11919427   0.79809582   0.14140815  10.26153374  -3.150208  ]
-INFO 2017-12-17 16:44:30,250 - [ -4.93846226  -3.95035601   0.34314692  11.08352852   2.58025932
-   0.44323844   2.63536525  -2.86207819  -2.73977518  -2.59548545]
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-  12.01092911  -1.87902355  -0.60001796  -2.08765411  -2.37581158]
-INFO 2017-12-17 16:44:30,251 - [ -0.9484911   14.1660862    0.59907532  -4.41918516  -2.25349212
-   0.44047126  -0.50178349  -1.49181783  -2.37893558  -3.21292591]
-INFO 2017-12-17 16:44:30,251 - [ -1.58618057  -0.73272884  -1.42250001  -2.84568048  -0.34402692
-  -0.43590659  -2.07197213  12.98192596  -1.07296646  -2.47049284]
\ No newline at end of file
diff --git a/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_2.txt b/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_2.txt
deleted file mode 100644
index 83a25d7..0000000
--- a/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_2.txt
+++ /dev/null
@@ -1,20266 +0,0 @@
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diff --git a/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_3.txt b/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_3.txt
deleted file mode 100644
index d1a1b62..0000000
--- a/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_3.txt
+++ /dev/null
@@ -1,20238 +0,0 @@
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diff --git a/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_4.txt b/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_4.txt
deleted file mode 100644
index d41b2b0..0000000
--- a/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_4.txt
+++ /dev/null
@@ -1,20263 +0,0 @@
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diff --git a/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_5.txt b/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_5.txt
deleted file mode 100644
index 776a936..0000000
--- a/cifar/Wide_ResNets/16_4/graphs/cifar_10_logs_5.txt
+++ /dev/null
@@ -1,20241 +0,0 @@
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-INFO 2017-12-18 07:38:12,120 - [ -6.43953371  -3.45142484   0.68053019  11.65019703   0.62989527
-   0.84328544   2.15570378  -0.84457034  -2.75475502  -2.47001648]
-INFO 2017-12-18 07:38:12,120 - [ -3.91550708  -0.57795811  -1.4197135   -2.83645558   0.50113571
-  12.97114277  -2.48690915   0.40155733  -0.96681768  -1.67107344]
-INFO 2017-12-18 07:38:12,121 - [  0.75119716  11.95612144   3.05926132  -1.59697104  -2.81289268
-  -2.18271136  -2.39522481  -2.38069248  -1.47290075  -2.92618513]
-INFO 2017-12-18 07:38:12,121 - [ -2.51208043  -0.9299444   -0.8785587   -3.33227825   0.33103147
-  -0.23839808  -3.81011486  14.22419167  -2.2563715   -0.59809226]
diff --git a/cifar/Wide_ResNets/16_4/graphs/evaluater.txt b/cifar/Wide_ResNets/16_4/graphs/evaluater.txt
deleted file mode 100644
index 3745561..0000000
--- a/cifar/Wide_ResNets/16_4/graphs/evaluater.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-python3 eval.py --save lolasso --depth 16 --width 4 --resume logs_4/model.pt7 --ngpu 1 --gpu_id 0
-
-
-
-
-python3 main.py --save logs_5 --depth 16 --width 4 --ngpu 1 --gpu_id 0
-
-python3 main.py --save logs_4_1 --depth 16 --width 4 --epochs 205 --resume logs_4/model.pt7 --ngpu 1 --gpu_id 0
\ No newline at end of file
diff --git a/cifar/Wide_ResNets/16_4/graphs/pytorch_ensembler.ipynb b/cifar/Wide_ResNets/16_4/graphs/pytorch_ensembler.ipynb
deleted file mode 100644
index 8dcfb24..0000000
--- a/cifar/Wide_ResNets/16_4/graphs/pytorch_ensembler.ipynb
+++ /dev/null
@@ -1,832 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n",
-      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
-      "  return f(*args, **kwds)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from keras.datasets import cifar10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def softmax(x):\n",
-    "    e_x = np.exp(x) - np.amax(x)\n",
-    "    return e_x / e_x.sum(axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "(10000, 10)\n",
-      "[[ -4.90083042e-06  -4.49831680e-06  -4.81777193e-06 ...,  -4.91496108e-06\n",
-      "   -4.86961810e-06  -4.92445519e-06]\n",
-      " [ -4.88604739e-08   8.74079187e-07  -2.49583755e-07 ...,  -2.49658926e-07\n",
-      "    1.00000092e+00  -2.47317021e-07]\n",
-      " [ -4.98662597e-05   8.86949022e-03  -9.17242644e-05 ...,  -9.12299545e-05\n",
-      "    9.91811430e-01  -8.20444685e-05]\n",
-      " ..., \n",
-      " [ -7.29081978e-05  -7.25324876e-05  -1.25106549e-05 ...,  -6.97059817e-05\n",
-      "   -7.22895147e-05  -7.24782733e-05]\n",
-      " [ -9.70507635e-06   1.00008639e+00  -8.69566493e-06 ...,  -9.81943587e-06\n",
-      "   -9.91263143e-06  -9.94954674e-06]\n",
-      " [ -2.94160565e-05  -2.87808958e-05  -2.93322451e-05 ...,   1.00026233e+00\n",
-      "   -2.90999918e-05  -2.96926985e-05]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open(\"cifar_10_logs_1.txt\") as f:\n",
-    "    content = f.readlines()\n",
-    "    \n",
-    "new_content = []\n",
-    "for i, l in enumerate(content):\n",
-    "    # Merge lines\n",
-    "    if l[:4] == \"INFO\":\n",
-    "        try:\n",
-    "            l = content[i]\n",
-    "            l = l[33:-2]\n",
-    "            if i+2 < len(content):\n",
-    "                if content[i+2][:4] == \"INFO\":\n",
-    "    #             print(\"hey\")\n",
-    "                    l += content[i+1][:-2]\n",
-    "                else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "                    l += content[i+2][:-2]\n",
-    "            else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "\n",
-    "            # Clean unwanted characters\n",
-    "            for char in [\"[\", \"]\", \",\"]:\n",
-    "    #             print(\"replacing: \"+ char, l)\n",
-    "                l.replace(char, \" \")\n",
-    "    #             print(l)\n",
-    "\n",
-    "            parts = l.split(\" \")\n",
-    "    #         print(parts)\n",
-    "            # Convert to array\n",
-    "            lol = []\n",
-    "            for p in parts:\n",
-    "                try:\n",
-    "                    lol.append(float(p))\n",
-    "                except:pass\n",
-    "    #         print(lol)\n",
-    "            hello = np.array(lol)\n",
-    "    #         print(hello, hello.shape)\n",
-    "            if hello.shape[0] != 10:\n",
-    "                print(\"LOL\")\n",
-    "                break\n",
-    "            new_content.append(softmax(hello))\n",
-    "            \n",
-    "        except:pass\n",
-    "            \n",
-    "    else: pass\n",
-    "    \n",
-    "    if i%1000 == 0: print(\"1000 more\")\n",
-    "        \n",
-    "print(np.array(new_content).shape)\n",
-    "cleaned = np.array(new_content)\n",
-    "print(cleaned)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler = 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler += cleaned"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "(10000, 10)\n",
-      "[[ -6.88311285e-09  -6.82021809e-09  -6.71568857e-09 ...,  -6.90769316e-09\n",
-      "   -6.89457826e-09  -6.91148780e-09]\n",
-      " [ -4.51994275e-08   2.82880900e-07  -6.16410693e-08 ...,  -6.16259045e-08\n",
-      "    1.00000019e+00  -6.14057739e-08]\n",
-      " [ -5.23434323e-04   1.54378808e-02  -6.01702816e-04 ...,  -5.83987867e-04\n",
-      "    9.89013143e-01  -3.33328514e-04]\n",
-      " ..., \n",
-      " [ -6.93338813e-06  -6.80913658e-06  -6.74814784e-06 ...,   1.72600785e-03\n",
-      "   -6.88697996e-06  -6.84799566e-06]\n",
-      " [ -6.54411366e-06   1.00004902e+00   3.78909490e-06 ...,  -6.53696053e-06\n",
-      "   -6.65006164e-06  -6.64101143e-06]\n",
-      " [ -1.03540179e-05  -1.01708944e-05  -1.03501255e-05 ...,   1.00008875e+00\n",
-      "   -1.03910003e-05  -9.84605584e-06]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open(\"cifar_10_logs_2.txt\") as f:\n",
-    "    content = f.readlines()\n",
-    "    \n",
-    "new_content_2 = []\n",
-    "for i, l in enumerate(content):\n",
-    "    # Merge lines\n",
-    "    if l[:4] == \"INFO\":\n",
-    "        try:\n",
-    "            l = content[i]\n",
-    "            l = l[33:-2]\n",
-    "            if i+2 < len(content):\n",
-    "                if content[i+2][:4] == \"INFO\":\n",
-    "    #             print(\"hey\")\n",
-    "                    l += content[i+1][:-2]\n",
-    "                else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "                    l += content[i+2][:-2]\n",
-    "            else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "\n",
-    "            # Clean unwanted characters\n",
-    "            for char in [\"[\", \"]\", \",\"]:\n",
-    "    #             print(\"replacing: \"+ char, l)\n",
-    "                l.replace(char, \" \")\n",
-    "    #             print(l)\n",
-    "\n",
-    "            parts = l.split(\" \")\n",
-    "    #         print(parts)\n",
-    "            # Convert to array\n",
-    "            lol = []\n",
-    "            for p in parts:\n",
-    "                try:\n",
-    "                    lol.append(float(p))\n",
-    "                except:pass\n",
-    "    #         print(lol)\n",
-    "            hello = np.array(lol)\n",
-    "    #         print(hello, hello.shape)\n",
-    "            if hello.shape[0] != 10:\n",
-    "                print(\"LOL\")\n",
-    "                break\n",
-    "            new_content_2.append(softmax(hello))\n",
-    "            \n",
-    "        except:pass\n",
-    "            \n",
-    "    else: pass\n",
-    "    \n",
-    "    if i%1000 == 0: print(\"1000 more\")\n",
-    "        \n",
-    "print(np.array(new_content_2).shape)\n",
-    "cleaned_2 = np.array(new_content_2)\n",
-    "print(cleaned_2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler += cleaned_2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "(10000, 10)\n",
-      "[[ -1.57441159e-06  -1.57772741e-06  -1.45891873e-06 ...,  -1.59082624e-06\n",
-      "   -1.58431145e-06  -1.59152312e-06]\n",
-      " [  3.35313626e-07   2.62089756e-05  -1.70342628e-06 ...,  -1.70323368e-06\n",
-      "    9.99985350e-01  -1.69302179e-06]\n",
-      " [  6.57110095e-04   6.19226593e-03  -3.55112923e-04 ...,  -3.51745563e-04\n",
-      "    9.95548429e-01  -2.72781988e-04]\n",
-      " ..., \n",
-      " [ -6.46500489e-06  -6.44906528e-06  -6.08788953e-06 ...,  -5.89226992e-06\n",
-      "   -6.45000146e-06  -6.44374483e-06]\n",
-      " [ -4.90078489e-05   1.00040277e+00  -1.11118866e-05 ...,  -4.76586889e-05\n",
-      "   -4.91237558e-05  -4.93641643e-05]\n",
-      " [ -2.99193856e-06  -2.99422431e-06  -2.96956578e-06 ...,   1.00002660e+00\n",
-      "   -3.02093184e-06  -2.95644880e-06]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open(\"cifar_10_logs_3.txt\") as f:\n",
-    "    content = f.readlines()\n",
-    "    \n",
-    "new_content_3 = []\n",
-    "for i, l in enumerate(content):\n",
-    "    # Merge lines\n",
-    "    if l[:4] == \"INFO\":\n",
-    "        try:\n",
-    "            l = content[i]\n",
-    "            l = l[33:-2]\n",
-    "            if i+2 < len(content):\n",
-    "                if content[i+2][:4] == \"INFO\":\n",
-    "    #             print(\"hey\")\n",
-    "                    l += content[i+1][:-2]\n",
-    "                else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "                    l += content[i+2][:-2]\n",
-    "            else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "\n",
-    "            # Clean unwanted characters\n",
-    "            for char in [\"[\", \"]\", \",\"]:\n",
-    "    #             print(\"replacing: \"+ char, l)\n",
-    "                l.replace(char, \" \")\n",
-    "    #             print(l)\n",
-    "\n",
-    "            parts = l.split(\" \")\n",
-    "    #         print(parts)\n",
-    "            # Convert to array\n",
-    "            lol = []\n",
-    "            for p in parts:\n",
-    "                try:\n",
-    "                    lol.append(float(p))\n",
-    "                except:pass\n",
-    "    #         print(lol)\n",
-    "            hello = np.array(lol)\n",
-    "    #         print(hello, hello.shape)\n",
-    "            if hello.shape[0] != 10:\n",
-    "                print(\"LOL\")\n",
-    "                break\n",
-    "            new_content_3.append(softmax(hello))\n",
-    "            \n",
-    "        except:pass\n",
-    "            \n",
-    "    else: pass\n",
-    "    \n",
-    "    if i%1000 == 0: print(\"1000 more\")\n",
-    "        \n",
-    "print(np.array(new_content_3).shape)\n",
-    "cleaned_3 = np.array(new_content_3)\n",
-    "print(cleaned_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler += cleaned_3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "(10000, 10)\n",
-      "[[ -1.61031776e-06  -1.57999949e-06  -1.47885537e-06 ...,  -1.61145076e-06\n",
-      "   -1.59693400e-06  -1.61415272e-06]\n",
-      " [ -8.93514509e-07   9.21749650e-06  -9.55381190e-07 ...,  -9.55941850e-07\n",
-      "    9.99998325e-01  -9.44531811e-07]\n",
-      " [ -1.30832186e-05   1.39098117e-04  -1.39199934e-05 ...,  -1.38905197e-05\n",
-      "    9.99969839e-01  -1.23690552e-05]\n",
-      " ..., \n",
-      " [ -7.42248002e-06  -7.37042783e-06  -6.69785813e-06 ...,  -6.63722754e-06\n",
-      "   -7.38086397e-06  -7.37749568e-06]\n",
-      " [ -1.82307294e-04   1.00139259e+00   1.60950997e-04 ...,  -2.00064307e-04\n",
-      "   -1.99131945e-04  -1.93583899e-04]\n",
-      " [ -2.66235687e-06  -2.64394052e-06  -2.68672693e-06 ...,   1.00002362e+00\n",
-      "   -2.70357056e-06  -2.61416855e-06]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open(\"cifar_10_logs_4.txt\") as f:\n",
-    "    content = f.readlines()\n",
-    "    \n",
-    "new_content_4 = []\n",
-    "for i, l in enumerate(content):\n",
-    "    # Merge lines\n",
-    "    if l[:4] == \"INFO\":\n",
-    "        try:\n",
-    "            l = content[i]\n",
-    "            l = l[33:-2]\n",
-    "            if i+2 < len(content):\n",
-    "                if content[i+2][:4] == \"INFO\":\n",
-    "    #             print(\"hey\")\n",
-    "                    l += content[i+1][:-2]\n",
-    "                else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "                    l += content[i+2][:-2]\n",
-    "            else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "\n",
-    "            # Clean unwanted characters\n",
-    "            for char in [\"[\", \"]\", \",\"]:\n",
-    "    #             print(\"replacing: \"+ char, l)\n",
-    "                l.replace(char, \" \")\n",
-    "    #             print(l)\n",
-    "\n",
-    "            parts = l.split(\" \")\n",
-    "    #         print(parts)\n",
-    "            # Convert to array\n",
-    "            lol = []\n",
-    "            for p in parts:\n",
-    "                try:\n",
-    "                    lol.append(float(p))\n",
-    "                except:pass\n",
-    "    #         print(lol)\n",
-    "            hello = np.array(lol)\n",
-    "    #         print(hello, hello.shape)\n",
-    "            if hello.shape[0] != 10:\n",
-    "                print(\"LOL\")\n",
-    "                break\n",
-    "            new_content_4.append(softmax(hello))\n",
-    "            \n",
-    "        except:pass\n",
-    "            \n",
-    "    else: pass\n",
-    "    \n",
-    "    if i%1000 == 0: print(\"1000 more\")\n",
-    "        \n",
-    "print(np.array(new_content_4).shape)\n",
-    "cleaned_4 = np.array(new_content_4)\n",
-    "print(cleaned_4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler += cleaned_4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "1000 more\n",
-      "(10000, 10)\n",
-      "[[ -4.31199980e-06  -4.19994704e-06  -2.74511017e-06 ...,  -4.34281428e-06\n",
-      "   -4.30919596e-06  -4.34810738e-06]\n",
-      " [  4.99193273e-08   1.46293575e-07  -1.73021390e-07 ...,  -1.73213024e-07\n",
-      "    1.00000101e+00  -1.71529301e-07]\n",
-      " [ -4.89897496e-05   1.26324436e-02  -5.63667124e-05 ...,  -5.64040064e-05\n",
-      "    9.87806949e-01  -5.31427947e-05]\n",
-      " ..., \n",
-      " [ -3.01398472e-05  -2.88805795e-05  -2.96235549e-05 ...,  -2.67090683e-05\n",
-      "   -2.93012213e-05  -2.97486135e-05]\n",
-      " [ -6.31877016e-05   1.00053530e+00   6.00985348e-05 ...,  -7.62089470e-05\n",
-      "   -7.53303524e-05  -7.64587367e-05]\n",
-      " [ -9.39931892e-06  -9.19098656e-06  -9.17715948e-06 ...,   1.00008256e+00\n",
-      "   -9.38361441e-06  -9.08778681e-06]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "with open(\"cifar_10_logs_5.txt\") as f:\n",
-    "    content = f.readlines()\n",
-    "    \n",
-    "new_content_5 = []\n",
-    "for i, l in enumerate(content):\n",
-    "    # Merge lines\n",
-    "    if l[:4] == \"INFO\":\n",
-    "        try:\n",
-    "            l = content[i]\n",
-    "            l = l[33:-2]\n",
-    "            if i+2 < len(content):\n",
-    "                if content[i+2][:4] == \"INFO\":\n",
-    "    #             print(\"hey\")\n",
-    "                    l += content[i+1][:-2]\n",
-    "                else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "                    l += content[i+2][:-2]\n",
-    "            else:\n",
-    "                    l += content[i+1][:-2]\n",
-    "\n",
-    "            # Clean unwanted characters\n",
-    "            for char in [\"[\", \"]\", \",\"]:\n",
-    "    #             print(\"replacing: \"+ char, l)\n",
-    "                l.replace(char, \" \")\n",
-    "    #             print(l)\n",
-    "\n",
-    "            parts = l.split(\" \")\n",
-    "    #         print(parts)\n",
-    "            # Convert to array\n",
-    "            lol = []\n",
-    "            for p in parts:\n",
-    "                try:\n",
-    "                    lol.append(float(p))\n",
-    "                except:pass\n",
-    "    #         print(lol)\n",
-    "            hello = np.array(lol)\n",
-    "    #         print(hello, hello.shape)\n",
-    "            if hello.shape[0] != 10:\n",
-    "                print(\"LOL\")\n",
-    "                break\n",
-    "            new_content_5.append(softmax(hello))\n",
-    "            \n",
-    "        except:pass\n",
-    "            \n",
-    "    else: pass\n",
-    "    \n",
-    "    if i%1000 == 0: print(\"1000 more\")\n",
-    "        \n",
-    "print(np.array(new_content_5).shape)\n",
-    "cleaned_5 = np.array(new_content_5)\n",
-    "print(cleaned_5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "ensembler += cleaned_5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(ensembled, y_test):\n",
-    "    y_hat = np.argmax(ensembled/3, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from keras.utils import np_utils\n",
-    "num_classes = 10\n",
-    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
-    "y_test = np_utils.to_categorical(y_test,num_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9529\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9483\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler-cleaned, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9482\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler-cleaned_2, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9486\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler-cleaned_3, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9496\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler-cleaned_4, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9505\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler-cleaned_5, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.9487\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(accuracy(ensembler-cleaned-cleaned_2, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/cifar/Wide_ResNets/16_4/logs/log_1.txt b/cifar/Wide_ResNets/16_4/logs/log_1.txt
deleted file mode 100644
index 855e6ce..0000000
--- a/cifar/Wide_ResNets/16_4/logs/log_1.txt
+++ /dev/null
@@ -1,200 +0,0 @@
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 1.461745286079319, "train_acc": 46.374, "test_loss": 1.097357508502429, "test_acc": 59.53999999999999, "epoch": 1, "num_classes": 10, "n_parameters": 2748890, "train_time": 105.08331942558289, "test_time": 6.562161922454834}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 1.0316256909724086, "train_acc": 63.291999999999994, "test_loss": 0.9611511034301564, "test_acc": 65.7, "epoch": 2, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.53286933898926, "test_time": 6.48010516166687}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.8644042199529955, "train_acc": 69.304, "test_loss": 0.906831101526188, "test_acc": 68.41, "epoch": 3, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.43698334693909, "test_time": 6.475081920623779}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.7463119200733311, "train_acc": 74.00999999999999, "test_loss": 0.7007935892177534, "test_acc": 75.18, "epoch": 4, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.40570259094238, "test_time": 6.47916316986084}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.6722305378188258, "train_acc": 76.63, "test_loss": 0.6836812394329265, "test_acc": 76.63, "epoch": 5, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.38727641105652, "test_time": 6.475099086761475}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.625322909382603, "train_acc": 78.372, "test_loss": 0.6982788356799113, "test_acc": 76.51, "epoch": 6, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.34799647331238, "test_time": 6.468293190002441}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.5879726947268562, "train_acc": 79.53399999999999, "test_loss": 0.6593171931520293, "test_acc": 77.23, "epoch": 7, "num_classes": 10, "n_parameters": 2748890, "train_time": 103.5339937210083, "test_time": 6.476840257644653}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.553233408485837, "train_acc": 80.94600000000001, "test_loss": 0.5575856064693837, "test_acc": 81.22, "epoch": 8, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.41291308403015, "test_time": 6.4851768016815186}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.532450068713454, "train_acc": 81.696, "test_loss": 0.7034656956226011, "test_acc": 76.89, "epoch": 9, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.36155319213867, "test_time": 6.476631164550781}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.5172826469401874, "train_acc": 82.234, "test_loss": 0.50364378245571, "test_acc": 82.36, "epoch": 10, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.33419585227966, "test_time": 6.457509994506836}
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-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.47008809355823583, "train_acc": 83.75399999999999, "test_loss": 0.5896720233597333, "test_acc": 80.2, "epoch": 13, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.3203673362732, "test_time": 6.4736902713775635}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.46424619003634926, "train_acc": 84.206, "test_loss": 0.4889691570137121, "test_acc": 83.39999999999999, "epoch": 14, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.36832284927368, "test_time": 6.476925849914551}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.45437669311947837, "train_acc": 84.32, "test_loss": 0.5214102762409404, "test_acc": 82.50999999999999, "epoch": 15, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.33553838729858, "test_time": 6.476226806640625}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_1", "ngpu": 1, "gpu_id": "0", "train_loss": 0.4461237724174929, "train_acc": 84.72999999999999, "test_loss": 0.6130215789698348, "test_acc": 79.46, "epoch": 16, "num_classes": 10, "n_parameters": 2748890, "train_time": 102.32000231742859, "test_time": 6.839731693267822}
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diff --git a/cifar/Wide_ResNets/16_4/logs/log_2.txt b/cifar/Wide_ResNets/16_4/logs/log_2.txt
deleted file mode 100644
index 558c8b1..0000000
--- a/cifar/Wide_ResNets/16_4/logs/log_2.txt
+++ /dev/null
@@ -1,200 +0,0 @@
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diff --git a/cifar/Wide_ResNets/16_4/logs/log_3.txt b/cifar/Wide_ResNets/16_4/logs/log_3.txt
deleted file mode 100644
index 61d4712..0000000
--- a/cifar/Wide_ResNets/16_4/logs/log_3.txt
+++ /dev/null
@@ -1,90 +0,0 @@
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diff --git a/cifar/Wide_ResNets/16_4/logs/log_4.txt b/cifar/Wide_ResNets/16_4/logs/log_4.txt
deleted file mode 100644
index a00604b..0000000
--- a/cifar/Wide_ResNets/16_4/logs/log_4.txt
+++ /dev/null
@@ -1,200 +0,0 @@
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diff --git a/cifar/Wide_ResNets/16_4/logs/log_5.txt b/cifar/Wide_ResNets/16_4/logs/log_5.txt
deleted file mode 100644
index 5135e43..0000000
--- a/cifar/Wide_ResNets/16_4/logs/log_5.txt
+++ /dev/null
@@ -1,200 +0,0 @@
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-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_5", "ngpu": 1, "gpu_id": "0", "train_loss": 0.00703172666101676, "train_acc": 99.956, "test_loss": 0.20418539775323263, "test_acc": 94.15, "epoch": 185, "num_classes": 10, "n_parameters": 2748890, "train_time": 212.04951524734497, "test_time": 9.173707485198975}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_5", "ngpu": 1, "gpu_id": "0", "train_loss": 0.007265857017129812, "train_acc": 99.946, "test_loss": 0.2065488906412185, "test_acc": 93.97, "epoch": 186, "num_classes": 10, "n_parameters": 2748890, "train_time": 103.16642308235168, "test_time": 6.550277233123779}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_5", "ngpu": 1, "gpu_id": "0", "train_loss": 0.007012003756430752, "train_acc": 99.966, "test_loss": 0.20720857619956323, "test_acc": 94.08, "epoch": 187, "num_classes": 10, "n_parameters": 2748890, "train_time": 103.18177533149719, "test_time": 6.538147211074829}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_5", "ngpu": 1, "gpu_id": "0", "train_loss": 0.006886654862147921, "train_acc": 99.964, "test_loss": 0.2037428918329975, "test_acc": 94.19999999999999, "epoch": 188, "num_classes": 10, "n_parameters": 2748890, "train_time": 103.16913437843323, "test_time": 6.556946754455566}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_5", "ngpu": 1, "gpu_id": "0", "train_loss": 0.0067240664285257495, "train_acc": 99.966, "test_loss": 0.20574695777289476, "test_acc": 94.23, "epoch": 189, "num_classes": 10, "n_parameters": 2748890, "train_time": 103.24545764923096, "test_time": 6.545358896255493}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_5", "ngpu": 1, "gpu_id": "0", "train_loss": 0.006984655025993924, "train_acc": 99.966, "test_loss": 0.2071010122853744, "test_acc": 94.03, "epoch": 190, "num_classes": 10, "n_parameters": 2748890, "train_time": 103.13028287887573, "test_time": 6.549396753311157}
-json_stats: {"model": "resnet", "depth": 16, "width": 4.0, "dataset": "CIFAR10", "dataroot": ".", "dtype": "float", "groups": 1, "nthread": 4, "batchSize": 128, "lr": 0.1, "epochs": 200, "weightDecay": 0.0005, "epoch_step": "[60,120,160]", "lr_decay_ratio": 0.2, "resume": "", "randomcrop_pad": 4, "cuda": false, "save": "logs_5", "ngpu": 1, "gpu_id": "0", "train_loss": 0.006674313736731744, "train_acc": 99.97, "test_loss": 0.20425087746374215, "test_acc": 94.19, "epoch": 191, "num_classes": 10, "n_parameters": 2748890, "train_time": 103.21909713745117, "test_time": 6.56070613861084}
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--- a/color_output/.ipynb_checkpoints/color_output-checkpoint.ipynb
+++ /dev/null
@@ -1,994 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Plot the output of a random network to visualize\n",
-    "    smoothness as done in Ramachandran et al. 2017 to gain\n",
-    "    intuition about different e-swish variants. \n",
-    "\"\"\"\n",
-    "\n",
-    "# Import libraries\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import keras.backend as K \n",
-    "import tensorflow as tf\n",
-    "from keras.models import Sequential\n",
-    "from keras.optimizers import Optimizer\n",
-    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
-    "from keras.callbacks import TensorBoard, EarlyStopping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Different activations to test<\n",
-    "\n",
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "def leaky_relu(x):\n",
-    "    return K.maximum(0.3*x,x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(40000, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "a, res= 100, 1 # Area and resolution\n",
-    "x = np.array([[(i)/res, (j)/res] for i in range(-a, +a) for j in range(-a, +a)])\n",
-    "print(x.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def create_model(act, l):\n",
-    "    np.random.seed(42)\n",
-    "    tf.set_random_seed(42)\n",
-    "    # with tf.device('/cpu:0'): # Makes it more deterministic (CuDNN indetermination)\n",
-    "    model = Sequential()\n",
-    "    model.add(Dense(128, activation=act, input_dim=2))\n",
-    "    for i in range(l):\n",
-    "        model.add(Dense(128, activation=act, kernel_initializer='random_normal', bias_initializer='zeros'))\n",
-    "    model.add(Dense(1, activation=act))\n",
-    "\n",
-    "    model.compile(loss='categorical_crossentropy', optimizer='adamax', metrics=['accuracy'])\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['e_swish', 'swish', 'relu', 'leaky_relu', 'linear', 'elu']\n"
-     ]
-    }
-   ],
-   "source": [
-    "activations = [e_swish,  swish, \"relu\", leaky_relu, \"linear\", \"elu\"] #e_swish_1, e_swish_3,  e_swish_4,\n",
-    "names = activations[:]\n",
-    "for i,act in enumerate(names):\n",
-    "    if not isinstance(act, str):\n",
-    "        names[i] = act.__name__\n",
-    "print(names)\n",
-    "names[0] = \"e_swish\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"963\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "e_swish\n",
-      "swish\n",
-      "relu\n",
-      "leaky_relu\n",
-      "linear\n",
-      "elu\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib notebook\n",
-    "def show(activations, names):\n",
-    " # Create a 3*3 figure to display images.\n",
-    "    fig, axes = plt.subplots(2,3)\n",
-    "    fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
-    "\n",
-    "    for i, ax in enumerate(axes.flat):\n",
-    "        try:\n",
-    "            # Create the model\n",
-    "            act, act_name = activations[i], names[i]\n",
-    "            model = create_model(act,5)\n",
-    "            pred = model.predict(x)\n",
-    "            # Print image.\n",
-    "            ax.imshow(pred.reshape(2*a,2*a), cmap=\"seismic\")\n",
-    "            # Set labels\n",
-    "            xlabel = \"Activation: {}\".format(act_name)\n",
-    "            ax.set_xlabel(xlabel)\n",
-    "            print(act_name)\n",
-    "        except: pass\n",
-    "        # Remove x and y axis.\n",
-    "        ax.set_xticks([])\n",
-    "        ax.set_yticks([])\n",
-    "    plt.show()\n",
-    "\n",
-    "show(activations, names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/color_output/.ipynb_checkpoints/output_landscape_3d-checkpoint.ipynb b/color_output/.ipynb_checkpoints/output_landscape_3d-checkpoint.ipynb
deleted file mode 100644
index 161c986..0000000
--- a/color_output/.ipynb_checkpoints/output_landscape_3d-checkpoint.ipynb
+++ /dev/null
@@ -1,5799 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np \n",
-    "import matplotlib.pyplot as plt \n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "import tensorflow as tf\n",
-    "from keras.optimizers import Optimizer\n",
-    "from keras.layers.core import Dense"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "def e_swish(x):\n",
-    "    sigmoid = K.sigmoid(x)\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "def leaky_relu(x):\n",
-    "    return K.maximum(0.3*x,x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2601, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = 25\n",
-    "x = np.array([[i, j] for i in range(-a, a+1) for j in range(-a, a+1)])\n",
-    "print(x.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act, l=5):\n",
-    "    np.random.seed(42)\n",
-    "    tf.set_random_seed(42)\n",
-    "    model = Sequential()\n",
-    "    model.add(Dense(128, activation=act, input_dim=2))\n",
-    "    for i in range(l):\n",
-    "        model.add(Dense(128, activation=act, kernel_initializer='random_normal', bias_initializer='zeros'))\n",
-    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
-    "    model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def calc(act, data=x):\n",
-    "    model = create(act)\n",
-    "    res = model.predict(data)\n",
-    "    return res"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib notebook\n",
-    "def show_1(act, act_name, data=x, lims=(0.495, 0.58)):\n",
-    "    # Part One\n",
-    "    fig = plt.figure()\n",
-    "    ax = fig.gca(projection='3d')\n",
-    "    plt.title(act_name+\" Output Landscape\")\n",
-    "    # Make data.\n",
-    "    X = np.outer(np.linspace(1,2*a+1,2*a+1), np.ones(2*a+1))-(a/2)+1\n",
-    "    Y = X.copy().T\n",
-    "    Z = calc(act, x)[:, 0].reshape(2*a+1,2*a+1)\n",
-    "    # Plot the surface\n",
-    "    print(X.shape, Y.shape, Z.shape) \n",
-    "    surf = ax.plot_surface(X, Y, Z, cmap=\"seismic\", linewidth=0, antialiased=False)\n",
-    "    # Customize the z axis.\n",
-    "    ax.set_zlim(lims[0], lims[1])\n",
-    "    # Add a color bar which maps values to colors.\n",
-    "    fig.colorbar(surf, shrink=0.5, aspect=5)\n",
-    "    # Show Part 1\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "<img 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\" width=\"924\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(\"relu\", \"ReLU\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"886\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(swish, \"Swish\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"917\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(e_swish_2, \"E-swish\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"833.6363455677824\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(leaky_relu, \"leaky\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"904.5454349399603\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(e_swish_1, \"e_swish_1\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(\"elu\", \"Elu\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"971\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(\"selu\", \"selu\", x, lims=(0.495, 0.58))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/color_output/color_output.ipynb b/color_output/color_output.ipynb
deleted file mode 100644
index 58a9e48..0000000
--- a/color_output/color_output.ipynb
+++ /dev/null
@@ -1,994 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Plot the output of a random network to visualize\n",
-    "    smoothness as done in Ramachandran et al. 2017 to gain\n",
-    "    intuition about different e-swish variants. \n",
-    "\"\"\"\n",
-    "\n",
-    "# Import libraries\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import keras.backend as K \n",
-    "import tensorflow as tf\n",
-    "from keras.models import Sequential\n",
-    "from keras.optimizers import Optimizer\n",
-    "from keras.layers.core import Dense, Dropout, Activation, Flatten\n",
-    "from keras.callbacks import TensorBoard, EarlyStopping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Different activations to test<\n",
-    "\n",
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "def leaky_relu(x):\n",
-    "    return K.maximum(0.3*x,x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(40000, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "a, res= 100, 1 # Area and resolution\n",
-    "x = np.array([[(i)/res, (j)/res] for i in range(-a, +a) for j in range(-a, +a)])\n",
-    "print(x.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def create_model(act, l):\n",
-    "    np.random.seed(42)\n",
-    "    tf.set_random_seed(42)\n",
-    "    # with tf.device('/cpu:0'): # Makes it more deterministic (CuDNN indetermination)\n",
-    "    model = Sequential()\n",
-    "    model.add(Dense(128, activation=act, input_dim=2))\n",
-    "    for i in range(l):\n",
-    "        model.add(Dense(128, activation=act, kernel_initializer='random_normal', bias_initializer='zeros'))\n",
-    "    model.add(Dense(1, activation=act))\n",
-    "\n",
-    "    model.compile(loss='categorical_crossentropy', optimizer='adamax', metrics=['accuracy'])\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['e_swish', 'swish', 'relu', 'leaky_relu', 'linear', 'elu']\n"
-     ]
-    }
-   ],
-   "source": [
-    "activations = [e_swish,  swish, \"relu\", leaky_relu, \"linear\", \"elu\"] #e_swish_1, e_swish_3,  e_swish_4,\n",
-    "names = activations[:]\n",
-    "for i,act in enumerate(names):\n",
-    "    if not isinstance(act, str):\n",
-    "        names[i] = act.__name__\n",
-    "print(names)\n",
-    "names[0] = \"e_swish\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"963\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "e_swish\n",
-      "swish\n",
-      "relu\n",
-      "leaky_relu\n",
-      "linear\n",
-      "elu\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib notebook\n",
-    "def show(activations, names):\n",
-    " # Create a 3*3 figure to display images.\n",
-    "    fig, axes = plt.subplots(2,3)\n",
-    "    fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
-    "\n",
-    "    for i, ax in enumerate(axes.flat):\n",
-    "        try:\n",
-    "            # Create the model\n",
-    "            act, act_name = activations[i], names[i]\n",
-    "            model = create_model(act,5)\n",
-    "            pred = model.predict(x)\n",
-    "            # Print image.\n",
-    "            ax.imshow(pred.reshape(2*a,2*a), cmap=\"seismic\")\n",
-    "            # Set labels\n",
-    "            xlabel = \"Activation: {}\".format(act_name)\n",
-    "            ax.set_xlabel(xlabel)\n",
-    "            print(act_name)\n",
-    "        except: pass\n",
-    "        # Remove x and y axis.\n",
-    "        ax.set_xticks([])\n",
-    "        ax.set_yticks([])\n",
-    "    plt.show()\n",
-    "\n",
-    "show(activations, names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/color_output/output_landscape_3d.ipynb b/color_output/output_landscape_3d.ipynb
deleted file mode 100644
index 161c986..0000000
--- a/color_output/output_landscape_3d.ipynb
+++ /dev/null
@@ -1,5799 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np \n",
-    "import matplotlib.pyplot as plt \n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "import keras.backend as K\n",
-    "from keras.models import Sequential\n",
-    "import tensorflow as tf\n",
-    "from keras.optimizers import Optimizer\n",
-    "from keras.layers.core import Dense"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "def e_swish(x):\n",
-    "    sigmoid = K.sigmoid(x)\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "def leaky_relu(x):\n",
-    "    return K.maximum(0.3*x,x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(2601, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = 25\n",
-    "x = np.array([[i, j] for i in range(-a, a+1) for j in range(-a, a+1)])\n",
-    "print(x.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act, l=5):\n",
-    "    np.random.seed(42)\n",
-    "    tf.set_random_seed(42)\n",
-    "    model = Sequential()\n",
-    "    model.add(Dense(128, activation=act, input_dim=2))\n",
-    "    for i in range(l):\n",
-    "        model.add(Dense(128, activation=act, kernel_initializer='random_normal', bias_initializer='zeros'))\n",
-    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
-    "    model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def calc(act, data=x):\n",
-    "    model = create(act)\n",
-    "    res = model.predict(data)\n",
-    "    return res"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "%matplotlib notebook\n",
-    "def show_1(act, act_name, data=x, lims=(0.495, 0.58)):\n",
-    "    # Part One\n",
-    "    fig = plt.figure()\n",
-    "    ax = fig.gca(projection='3d')\n",
-    "    plt.title(act_name+\" Output Landscape\")\n",
-    "    # Make data.\n",
-    "    X = np.outer(np.linspace(1,2*a+1,2*a+1), np.ones(2*a+1))-(a/2)+1\n",
-    "    Y = X.copy().T\n",
-    "    Z = calc(act, x)[:, 0].reshape(2*a+1,2*a+1)\n",
-    "    # Plot the surface\n",
-    "    print(X.shape, Y.shape, Z.shape) \n",
-    "    surf = ax.plot_surface(X, Y, Z, cmap=\"seismic\", linewidth=0, antialiased=False)\n",
-    "    # Customize the z axis.\n",
-    "    ax.set_zlim(lims[0], lims[1])\n",
-    "    # Add a color bar which maps values to colors.\n",
-    "    fig.colorbar(surf, shrink=0.5, aspect=5)\n",
-    "    # Show Part 1\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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-       "<img 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\" width=\"924\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(\"relu\", \"ReLU\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"886\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(swish, \"Swish\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"917\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(e_swish_2, \"E-swish\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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yWuAqriSIlZfX/dX3ChJwBONA8WtlDjEekEl+899QPIcsrK6jQQ9uK6mhcqk7q9x+0b21v/6r/9qB39r4QQCeRlQrtn9xgKLG7XbsDYCp3FQ6eP+SqIpXLqvuOIKOeusszrRr4guNXQFFCpD32FdXwgKKFSGsIulNShUBrSZnWgpcVBpk4oAE1jmoiXKyDxKVksaJR2ICbznnntMzUfbSKpik/DEQSVgiVssmTNxdXQTubjy0w+LFKUbABmyb8aV8IjbMuLlRowYYSyjlIIA2NzGOpgzwMr8beZZ+hDjSYIYLFp2/WQSxULLeS54WL34L1DhNp9EPVhz7bUttOIi7Fq7bAbUKFTiPsu5QKBv88n+aq3C7D8xtLYBZQA4cbNpoZL9AJAZp1qiHlyeAWwsfG6bNWuW0cfGo9pEPVi3KelhLaj2HJuoh+RCJAMibvP73/9++5C8qNhzzz2Nm28cVGJdjEvUQ/8lS5aYlxDMgfhP7hfmQDyv27A2sxbuK22qQDMpoFDZTLulc+2sCihUBrTzCpUBbWYnWkq5kiJY1iiDwMM0mWB5QObBm3IPuAny8E5btGhRu2skmVrJuEpynd/+9rfmARsoKweVnM8xLILA6Ve/+lVTgiIKscCpzfw5Y8YMU5/Qt2ERpbwF/8V1luRCZGIFLnBbxSpJfF/UGkecHKBL2QkSwJARFD0orUFGUxc8bEZasqJS6xD3X2IIsdRGS4pgXQMqsKwBk8AyrqS24XqLqy4WYizAzBXAww2V60ahkvkBO1jncBVGu7jaia5eABfrjmb2tX3QiT64vwJK7Cv/Br7RACDkPkgLlVyHWpeXX355e0kRIBWAjZYUwe2Ve48EOOiLyzX3IfPgv/fee69QxoSGTkA9cZa2pAjZWXGB5h7gPgNSGQfLL3vFSwLWwT3A/QqsloNKW1KEufIiBDjGYvrlL3+5XV6szFh4yRJLzUvcwdH62WefFUrycF9h1damCjSTAgqVzbRbOtfOqoBCZUA7r1AZ0GZ2oqWUg0okAHbIiInVBasf1kmsPCRCcetUAjVY2EhmwoM3FihcC3nwx6pYCSqt1Nb1EMslD/i2/APHrSsmAEByl6SN+ECgFUsi8ZVkMcUSibsu9TCBp2gDPkjmAnjiBkxc5ze+8Q3TDViIurqyTmLqgFeA28Kfqy+AhjstEIOWwAVumLZ8hZ0DLr7oCZBvs802xsIKjJCgKAqVgA3QB3DaJElxcY1RqAReyzVbexGt2RfiFqnNSFwloM1LgwsvvLAmqGSO//M//2P2hbn069fPgD3lRwBlm6gHgMOlFQAHzLD09erVy4AkYBqNXWRuuEtjAQVIibcE7rFMYrWm8RKAdTEm4wHJvNDgnovurY2p5CUE0Iu7NhZrzsEdG8t5tFEXlf3CJRyrNntIKRleaPBSgRqW2lSBZlJAobKZdkvn2lkVUKgMaOcVKgPaTF1KoRSgoDyWJiCEh/9mapWgvZnW0VnnaqHStcp2Vi103Z1XAYXKzrv3uvLmUUChsnn2qupMFSqrSqQdVIFUCuD+igsjmWajsXWpBqzjSQqVdRQ7h0spVOYgqg7ZdAooVDbdlumEO6ECCpUBbbpCZUCbqUtpuAKvvPKKcR8keyxxlriekjW02ZpCZbPtWMf5KlQ29/7p7LNRQKEyGx11FFUgTwUUKvNUt85jK1TWWXC9XNAKWBgjqyaJcn7xi19Ily5dmm7NCpVNt2UdJqxQ2dz7p7PPRgGFymx01FFUgTwVUKjMU906j61QWWfB9XKqgCqgCqgCqoAqkLsCCpW5S6wXUAVqVkChsmYJizOAQmVx9kJnogqoAqqAKqAKqALZKKBQmY2OOooqkKcCCpV5qlvnsRUq6yy4Xk4VUAVUAVVAFVAFcldAoTJ3ifUCqkDNCihU1ixhcQZQqCzOXuhMVAFVQBVQBVQBVSAbBRQqs9FRR1EF8lRAoTJPdes8tkJlnQXXy6kCqoAqoAqoAqpA7gooVOYusV5AFahZAYXKmiUszgAKlcXZC52JKqAKqAKqgCqgCmSjgEJlNjrqKKpAngooVOapbp3HVqiss+B6OVVAFVAFAlXg/fffL7uySsc4qZbjnMufD37wg+aPNlUABRQq9T5QBYqvgEJl8ffIe4YKld5SaUdVQBVQBXJRoBagqgZktYwdPbfav6PiVLs2/deuXSttbW3Sv39/IKB9CJ9zbWf6vvvuu7LJJpvIVlttlcse6aDNp4BCZfPtmc648ymgUBnQnitUBrSZuhRVoJMpUA08ajley7lR0MsCxl588UXp3bu3bLbZZhV3udq8a71FXPBLO5Y7xsqVK2X27NkyduzYRMO5Y/z73/82cLrRRhtJ165d1VqZSMlwOytUhru3urJwFFCoDGcvcRkq768U0Dp1KaqAKrBegWpf+1qO13JurTD2yiuvGKjYfPPNE293tXlXGhDA8Tk/LZAx9sSJE2W//faTbbfdNvHa3BOi1kDGBsri/lvpWPQc29d+7jsuMPj6669L9+7dO8zB53x7Lda38cYby/Dhw2W77baTLbbYoiaN9OQwFFCoDGMfdRVhK6BQGdD+KlQGtJm6lLooUA0e8oorS3LduL72Mx7geejGVdC2amNXE7bW86uN7wtjU6dOlcGDB0uvXr02GNJ3jHJziYOxeoHYv/71L1m8eLGxVAJPaSAwDvqS7hsa8AeLYNx/Kx2LnmP7vvXWW7J69WppbW3dYFzf8VjHggULpEuXLjJw4EC1Vlb7QnWS4wqVnWSjdZlNrYBCZVNvX8fJK1QGtJkFX0q1B9hajmd5Lg/wxGfhZuhjgap27UrbUsu5PtsdB1L33HOP7LXXXsYy5NNqhTGuEQWyLKxccRaxp59+2oAXcOECXxKrl0/fNPtWDcTs8TiQQkOssMDypptumjnYVbq2eyyLeyF6zz3//POybNkyOeCAA3xux9g+3Avo8+ijj8ree+8t3bp1U2tlajXDOVGhMpy91JWEq4BCZUB7q1AZ0GbGLMV1D4tbabWH4yRWt2jfWsautivVxq52fqXja9askZkzZ8ohhxxSNX6t0jhZPIBXclVM6244b948YxXacsstY10ek7gvJulr55t0bypZxKIwhBWWRC28EPABpSR9on2TnFvrvYCL6LRp0+TQQw9N5dqbVPN69udFwKpVq+RDH/pQ6styH/IiaO7cuWbfcREeMGBAB2t86sH1xKZVQKGyabdOJ96JFFCoDGizFSoD2syYpbz99tsdYr2ygrEXXnjBWAbiHgRrfYCu9fw4yxjrtn94AK0EQ++8847MmTNH9thjD2MVqmS5Kmdxq+QWmQbE7NyT3K3lXBW5J4Au3F/LWc98gMnCXtZ9o5axJPcDVthhw4ZJz549k0hV+L5vvvmmzJgxQ0aPHm3uyZDak08+KXzniIdM2yxUvvHGG/LII4+YYcaMGWNenCS5f9JeX88rpgIKlcXcF52VKuAqoFAZ0P2gUBnQZsYshYe1JCAZfQArB2JLly41MV4jRoxoWIKPJHFlaXYZuKHmnWsp84nx8ok58wGxcjFoPuNboIxb95QpU2TQoEHSp0+fNLIU+hyS2QAnoUElMYf33XefyZAaWh3Gxx57zLzc4CVO2sZvAb91aDNr1iwBwg8++GBjtSYGVVvnVEChsnPuu666uRRQqGyu/ao4W4XKgDYzZilPPfWUkLK/HIBVs5olVSdt8g4fUMobxOz4rHn8+PEm0yaZJENrJLPBNbBv376hLc1kSCVetEePHkGt7bXXXpMHH3xQxo0bF5zljXIiWBS5J9M2GwcNVC5atEiI0+TlAvcBSanUWplW2eY+T6GyufdPZ985FFCoDGifFSoD2syYpeCmipXDx8KWBNqIXSMpBi5meSfyaMQOhQonaElsHoXm+/Xr1whpc73mhAkTTKIW3yREuU4mw8F5MYRb5+GHH57hqMUYClgG/nbdddfUE/rnP/8p7733nrFU8pvHn6233lr23HNPA6xupuPUF9ETm04Bhcqm2zKdcCdUQKEyoE1XqAxoM2OWwsMWb/GzbsQukcwGqAzxgW3y5MkyZMgQk0k0tHbvvffKzjvvLC0tLaEtTUKFSuKXSbDE9y20xu8IiaN22GGH1EtzofKZZ54xL9JI/kN8LcCqsZWppW3qExUqm3r7dPKdRAGFyoA2WqEyoM2MWQpAyQNX1o1kL7hRErcUYqHx6dOnG8tJiNY8Er7wAF/LQ3zW91NW4+G2TPIoSkqE1F566SUhoc2oUaNCWpZZC5bz3XbbTbbffvvUa8NKyW8dXhPoZGPBibMcOnSoia0M8eVXasE6yYkKlZ1ko3WZTa2AQmVTb1/HyStUBrSZdYRKYjGxClFbbptttglOxCysJ0UVhYQvwPKOO+5Y1CmmnleoULlkyRJ59tlnzUuc0FqtGXsBSOuRAVRi0d18883NPY5rLQmAqO+p1srQ7pzq61GorK6R9lAFGq2AQmWjdyDD6ytUZihmAYcC/niLn0cLNdMmWj3wwAPG9RU30dDa/fffb5L07LTTTqEtTe6+++4gEyyRaZk4wQMPPDCoPQMIa02KxRj8xvFbB1SS+AdLNfc3Vsu33nrLuMF27dpVrZVB3T3VF6NQWV0j7aEKNFoBhcpG70CG11eozFDMAg6VJ1Ti/rr77rvX5LZWQMnMlChLQAF13PJCa6ECswWU/fff3+xdSI1spsuWLTOeASE1YHDSpEkycuRIA31pWhQqsU4SL4ylcu3ateYFEfHRWCtxg9VMsGlUbs5zFCqbc9901p1LAYXKgPZboTKgzYxZCg9c7777bi6LDNmNcs6cOdKlSxcZOHBgLto1ctAssm02cv7lrh0yVFImY/ny5cYKG1ID+oipPPTQQ2WzzTbrUPOW/eSlWLWyRxwHFIFSLJXEDPOyC4ikUVaJxD2UGAEqN91005Ak1LVUUEChUm8PVaD4CihUFn+PvGeoUOktVVN2zBMqseZRx5HyFKE1CrJTngALR2gt1H0LOc736aefFsr47LPPPrG3o01MY0EsyX/LwVs1mKt0jSRASIIdoJBzkjS3TBIxlSTkIdMrHhT83SZrIlkPLt+8IOrTp49aK5OI3OR9FSqbfAN1+p1CAYXKgLZZoTKgzSyzFB6q8mjUqcS6MGjQoDyGb+iYjz/+uMkmycNpaO3hhx82yZVCc+1NC5U+QOYLSVnAXNwYZFtmfWQwdY+7f09ynwJkPrVrbZ8kNWxt3+g5cf9+8803ZeHChSZjL8ej55b7t50/a0aX+fPnC2WO9tprLyFzM+O57rRA+auvviojRoxQa2WSG6XJ+ypUNvkG6vQ7hQIKlQFts0JlQJtZZim4vya1AvioAnhhISAJRmgNlzkSfPAQGlp75JFHzIP1gAEDal6aC2SNBi9eAqxcudIUvQdGopa2Spa3JEJUAzIfWPMFLzsWJUVYHxl7k4CXC2ruvIoSV5hF/U32FfdW3LqxRpKc58Mf/rDJAGsbv4G46/MihQRcxNwWRYMk9572TaaAQmUyvbS3KtAIBRQqG6F6TtdUqMxJ2AINazMjZj0lLAw8zJVzycv6evUcjwLqr732mrF45NmSWLYsFEX/mxTmVqxYYcAE+EoCXuWun0QfC2RZWsDsWOhAQhvAC6Dwgbs082gEjGCJY3177rlnErkL3zeLUincl0Aj1shVq1aZl0GUXtl44407rJ+SLCQ7QscxY8YYLwttYSugUBn2/urqwlBAoTKMfTSrUKgMaDPLLCUvqGx0RsokQJYUvHCVw52ODJJZgFe5MZLefb5Wqkquhzx4Ey9KzFle4FXJbTHpmn37YzWn5mEtmUR9r1XvfnPnzjWur4MHD673pXO9Hr8hS5cuNZbFtM1CJb9zWCP5ro8aNWoDSyTHqT8LbFKaRTPBplW8ec5TqGyevdKZdl4FFCoD2nuFyoA2s8xSbGFw97B1W4zLruiboANXQ9zXcCnLE7zKwWPSnasEOlE3QSwfxKLWE7zKWc1cQEy65rj+oSYhsoQgbJIAACAASURBVOUpLDBkoVVRxgg1G3EWHgEWKvmeAN+8ECKbbJxFGR1JeERpFmIu1VpZlDs8n3koVOajq46qCmSpgEJllmo2eCyFygZvQB0uT3wgrl+u+2LSy8YBj80sa9/4V7OipXE1TBp7VsnylmTNWE9wpzvkkEOSnNYUfefNm2ceuPfYY4+mmK/vJHkRMHny5HYrlO95zdAv1ORKCxYsMLUkSbCTthFrygsFfiteeOEF81uHm3DPnj03GJLfQr7bu+66q7S2thprJedpC1MBhcow91VXFZYCCpUB7adCZUCbWWYpvJkn9tHH1bEcGMYNTcwhyTHGjRsXXNKLl19+WUhENHr06OBuEOLzeMEQWmZbC5UHHXSQbLnllkHtW6i1RbN4wYEnBlCJSzfutG1tbaYWJfHQUWvlE088YWIuuVc4DlSqtTKor0qHxShUhru3urJwFFCoDGcvNaYyoL0stxTe5PPglXWjHACFxgGv0AqKk8wGV7mxY8dmLVvDx+PBmofw0LL24q48ZcoUk6Rliy22aLjOWU6AOot9+/aVnXbaKcthGz5WFm69LlTiTsvvEnHDxJ/26tWrwxpxj+XeIJvuDjvsYP6otbLht0FuE1CozE1aHVgVyEwBhcrMpGz8QGqpbPwe5D0DrFJARNYtZMtQyFZYSi4AYMOHD8/6lmjoeCFDJS9vLAQ1VOSML/7QQw+ZuOX+/funHpnfNsASSyX3No2kRsR877vvvh2slbgRA5pYMHGVJXM1sZVu+ZHUE9ETC6eAQmXhtkQnpApsoIBCZUA3hUJlQJtZZil5QaUtNr/ffvvJdtttF5SQZH4lU+Rhhx22QWmCZl9oFnFsRdTg7bfflqlTp5o42C5duhRxiqnnNH36dBMHSDbikBrZWlkTZWDSNOK6bSIyXPdJQoUlEgDHukvdSupS2sZnWHsBS/5urw1Yamxlmh0o9jkKlcXeH52dKoACCpUB3QcKlQFtZpml2IQ6eayUEg7E5kXdzPK4Vj3HXLNmjfAgT2mC0GKuSFaCi+Dee+9dT0lzv1bIUIlbL4CEC2xIrVZY5rfNlkwCCmfPnm0sn4AjCXuWL18uvPSysZVcD7fYHj16mNjL5557zlgrqdmq1sqQ7qx1a1GoDG9PdUXhKaBQGdCeKlQGtJkNgMpaHwqLqn7IrpRktSV5Ew/TITWyiE6bNs2UkwgNECZNmmQymob28qbWdUWhkoRGLS0txgJp61YOGDBA+vTpY5JTYcnmZco222xj/v3AAw8YS+bOO+9s3GDVWhnSL4JCZVi7qasJVQGFyoB2VqEyoM2ssBQgKY8WagIRkhtNnDjRFGXHihFSy6I2YBH1sNblEKFywoQJMmLECGNha2Rz69vG1br1rVdr6+OSNApXVdyV42rmlqtRaz/nHEAQcORFArGnu+++ezt8u9ZKIJPj+++/f3siJ8qL8H2w1srQ3KYbea8U4dpqqSzCLugcVIHKCihUBnSHKFQGtJkVlkJSHR7Esm6h1s9Dq/HjxxvXudDiRXnQJrstawupNYPLchTK3Nqx5QCKPo888ojstttuBoaqgVYex90xk9wzlcoYcYzyHmRf3XjjjQ0cxvWP1re1/eznS5YsMS6v6IOlGnd8/k1zrZVYIrFkUnKGRD40tOUzalrusssuZi4k/NEWhgIKlWHso64ibAUUKgPaX4XKgDazwlJs3FHWqyVFPw+EQ4YMyXroho+HpZKi7I22DmUtBHFk1OHEYtNsrZJ1DEABvqg/CBi41iwXinw/j8JZ1AoXHccH5pK+2LHghOUcEAKoXPiKAlYttWjLQV3az5lLtE6ke79lEQPLnmBtxOLJSxI8J9h/ANK2RYsWmfudmFRKmBAn7c6L8iLEGVtrZWjlaJrtO57lfBUqs1RTx1IF8lFAoTIfXRsyqkJlQ2Sv+0Xzgkoe5rCChlaegg0iOcqgQYNMPFYoDagBKpctW2Yevn1AyMealsc4cfCXdB/SApGv1SwKcVHIi44TtbpVOt9CGd9dYg8PPPBAY0kLpWWRYZl7E9d+6zEBYOKy7sbUoh+wyfcYuMRS6TbuM0qb4OZuk/zwokxb8yugUNn8e6grCF8BhcqA9lihMqDNrLAUm3Y/69WGGp+HTtEkRNZKliSWrFyMWTlQq2ZFS2Mdc6+VZP8t1FQDoaJYx3B/xZWRmEoy9laykiXRodF9rUXv4IMPbo8FbPScsrh+FrVgubd5qYU7Ny+4+DclZaIurLxIIdsrsHjAAQdsMP1XXnlFHn/8cfOShTJCW265ZRZL1DEarIBCZYM3QC+vCngooFDpIVKzdFGobJadqm2eeUElBcSJacI6UK25UNYo61eS6/KwasHKnldtje5xe27e1rKk1jEeoLFUkgXTdaeMg8ck62103ywsX41eQ9z1myFWNI1u3IfUlQTi0jYLlZxPJlcAPOreyjF+/0jSw8uGOKjktwlrJt95LJm4z2psZdpdKc55CpXF2QudiSpQTgGFyoDuDYXKgDazwlKoS7hy5coOGRarWdGqWcWs6xmlHHDLq9Y/bTxZHJyViyXLMsaM0hsk6aE2YBprXFEtZVhsiDPD8hVSCxUqQ10Xrqp8x7Aspm0WKvneM9bixYuNm/Cmm266wZDE26Il931c6RDiKnlBhls4JUeIrSzqdzitXp3tPIXKzrbjut5mVEChshl3rcycFSoD2swKSwEk5s+fXzbDYlprGlYUHg6JPXThr1osmY91rdEPdKFmts3iYb6I35rVq1fLfffdJ2PHjg3KykRNUaxohx9+eFB1FF988UUDgSNHjkx9O5HACOsiVkV+4wDL1tZW6d+//wZj4t6KdZSSI9SxjDZcZJkTtUDpY7PSpp6cnthwBRQqG74FOgFVoKoCCpVVJWqeDgqVzbNXtcyUN/okrMi6hfrAi05kiqRuHVkjQ2q4vmKVIfYwpBYqVL766qsya9YsGTduXEjbJZS2wXti3333Tb0u3Fr5XQMqn3/+eSGTKy6wuONHrZW42tKX47jARq2VACn3EH9wDd92223VWpl6Z4pxokJlMfZBZ6EKVFJAoTKg+0OhMqDNrLCUvKAy1HgvpOQhlIfV0Mql8OD95JNPmtizkFqoLziWL18ujz76qLHAhtR4sUEZmBEjRqRelguVJA1jPKCxe/fuG1grcX8luysWTWpSRq2VWDL5vvObRuwlL5PUWpl6awpxokJlIbZBJ6EKVFRAoTKgG0ShMqDNrLAU4hlxE8u6hVruAJ3IJsn6hg0blrVsDR0PF8B58+bJmDFjGjqPrC++atUqk6wFi16jXaezXBtlMACe0aNHZzlsw8fCHZ+XXUOHDk09FxcqeVFCAxwXLFhgrJGutZJ7Y4cddjDfacAyaq0E3G1ZkdmzZxtrJTHVGluZensafqJCZcO3QCegClRVQKGyqkTN00Ghsnn2qpaZ5gWVjDthwgST3IKHuZBaFpaUIuqB5Wvu3Lk1Zd0s4rpChUpiYBcuXBicuzIQh0WQeOw0jd8em9UaV1Y8CwDAXXfd1dSd7NGjh/m7bffee6+xPvI7ReztzjvvLC0tLe3HcTGmliUxmcwNq+XgwYNNeZFNNtkkzRT1nAYroFDZ4A3Qy6sCHgooVHqI1CxdFCqbZadqnydFwvNoFGbfY489pHfv3nkM37AxQ63BSU0/LDEkfgmp2bqHRxxxREjLMlY1kshQ6iKkBsQRt7jbbrulWhZQidURaydQiXsrbq877bSTYN11rZX0nTp1quy1117mmiTk4Q/WSls6BNDELRawJK6SRF245mKtBCxDsn6nErwJTyogVG4rIlvVQco3RWRVHa6jl1AFalZAobJmCYszgEJlcfYi75ng/pq0rIfPnLAARN/6+5xX9D48yBN/GFfXruhzrzS/UBO/hAqV1IIFLGvJklqk+9XWq8UdlRdRWAurlSOK1pelP5lf+S8WyY033lgefPBBMxaxknzOv3v27GmslcAnv1P77befAUTOJaPujjvuaCyTtGnTppmXY4ApDcsn4xBTTWylWiuLdBf5zaVgULltF5HX1vpNvdZer4oIZnoFy1qV1PNzV0ChMneJ63cBhcr6ad3oK9m3+lnPwz4cApYhNWvNoO5dSM3CV2ixh6HCMi83sLztv//+7behhbAojEXhK8t/x4EdnyW9RtIXW5XKHZGUh98dYiVnzJhhSoFQEoTGCyFc2MkEy28fv1NuDUvKmQDs9qURUIkbf9euXc351LTEmjp8+HADmmqtbL5fwYJBJb7Wi7+cs6kSE+X/rtsq3pa0Nd+u6Yw7mwIKlQHtuEJlQJtZZSl5QSVuZ7zJHzBgQFBihlrP0cYehlb3kPIUuCz6lN6w1rIkQJQVVCW5ZtTF056b5IuG22a0Lqz9LG192iTnlbs2wIbrK9ZEd47RscutFW0oS8LLH4AbSyQAiLsqzbVWYs3kd4oyOraUiLVWAqSAKO6vAOjmm2/efkmSCfG7ueeee6q1MslNV5C+RYTKb4nINjnq87qI/FChMkeFdeisFVCozFrRBo6nUNlA8et8aZvUIuvL4ibGgxquYyG1ULNuZlXPsZq1LA8rmoWquGvz8P/mm28aS1M1cMvSWlYJiFxASgt2WNzWrl1rrHC+14qCZNG+lzbB1z777NPubpp0joyBLtaNlRho19LIeNZayQsvkh0dfPDBHS6DtZL6lkAjccaHHHJIe4wlHSlRghstGWoBU16eaWxl0p1qXP8iQuV5IkJgZV4Nf9dLFCrzklfHzUEBhcocRG3UkAqVjVK+/tfNCypJ5Y8bGkkwQmoktJkzZ05u9QFrsZZVA7pKUEVsLRlgsRDRomNV+3et1rIoXCWxelWCKgBjyZIlJptotF/0GkktdY28r0P8fmElnDhxorEMUsYjTeM+5F7GUknMKX+PWhqttbJLly4GEDnuNuaBWywZYSm1A1RGG6WFLNRjBWUsbc2hQBGh8jt1gMqLFCqb4wbVWRoFFCoDuhEUKgPazCpL4QEKsMy64YKG6+G+++6b9dBVx6sGV7VYy3iQ5GGVbJI+AJjUPbJR1jLuASw4JCghwQnAlRXYlRunHtYdQJlSEGPHjq163zRTB2pU8t2tpZ5j0dZLJuopU6YYyyFlQNI0C5Vog+sq/41aGhmXex0wx8qIJTPa+I7zG8Z3IS4Z0po1a4y1knubFxZkiK3H/ZxGEz2nowJFhMoLRWSdg3Y+7TUR+a5CZT7i6qi5KKBQmYusjRlUobIxujfiqnFQ6QNL1SxXPMwDlWRZdPvWAnTVrmmPJ9ExzspVyWLFQyvxh1j0KDuQxvUwzv0xLcAlWWulvjwkT58+XUaNGmXqBIbSQq2/GaJ7OW7KJNYZPXq0bLrppqluQQuVfC+BRuAxDirpx7XI3hqXydke53tZLikX4y9btsy4yZJhNu2cUy1UT0qtQBGh8gd1gMpvp4PKgSLyUxHBnP+GiNwkIueLyLtVNuB5Edkxpg8m/bdLn+MWw1hkGxsuIu+VyVf0foVrbS8iy0rH4/q9LCJ9Ut8semLDFFCobJj02V9YoTJ7TYs6IuDHG/co+CWZbxwQ8VBGPBvZEdPEkCV1RfSBsrj4taTWhVDhCwssmS5JWuImJUlyHxSxL+6L8+bNkzFjxhRxeqnnhPUV+MdKFkrLIlkUvztYPHnhA/ABfmjUt2/fDWQiXvL111830BhXGmTu3LlC9mBqgWKxjDabCbZ///4m06zGVjbHnVhEqCSJTt6WSpIBJcz+ypQeF5GnSyGZ/UTkShG5RURO94DKWSJyRaTfg0RXlD4DJMeLyEMi0k1EhpWByvUprtcPBty+JSJufA3jAsC/c64J/M5ujjtTZ+kqoFAZ0P2gUBnQZlZZCnGPWHNqsbjFXQJYJbMi2URDasRoTZ482TxoAsyhNO4DCsHX4npYRC1ChcoQsytn4aqM5wXfUaCSWNpFixYZICQTbPQFEtZeQBYrI+6r0bZgwQJj6cTVnT/RZhP28DswYsQI83sQkpW/iN/nLOZURKi8vERVWawvbgwKVH4jOVSeKyIYOHcQEYagfUlEril9trTCfLFU3lEFPjcSkX+XxvheaYpbeWjAl/E5EflvEfmx0x+o/KaIIKe2JldAobLJN9CdvkJlQJtZZSlYKHkIy7pllU0063nVOl4WyURqnUMe54cKy6Fm66X0BglisJKF0rAsUkMSa3na5kIlGVxJrAX8kem1T5+OXnBYKgFOwBIX2Ki1Egs3scZYJEnmE7VWch5j4AHB+FhD1VqZdufqd14RofLqOkDl15ND5fQSTH7C2R2S1AKYJ4vIb2qESvf0JFBJstyLS2Dr1txUqKzf1yj3KylU5i5x/S6gUFk/rRt9pbygMlR3SvQaP3687Lfffu217xq9h1lcH1flSZMmGVdAHoxDaaFCJdlJe/fuLTvvvHMoW2UytvKnXAyjz0KBQO5lLJVPP/20yUBN0h/gkqRhrrUSt//tt9/eJN4COKNaAoyAO7ALMEaPYwWnJAnnMj7WSr47aq302anG9SkiVOKz2T1HSVaKyBnJofIVEblBRM6JTG2JiNwc87nbDUslpTfJuEWsJICKB+68MstMApWMwZKib5+ASoCX1NG4xuJai+XyxRyl1aFzUkChMidhGzGsQmUjVG/cNbFSJc06Wm22PNzdc889NZUHqHaNRh2n7AEF1W35jUbNI8vrhrpfNssnCYhCamQ27devn+y4Y1wujOZcKa6quMDywiZtc6GSeEoaycLQa/DgwdKrV6/2oUnUQ51PrJsAaNRaCXTiGguIUu8yehz3WoCUsknUxcRqDKQCllgvtRVTgSJC5S/qAJX/tW47SHX8krMzq0WEP3ENGLxARC6LHJwvIveVXGHLbfJPRIT4SYAO33LcaHuXYiAXxZzkC5VDRWSuiHxZRK6NjHNjyeWW5DwUyGburIFYTRLgamsiBRQqm2izqk1VobKaQmEdzwMqsyhkXlSVKXtA8o+oO11R5+szLxKcTJgwwTw4b7MNL5jDaFm4VBZRiXvvvddYzoCeUBqur2SA3XvvvVMvCSslkAjUETOJlRLYAxpfe+01Uz4ESOT3iRjiYcOGybbbbmuSlUWtlWiMW2uPHj3McSzDbuzlc889Z8bEQkn5ESyX++yzj8ZWpt69+pxYRKiEjnrkuPwV5QmQaiYAXdZQGR2PTFkLROS3InJaDVBJTqMzSxldbZxnOeUAUJL04C77oxzl1aFzUEChMgdRGzWkQmWjlG/MdXkQAyqybiHCFxpRegPrB5aiUJp16yWhCQ/ZobRQoZJMvbvttpuxjIXSKtXedMscRWu/uv8GKLt06WLcX0lmBBBizSUjLNbEIUOGGA8DLJp8j4HMrl27ytKlS4010sZOWujECsn3gfsIV1eO29hL/s0LuT322MO43DI+0Mnvglori3tXFhEq8THNGyoJgkxoqcT99XoRIWGP23zcX+NugH+UlhnniuBjqYQzcKt9VESO9rzDyF6LZfXTnv21W0EUUKgsyEZkMQ2FyixUbJ4x8oJK3Mt4oGttbW0eMTxmOnPmTLMmygiE1O6++24Td9atG9ndw2jAAlYqahWG1MhADCBhPfNtlerPWjCL1oKtBHDRY0lq0MadSxw2jYQ4ccd91wls8/207qv25Q8QSAkRrInEWgKBI0eONDGQXI84VRs7yW8ilkpcccnqyvGHHnrIuM9aa+X8+fNNbUpcaGlYLoFPLK1AZUileXy1b4Z+RYRK6mPkDZUnrtsc/mfsJreptGXEQRK7+EmnE24suJJWS9QTN26tUHlQKTbzMyJyq+e9plDpKVTRuilUFm1HapiPQmUN4jXhqby15w1/1o2HOiwFWPVCajx8ug+XoawN91ceiLt3zzNlRH3VyhoqLZglga9o3yTwVe5cEsMAOwBYuT5xnydRP66+rE892Gh5It9zXnjhBeOuivXV95xoP9xQiXXEjZs4SoDPxlFireSzoUOHGmvjww8/bF42YNWkudZKLJB8z0kaBDjSiM/FRddaK+fMmWOsmDaBD7+jgCov0nBLxgKqsZVJ7rj69C0iVOIT2jPH5S8XkeOSQyUWSlxHAdFVpel9UUR+6VFSJLoaXCoIcibBT1yNSx9LJaGnx4sIgdHr3kBVbtTBfLiUUEjLjFRTq2DHFSoLtiG1TEehshb1mu/cvKCShy5c0QYOHNh8olSYMQ+jxB1iEQmp1SsBUTkwyxK+rKWLchBYp7Do+cJXJetc0oRWFrB8IMn28YEyktoALoCl29/nej5zidZ0rMd9XmtGW/YGayfgSBwlAEhCLTK42sZn3BPUncTS6JYvsdZKoJZzcJ8lwZPVgvF5UYb7LC/KopZQrgEYL1682FhDgUq1Vtbjzkl2jSJCJWa3vKGy5P+ZxFLJFwdL30IRuUREiPe4shQX6YLhJBEhY5itb/RZETlKRO7kXU0pUQ+AigsMAdPUmLTtmNJfjhWRj4nICaV/z+Lr5PTbWESWicjdTh934ynDydvrqSKC2y6JekgOBHzu5UBxsptFezdMAYXKhkmf/YUVKrPXtMgjYqUELLNuPLTxILbnnntmPXRDxysKLGdtLeOBm2Ql1t0vS8iLWueSbGAUsnygy/bBzZHkL6wrDqZ8AcvXaueCIXPIA85CjX/F3RTYS+sub8sj4YZKKRkAE3dut0QOnwGvXIM+uL+6zVorsXDiNn3QQXjcrW+cs2DBAmMJxR3WtYTSi99SrJUAP+7xaq1M8k2vT98iQuWfSua3vBSAskr0lgQqmc4gEaHiyYdF5A0RwVMXWHOLWwNyO5X+cM7+InJpCewI0MfKOVlEviMiT0XWSBmQuPaFSB3Mj5Yyux4pInfFnACQYlUdICJdRQTjLP3OL8FoXtLquDkpoFCZk7CNGFahshGqN+6aeUElkELhcbIjFrWlsV5hJQIWeDBNEnNWzQLGcZ8+ds5JNK0GZhwHvnA/JMbMx+JVbcwsgK1WMKPkA5ARhYMk2hWtry3/AhABLaG0WhN78d0hFpI/WCv5N66qUWshpUZWrVpl3F6BTrdZayUgumbNGiFxldv47gGTuIhjkbSJfNw+fP7888+bJECMg7eGtuIoUESo/HMdoPI/121BUqgszsbpTDqVAgqVAW23QmVAm+mxFPsw5tE1tku5zIy4gr366qvGUtlI+Kpk0UuyZgs4jMfficsqCnxVAzgfi1mtD/VJtKxXXx7wuQ+JjQulEe9Hoh5AGatyKI2YXl5AEYedpvEbgzbc64AjMZAHH3ywiTt1G7CItRLgi0Il/YjJxErJcdxYo43SIU888YT5TbOJfNw+vKRjfNZh3f9t3Gaadek52SpQRKj8Wx2g8uMKldneSDpargooVOYqb30HV6isr96NvhouYY8++mh7zFnSrI5J4swsmFWDIF93w3pYy1yXRvaqGSywae4p6vbhzhdSmYoXX3xR+BMSVPJ9paQI8YChxOxlUSfVhUrAjxIlvNAiBjLagD4smnEWbMbBFReLfdRSyTj83nE++8D5tsSIew0LpiT5oQ8eANqKoUARoZK0qP55nJPr+LKI4D+qlsrk2ukZjVFAobIxuudyVYXKXGQt7KBkRQSUXOCrBH2+QLhy5UozLhkWo2BWWDE8JkY9O1tI3aN703QJsf4mQIm1Mho71zSbEjNR3JQp1zN69Oj2zKTNvB7mnoX11UIlvzXWmoglF2tj1FLPSzS8KMh2TNKtaCMZFzrzMiJq6aQvLtX8iXOv5ThzYY+wUAKmuCmrtbIYd2kRoXJ8HaBynEJlMW5AnYWXAgqVXjI1RyeFyubYp6xmaRNcZDWeHYeHtlmzZsnhhx+eS8KSrOfrOx4Pk7jWkawjpIZ1hvIIJBkJpeH6itWIh/9Q2urVq03M4NixY4MBFWKvuf9qAWUXKtlzXiiQqGnYsGEb1F4l2RYgi6WX49EGdJI1mMRBlAiJNhL24ALLd6VcFmiuQewmLrJApVori/ENLCJUThSRPjnK85KIjFWozFFhHTprBRQqs1a0geMpVDZQ/AZdmgesJG6sPtMkdf/MmTPlsMMOi33b7zNGEfuEGKeHzlhWyFjJn1BaiFCJlZxyFuPGjQvmZQ0AR9ZUXkClre1ILCO/Y1gESZTDSy1iGnFTjSYL42UXZUMATyyZW2+9dYdbHksl7q9AIS+PotZKEkBxb+FCy3H6Rtu8efOMR0Pfvn1ll112UWtlQX5UigiVpE/NGyoPVagsyB2o0/BRQKHSR6Um6aNQ2SQbleE084BKrATE6ZEsI6S39JQdIJEHbr0hNaxf/fr1i7XMNOs6gYtly5YFZVXGrZwaigBYKG3FihWCZQ/ra9pGVlwgD6jk+8nvD/UkiX8EKrfdluoG6xovuzi2fPly8zJt6NChHS7LdwErJV4JWCOj1ko+BxgBWdxniUWOttmzZ5t4S/aLTLDWWumTNCutBnpedQWKCJX3ikjf6lNP3YMCj6XiOEmzv1JkOlpShDIdbkmRuHk9X6pdGT1GKuS3Sx8S7MxYpFgeLiLvichWMYP9RkQ+H/P5R0p1K+2hTUXkB6U6lqTFvk9EqKcZLWOSWkc9sX4KKFTWT+vcr6RQmbvEhbsAD2O4j2XZski+keV8shoL1zeSgOCqF1LDUoRVhYfpUFqIUEm84GOPPWY8AEJpuJOTsXXUqFGpl+RCJa6pwNugQYPMd5Xft+HDeW5d10h0tMcee5iYVKyWFvqix7Fy2thJNyaSWHHGpP4p9XiJm4wmTcKazEsaXHEB2v79+5uMsnExmqkXrScmVqCIUHl/HaCyFKyRBCq3E5HHReRpEblERPqJyJUicksJ1ippD1TOEpErIp0eJNdV6TO+kISTPiQi3UQEP/RyUAkTHxcZ60kRed357Jci8hkROYskzqV6mruIyJBIv8T3jJ5QfwUUKuuveW5XVKjMTdrCDpwHVLLYiRMnmlpuacsEFFGwbTYbLgAAIABJREFUEC1F6MxDMJkycdULpQEEvASIy+LZrGvMAsCKtnZb27FcPdFyZYvcckH8huHuCvwB3XhHAHIk3KG2pHVzxboIVNp/0xcAJVMsjZdheFhwHAjkZUvUWglIAqTEU+Iqi/vsgAHUXV/fiBEdOBBDjxiwtddjXmqtbNwdWESohKrytlSWKrImgcpzS2BGPMSrpR37kohcIyJ8trTCLgKVd1SBz434upXG+J6IfKMCVFLbZ48K1yMRANc8TUSuLfUDVF8UkYtE5EeNu+P0ymkUUKhMo1pBz1GoLOjG5Dgt3vLzsJV14+GMhy0sYKE04qxwqQsppo29sUXdcQsMpYUIlVi+yECcxv26Us3W6DH770o1ZrMajwzU/AYBanHz8I33JtEUf3AP5kWWdVt1wZFr4f5KLCQQSuIjwJCalUBk9Djxk1FrJa6txGRyLV4yMT7jWWulBVOyywKcjI/7KxCq1srG/roUESpni8j2OcoC/Y1YN34SqJxegslPOFPDhxzAPFlEcEst13yg0j23VqhkPteJSHcRec0Z+C8lK2gppDRHkXXoTBVQqMxUzsYOplDZWP0bcfW8oJKHt7iYpEasMatrhpqACDdA3PTKZbPMSr96jrNo0SITN0cGzqyatZqVg7AkoFUJ2ModI1PqmjVrzF75AJ/bJ4kGWdSA9S1NhEsvMZBYyTnHp2yR24d1AX/PPvusyfQLxJFwytZcdcHRvkAh1tu6opLtlfjHIUOGCN9vvgv2OPphrWxtbW1PYoVVn9813FvZbyAWWLSWSQumWMixTFrwxFpJDKZaK5Pcidn2LSJUPlYHqCxFDSeByldE5AYROSeyA7iW3hzzudsNqKRWDwVaiZUEUL8lIvPK7GY1qMSt9R0RISaTMb4vIrc7Y2GJPDEm3xExlqfknAcp2xtURzMKKFQGdCMoVAa0mZ5LwUoJWGbdsH5169bNuKGF0nigp6ZjSMXn2RsejLGmxCUdyWPvysFZGtDinLjzSKbCfvXu3dscTzt21GqXRA8LZ0lBKQp19nygB5dOoKlcn7hrJbm+rVmbZJ219CUGEvfVuPIePuOyP4AcCXbQhayu3Me9evVqP92CI6DJ3/n+WjdUss9y/wOBwC2WRyzB9njUWmldW3EXp1lo5Hysn9bl1oIp88O6yTE8N6ifCcRqq78CRYRKAhcJWMyrQYEEForIh0SECiO2rRYR/sQ1YPACEbkscnB+KQkOrrDl2k+IqCi5nxJP8e1SKc69RGRRzEmVoPJrIsLDCTJhKf0vESFL2adE5E+lsX5VykW0zt98fcOllnhQkvhoayIFFCqbaLOqTVWhsppC4R3PCyp5eCPdPgkzQmlZFGrPUgs33iyJlSwKSiS1wf2QB2Uf10cfK1m1+STRwYUiX0ACKIGV7t27x1q/fC1p5ax21eaRB5zlYX1Nsg959AXiiIXEUpimcZ+xz5T5AAABTGK5cVG1Dbd1wA5LPP0OPPDADpci+yy/VdwruBePHDmy/Ti/j1grAVYsllOmTOmQUdZCIxZIfusoZxIFU15w8HuISyzWSsBSYyvT7HZt5xQRKklPmjdUdoz4bdfwQhEB6OJaLVAZHY/4lwUi8ttS3GP0eCWojPYlFpPMrtQBGlw6qFBZ29eicGcrVBZuS9JPSKEyvXbNeiaAwENZ1o0EFTyQRVP2Z32dLMbzSQaCTlh0cY8bPHiwsTy4cJUEtLLs67t+CzlxMMSDOJ9b17w0EJeFy2Q5y57vGt1+wAEP82T3DKVRLgNACmlN1n00muzGd8/47vKyh+8m8Md/bYykOwbXoXE86hJtoY84TEqccL7bbH1aoJBrWNdW2weQnDt3rvkcyyfwjyuu2wBXLJS4yeIuq9ZK3x3Orl8RofIZESHTTF6tTURKvkJJLJW4v14vIiTscZuP+2vcUv4hIj1EJC4WIQlUMvY3S8l3cK9dW/r7CTH5jtT9Na+bKudxFSpzFriewytU1lPtYlwrS6h0rVM8AOOuR2bFWl0PfaxnPn3KzcM3GYjdMRv7VaurYa3WMh/3SnuNSpYRrChYKoHlUFqIAEY5C+Iqqb0YSqs187D7+8WeY60E6KJlPnBTBfxInkN8o9ustZGXYMAelk63WWslZURwryVTrQuFnA808qIJKyRxotFrWDdb9g4rqlor638HFxEqCUDMGypLhaKSxFQSB7lSRD7p7BJxkiTCqZaop95QWS5Rz59LyXs0UU/9v2o1XVGhsib5inWyQmWx9qMes+GBiQeiOODysajVmgykmhthmuQdWVjNys3rnnvuMXXvbExVPfYo72vU6oKY9/zSjA9g8CAffbhPM1ZRziH+EKucW3exKHNLO48ZM2YY11L+pGn8/qAJ33msuPyW4YYazToN+HEtEvSQrTXasDbycoXMsXHeFVgryQSLpZOamtGXNNbaCXji+RE3BlDLeby8UWtlmt2u7ZwiQuVioPID+T1Gt73/vkn7mjD7KxbK80rnrCqp/kURoR5ktZIi0U0iuS11JUnwc3rMDiaxVOL++kApCZAtM2JLipxaygLLJfB9p6QISX20pEhtX5u6n53ft6HuS9ELKlR2vnsAqORhqla4i57P23oexHAJix7LI96sXjtHTBUPrTw8htLmzZtnHnYpCh9KW7hwobGU47IYSqNGInBk6yqGsK5aSw+5UGnBDmDDRTgKfsRV8qKBmEks825DV5LwEFsZlzGY30kyWtOvXEkXxifZD5bIuFhym4mW2E4slTbhUgj72AxrKCRUbr55/lD59ttJoRIoIznOwlKyG8I+ryzFRbpgOElEdlzvYSufFZGjROTOUi1LEvUAqNSN5If4Oec+Oab092NF5GMiggsrbZaIvFAa90YR+b2I4CXMnEjUM0pE/lNEbnPGAnY/LSJniQguugAxXr8Ear/eDPemznG9AgqVAd0NCpUBbWaCpfCmP6kLaLXhgUpgZcyYMdW6NtVxsr9Sz5GSAqG0Zop/9dU8RKjE0oXbZUhuyhMnTqzJ8u9CJb85WKitpZDEO27j5RkvGrBixmWlxhUX92KgE7iMNn7PiLkksyvJhaLNJgQiy6wtMRI3BnBJwh5eeETh1vf+1n7JFSgkVG6zjbRshAEun9b2739L6+uGq5K4v9KfDHs/FRGCg98QkZtKmVzfdWY6VUTwri152Mr+InKpiPB2kmytWDkni8h3RIScRG57v8yKv1CqgwmI/rpUZpNUzlz34VJG2vGRc/myEkMJmHYVkZkickYpQVA+wuqouSmgUJmbtPUfWKGy/poX4Yp5QCVWAx7Sxo0bF1SmwxDrb9Za1qEI93B0DiHGH9q4vXLAUsR9qDQngHDChAnGm4Ham2mahUq8IYinXLp0qYmbBA6jVmpqWBL3CBgSdxlNlvPAAw+Y5GK4tseV1yH5E94XACmZYOPatGnTTMKrcsmUbMkRroHFGauqZoJNs/PJzykkVPbqJS0xLyiSry7+jLZ//UtaXyHvTmKozGoKOo4qkEgBhcpEchW7s0Jlsfcnr9nxZp+HsywbD0/EMI0ePTqot/G1JhbJUuOsxnryySeN2140QUlW4zdinAULFsjatWuDWhNQhIUL98kQmi3RQ4kP4CpNAwL5/QIqKY1DbCTQDSCSFMeFVTK3kuGVsiK4r++8884dLsnvFZ/bZD9RKyIvKnCfZd7EZcZZK3GhZT640OLiGtfwdgBoAU/6qLUyzc4nP6eQUNnaKi0bb5x8MZ5ntP3zn9K6mMhNhUpPybRbgxVQqGzwBmR5eYXKLNVsnrHygMqi1XTMajdCe7BHlxCteiFCJS80SCSD+3UIjVqiABaJb+LcTX3WSOIcfr8APFxfeTmCBRCXbo4NGzasfRiuheswv01YHbFWkriHhvs/8Z30ZxxcZ6Musri/AoDLly83cBq1VtoxAGQsouVilIFX5sA8sZryX/pry1eBQkJl//7SsskmuS287b33pPUZQhIVKnMTWQfOVAGFykzlbOxgCpWN1b9RV+fhizf+WbYsXNuynE9WY+GCSLmCuEQcWV2j3uOEGH+I9ZX6myFlSsXSRjzgTjvZEKZ63ynZXg+rH2si7hoX0DSlh/jtwnoLlOLGjcUSS6V1M8Ua2LVrVzM20IhLLP/mui0tLQYOaYwDdFKjEtdZXkpgjXStiCTi6datm7kGpUWi1krgFksl4Mpc4uplAp64yGJB5Zp4cgCZxHFqy1eBQkLlkCHSEkkalaUKbe++K62Pk3NHoTJLXXWs/BRQqMxP27qPrFBZd8kLccE8oJKFUX6DN/+hld/goTKkTKkh1nQMESorld8AVuwfF86SglrS0kJJyw5F68kmSRAWVyqIl2FkW8XaR2kc4hmthZHERlgw+a5iGUQ/4jfpg4sr7rIWDLGa4jKLKy6uqdbN3bUKcxzrJC6y9913n3GfBUxtA0Y5jzG4/+y13R95C6/8Lt51111y9dVXy5133hmM9bkQ/0MrM4lCQuWIEdISkxQqKx3b3nlHWmfPVqjMSlAdJ3cFFCpzl7h+F1CorJ/WRboSD2Y87GTdeCPPA15ImVJDTGrz7LPPCsXhsayE0mrZJws+0f9mCVxpYA9oAXh4qREHjkn2zi3zE1dOyPezKOhVKk0UPYalktqPWBOrzSeuDBH7w32LSzqwCMjhHmytj4z/yCOPmGP0BfgOOuggoyG/eVgrsfoChrbvoYceauby0ksvGbdwN6EPVkisoLwkw1JJ0h6glP40WyeTMcgyy7yi1koXPE888URTzonrpI0pTbLnnb1vIaHygAOkZfPNc9uatrffltb7708DlQNjsr+eX8rCWmm+z5fKgUT74N9tapuISE8RYSyyxQ4XkfdEJBpUTXrls0slSgaLCF+yuaVMsvdGBo/LJPuyiIRT9yu3O6R4AytUFm9PUs9IoTK1dE19Yl5Qydt80uuH4q7HJocYf7ho0SITJxZXn6+WGzvOcha1VKWBKx/YAxIYm4f1JJBo5+y7bgtVWdd5ZbzomLgp833CMpcU+qL9fdeXZz+shdx7lOhI09grXE4BRxLeAHXUfkQj23BZJV4Rt2H+TvymzbZKwh7mABiS4IcXEXYu3DtAaO/evWWXXXYx9xA1anGfxd2W38yotZKSJuwRlkoallOu5dYVtbU0Ac+bb75ZTj/9dLn99tvl6KOPTiOBnpNAgUJC5aGHSkuO8bRta9dK61QqfyRyf7V1Kp+O1Km8RUTcOpVx6gOV1Jq8InLwQUKXS58BkpQFeahUw5LA5yhU8m8yDP0GpycRIT7nSyLycRE5vFSqxF6CcSl/8jvnmpQgMSZabc2lgEJlc+1XxdkqVAa0mQmWwgMUD2dZtxCT2pDggwfDciUDstaw/f+a778fa52q5uroY13jgZoYNB68faDPZ0zbx1cPa4mqBZbcc1kTawEK4gDNxzJWro/9PM565rveNP0mTZpkAKVXL8q2NX/D/XTZsmUG6tI0C5W8EJk/f75xNyXW2XW3x5IJ3A0YMECwyGOptA3vDMAQbwr20rrD2uPMDddwrJXcz677LH2i1kpb0sRa/LFWzpo1y/xWEMdJe/nll82YgOcNN9wgF154obGk4gKrLV8FCgmVRxyRP1TefTfCJqlTeW6pJuUOIvJqaVcAumtEhM+WVtgpoPKOKvCJ1dGmm/+eiHyjjKVyaxF5zbkW1sv5IkLmoY85nwOV3xSRy/O9g3T0eiigUFkPlet0DYXKOgldsMvwcEbMUdatiMXaXetZUisZ5/JQiEUE64UvXPn2q2SBS7I3cdazSpY0MmaS1AbXwbi4tTRQ5gttLqAlWWO1vkAGerpWomrnFP04NR0pk8E+hdBqjeW1UInVkBdYuJZSFgdLrm304RhZXrnHATi34X6LqysvVMjEus8++7Qf5ntLHCWWTzR/6KGHjCXTZoyNWiuxumIhd0vzALS0oUOHmv/iMsv1AM1LL73UJO3hZQHJv7Tlq0AhofLoo6Vliy1yW3jbmjXS+te/Mn4SqJxegslPOBOjkCyAeXLJelhuzj5Q6Z5bDirLjX+riPQXkb2dDgqVud1B9R9YobL+mud2RYXK3KQt9MBpoNInMQiWAWCVuno8oPnCVRrY87XY1ZoYhOtg4cDdLq3LYy3WuGoxbGmsZ9biElIGytCgkvt2/PjxxkXZhaZC/7BUmVytyZTc3y2simRsjdamZAq4pVJiBFfoqIcBHhpYK8nGynfHwp+d+pIlS4yLLhZQSorgtmrdZ+njutDi+gpoDhkypH3lUWslv4l4BZCo5+tf/7qJvfzDH/7QzNvYNHMvJFQee6y0lKlnmoWwbW+9Ja3r7q8kUPmKiNwgIudE5rBERG6O+dztBlRuIyKQMu5PAOq3RGRemfUkgUrq/zwrIsRUHh+BSoAXy+ZbJddaLJcvZqGhjlFfBRQq66t3rldTqMxV3kIPzoMVsJTE/THJgpJarmrp7xPn5gN27sOjXevSpUuN+9ohhxySZPmF7ov1BNc/1zWw0BP2mBwAwP6FkqUXWJk4caJxFSWmr6jtVyWL2zsJJnj62zZ/R4KT8J9z3PbXrl1rrIok6YnW8eQ3jSQ7JOiJc7XFpZ3vNdZIyoG4jWuQ0AfoxEMh+uKF30yO47mAyzUWR16iuY17kTkArIA0DUj93Oc+ZzLI/uQnP0m2cO2dSoFCQuUJJ0jLVtFwwlTLiz2p7c03pfVmOFA+JCIvOZ1Wiwh/4howeIGIXBY5iOvpfaXYxnKT5GYmfhKg26XkRttbRPYSkUUxJyWByvNE5CIR2U9EHnHGurHkcktynj1Kc2cNxGq67rPZCasj5aaAQmVu0tZ/YIXK+mtelCvylp8HqHKujj4QFgVBLGBYEEKygGH14CGR2nqhNKwxWFDSJkwpog64HXI/hgKVWNRwkyQWLy5T6E0R98nsczmvy5Th2+oFlXhC8PIA6x/xi9SVjNaPZM4k81m9erV5cWLdV+1abLkRoDJqqaQPsZJ8PwDGuGRW1lpJrUziOW32WTu+rZmJay3utuwf4HvYYYfJxz/+cfn2t7/tK6v2q0GBQkLlF78oLaV42xqWVvbUtjfekNbrros7fqGIAHRZQ2V0vL4iskBEfisip9UAlWNFhMDj75fAspJc+JqTpAcI/VEeuuqY+SmgUJmftnUfWaGy7pIX5oI8WCVxDfWZeIhWPRJ/8IB6+OEkoAujhbhPQCWJW1xXxCLt1t9KEPhmwkn5wqJvv4SX9wbLekMlybOI4QYYsRpGyxhxDEsjlkEyxEYbSXiAU5LyRD0UsBJzHGiMxmQyjrVW8neS/hCfGW24YzMOv7PUuaSMCS6w559/vpxyyilJt0H7p1CgkFD5la9Iy9Z4bebT2lavltaf/5zBk1gqcX+9XkRI2OM2H/fXuIX8Q0QIBMfCGG0+lsoRIkIK29tE5POeSj1eSurzac/+2q0gCihUFmQjspiGQmUWKjbnGFhCsFRm2cjKyMMcb+RDaTyY4mY3bty4DR4+m3WNNh6NcguhtEYmibLA6KNlXlDJtfMAyzyslbW4v1pLJR4EuLGSVIdEOMCfC4e8CAI4iWEEHG1tSbtHM2fONIl8AL3u3btvsHWcz7lYOnlZEW24jxN7SWIoN/us7WfrUzIHMtHSh7kST/nRj37U51Yp2wcvkzPOOMPEhpJllvqXF198sbHaVmqUesLKGm24EruJgwDqCy64wNTUZO02ydDw4VSGaJ5WSKg86yxpydGdve3116X1yivZpCQxlcRBrhSRTzq7i889rqTVEvXE3RC1QCVJeWaKyJxSxlffNPUKlc3z1ewwU4XKJt24uGkrVAa0mQmXkgdUWgDDqhd9iEs4vcJ058GSB1BAOepGV5hJJpwIGW1JZDJ69OiEZxa3e5ZQOTuhVXFVAlk6C1TuVEWTo1LEVfISDBAEdGw5DzKvAlfELLqlV3gRhHUQ99M4S+b06dNl6623Ni/WSPYTbVi+iZnEEsk40cY8+F0A1Bg/rmGtBH6ZI7+Hra2tJjMttS/TNiy0WOOJ4zzvvPMEV/azzjpLjj/+ePnZz35WcVjmCiCefTY15tc3XHwtkFOXl/ny20BNTdZ5ySWXGIDnNwOra7O0QkLluedKy7YkVs2nta1aJa2XXsrgSaASCyWuo5xjf86+KCK/9CgpEl0IRWMJJCawM67GZSVLJSZ/gBLA5Y2n788lbzseLiUU0jIj+dxauY2qUJmbtPUfWKGy/poX5Yq4cOGelVW7zCNF/jkpHiSzml/acdasWSM8gJIFMpQyACHGic73uP/sPeBbTMf3iYZxfcEyyZiMm8T6mKSvz/dhoIj4ptRZ4zOg0ycNVFp3UqASWASwAEJbqoQYRgtHJOoZOHCgKTuCuzeWTPuiC5CcOnWqgTNAKS6DLJZKrsP5xGxGX5Jh3SNhDy6yccfNPbFqlcyePdskAyK+k/kxlzh3WV/5KEvygx/8wNTM7Natmznt2muvldNOO818hjW0XAMqjzrqqIrwedlll5lamgB1ly5dzFAWzG+66SY54YQTfKfa8H6FhMoLL8wfKr/73aRQSU0eLH0LReQSEeknIpg7iYt0wXCSiOxYKvHBNT4rIkeVYh+pZcnbFQCVG5M3J885N8Expb8fW7JA2htpFkmVRYSb7f7SGMeJyHLnXLzrsVzSqHG5a8k9FrddEvUQpLy2lBzI96e44fenTmCdAgqVAd0JCpUBbWbCpTQCKt0p+r6r9e3H2OTUS/OwWkk63O0mT55s3OAoKxJCw00Z17axY8mFUNz2fAJQTAJrjYRK1K401455RNftTXavftbvdRLHdx+wTAqVw595pkPJIZ+yQkAl1kj+AJLUW8X9lP8CeLiyAlrEik+ZMsXAIt9ZLJm4oForm7UyApoAE14bUddOLJ3EaXIca2UU1oBEWw8z7rjZ5zffNLUuySTL348++mgz11o8HkiuxRpvv/329s0EXvnshhtukJNOOqnsF9oHKr///e/LFVdcYYDdAjqAiYvwjTfeaFxtm6UVEiovu0xanLqqWWvZ9tpr0nqOqQySxFJJ/0Ei8lMR+bCIvCEiN5Vgzf25JM4RJwTriEARWMyigB3/qwboJovId0TkqcjaqC0Z175QqoPJmC6Eun2BTnvNj5WsqgNEpGsJPu8SkfNFZFnWeup4+SugUJm/xnW7gkJl3aQu3IV4QAMss2o+lkr3Wklg0bdvkkTtvn3LpVQY0YRWV6t/I5MPrUoAikleOSeBSnTwAcskY9q5xjtCbvgt47W6bwsRKreZODG27mulWrB4DQBzxEgSV0hfrJE0SncAbLhuAolYKnHrBCpx3eSe33fffdszxwJ7ABrn8HesiLjD2mYtncAgMciupZM+K1askCeeeMIkAYpaQu0YwBhu2UAu8/7Wt75l3FXjShf53gsA9cknnyxYFN0GAGNFjH7u9gEqX3/9dUFHyq2w/h/+8IcGzG0D1tHwa1/7mnGrBcDPPfdcA+lkwS5yeZuohoWEyquukpaShdl3z5P0a3v1VWk980xOSQqVSS6jfVWBzBRQqMxMysYPpFDZ+D1o1AwUKv2V983VVy7aaPuCASgPu5RjIPlQLW2zknucHeOd98u9jF5/lSSgmKRvEgBkNkmhslqMYLkCcOX0DQ0qWaePtdLNwzo04fcCwAH2iCfEhduW6uDaNjEOcIglEEsj5VhIXsN5WDIpN0MZEaxwWOpxaQfwgCWahSvrHkvso7V0ck3XbRWQxN2U62EJjbNWkkCIZD5YKoHeq6++WubMmVMTVAKDWBPPWWeNam+sDdjGFbZc++pXv2pAGxBmXrjREl/NnNy40AcffNBYVTlGA0bHjx8vu+++ey0/F3U/t5BQec010hKTGCorcdpWrpTW00wlD4XKrETVcXJVQKEyV3nrO7hCZX31LtLV3ELiWcwrWjcvbkyCLmzztT7S37evr/WRMZP0rRUq47SolO5io4QP29HxN4spcbDBHN70xLAE1mwfqGQevrDo248xfVZDWkHbfKAyyVzpmxdY5mGpZL6+LrBR99dy1tik608KlVggsTpiJSTeMVojkuQ6QCLQREwk0GhjIUlAQ9ItQBH374ULFxroNPtWcmXFkgmo2jqWWCe32GILA2BArJvQhuyvWD8ZDxfZOGsm0Ml5QBnwSZKcu+++u6af21qgMnph5oyl97jjjpNrrrnGHEYXkvTwB1dXLLmXX365LF682MBz797UtW+OVkiovP56aelBtY18WtuKFdK6rmSNQmU+EuuoGSugUJmxoI0cTqGykeo39tq4ZPHwlFXzgUr3Wr6gyDnr0lFs2NpiPvOFRd9+XMIXKunrmxvRtx9jdskjBscXKpmAJ1hmDZVc2gVLH9dSH2sZ4/re+UnANilU+Vor84BKnEZ9NPDVE02Trj8JVPJ7hbs+CXKwQgJX1KB0Yx1x7QQmiZ989tlnjXunbfY8Yidt8h4g0jYsl4xJ8h4bC8n5WD2B2WhcJvCFBRTrJvOKs1YCwLiaDh061AAsMJuF+yt1LknY4zYf99e431DKmwDpWCdpxxxzjADMxIvahh6A+qmnnmoywTZLKyRU3nKLtPTsmZuEbcuXS+vxxzO+QmVuKuvAWSqgUJmlmg0eS6GywRvQwMtnDZUsJQlYZgGVcfL5AmASqCQbgNsWV9g3X1j07celQofK3hUest5b7iYBrP6FSQJBPlCVNVS61tIkEc2+VkXffijps376+WqaFCoZ2xcs+b2yZZCI+8P9FJfPaI1Isq3SAD6ysrqNTK+MQVwgyW2IHbTNZmrFOol1DqvnIYcc0u6qCqQCXzYuk7FsDUrGwFqJuyvWTGsdpQ8WVayBRxxxhIFh3EhrqeML6JI057bbqAu/rgHT2223XdVEPXHfnihUkqkW3a6//voO3Vk3EH/rrbdW/xIWpEchofKPf5SWXr1yU6jtlVek9VOfYnyFytxU1oGzVEDf+fVHAAAgAElEQVShMks1GzyWQmWDN6DBl+fhyc266JOBkT5x/fhseilphu+yfMGynKUy7jq+UMm5vmAZhcpK69uwjPr63i85J4YIleZhvhRXuZnzwL6BXm1xNuZ4VYsKlXExlr7wZVeaB1Qyti9YZg2VXLsaWFKIzm09PV29XajE6ohljRi/aA1JXFJJjoMbq2uJ5JrWAmnrWQKlbgNIKaNBJlWsjCNHjmw/jFcH1kigi/OJQyRWEtCiAavEbbqxl/QBYOmDpZLrYWHccUeqMqRrnI+1EHdUrk+77rrrjBWxWkmR6BUBc+p7kuDH1rj8yEc+YmIpsfjahEK4BzPnL33pSyaxT7O0QkLl3/4mLTm6ELe9/LK0fvzjbFFSqMR5IZr9lYyq1X4mni+VGYneFpQIsZ7zmGYZi2yx1JR8r8L/fsnuerGIkN31xVJ22V9HBt9URH4gIpQl4X/P95VKn0QzzjbLrdqp56lQGdD2K1QGtJkplsJbc8DSp/GAwRv4uOyM9rO3R4+uOFQUJTobVG7wf91W/r8f39YuXm8PzdRSedhh6y6YxP3V8x4x467ysO0VGCqjli8DDJ7W0maDStZW7YmRPj7raindxq/5/Jg4fZJCJXBJkjFqx2Kdc62NDMvxGTNmGAshiWuiDQsksZUk7MFN1m0AKccBKP5ODKTbsJCS5IfPyRgL0OJ2alvUWgn4cpw/APBVV10ln/0spf3SN66Piy7jnXfeecadliytxEVaMGT0MWPGyAsvvGDgmPb73/9e7rjjDjnyyCONyzBxogAqSbsASAvHf/3rX+UTn/iEmaeNqaTECH2AdaC5WVohoXLChPyh8vDD2aIkUGnrVD4dqVN5S6ROZdzWA5XUmrwichB/apu5DZAcLyIPlaJZhpWBSoKcKVtynYhgEueBghqU1Lb8kzP+L0XkMyJylogsKfUhOmIIhvtmuT91nusUUKgM6E5QqAxoM1MshRgfHsJcUIwDR4DSJw3+5ATlIpiuL1RG+7oJf6LLzsNSyTV8rZWVLJXRuXapAJUd+kYsKhW3eitf+2sCsMwaKlmAJ1j6AN0mrpujD9RaAVes8PrW+MzBDuQDYO5Ffa2VvtZHxk7SNwqVrZtsEqvJW+9hXKjekkIlI/qApeuub+tScq5NpuPODIsjLqFYB4mTdJuNuwT0olDJNWwsIedFa1faLLJAHSVNcGt1X0JErZW2LAnwC8ROmjTJJA+qtZFJ9owzzjCW065duxr4I5MrmW5t4zrERvKHhustpUHmz59vXH+xcpKM56KLLtpAhz/+8Y/y4x//WEhuxJiAOxlnce1tplZIqJw2TVr6JPFTSaZ420svSeshh3BSEqg8twRmJGd+tXTFL4kI2Zv4rNL/crnB7qgCnxs5P0vfE5FvlIFKwJP/ga13ERD5Xcm6Obg0L95fcU1S3NpUxzgzYdW8SER+lEwx7d1oBRQqG70DGV5foTJDMZtwKBujlNXU6wWVleablavsukex9U2h0vMu8YW6alBpLapctvRg7DkDP2spgzURVDJdX1js6ZP9N8H66eoLlfRNCpY+UOlmq8bSCPwBRmRntbUq7f2BtRGLHhZHMq9G27Rp00ypEDKyRhsv2igxAgSSYCfaSNADlOFKS+bXaN1GLIC4j2LNxJpKH9x1sSwCg9G5et/T2jGxAoWEygcekBbf72fiFYu0LVsmrfvjZZoIKqeXYPITziV55wtgniwiv6kwFR+odE8vB5WbicgbIvLfInK1cwK+vH8VEfzMuRbzwZLJ+1v3p+YvJSto7W9tUuiup6RXQKEyvXaFO1OhsnBbUtcJhQiVCOgLlkmsml3KWHBiN6zFOgPGb+fa554zB7wtlXT2tVbmYank+r7WykpQ6YJiNah0pcsLKrmGB1gmsVQaq2kS12LPzLpGDt++vtf3WLvdhiyhcr3D6LrR3/W4t4BK4hrxmMBlE4sbMYoAIIllNtuMZ9J1DUsl7q/AJ8f4u9uwIPLbh3vs5hHvCqyVwCCwGnV/ZQyslVgI6RdnJbWZYqn7iLss18fiR4IdYDQKoXX9we9kFyskVM6ZIy3bb5/bTrQtXSqt6+LZk1gqXxGRG0SkY/HTda6lN8d83uHXWUS2EZEtSrGSAOq3RGRd8dcNWzmoxBL5uIh8RETcujv4Wy90PscSeWJMonViLKmlkp8ZOLdd69wDK1QGtP8KlQFtZoqlkAqf+KSsWhEslayl6FDZrrcPKD766LruPn3plyVUuhDo8eDfvi4fa2WzQKXVICnY+oKdLygibpK+PtfPECq3dFy533figX1+W5JCJbUfiRUEColtxKq46667tl+KWEbiBnH9BO7cuEdgcOrUqe0JebAgRhsJd3CxjXOfpS9AyxwOOuigDdxrOY61kkywjEEGWdxeP//5z5t6mDYzrI8ucX1wu426vl588cUdXF/jzsNiS4xltGFFtWB90kknyY033hg7NeIvzzknyhxpV1Gf8woJlU88IS1OHG7WSrQtWSKtg42nKAHBbm44cmiVy6OFb/sFInJZZD7zS0lwcIUt134iIsRP4n5KXCMxkBQzhWwXxZxUDipxeZ0hIqRsfsA5j6KepAA/Tta5wv5KRA4SERILuQ2XWurdrPcBz1pcHS8XBRQqc5G1MYMqVDZG96JcNWuoZF3PJ4yr3NJTDF6F2lYtd6gvVDKer7UyS0tlhyX7wuJwch14Nt+YnSSgmKRvkaHyiCPWi/iS+8xVRdu8oJLL+sKibz/G9IFK+lUDy3WudOuapwZJoZKhAUuAz/6JZqXm5RdWSmL82traTEkRsrsuW7bMWAQBTEp80EjUAyxSI5J+WBQtzPGbhyWyf//+Bv44z41F5Hygkn4k4rEJbNy7A2B84oknZNiwYaa8R7RhrZw5c6ZZy6hRo+Tmm2+Wyy+/3NTO9IlNL3cn2iQ9JMtxk/Qcf/zxHZL0xJ0PVGJ5PfvsszscJk7Szon54f7rNkqIXH311UIdT9bbTK2QUPnssxtkLM5SU74brc4LFmfsC0UEoItrtUBldDwSPC8Qkd+W4h6jxxUqs9zwAMZSqAxgE+0SFCoD2swUS+FBjYenLFs9oLLafLsmcFVd65mAJEioREhfWPTtx5g+UEk/X2ulhRkXCCvdBL7X72xQGZckxncP0NsTKumaFCzv+NvfTKmiSo2kNMRBYoG0dSY5BwjcYYcdpLW11YAclkiS7FBWBFdV4hh7l8o4YJmjPzAJKFEeBGum24DOPn36GGujC6u2D5/j0sp8RowYETtlMqXipkvCnCuvvFImTJhg5lJLw1pIQh5Kh1D2hHbttdfKaaedVrWcCFB51FFHVYXP6PyYP6BJzc1ma0WEyhdeWJw7VO64o8kqnsRSifsrhUlJ2OM2H/fXuNviHyKChTEus1M191fe+pGwx7Y491dKiUSrE6n7a7N9QUvzVahs0o2Lm7ZCZUCbmWIpbvKLFKfHnpIXVHIx11pZ8eEzAVRKTCKPsmNXiZVsP6+apfABx7unkZZKJuwLi779GNMX6gAa18W20qYmgR/fOTQbVLIuH72qWR9dnZPomgNUfqBUz3TpHXeULVmEJY1YRjKYAosrVqwwrqV77rmnWQmQRd1G4hdNvdzp040VE6jETRa4w0rHOLigkuQHWCKhTtTKyflAKRlPsUZirYzWleR6gOVbb71l5kN212jD6oe7KXUtKcnBtW677baafmaJywQmb7/99vZxgGs+u+GGGwT31XItDVRSrgRYJ/MrltFma0WEyoULF0u/fpVj7mvRecmSNtl9dwOVSWIqiYNcKSKfdK7N/25JhFMtUU/cdNNApU3U800R+R9nUOpW/s0jUc+fS8l7NFFPLTdQA85VqGyA6HldUqEyL2WbY1yFSpGGQKV7e/Tv73ezEFvp6wJbDWrdKyaBxWp9LfAkgUq/1ftbNe14PnNIA5VHHeU342pauaMk8RbwcWttJFS6GVUTQChyvHP//WW1xaOCrK54V1DuA3dWm0mVY1gBcXklEQ6WyJEjR5rkPTaxDplccVWl/iQxkcQ68vsHqBJzacGR/riuAqj0xSoaTfYDqAKUzIP+0VqZLIIsscAs7Ze//KWxiP7iF7/wu3fK9GKMk08+WS67rGPoG/M/4YQTNvjcHQaopJwKLsHMG0D94Q9/2A7mcZfEZfeb3/ymcROOcwOuaTF1OLmIUPnYY/lD5dChiaESCyVvDTjRFhr+IreuR0mR6E6ShejJUoKf02O2uVpJERL+EDNpG7UycQeIlhQ5tZQFln681SGm8/taUqQOX6yML6FQmbGgjRxOobKR6jf+2m7tt6xms1HCmMpNunTZ4NJta9fGTsfXUsnJ3i6weVgqmYAv2PlCJWP6QqUdcwZ5DyLtQOpLp2xJQMkH6pJYyZL0ZXk+16efBUuf+oFJYI2xffXKGiq5dpK5VtM2LShmDJXEFD7yyCMGHCkn4ibnwTKIBRPQpA/QaLO+UsoDyybwRwzmc889Z9xaacSgueBIqRCS/wBdWDYBVIAMi6VtWDA5hhWPpEBxpUUAV65PQh8slPT97ne/m/KLt+40YBCrYTRhDllwWQ+usOXaV7/6VVNnknkAibjRYj2dM2fOBu6/dgxce0niU6vbbk2LruHkIkLlrFmLZfvt87NULl3aJh/6UGKoBMrwbybLKsluSNB8ZSku0gXDSSKyo4jYN6GfFRHest1ZqmWJHzmAim/23iKyLs35unZM6b/HigjWR1xYabNExGaQ4n9OU0v1J/8gIqNKCYQ+LSJ/dMYCdvnsLFmXoRYgZk5DROT1Gm4ZPbUBCihUNkD0vC6pUJmXss0xblGhsqx6vqDGAD6uqrihhgiVrD8JrPrerr6QxHg+UFcNZtx5+fR1XUN9rs/4PuPaeSQBNc5JopcvWPpYKrm2z1xtEp4k8JdX3yqWSpLfYKUkVhHww7IIINlmLZIAIO6pQKVtWBWBP+IxcRcF9GyNSsYEmrDEAY5YF7GIcj7giAsr4Im10ib7YQ641gK1wCNjRJPYUNYEt1QAFYvg17/+dRP7WEurBSqj1wWuAfDjjjtOrrmGGvcdG1lmBw0aJD/96U/l9NPjDE61rKQ+5xYRKmfMWCx9++YHlcuWtcmBByaGSjZkkIj8VER420K9yJtKmVzfdXYL4KPwqy3+ShavS8lNLiLUtcTKOVlEviMiT0V2+f0yu/6FSB1M6lJeLCIDStZHxqfcidtwlSWGEjClhPRMETmjlCCoPjeXXiUzBRQqM5Oy8QMpVDZ+Dxo9Ax7GsmxZWCrLzidrqLQX8gVL336M+8wz/rL6AqCvpZIr+47pP8tkkFQL1JWzGiZxV2VdPnNoNqhkXeXA0tUtybryAsUk4+6/v7xz1VWxdyMvv2ymaqyVQB3ZW12o5EQskrh4Anm4v7qNepY06k8CpS4EYqm0WWIBTqye9nzrWsv1KFNCmzVrlknkQ2Iga9kEUrfeen0uadxqmR8gOmTIEFMGhNjKWhrur6eccoqQsMdtPu6vcdf96Ec/aqy7AHe0XXDBBcadFl169uxZy7Qbdm4RoXLSpMXSp09+UPnSS20yZkwqqGzYPumFO7cCCpUB7b9CZUCbmXIpuEIBlsQXuWn8eZCLfuZ7fMxHqF/s3+JcYGPPbiao9LGU2kX6roukPrZuZTV5iwiVcUlmkoBH6FDpE6/pq0ERoBJLaJL9xVoZA5b8FmGp5PcH6+GUKVMM6NhEPfarYC2SgCNlRNxmE/RwHm6pJNCxDXAkjnLAgAHmOiThIbGPbbjL8hnuo0AifYFMm1EWYGWOxG3aRrIgrtGlSxfp27evsYSSFMi65Fb7+sYdxyWXuFA34Q8QTaKgaol6kkIl66N0yV133ZVmqoU4p4hQeeedi6V37/yg8uWX2+TIIxUqC3ED6iS8FFCo9JKpOTopVDbHPuU5S966k2afhyUe2Piv+/e4z6od3z6JRY9YoZi4ykJCJZPyXVseUMn1PbPFrumzi2zxzGOJb51X+qx/MI47eaut/IbcYvPK5SHaR0kCHb5AZQf3sVTSNw7AKmVZ9XVB9XVpZQ6+rrJJNMgDLImvzGPc0p75QiVwhjXR1qa0W46FEDA86CA318e6o7ik8lsHDAJNbrMxmUAb1keyutrGeLjIAp2cS3ZYLJ22rMcbb7xhrJeAKGVGsJROmzbNuNiS7Ie+1JLEzZTjaRsWyksuucRkuiWmlHbdddfJqaeeWrWkSPSaWCBxbyXBz89+9rMOh7FcAuU33XSTOd6srYhQedtti6VXr/yg8pVX2uSTn1SobNZ7tjPOW6EyoF1XqAxoM1MuxVoAUp4ee9pmY8cmG64HJa08m2/fcv3iktf4giJT9O1bAKj0VLSsN2Xc+b5QybleYFlvqIyDxSSQxMJ8oZK+vmDZKKi0SXiS7EMSvZKMKyJv/fjHsbctv1M0LIJYAYkvJH4SF1S3EQ+JSydQhMXSbQAerrPEY7pJfujz7rvvGnAkCRBZY11LJsfJ+Mr5JK+5995720uW2PG5Li/gsJ66tTBJhHPEEUeYrKskvaml4fqLKy1ZbinxQcmPs846y8RFumA4ZswYEwvKnGm///3v5Y477pAjjzzSuPDinQKgEj9KUqNoZtevfe1r8qtf/crEnhI72qytiFD5f/+3WHr2zA8qly9vk898RqGyWe/ZzjhvhcqAdl2hMqDNTLmUYKESPXwBNAkARqHSrTkZ3QPfcX3dXxnf01JJV6yVPi0JIyV5xswaKhfJLt5buvXmbn6JKiokgaS8oJJxfcDS11KZ1PU0Cfwl0SvJuKX7+42Idcy6v3IYKyAZWQFDspcCjzaBDsdJnEMyHtxcsSy6jXGwMuIu6loibR/KgODmSrwk4OY2C51YOOl34IEHyqabbtrexbrXUh+TOQJr1ML8+9//LmeeeaYBNKCz1kbcKPGZADBWzxNPPNFkcnXnwnWJE+UPDevtueee264NVs7Ro0fLRRddtIFGzJ2ERbja3nrrrbVOt6HnFxEqb7klf6g8/niFyobeeHrxRArU/quY6HLaOU8FFCrzVLc5xrYJMLKcbSEslSyoHlBZSThfqyZj+IKlr0tnk0Dl5KkbeRt/k2xpiFD57/3XlcHY6KWl/l/XJFCXpG9SsEzyMuSkkzZYH1AH8GDxw40V11dgiQymNraRk7AMYsXEWkmZDRe2OI5bKnDJ+fRzm7UwYs2zNTDd47b2JHMYNWrUBpD46KOPmjFJqEP2VFxwKfPx61//Woi7zAIq/TdeexYRKq+/frH06JGfpXLFijY55RSFSr37m0cBhcrm2auqM1WorCpR8B0UKktb7GtVTAKKSfp6QuXqjSkB5tfI6eOWGKx0lq+10rVU3nNP5Xn4WjWTyOT7noCZeYNlEkCyS/YVzHF/XdqHGt7xzXP7iwOVSTYiSSZkXobEQCVJegBLXECx1lHyg8Q3WCWJXbTARgkPLI3EDAJ3u+yy3loPTJLkB9DEGof7bLQBnbi/RhP90I9al1hJiefEkhdtJM2x7qRYJknsQ13Jhx9+WCZPptKCtnoqUESovOaaxdK9e35QuXJlm5x2WiqoHBhTUuR8PMOr7BnmcGpXRhsFqN92PiR1MiVLDhcRfNn/UqozudrpU67sCF04f1mpb1y/l3k1W8/7S6+VjQIKldnoWIhRFCoLsQ0NnQRv/wHLLJuxVEayL1Yc3324q0YqSR5mk/RtEqhER1+w9E0Uy5i+XpX09Q0NawaovO/h9S6MpdwnXl8FXw189SoEVCaZRBJQTNK3pH4cWJKlGpdX3DqxNGJZxFqJKysurTQysxIvCYSSfAdrpc24CpTOmDHDxBBSe9I9ZjcdqOQ3MVoixB4HEEnkg4tpXMNSSkgBFsu99trLuKpy3d/97nde91W5Tlg+o26vF1988QaW2Oj5gDPxldGGdtEYz3/84x/GlZa4U8AbXW+++WYD4M3YigiVV121WLp1y0/PV19tkzPPTAyVfHkeF5GnReQSEeknIleKyC0iUq1IKVA5S0Si9XKoU2PhD5eA2aV76DzC7UXkchGZKyJHOfdWx5TN6w5QL/MtEdnL6ce4AKr7pQJ+7TWa8XbttHNWqAxo6xUqA9rMlEvh4csmwUg5xAankQ2xx49+lGw4X3NVElBM0jfJA7XtWymektX7rom+Ca7fLFDJsnzAMolMvlvKu4kEkpp7NUSoXCrby/Y9qhkbSl/VJBbbJKDo23cgxpJ1bU3MSylgD6AELLEC0h5//HEDgbacBzGTJMsBMok7BKosFNmSI8Ak2VqBSxeY+C3kfGIOgcJoyRKu99RTT5kEOSTssRlY3R86LKmAJVlkyfp67LHHmiyrV17JM3q6ZhP0UOLDTdBDRtlo5tboFVg/WWnPPvvsDofQz3XHveX/t/cmcHdN1///an9tSWIKCaJUVanGrKpiqKhZYp6nIFQJomJKRMxf31C0pSJVQ2ue/k0MNRYRU0toBUFUUxK0TVHE8C3l/3rv5+4n+57nnHv2Pvece8891nq9nleS++yzz96ffe7J/uy11mddfbWpgUm7LbbYQniHI0j0ox/9qIdSbrZZtP6qMpLK8eNnS9++xZHKt9+eI6NHB5PKMSIyVkS+JiJv1VbqEBGZUPusUbw9pPL2FPK5l4hcIyLf5itU6x+P5d0iwhf58YSng1CCWSJyvIi4Cl6QyuNqxLT1D5beMVcElFTmCmd7O1NS2V78y3B3TtHZbFFPLWqEi2UxwsT6Rgp0p/bjyyx8WQU3DGkbwkAC2voSQNLZ1ljW/n/eGC3fPunF11tZhKeS+2cllXfdFY9BAPQdRSqZre/cOMvw/brQrzeppHERxJJ8yhdeSH0FuA3u/e9/E+vmQhghbAj0WIEcSBKeN1vKY5FFFjEeOgigFfOBnJH3iJeR3xEi6wr94AnF0wmZJAeSPvv06VM3bkglKrAoy8aJ/fDOhIwRQsv13GuPPfaQ449nX5zNUGrFg/jqq692lzEhV3PEiBGppUQglUOHDm1IPlGBhWCPHz9eDjvssGyDLOFVZSSVp51GOZjiSOW//z1HTjklmFROrZHJHZ1lpGYN/yENF5FfN1heH1KJt5FaVfPr9IjAJf5V8ziemtA/Xs0za8R2jtNGSWUJv29Zh6SkMityJbxOSWUJF6XFQ0IFkJP70aNHJ945Si454W5EODnxX/R//idsJiG7ZF+y6NsuZEcf2NaXAIZopIQQQLuXd8ruxa5LSJ8hoZ++OZghEXa+5Mu3nQuIr7cyBAPftiHPQMjXpTBS6TsxAA4klVzy8vLL96iZy3sHLyIE0taoRLgHkgeBwjs5aNAgc0hm60taMR/yHK1ya/R33M/WmyRfkxIhcaVFIJuE06ISmxQiS0guYj6MA48mtSVRac1q5G9SE3Py5MndXZBLymeXX365HBCTg2ob+pDKiRMnyrHHHmtKjESFjbKOuQzXlZFUjhlTPKn83/8NJpX/FJHLRSS6CXhNRK6K+dxdXkjlorWQVnIlIagniMgzTiM8kX8VkT0jz8UjIjI75nPbjD7eFJForDmkEsK7SC00Fo8nnstXy/Dc6RjCEFBSGYZXqVsrqSz18rRkcIQ8kSN02WWXZVYnjFM17H399WHjpxj5s8/6XRNCFn3bhjCQgLa+pJKJ+5KKEAIYspf3jVL0W6SuVj6eStoVQSoD+b8Zry+ppG0cp7qdQDDHAh6VIAyKJpU+5Wh6/ztAhTbkQazhFxcCawV7IJeQSryVKL1SSoQQWDyRkDBLOKnViIcSAohnklxKGzrL7yBShIfyDuPvkEm8i9ariSfTjeJAiKdfv37GQ8oYbNitu+Z4O+kP0kfuJzUit9ySaL9shuDQ8OHDjSfRta9+9auy33779fjcbQOpREAIkgsZB5uzzz67LrQXUgp+hLqSp4l3d7XVVjO1LLfZZptsgy7BVWUklaNGzZZFFy3OU/nOO3Pk/PMNqfwuqfLOMiCI44riuCsEGRwnIvUPmAj/IT8qIoTCJtkFIkL+JIQOVSzCaJeq5UBCJDFyNe8TkUMjnfC2JKk97suBZ5Ocyx+JyCWR635TC7lFnGe12tiZw5oi8nYJHj0dQgACSioDwCp7UyWVZV+h4sd3zjnnmOLYCFjkKXmfiVT6ThcC6mmfLo1o3Hz74pQGKoy+DMC3XYCoDiP0JZW09SWWIXv5qpPKpJBa9/kIcb75EOaAR6VQUjn9hfmCRI2+OgFfLdNNrsTSyamk7w8SGD5eRsTF8BjyA7lD9XWhhRYy9SAhhfZdZhVbCZeFCEIc8R5ihLvi2YQYkgOJ9/Gvf/2ridygTwgkYbRuzUrUXyFqhMUi2kNdSu7rGnmZ1NEk/xPCRkhuXH6m5yvMkEFUZKPRJBA/xkoobJKNHDnSkOivfe1rZm6E0ZKTSt6nVcbdeuutTdgvc+L/gwEDBshFF10kCPdA0ldddVXfoZaqXRlJ5eGHz5ZFFimOVL777hy56CJDKqN2mogkhZk2Qyqj9xlAXEIth3JE7ZdZSOXZInJ0TdE1LS8EAopID+GygWIOpXpkP5eDUVJZoWVXUlmhxcw4lVtvvVX22WcfeeONN+qKiGfsrvuyspLKRvPyJWr/IhPE09L6dPVIqkgqgcmHfIV6KtNEgu3y+Dqqbfu8SSX9Fkksp03zexBDPJshxNKHVL67UNfBziLzAjybDUil66204Zp4ISkxgueS+pCuzZgxwyiwQpogmS7BozwJSqgQzdmzZxtiiecSmzt3rhECcutdQhAhc5BQW5fSJV2Q3alTp5o+fvzjHwv3hmTi3cxqzZDK6D15zxMOzDt/wgR0WMR4Ue+991655ZZbZPvttzefQdwh02B55ZWkxHWelZFUHnzwbFl44eJI5XvvzZFLLw32VBL+epmIINjjmk/4a9yD8TsUDWoiPPye8NeXRQTBHteSwl/hGYTV/llEdvB88lCvxbO6h2d7bQZ57B0AACAASURBVFYSBJRUlmQh8hiGkso8UOzsPsgx+ta3vmVUDfOUjq8yqWTFfYllGql0n54QUhmip+K7ny3CU8n80kilJYi+xDKEpPnO3a5Du0llyPMSQhRD2vqSSrzgvm3BN5RUck2StxKSiOqrDYGFaCKQg+dyo402qnspE96PJ5NwVEJZed9ZIyyUHMjvfOc7JoyWtng1MesBhRBSpoT7uUJA5DWSz0mILDmdZry1/hjDueeeazx/kNVllqmPmAj5X4PwV9IUCEd1zSf8Ne4+Q4YMMXP94x+JWhQjJHTjjTeanFLX67r//vubsFg8tp1oZSSV++03WxZaqDhSOW/eHLnqquCcSvIgyV3cyVln8iQJJU0T6ol7NKKkklOJ1SNlQeASc0XkFzEeVE6FGBM5mDd4PntKKj2BKlszJZVlW5EmxqOksgnwOvRSTuX5YVNmlV/JNyJf55vf/KbZpCGBzyaNTRV/un+3n6X9jpPuDUIxcneojeI2A3ay0fDXRkMK2dD7kkru59tvUaTSt98o5D6lRn08hmmk0q6JL6mkfRmIpe+8GGta9RmLQQj5K6ptCFkM+CqaKYYSyyRSab2V9IknDyOkk/xBhHaiofzUXoQ0Qe5s2KfFHPEdjHcf77WBAwd2vybwXHLgRm4kXkjCZfFc2hqPkEqIKu9MzBLNTTfd1KQVUFvyqKOOkp/8xK2IEPZiJA8Sz+ikSZO6L2SeqOCmCfXE3SlKKk8//XQ55ZRTYkklIb54azvRykgqd999tvTpUxypfP/9OXLjjcGkEg8loaNc+O/aWh8sIhM9SopEHw1OT56vCfzYGpe2pAinOYTCYpuLyL0JJUUuFpF9RWRJEfnQ49lDVZaYDYSGqH+p1kEIKKnsoMVKG6qSyjSEqvf7vfbaS65vIKKDtPzPf/5zsykjlCz6Z9xnto37O5Bb9z+e9fEszAE71HcX5P+bdPPd+NOTL/mjbRGkkn59CWCIp9K3T+7vm4M5b1469raF7xp0Aql08zJ9vaBFkb8s/fqub8BXMRdv5QeLJXnyPkh80KLeyhdeeMGI8RCeinfPNVs/knzIKKm0ZUmoO0kOJYdr1iCveDIho3g6IVnkbPKuw8jRhLCi9ArRdBVmeY8SOjp27FjB65fV8FCiIIvH09bGvPTSS+XQQw9NLSkSvSf4QIAR+LE1LiHGeGohrTvu2FVVAmwJf4XQavhr1pWruw4mOXuHHWZL797FkcoPPpgjt9wSTCr7UvJVRGaKyFki8lURobAqtSUtMWQyiO0sLyL2CwJZHCoid4gIse0I9UBQFxeR79RqTHIdpz7kPKLaCnnFrQ/5m1673gXqSyLyhohQVGq/GOSPFZEVRWSKiBC2i1AP4kCQz7UdUpzLomknxSOgpLJ4jFt2ByWVLYO6NDdiY0KIFrlI9gdlQcQZOE3ntD4vwZ7evkUSLToBO1lfUknXvmGdvu3oM4nUbc75a8RCyKovAWw3qWSKvsSyHaQyKsoTourqO15fUglWWQigz0vD9uvzVYvo4DTsPuCrmEoq3Wc6pN/5A4wnloSjQn54X+GtxKNG+CohsJAk9z2GB5LcRrx7cfUl8XJyLYI2iOy4hmLsrFmzTNgsxBWiZY1+IZqLLrqoIWG0hbgh4HPiiSea61B/bcYgxORt0j99os46atQokxdpiSH9b7bZZqb+Jqq2GJ5S7r3tttsaUoxQDwQVIkxIKweI1nbddVeTC0rEihXque+++0zYcDMiQ83Mu9lry+ip3Hrr2dKrV3Gk8sMP58hddwWTSqDG1X6hiAkwek9ECFmFrLknwxC5r9d+uGZ9ESEmG2JHXUu8nCjhnSwiL0bWD6KKUixKr5+IyG9rQjxRRdohNWXXbUXkzphnYLsaMcXruXAthJZ2J9XIaLOPjV7fYgSUVLYY8CJvp6SySHQ7p+8TTjjBbIxuu+22XAfdaaSSyfsSyxBSF0IqQzygdrMeSSHrsYa+RJULfT1ZtPUllbT1IWpRT2Wj0NoQQsf9O5FU+ojwhJC0vEml+13Ju+/6hzjZW4mCK95EDsMIY6W2JKQO4R3r1bN9PfDAA4ZoEspqQ2bt7yBaCO/gpYRYugZ5RfUVQkoILTmUrrmCPpBKPJ8QV/IgF154YVOuqVlDUIhQWsJv6ZO6lyi5unUl8aCiOMsPhod1zJgxJi+SsFzwoC4x4a5uXilt33//fdOWKBbGDyknHxSsOtXKSCoHDy6eVE6ZkolUduoy67g7HAEllR2+gO7wlVRWaDGbmMoVV1xhcmrYuOTlpWQ4vUMYEuU3FqvfzNHHIh8R4dLTivBUcpdOJJU+S+9Lgt35r7tu455DSeUTT3yUOtSFF14wtQ0NQkkl1/gSSx8CHDoG61H0IYohXs0iSKU9WAghiiFtQ8Y8/2GIJ5bkOZK/TTgq3salllrKkCKIpltD0orsQDrx2rleOu6BxxHSiShPXO1JPID8IMhDDnrk/1Hj0SPvkbHYvMztttvOkDLIn1rrESgjqRw0aLYsuGBxnsqPPpojjz2mpLL1T5veMSsCSiqzIlfC65RUlnBR2jAkTr/Z/JAPhOx+XpYHqUwaiy/54/qQTXJIv75ErWhPpc96+Y41ZP6ffWY1HXxG4EcWfUllKKkLIZW09SGWltjmTRSLIpU+q+S2CSGKIW1dUul77tSvXzypdMuLEG0BWbQ1JF1VVkqJ8J4jn5IUAMR2CJO1Rj+Ex0JO4zyZkFfUZVFHtSVHXKyo/UhoLDmZeBJRi+X+hx12mPEwqrUegTKSynXWmS0LLFAcqfy//5sjTz2lpLL1T5veMSsCSiqzIlfC65RUlnBR2jAkQr84ZacAdly+UdYhcWq/6DvvBF0e562M6yCE/ISEdPr0azfQvkSN8fsSS99NNn2GhLWGjNUHA+5fVVL5pz996v3MLrdcl2CLj4WQxZC2kDTfNQvxEoYQxWjbtOdtNbKwAiyJVNIF7xlIHyGq5B7ibUR8BnJpwzwJW33iiSdMuRFKaUAuKclhjTBa3n94MvkcQZ+ocR3tqN0YjejAO0m4KR5RcjIpz8Q9Lr74Ytltt90CZtqzKWQ1Gvp65pln1oW+xt2AOeBdjRrq31a9FiKNUm3UKDPSSNCtqQm16OIykspVV50tX/lKcaTyP/+ZI889p6SyRY+Y3iYHBJRU5gBiWbpQUlmWlWjvONgQERKGiMPuu++e62B6+8ZIftQVGulLKmnru5HOm1RagEL6bYYAxon/MIZm+my0yL640oc/sSzGUzlrFvW5fc3XC7+Ib4dSNKn0ecZCyF8epDKJMIaMI5RUsiCNvJV4IiGF5AIimkMdRnIJ8UiSd/jmm2+af1NuhJzDN954w3gSLTm0pHOllVYyv496Mrk/4jaE1qIu279//x7PCLmcEECILL+H3OLdjNbN9H64KBRYE+lhXK5Iz7777lsn0pNEKvGqHnPMMXW//t73vtc9b0sqSYFYxVlAxu6q4IaMuSxty0gqv/nN2fLlLxdHKj/+eI785S9KKsvyDOo40hFQUpmOUce0UFLZMUtV+EDZbBH2ddJJiKjlZ96ksnbL1z/xKxVC82ef9c+t89mchxBV2vr2WSQB9PWAfvyxX6jqSist5k3Ww0glrdOI5dM1EcGQ52+hgMblIpU8v0mWB/nL0neUMIYQxZC2SaTyE3QhE+ytt6Z1186N1snFUwnhQ3WVEFV+j2eR0iJ4DKk3ifopZBHPJqGwlNawpUcs6YQA4vHEy4e30TX6g6ByL/Iqo95Km7fJAR1joN4lSqyEwmY1DvrIyXz11VdNSRPskksukREjRqSWE2EOQ4cObUg+LanEixvNFc065rJcV0ZSudxys+VLXyqOVH7yyRyZPTsTqVwlRv2VzUBaXTBUoerlkrsegF4i4ibRUzMIdVnUXz+uqb+O4hzZeV5+LSJxtXe2qZUYsU2/IiIkKlNyBAXYR2ulT6KKs2V5FHUcDRBQUlmhx0NJZYUWs8mpHHLIIeZU/De/+U2TPdVfXjSp9B1s3AaeovRx5uup6yRSyTx9iaXIv3xhrZUK820+P4ct+QqU6UMtb2KZ3VM5ezZq/PHWty/7Hz8LIZX06EPorGfbp60dZVFtfcccRQtimVQXl8+XXnrp7rBQPIcvv/yyIZL8HWJp8yFnzpzZrXLKdS7pxFNJezyZth4l48DrCEHl2jXXXLOb5NkxQlZpgxJtr169TIkPypQ0k6dO+RLI5OTJk7uhQMmVzy6//HI54IADEh8oJZVfKNN+1dSpXHLJ2fL//l9xpPK//50j//xnMKm0dSpfitSpvDpSpzLuWYNUPiEi50V++cdaXUo+tnUq+btbp5JTROpcWoNUbiwi+0T6el5E3DyaiSKyp4hASglVofQJNTJXjbTze9lqq7YiUKYvaVuBqMLNlVRWYRXzmcO5554rV111lTnBb6cC7Otf6qkAmzTDRp6e6DVFtCXtypdYFhWq6uupBI/OIJWMNJRYFk0q2Ws1Mg7h/awoYrlgmhPYGV4RRNF6GEPCWkPGYZ6KxUQWXDC5vAg1K/EWQuogg/zdivMQHkuIq80ZJ6+QHEj+TakQPIEIleGtwxNJKC1hrJBUzKrDUqqE0Fn6W3ttaq3PN8py4M2kXAl//+EPf2hqQrrE1O8pmd8KTyp1hKkf6Rp5n/vtt1+Pz902kMp33nnH1CVmTBDUs88+u67upPVUEq6Lt5YalXvttZcpOwIx7mQro6dy0UVnyxe/WByp/PTTOfLOO8GkckyNmPGf71u1NT9ERCaICJ+93uA5gFRSiPWIBm32EpFrarUwrTcRj+XdIvI9EXm8di2kEmnlRhnXgMc9R+C0r12HC/9VETldRM7p5Gf28zh2JZUVWnUlleVfzHvvvVfId2GzQvjW4YcfHhvOxIZq7NixhhiyeeJ0nsLY0XpkSTOmSDbiDJzYu6qIzSIUqgDL/d5ayI9YUvC9UbicO/YiSCX9+5LKtLbuBjtN5MSdVzGkkjv4eivjS77EPzc+nkpDHwIfuyyk8pmUe/iHYYsURyp9DyNCCFpIW0AK8ZqGkEr6tmPxLffCNUnE0irB0sbWoZw1a5Z5p1Gj0Zb6sAv/3HPPGQKJ1xGPJl5F/o4RtgohxLPJIRvv14cffth4L21orc3ftP0R6UGtS7yZkNmf/vSnMmPGjKYO6ZjHGWecIaNHj657Xsnr5B1PKGySjRw5UsifpO4m/3cQRotKLaVXGCPG3xHkgXBCIu+//37hgHHzzTcX/k/oZCsjqVxwwdnyhS8URyo/+2yOfPRRMKmcWiOTOzrrzUsYgjlcRCB7SeZDKq8UkTVEZC2nE7gE/8kQEntq7XMfUsl4LhWRJUg5dvr7rYhALgd38jP7eRy7ksoKrbqSyvIvJiILd911l9kcTJo0SfbZZ59YUnnooYeazcH5559v1AvZQLCRYOOEcEWasYlCDIJaldHi32nXNvo9G72F3rKHn349+ZJKeoNY+phb9uFLX2p8RQgBzYtURkf09tt+OZAi//CZfq3Nm7U/B3pc40sq6cqXWOZBKp+KGbtvuKpvO25RPKl8++3kUFlG0L+/f7gs7UPIotu2ppGV+EyEEsVo+0aEMWTMdoCNvJWos/LOsd5KQlLxOpLjyHuQd5w1K85DHiZlRjDyLDH6gRhSsxJlbAgntSghX/T9zDPPGHLp1rSEvEJOIZuI3vA7al82Y82Qyuh98bAyLv4PmTABJ1S8XXTRRXLEEUeYg0yw6VQrI6kUKZ5UigSTSl7gl4tI/clFV2jpVTGfu48EpJINRu9ariQE9QQRcU/t8ET+tRay6l77CCHBzueQSsJa/6+Wk0kfZ4jI/NjvLk/kMBGJJq+QY3lQzOed+vh+bsatpLJCS62ksvyLyQbJhk8l5cjMmTPHCEuwUSA3EuOUHXJ48skny/HHH586UTZf1Fi78cYbTS5Qntbb191Su+l/vjlQfEVjfUklXfvUE6RdHKlMIqKdSSp9VjeEVFqySr/fSukcYpl2EhBC/ridb3vfdvTZDKmc0wCDMC9FCLGMI2hJhDGUzDUillHSGEpCG41l8YXidUI+SAhRIOQVryLvTMgf9uKLLxpvJaU+oqVC8Cxa8Z3evXvXKZ6i5EroKCGvvE+nT59u1GPxXLqEFMKKEUKLJxDv5rbbbmu8lngv7Th8vnXRNoS/HnTQQUaZ2zWf8Ne4+w0ZMsQo40IYk2zu3LlGwIj/T6iz2alWTlIJtwp7B4Thz7vHeKG/SyUr51oEcVxRHLdbhHPGiUh9jLUIUmKI4HRtKuLtAhHhYSL8lBuT37iUiBAbzmQx8gfuE5FDI13gCkd0h1BY7CgRQarruVq4Cg8fv6Mmz821Nr+q5V0iLOTasbV8UPpT6yAElFR20GKlDVVJZRpC5fp9EqlEsOHggw82OTHkB1nbeeedzWaIvJk049R99dVXN8IPKAumGe2tAmNUiREi7H721cBalZBKX3NJZRoR9SWV3NvXW9mI1Nb2mt1TCSGg/p5Kuvf1VrrkLw3hrKQyrd80MUF7fQgB5Bqf9j5t7P3jSCX7piQL2Sj6t21EKqPPe55kzs7SEsbQvn3a9+s3H8uVv+77XHRdk0QqefdwQOZ6KyGG5E9yyBYtk8H78emnnzZiOuRPulEa9jo8j+RgWvVYO2pII55EamNiRHtwDR5KSjMRSkrqwt577532pUj8PZ5RPKVEqVgjT5L3fJpQT1ynSiozL0WzFxqhHhFSCufXR222057X41yMPdg7zQkzjV7WDKmM9jWArJBaDqXdSPiSymhfFAGG1PLitpsCJZX5PzRt7VFJZVvhz/fmSirzxbPo3pJIJZ7IK6+80pzGu0aO5WWXXdbjc9qwSbnhhhvMBoyTfX4oGE7uETmVfE4oLXk2UZJoCaXvfHdwiG6Pa9ydZe2XWUll2njiSGUSEfUlldzT11v6zDOfpg3R/L5v3y9KMaSS3n2JZRVJJfOPI5azEtaF/ZGv+RNFP0/Fh+bGvXqFeExFfMicndH66/vOLaxfemUcMV/txBuGkkqKtH4QKflhO+fdRa6k660kDHXhhRfuUTaDd9m0adMMaSQ0FqEa16htyfuP96L1Qtrf2xxK8ix5T9r8SUJot9hiCxNKe9555/VQifVHXYyH8qyzzjLhuYwBu/TSS4V0BzyjlC/xNRRtGRMCP+TbJ9mFF14o5GN2epmRcnoqcf4VTSqNzk2Ip5Lw18tEBMEe13zCX+Meo99RVrYmwsPvCX99WUQQ7HEtGv4a19dxNfEdwmt5KRL+SimR6MtZw199XwQla6eksmQL0sxwlFQ2g17rr00ilagMImVPuJZrCC5QMBvCGLU777xT+CH0y/6w8WKTtfHGG5vPdtllF7NZshL+cVL+Pp/1DnER2oGu5eb014/+PwvOJwa+hI4eQoaR1G9cOGHIGHyJpcgrAQ9YXNskQuRLKrm9L7EM6dPXIxXiVWSs0fZJQjz+m/Ce+5ZGSxJCKucllHaL7z+EWFpS6SN800wIbFI5HncGIf3XkcqAcPk4YmkFe3iXEXrKO8rmNkIACXN1DbJI7jniN7Zupf09tS8hnZA38iytkA+/p38O4gh/RRANzyUpBCussIJpR81fQlebMYgrntCVV17ZvMtfe+01GTVqVI/celIWXnnlFeMtxa677jojtEMYLmPHywpBxTP75JNPmjFi++67r/HeEuK74IILGu8qAkNc53pHm5lDu64tJ6n8k4QIe4Vjh1CrUSUmsbJRHL7bNXmQvMR3cj4kTxIhnDShnrghRkklQj2r24HVLoBLzBURTjesUE9cX1FSmSTU8//VxHtUqCf8oWnrFUoq2wp/vjdXUpkvnj69EbqEYEKaoc4HsXMtT1IZd39qVLIRgpzmWVaEkLB+M2akTbn+9w1IZbSjmX9vTED+XdO8yYNUxk0ijVS6RNSfVHInX2Lp244+ffcZtE17Tt0wK19i6Usqk7yHcSsQohZbNKmEMPpYXL3w+OviSGUSaQzxVHK3OOKX4AA0gwshitG2K38zxVPvWyDWwvTss/LB1lvHgma9lURdQDI5dMNTCemLKmLj1Zw6daoROItTy0YhlRIieAqtkI+9KfmJeDMHDRpkwmghcPaH/HTCTZs1xNOOPPJIIxzEHIYNG2aE2Nz/HwYPHizU1+QHI9x3zJgxZmzUtWTsP/jBD0ypEHeOEM1rrrnGEFJIM2STcF2ujf7/0+w8Wn19OUklTruQd1AoapBKI64UQirxUFI/kmusQtzBIkI9yLSSItEBMjnqSiLwY8uM2JIi/Idh6zNtLiL3RkqKRPsi/PUPNREgW2bElhQhPxMVWIycH3ITEPXRkiKhj0yb2yupbPMC5Hl7JZV5ounXF6FLeBbTjI0ESn2uNQp/pZRIlKw2Cn+Nuz8bETZHhNGyecnLONHvcx95+gE22P/AMY1UundNI4C27b/+5R/W6tsnffu2/fhjNuAhZNG3bZ6k0kXWVwGWa7pCO9PNt1RIq0jl+ylDDglra0Qq+/S4z4ABPT9LGkwjYklt1aiFlAxJIpVJn6cSSXcwllSGxJ6TXxlDLF1vJQQTYRoIIaI9lOJwCROEEcJGfuSGG27Yo64k3j28kHGkk3cbYaKLL764eW/iUVxggQVkueWWMx5O8jHV2oNAOUklEZ8hYfWh2HEQuCEXhZBKSBniODNrYje8yM6v5UW69Sf5T5wX1zdro4IsDhWRO2q1LBHqgaBS2oMH354MfllEkO3+rEZeCRU4V0Sm166nO/r9DU520pNrRBGhnk1FZBcydhwkILt7iMgo6VKohRAzJpKb3wlFTNu3FwElle3FP9e7K6nMFc7COwsV6iF8FfEeH6EeBo8XlRNtTvUJh8rTev/+92HdrUsNZD/755eWEeuNTLvCl9TRT0hb3+mF9Pnxx42EYaIztUJ7aQjwe19imeapdO9VBKmkfx9iGUIq6TPqKUgSRaRtSFirD6l0ver+ffuQSpcchngUk0hlEjkNIaF1pNJHqcqnjfvY1Yq6fnAwjpV6I+wfNdj333/f1GPEmwe5RJDHhn9yhVVyJVSWUNBoniLE8cEHHzRezrj3ImQSssq9+D3qqZBJQlVDch59vrnaxh+BcpJKRPOi1TD855TeEl0FcyAbQippTy0dakZuwFdCRAhZRcnVDS1h8BxL2aMpsrKRJcaLyEsYL+f9InIyosuRsfJyRCkWNVcUXqkrebSjSAsRvUJE2HiQSM59p9UUae+O9LWAiJBDSW4lp98w9SNrAkHpEGmLUiGgpLJUy9HcYJRUNodfq69OKykyceJEowKLkYuDkuG4ceO8SopwDZunZZdd1hTb3nNPykXlZ73vumt+Z2mFImkZSCp9R+qGwKapxYYQwCyhtWlEuDhS6R4gN0KuU0glc0gilnH1Pv3JXBipZH9TTN/9+/dpKvTUXeU4UhgSNht3fSOi+cUXAkPfXWJZI41p3+84Umm9lYSokmtIrjhEz6q4EhqLcfBGmCjexX/+85+mJnA0/J/wWEjjRhttZDyarnEfojzweJKzSc7i9ttvb8R/om3T5uH+njSEaNjrmWeemRqWyv8ThLNGjfGQNxk1xk8ZFPJDb7rpJtl1111DhlnatuUklUR8Fk0qt2BNQklladdRB1ZtBJRUVmh9lVSWfzHZHBBehVHqgxNwK/7g/uePGiBqrii2EqaFYiAbKQQoKPrta5zmszGivmWeVkcqfTsmrzJmExS9HE+lr2Uhf7bvRiS0mX4bjX3uXDdXsVH4ZYinMiRfMdpvUv5qqz2V0U2zf45iNuLnGw4eRiohi77m43108yED0pIbqsbGqbgGnPmY6SUSy6STlZAvVA3AOGJJniCqp/zwXoMYEupKzjrvSYy0gVmzZhllWH6HME7//v27l4XDNqI9CJnF8+h6OW2jl19+2RA5wmcRyCEnkXtmzU23Aj0o0roCPQjrNFJuZTyQSkjiMcccU/doxZFlGlx88cVy2mmnGXVbJZW+38bgdrWSIkSKUsaxKKO81LZ0rqSyKIi131wRUFKZK5zt7UxJZXvx97n7r3/9aznwwANjm7LZscbmiRxKcisJ52JzgzR8NC8z7Z6QU8K36CdPIy9p2ccRKQiwgF3xu/1MwWdJKvbu3jVkv+rrrYz22YiA+vbJmOtJZRp2XcqP9RaXv9gMqWw0Bt+8zjjvYVK/vqSrWVLZ04Mzf0RdSpl+ZkmlU4SxwYX9+y/h1610na9stJF3c/H9+tjAgZC+fUnlJwS61ewr0yg552k+X9JIxMP7++9fVx+X9yP5lJTe4J0IacQgkBAoS7L4PR5Kfk8Y67x58+pyIa2QD2QNTyc5mdbLaWfDO/OZZ54xZUluu+02ufbaa00eZlZSiYAOYjyMjXxN7JJLLjEHi2mlRJIiWuKQx4uLcA9K4cOHD1dS6fl4ZmhWI5W31qI7M/TgdQmHe9vTUkmlF17aqN0IKKls9wrkeH8llTmCWZGu8HRCZB977LHMG6I4KMhvWuxW/kMlTc4nT07Ee1dMYkaNVPosg0vq2hECG0cqk8bRPKlMQiSJAEa9oSEeUF9SyZjyJpYhpJL7W60JnycmSip7pVwUW3w89hqXVHo45YNIJTe0xNIn4jyEVNK3JZYucWwETGZS6TP42o1vqXkfo+OgVMjAgQONCA/vIjySlBDp16+fiegg75IyIISI8u4jN9LWhUS9mvBWyCTiO5A20gRcw9v50ksvGbJ5zz33mAiRu++OpoL5PGtdbb7//e8bMjl58uTui1Bx5bPLL79cDjjggMTOQkglonF4RSGVeGDVU+m/RoEta6Ty5haQShO+rKQycIG0eXsQUFLZHtwLuauSykJg7ehO77jjDpNTw0l+9DS+2Yn1vpn/UAPMdbWkENGspDJtNFEC2OoQ2FmzkkJeP4oZepynMmmGIQSwEbF0xxfSZ16k8r/OBLu81X6WRirdfumRHkRvFwAAIABJREFUMmu+lkYq69duueV8BH667p1G/FAtdi2h4kbiRBr1H+17c4oCBFhDUhlX+NLNw/a8z9933tkQR7e2LjmDGO8zxHgw1yOJ0jZmy4WQX8k1a6yxhvkcMke+4aabbmq8hHgrCaXlPtYIfcVbCQGFsM6ZM0co0ZTVIMF4DsePH1/XBSG7++23X4/P3UaQSkTXGAs5nRDUs88+W1Zfvf4Zfvzxx82cIMCYksqsq+V1XY1UIm46P7Ta68qgRpR+RJRVSWUQbNq4bQgoqWwb9PnfWEll/pgW0WNWwYYsY0HEYsUVVzQbDTYneVpTpDJtIDFKI+/Om7/pcy8PCT/Nq200dSyk32RiGQdKI8VYV+U0hACGeCt9FGup5RhCKhuFproYhJBKroNYRslj0oMWQirpA2IZR/x79h9CKrka4hcleEmjzkIqffvmniHE0pDKOPKYNPgMpPKDGA+eW14EUgkZtN5HctSp7dinTx+j/Iq9++67xiNJeCyfExo7c+ZMI9JDKCykkdIhqMhaw0uJUA9htkR7QODw/mU1yCCCaaNHj67rAu8qHlNCYZNs5MiRZuwItfE+J4yWQ0IUcMklxcCENttss42pXQkGSiqzrpbXdTVSiahq0aRyGAMK9VRSvyyq/npSRP01bqIURo0LEyGcw30BInxA/6i/flxTf6UkiP1PCdUskoApUTKQNGwRebqmJPtQ5Mbz837m/4Jk0iIVkLwWWRuFI6CkMhyz0l6hpLK0S9M9sGYEG7LMjk0T0vnXXXedbLGFUZHLzXpPpLxUA4tTBPFNDKPbAAnLGS/EE066cTfVIeQvpK1P5YS/ow5vcsDS6iO6mPqQOtvel1hSgiTiAktcxpD7sw/wsS6VznRLI5WQWddCiGJa26hQ0XfTh1tr0YhUzp7N/qvettiiXn007UZJxDLuGdxxx7Te6n/fiFTaspPuFQMXDDmgEJEkYtlAbvaDGHcruZW82/BW2ggMciDxaEIG8QxCwqxBwFBKxXuJ1xHRnfXWM0XlBVEechH5t82Z5BAOwoooGoRy7733NmGqWa0ZUhm9J6G55Nbvs88+MmHCBPNrSCmkFY9t7969lVRmXSj/62qk8jIR8cu39u/abcl7+iA+CCGVtk7lS5E6lVeLiFunMm5IkEqUBM+L/PKPtbqUfGzrVPJ3akraOpWQRkgkRk7MbBH5tYhQf4zTvkNqCaIQUUqVWINUQlCvdT6jBAm1MNU6DAEllR22YI2Gq6Sy/IvZjGBDltkhbkFuESqDRxyR9v9J2B1SSWVcd2lE0fV8pLV1+m9EKqPD8KnFbglg3sSSsfjc3475vfcsuXFVY5PWyZdUcr1vbcs4okh96jjzJZVcG0Iso+Qxaf5pRDF6nW3vo3TrTyq5C8QyjkDGjTwLqfQ5xLD3CiGWkMo48piEeCZSGVIYU0TiSKXrrUTFFSNElPIf/JvojAED5help8zI9OnTjVeQcFfarr322uY6RNHIu7Q5mXwGCSUHk+gO+oGk4iXMKtTD9ah88/53zSf8NQ77IUOGGCJMnU6EiBgnHsw99qCGvJiwXt77hOzuuOOO5mCx062cJUV+KSL+4lzha8B7/0fmlRLw0h5Tq0nJqcpbtXtC6DiB4LPXG4wDUnl7CvkkHveaWi1MW78SokjS8fdEBAU/XvA8dG879+KzZ0WEvI7tnM8hlceJSPZQgHBg9YqCEFBSWRCw7ehWSWU7UA+7ZzOCDWF3mt96t912Mxukn//851m7iL2OsLKlr3UPFz26DyCKQZ7Kf/uXIQkhdSFtQ8rxhfQ7n1im4ZtEKuMIqS+p5J4hZNGHoNGnr6eUQ3dfSyOV0Xuu6tuxiKSRyiie1Pz2t0bEMvqsDLW+AM/uG5HKuEOTEBLaiFR+sHRPT3Pvh+/xHPX8ZnGkkt8i0ENJEddbSZgrIavkTy6xxPzNPodrlHLiMzyc/FBqxBopCYj7ED6LQdYQ71lqqaWM+A+kkJxFal9mMd773HvSpEndl0Ns+/btmyrUE3c/l1TaUNekcTGHv9tTsiyDL8k15SSVONiKJpVHsgIhpHJqjUy6cQoU/oVgDq95D5NW1YdUEvNLgvJaTidwCV6wAHJqg0fmhlqeQtcXrcuUVJbkO5bHMJRU5oFiSfpQUlmShWgwjGYEG7LOjtIkDz/8sNx5552mC0752WTF/el+lvZ7NnQDf010S4BRnC+itJh4dQABfXeh5khlUk32EPIX4j1K69cdjz+pBElfb+UbAYvmky9JpBPmSypp60MsQ0glfUIsffqlbQippL0llj6EPBupTHsuGEVWUunrdQ8hlYwHYhlHIJMesLyIpfVWch/CSzHI04wZM4wXEsLmGr8jl5KQVkJEKRdizc3J5PdTp041obLcg3aI/bgkNODLY5rioaTW8OzZs7tVaC+99FKh5FNaSZHovQjdZWwI/FDjknBf1Gyjc91rr73k1FNPNWkPeGg73cpJKn8qIl0lYooxeODRdB1CKnkJE6tdn8ArQogJtcWin7tDh1RSCJuQVmL1IagniMgzTiM8kcS97xmZ8yO1kNfo57YZqloviwg5lfs610IqmSieTXJD8HjiuQzJvSgGfu01GAEllcGQlfcCJZXlXRs7sjxza+JmSxjXIYccYk7x7Q8n9/wdIskJ/WGHHeaVX2kVF6Pqi/zbfrZpjah6I+9T8d121qhtjOhQs8Qybg4+RPFv/DcsIj5t7T18yINt+/zzXWqX6QaZ8iWV9JY3sbQj9CV1dqxp7clZ8hXgmR/ymI5XGqmMjivNE+re0YdUzienAwaE1M5MJ5bu85WVhCbhFyWnoSS0IalMKJj5QUJ9FuuttII9toQI+ZRWqMfOA4JoSysRdrr88vV6JFYlljDYKVOmmDqXhLziZUQxFrKZ1WwuPYJAJ554ognBHTVqlMmLhBha22yzzQTlWcqiYOTC33777bLtttvKMsssY8YDQaVOMKG+iPHEmQr1ZF0p7+tqOZVnt4BUwunMiVYtK9+MEUEcV6nNHThkcJyI1EsNd4WeUlyWUNgkuwBHfY3QEWowVkSWEhFixW0CNbma94nIoZFOCJslFp1Q2Dgj//L0Wojsk04DZJW5lpCY1WpjZw5rRsJnvRdHG7YPASWV7cM+9zsrqcwd0tw7LJpUIkJBGRHyiuwPGwyUC5GhZ2OEYiAhUXGk0ZJFfueTP9S7QX21bvCiBDAvYhldHU/P5ruySFBeY1FkkY1/kofUnZo/qeQqHy+a7T2NzLmj8BEXmlW7IKRfXxJcBKlkuOyXfC2EVNKnJZbpa9IMqfQ5oAghli5J9PFuNkUqE0hkdEWSSCXREhBL3lW8W63SK3/fcMMN68qE0CeeQgjbt771LUPSXOPwjRDZtdZaS/785z8b7x4ey/3339/065Yc8X1i6r/Hz8uRRx5p1GYXXnhhGTZsmMmDtDmhtB08eLAR2eEHwwM5ZswY4ymF2JLG8IMf/MAovDKHJFNSmWWFgq6pkcozRSQ0miLkPqQkItraw05rEGbaDKmM3ohTuhdqOZQjar/MQipRCbxDRM6oEctGIBBai0gPJPScELS0bfsRUFLZ/jXIbQRKKnODsrCO8hZs8BkoGyIb0mVzhnyu82njRSqjHYWQypAaCp6kkuG8Ps9fuMLdWKelJvls8C0cIW3jiO3s2UkezHQS0zWGEPJHex9iSbsQDygRU74WQix9vbshns00UhnFEyV9f0sjlm+8MV/Rf9Ag37IsXff3IZVZvZu+pHLatK6xfH81qx3ijw0tk4glQjt4IfFW4hFEBZY8S8R6osSRSA2IIrmRbvirHQkCPZBHhH022WQTufbaa82BHAqxPodsYTPS1iEIlDP8lfRB0hWLMlIPTIpiiKeS8FdkaRHscc0n/DVuIr+rSdwiwoMR/koYqymg6VhS+Cuna1NEhITi/T2RotgqntUu5Sm1jkFASWXHLFX6QJVUpmPU7hZ5Czb4zIewV8LBTjnlFCONn6dRL20FFAdDamCGkMoAoijRfhuMKSupTMMujigmEdFmSWXSWJLJZtwVSRFUcW3TSKVbXsKXWIaUpAgZK2XVfCyEVNJfSPtspNIlj0kzaJZUpj17PiTUHVuUWFoCmTT+LMQyzVsJGZw7d67x8hGJQT1Kaje6ZBDP5oMPPmjKiwwaNKgHUSSsFC8lpBRSed5558ndd99twmbV2otAOUklzrSiSeVZAB+SU0keJOpsOzkrRuw2bs80oZ64RY6SSoR6OGHrkk/uMrjEXBEhltsV6qFYLGTzTzXF1541leIfKyWV7f26Zb67ksrM0JXvQiWV5VuT6IjyFGwImS0hU9/97ncNsczTUEzsP2RIWJdxRDFJvCeEVDKKAML66WpE2cRblAj6hAHantI27O4dfcs3hNSNbx+pdGfm6y0l/LUsxNLHE0rUW4g1Ipau2j59hpFQH2Jp833pffBg/3H7kkrrQW9U3zLurr6k8tV580VQ+vX7IHYCeCmpW8mfiNhQagP1V8JM3TIhXIyoDZ9DNKk/ibKraxzAQSDxaHIAePzxxwt1ISdPnuwPXqQlyrLRsNczzzyzLuw1rnPKhJBfGTXyRiHFGOG65GjincVLC5lGmIealVEvbeYJlOTCcpJK9GSy59qmQ/uOiPyEZiGkEg8lbJdrrMrawSJCYem0kiLRIREj/nxN4MfWJLMlRYi/JhQW21xE7nVKivAZJ3AQSgjupiLiWxsKVVniGhAU0jIj6Q9JqVooqSzVcjQ3GCWVzeHXiqt9BRvyHgviPKgfXnMN5aXyMzZhfTbl/4sACyWKPu0tKc2JVEZn40MUbX6kT1vbvy+ppH1IbmdamK47v4/mR1WmLmKjfj/+OFq/0pdY2n1J6u0D1WV9+rNt+ng2DiWVxXk2Lal0iWOjSYSQSvpxiWXas5cHqXQJZNw8kkglbSGVEEFIGKQLUgmZQ9V1nXXmiybZnEtqT9LO/Z29J/mLeDk33nhj+eEPfyj9+/eXiRPZj4ebfd8TausK9FA32BXoiesZUslB4DHHHFP3a9f7es8998htt90mG220kSGUCPmQb0ldSgjnAgssED7okl5RTlKJMqt/KkU4tERnoDAbRCpJ8sTTN1NEcHN+VUTOr+VFusWqEdtBrQpvIgZZpGARuY/UskSoB4LKyQ4lQGzSPFLL5Dyi2gp5RSkW8je9dj19ESqCe58+9ql5Me30/6/mueTfx4rIirXwWMJ2EepBHOjDmifUR3o8HFa9ojAElFQWBm3rO1ZS2XrMs9zx+efTBRuy9Nvomp/97GeChD311/LODeodUszckj8fomgnVFDbTzdPEqnriWQIUUzbgLu9x5HKJJIQ0m87SGUUtZ4kM+4JtcTTl1iGlCwJ/RZlIZZp0Vw4BkIsxLMpAjnyNR9S6QpH+bR37+1LLO0z79vevUcjbyWCPS+++KIJXaXkBlEUvO9QcYVkYXgxKTnCZ4jguL+z96EPDuBIGTj88MONMM5pp6GLEm5EpiDGQ9mQxRfv8rpecsklMmLEiNRSIpDKoUOHppLP6Kjuvfde2XLLLeWRRx6pRCkRO79yksrDW0AqLwKCEE8l7b9dqxlJLZn3RISQVcjaf5znhTzHr9d++Hh9qt/UiB0xvRC6+0XkZBF5MfKcQVRRiuU/0U9E5Le12ic2R4F+LQmNPqK43/k9tl2NmOL1XLhGPql9hjqRbw5F+BdTrygMASWVhUHb+o6VVLYe806541133SU77bSTkAOJoEWeFkQq7Y1DiOJaa4ksvbTfkEP6DWjrlitJ8wyFkD8m5eutTLuvC9C/A893Q7yVUcXaRoTbj1gych9SaQlonsTS9TKghu9jIZ6JUFLpTxIZaVZS6aM63CypTHuumyWVREnYH1uzEsKIsqoV4Zk+fboR3iEMFiM8FoK3/vrrm5BRjDBY1/gcr+e8efPkuOOOM6Gr1JPMYoTQQibd8FlUXPns8ssvlwMaqGdnJZVPPfWUIMj2wAMPGDXZqlg5SeWPalyoKJThg7+k81BSWdSAtF9FoCECSior9IAoqazQYuY8FSsxz4aJkiJ5GiFnqwys97B8Ic2DEkDoBFLpa26/PkQ0YBwhdTDTNtQumfRpa6dfFLFshlQ2WppGuaiffRYNl/UhltzNN6yWtgGxvabEmo+FkEr6c4llvp7NRqTyXzHivuvji/A0Xz5iCepGG3l2XGsWQirtcz9v3h2GSEIi+Ykz6lPiZcQgcCi6QiJ79eplRHxQdoV0vfPOO6bWI7/r3ZsIvi7jM0gfoaQXXXSR8Vbu6CtxGxkQat/Dhw+X8ePrSwZSJ3O//fbr8bl7OaSSMRLCS4kUCCploaIkmGsQIOKHMR9xxBEmvxIvbd4HiGErnG/rcpLKA1tAKq8ASCWV+T5O2ltBCCipLAjYdnSrpLIdqHfGPTl5p8bZVVddJVtttVWug2aTtmxtE+fb8Rci3oFUT2RWYpk2oLTQXUdBNm9SaYcWQiqLCmtlLCHE0o45LSw4RODos8+6Cr7HW5RIloFY+pb1CK1j5+/dhFTGkcckFENIJX1YYunj2aR9CLGMI5U+hyZf//qrxvtoa+q6f0KsCH+FhPE5BBSSSEklvJcvvfSSEeuxxIzfLbTQQnU1HxHqgdARpnrFFVfILbfcYnIWs1gzdYlHjhxp1GshyJBFwmiJNIEkRw8GGR/hrhghvbfffrvJsaySlZNU7iciCxUIM9o2VympLBBh7TpfBJRU5otnW3tTUtlW+BvenILb1DsjjwchiFVWWcX8GbXLLrvMnEYTokVxazYS5NU0a2yu1l57bdlzzz2FzUqeRp24xWr5QiH99iCWjS4uSjE2jVS6YwoQAXrLaBt0WdpGOYRU0l9RxDILqfRZ7yRiGUdIGxNL9249VTGTxxISC/z/fKbktCmCWEZJZWPv5pe/7E9C00hllKCmtY+C5cu97DPs2969z7rrxqvA0saGwfJ3CB2G6A557BtssIHMnDnTeO94t2LkWPIe5ndf+UqXp5qSI4TLzpkzx3gHKSty9NEIsoRbM6QyejdUaPl/Y5999pEJEybU/Zo8ULyy/D/D/x+87yGZNpc0fOTlu6KcpHJPEfHNxc6CKWWcrudC9VRmgU+vaTkCSipbDnlxN1RSWRy2zfbMaTdhSZw8s7Fh8xMllddff72pIzl27FgjDnHDDTcIJPOhhx4yIVrN2h577GFO5S+88MJmu+px/Wd90v9jnR9g1nV506QyaRYB5E8sqfStsxnQt0ss0wD39QTRT0i+5CdIKARYSN/kjvrkj4Z4K595Jo5AJRHIMhDLrKSyi/AkW32pi0YtQ0gl/YSco9A+5NXjkkSfw4+8SSXj5ZCL9yvkEW8lf+cwjxIbhIUSsbHCCisYSCFfjz/+uFF4xftn61iiuvrcc88ZwRsIJrmZWQTOCH896KCDBMEe13zCX+PWfMiQIYYIE9qaZHgzl19+eaFsybHHIq5ZDSsnqdy5BaQSDRwlldV4iqs/CyWVFVpjJZXlXUw2NmxwMMQZpk2b1oNUcnpOrs+1117bPRFO0NkE3XEHKt/N2bhx48wpPKI9eRthZOt8//tB3fbp6xEWmEUtllEEkL+gXXajHXnkd2UglUARQixDSaXvgke9ko3CZuOJZdydGoXL0h5VfGvNeisRJowz3zxMe2364UtXy+yk8uOPG+eRfvvbvkS4aySNSGWzeZv070MsowR16NBkbyXEECVY3rc2pxCvI/mU/Hu55ZYTSJ01xHvw8PGupTQJ4a8bbrih3H333UakZ8qUKaZESRbD07nEEkvIpEmTui8nT7Jv376pQj1x9/MhlVxHXunmm2+euRRKlrkWfU05SeX2tYoaRc2e5/xWOldPZVEQa7+5IqCkMlc429uZksr24u979zhSSc7MiiuuaDYfrijEBRdcYBQIqa/WbM0xalRycs0GKsupe6P5EX61slMPzgcLL1JpOwohiV27qp5DSCKEIa6bgtp+utj8cFkf7EJCVUNIpW9+Ht5HHy+lnUta7qU75z/8Yf6/Zs1KE7ZJI5a2r3/4wOq0+W9A+xBi2SypTArPXcJ7vFlIpe9zwSBCPJu0t6TSx7NpJ9mIVNIm6q2EaBIOimdy4MCBxjNpjQO/Rx991Hj3CBflgAzVVKJE+CGiJOv7Eg/lWWedJbNnzzaHgxilnVCTJcUB76mvQX4plYLAT6Mal9wLryupE8cff7xv96VvV05SuXULSKU5BFZSWfonVAcIAkoqK/QcKKnsjMWMI5V4IjmFJveHvBlrtuZY9PMsM8U7SlgXmxOEK3wtKt3vqi+yIePfhFz1X3NN3y5NO2jUAj7eSttrCLEMIX8h/YZIVjLugHGEEMsQUskwiiCW9BsS2nrzzf6Ph0ssG101axaS+641EvAJIZZ5ksqoWmyIp9BXBMSfVIJWlFimeajXXdd/7dJIZbN5m3YkjYglwmR4HV1v5csvvywoVZNbjqfQNT5/7bXXjIePA7KNN95YzjjjDBNRcv/9lOrLZoTbrrrqqrLyyivLiSeeaO4xatQokxfpEsPNNtvMjI0DP+y6664zYjvbbrutIZ4cOkJQ33rrLUN6bfgu5LRfv35GnId3OmMnB/TDDz+Up59+2nhJq2LlJJVE54R8n0NXg6iDqVykpDIUOm3fFgSUVLYF9mJuqqSyGFzz7jWOVOJF3HfffQUxhqWdUhiWCGYpZP3jH//YnNgTCsYP0vSQVCtSseyyy8pRRx1lco7iiKL7uQ8G62WQ3Q/xzy3geBcajgciF0DmTF8hxDKkbxu+6wPgQr4EotZZSL3RkLYN8kv/OqfeKxdCKkO8ldOmdc3R55qexDIJ7BDFWFQXQyzEWxmyCfV9JtLIQ72Hc8CAtJzO+rmHkEquhFgW6d3kHo1IpRXs4b1mcyupO0n+JId2UQ8hJJR3LB7M9957z+S+E/rKe9NNRwh5ImxbDgTpC28oNTSHDRtmvIhWGIh2eEYJz+UHIwd0zJgxxkuKAA9eTvLsTz/99DqlWmpdolILmeRdj1Is6t4QWFV/zbJa3tfgdn65djbqfVHGhm+JyIqk02e8Xi9TBFqGgJLKlkFd/I2UVBaPcR53aBWpRPQHeX3CZtnAoER40003yXrrrWdyivjZaaedYqX5XZl+n7+zifugFt4VglEhpNIOIIQoum3TRHuKIpWMO4RYhhDFkLaMIw0DZ5Fn/q0xoXKJoQ9JpGtLKn2eJbyac+dGPZatJpZFkUrzUAR4Q/xqaOZBKht5N7OQ0EZrHUdQDzggOa+SvvBUQhZ5B/JjSSUEbZ2YUH28hH//+99NzUp+v/vuu5tw0/PPP9/nMdQ2BSNQMk8ls4VY+p76NIMOJ1xKKJtBUK9tGQJKKlsGdfE3UlJZPMZ53KFR+OsLL7xQdxKdZ/grY99iiy1krbXWktNOOy2PqdT18fDDD5uwLFcEgwbvNFCGLSWpTEMmxPtIX/38RVdkwRAvVtpAI78PIaxR4hxX0qXWfRqpdEfhSyq55te/9p+fb7hsPPlM8mCGeCujpDItd7KX/+SCw+uKIZYhZylZSWWIdxMAGxFL11vJoRqho4SE4r0kDQCvoWt4+vBWEhpLiCzeQ4jlCSecELBW9U15n0e9lKiyul7KuM6pk0k4bNQIa12w9o74/e9/b/Iz8WpSNoVrDjzwQCFCxZZTyTzwEl5YQlJZQpR0SIpAexFQUtle/HO9u5LKXOEsrLNGQj2TJ0+WHXbYofvelP9AXIeQrLSNiM+AKWuCkAM5O3nbn//8Z+nVq1cdKc77HvTX29eLltVT6TPoEGJZBVLZCJO11or97cy/dKkduxZCKn2JIoJBvm0Zi79Xk9b/8nkaam1C8jBDSCXdhxw29CSVjUiG7+MZQioZcRyxzNO7yT3SvJWEryLSQwgsxIu8Ssoq8W9yHaMGqUSUZ9CgQeb3EMD9998/4BmY39TmU6600kp1+ZSkOTQS2qEHCCLE95hjjqm7N2G5VjRot912MykNlIoi7BVySXgs/77iiisyjbnMFympLPPq6NgUgS4ElFRW6ElQUtkZi9mopAgbiauvvrp7IhtttJFRJMyjpAidoib7y1/+0uQWZVU0TEKZ8DHk8imLUmbrvTWKfRELIaBcWhSpNPwhhEAEIB3iqaTbEBaRQCzjRjfjhZ5kM9rOKsv6kkXfdtyHvv2JZVGkkpE0IpbNiPtIkKcqT1LpCkKFfqVCvZsgmEYqrbeStoS28oMYDwdgEEfr9bPPH/Uf33//ffMOQ4371ltvNTmKWQxhHXInUXldfPGumAzyH0eMGJGq/AqpHDp0aEPySb1KRHpcQ2n2pJNOMgQ6+rsscyjTNUoqy7QaOhZFIB4BJZUVejKUVJZ3MTlRtsTwoosuMifmNldnk002MQIReA9RBaSe5Kabbio33HCDCW+aOnWq2QDlYYTTbrfddmbTYWu45dEvfaAAiygFYWNVM3KzHnjgAeM9sKUBqjRHFCUJB0Sl0lrvkJohOZNKO4a03ErX8xlKLH3Xb+7c932bikjjGpH1HYXkYXKl/2FDaPhjGrG05xFp7aJAhRDLIkgl47HeSsgdhJGakwig8T3Gi+gaYjq8F/nhPUa4bDM1KiGTRJ9YQ3SHzxDY4XAxyXxIZdy1d955p1GMnT59uqy++uoBz235myqpLP8a6QgVASWVFXoGlFSWdzFR9bMy8NFRQlYsEaMu2vjx481JNiqtnDxzYp2X0S/12Nh0cBKfp7FhY1NGwe9ma2rmOa68+iK8DGVeNnxVM55PlIebPbzo7ell9fFW+hJL2y6NVLoENIQvF0cqGXkIsfQnlaFCPL5O7CJJJWgkEcthw+YZQbEsRvgrxPKll14yfSC+wwEY+Y4bbLBBt1eXXMsHH3zQ1HhETfVHP/qROSTL6vFbcsklZfjw4eZ97ho559SajH7utuEdQ9QHh5EcEPBOPfvss1PrmTl2AAAgAElEQVSJ4tixY01JEbyYhPlWyZRUVmk1dS5VRUBJZYVWVkllhRazoKmwweKEnpwbTrTzNELNIMgIAVWpPprFiE0oYh5rBtbjzBPjovoiZxfSTH2+aEhgUfeM6/dvf+vd4+M0b6V7QRqxdNuGlEOBhL79tq/HMqu3srG4T69eYaQqVIzZl4OEEEtfT+UvfhGv5Mr7ikgNyoAMGDAg86PI9/aZZ54x3njCX3lX8bxD8Dhkw4hG4F5EI5CvCKnDU4lybBaDDFLrcvTo0XWXr7baaobMEgqbZCNHjjRlTciVpEYlYbQQ4T/96U+G9MYZpBmBoYMPPlh+9rOfZRlyqa9RUlnq5dHBKQIGASWVFXoQlFRWaDELmgqn8eQLsWmiRmXexkaNzZ/dqOXdfzv7w5M3c+ZM4zXIOx+1nfPi3jwXDz30kNlwR2v4tXtscfe/+eae5JN2RZJKXxzefvtT36bB7UKIZRlIJROEWCaRRvvsRWvlunVz8aJz6GHr69K2UXv3Wvt3yCneu+WWW6470gDBMiI38M7jwSTSgpxK8thPOeUUufjii00OJqkJWawZUhm9H+8eiDXpERMmTOgxnHfffVc23HBD8znv4D4NFLezzKUM1yipLMMq6BgUgcYIKKms0BOipLJCi1ngVPbaay/jjSK3M2+jWDeEK05ZMe97tbo/5Pwpm8KmE5Xbqtlzzz1nlDKz5pCVEY9jj40nn4w1xFNJ+5CQ2aKIZQipZMwhxDLJU3nrrS8Ek7gQ0gfx8zW8hrxfIIE+9XNtG3sIxIEJ3ko+xzNJuD55lRyEodaKgA+pCHj6SD048cQT5eSTT/YdXl07wl8POuggQbDHNZ/w17gbDhkyxBBjiK9rhPZus802JmT3scceM8S5iqaksoqrqnOqGgJKKiu0okoqK7SYBU6FU/j77rtP7rnnntzvgleBMC1Ct6pm1ptnN6FVmx9eGUJ8EY6qmieWtWIzTn6cTy7x5pv3JKNlIJXMI41Y3nTT7wwJ5MfXQghaKKHLoz054PTTzIHHxx9/bIgkxNSGtCKY9uabb5qQV8TLULDG40duIh4/yCWhqlmMiAbSACZNmtR9OSG11MFME+rxJZWsMSVEEGAj0qBq4jwuDkoqszyFeo0i0FoElFS2Fu9C76akslB4m+78pptuMuVCUNrkVBxyQu4MBavdTTxiPYgyWLEe8mnyFOtBZZYC2eTq5E0eOEknD+kHP/hB7n03vQA5dMDcqBeK2EfVDI8HQiXrrbeeLLroom2bXlz4YojnKyk8ku/cvHnzjNhSlhBKdwy+3jW+X1k8a6FEjHuQUweJYX4hJNGOsW0L7nHjt956S5566ikTJZA159eWF2HtbM1fci3xVpInTfgrBysQTPISCSGF/GU1PJR4OwmztYrRqHkfeuihqSVFovd8/fXXzTsHgR+3xuVhhx1m8uPvvvtucxhUZVNSWeXV1blVBQEllVVZya68KP84ogrNu1OmQu4Oqn477rijydPhdPmcc84x4VV4D7Hrr79e9t57b3NSDjGjrAgkk1Po9ddfP5epsjkjr3LOnDnm1DxP++ijj8xYOd2vYl7PK6+8ImzwmlVJzRNz3754PdgfS6qiZI3wZQglxKQoche9Z/TfvvMJJWvcB3KCtxJSEUrcsrTP+9CmETazZs3qVvBt5X1916uZdjyLeA55b5L3m9XivJUzZsww6rCExXLoAMHcfvvtzXccUpjVOMQgDYAyPYTRvvbaazJq1CiTF+kSw80220x4r+AlxTj0u/32242QGuG6HP5BUHl2OZC0KuKMjf8njjvuONl5553rhjlw4EBT37hKpqSySqupc6kqAkoqK7SySirLvZhxxaoPOeQQQxzZgLBpRYwCwnfttdd2TwaCxkm3rXPZ7Cw5kWcDRQhs3mGqbP6mTJkibGqWWmqpZodauusJX3v88cdNHVG3zqclYEUSsTy8db6AZiFQId6xrP27OXJZiJMlJj4hsL5YlaWdPdCpai1VSBneWBSKsyqyWm8la2ZreUIk+U5z2MBnCOJAKPEoHnnkkU0tLyVJ6ANvKO/cYcOGGSVX6ymlc3I4SRvgB+MZHTNmjHDAQ11L3v0cMJ5++undYkX2OiIL4swtU9XUBEp0sZLKEi2GDkURSEBASWWFHg0llZ23mCgMjhgxQlDvmzt3rsn3IgcHb6a1Cy64wJxG0yaP+o8QH2TpTzjhBLPJydueeOIJU+A7j417nHetncQNIRs275ZQWqLnGyRgvWuhXrZWtecZwxtCKJ3ddOf9fLSzP7x5hDh2oqfZBzdKTvCO4FCnasZ3jygI0gYQu8lqeCXpi++wrX1JWDsKswj28G7kh3czKtlq5UBASWU51kFHoQg0QkBJZYWeDyWVnbeYhEJx2kwoKp5IFP443ea03BphsltuuWWPz5uZLf1RL+2kk04yJImTc07/m80143o8sghikN+VlQC61/nOM+r9KoqIQUzwOrABDfW4ZfGu+c4/j3asH89jVT3NFJN/5JFHKhuejUgWKr4cCmT15uXxHBXVByGiHL6RCpD1uxTnrSS0FOVX0hOWXXZZ47WEwJLDmdUQvYp6Kc8888w6L2Vc34yBcNiooT5t80nBgL7wajJuDoDwuFbZlFRWeXV1blVBQEllVVZScyo7biUpT8Hm77zzzjPCOddcc43su+++Ji+KnDZr06ZNM+IRdjMcOtFjjjlGEAnihN7+sEFx1SEJyUoqA+IrNmJDExG/oH/yn4oKiXT7tePLuskMxZMQPLwa66yzTuilHdEerw2b1Cp6u1gANuKUe0gqIt8Ri5QwSL7TU6dONXl8nVBvNBRrDsB4b/LdIxoiq/GOAivrrbRh+/TJYRjPPt/zrPmbNp8Sr6qbT8n73c2nTCKVvO95b7tGqoJ9x0Ekt9pqKyOqBSHmO6ukMuvToNcpAopAXggoqcwLyRL0o57KEiyC5xDwTLJJQNGP0h6QpKJIJWQUBULC4vBI8oMQBHk+nHbzOafjeN/ivHuhZI08IEJgyQOqoreE0gN4g8iFCsXG8/FoazOeTbyxeGmqOL+qh8BSr5BDj3XXXbetz1FRN3/mmWdM+Opaa62V+RZczwEb712IJaSSPET+vdBCC5l3Fxjy9yyGsA4HdSh4W/J7ySWXmFQHPmtE+HkXo/bdiHxCiG3o7qmnnirnnnuuksosC6XXKAKKQK4IKKnMFc72dqaksr34+94d0oXYBBt2Qqxs+QYb/krYFII91ooIf0Wkh4LZhFHlnTtH6CsbNEhz1RQIWRNbeoMQPIh41cwKvpB3mHVTXWZMEKriQKWqCsWQIbyx1Fvs3btnvc0yr43P2KxYVjPzg0TyPbbeSt5ZeEA5cOP5OOigg7rF03zGFG1DjUrI5OTJk7t/xXufz9JqVPqQSvd+SiqzrJBeowgoAkUgoKSyCFTb1KeSyjYBH3BbwkK32GILc1r92GOP1QlOIB2PuA0bkR122KG71wsvvFCOPfZYc3LuqgYG3LZHU7xRyy23nMnHIUQrb4MsM5cqhuCBFd7f5Zdf3uRfVdEgXYihMMcqGt89QsxteYaqzRFSSV5g1vDNsuOBWiuHce7hW+iYIZKUGMHjx0EKfRISzTuRCA4O97J66gmvHj58uIwfP75uWHynqDUZ/dxtBKmEOJP/y4EfBJW6xauvvnrsFJVUhq68tlcEFIGiEFBSWRSybehXSWUbQA+4JZuYnXbayXhJIF1xOWtsksinufrqq7t7JgwRj19eJUXomBN6alT+6le/MqFWeRsqlHhJmtn05T2mPPtD7h9D7KiKRgglHpuq5o1ygEMYc161X8v2DBDuTpivjYgo2/iaHQ9555A+5ueW9gnp1wr24LVE9ZjvNM8D72UI3P333x/SXV1byOAZZ5who0ePrvuc9wUeckJhk2zkyJEmyuNrX/uaqVFJGC0CTLxT4/KAlVRmXia9UBFQBHJGQEllzoC2szslle1EP/3e1KSExCHMw8bCtbXXXtvkNlL4GkXYcePGmVqI1LC89NJLjfhGnmUQ2EhBXimajUhQ3obIBRs1am5W0fD0UleuGXXIMuOCgi/iH+SNVjEv1obANhNCWeb1wwPHO4O8Q4RnqmYQQsJV8TQTcZHVwInDPp53vs+8l1HGnjlzpvksq6eyGVIZnQsEGjVw/l+YMGFCj6kqqcy6+nqdIqAI5I2Aksq8EW1jf0oq2wi+x62TpOK5FK8Cv8cuu+wyEx5FiCyevrPOOqsQbyKbFE75qceWt7ERYmOGum0VDaVFQiiZX14hyWXCCSGTKVOmyJprrmnCKKtoVQ+BnT59ulm2NdZYo4rLZ7x4vGcgglnJn/VW4tlFRRVxIw7a8FKSy45gTxYj/JW8TAR7XPMJf427H6WmILl//OMflVRmWRC9RhFQBFqCgJLKlsDcmpsoqWwNzlW5y2mnnWaUZ9k85W1VJ122BAFlWNhAVtGeeuop6dOnT2VDmF9++eXumodVXL8333zThExy8JG3GFcZ8EJohzSCZg8+6If6l5QZgYCT8w75Q1E1a84teZB4iCdNmtQNFXmSpBykCfUoqYx/urROZRm+dToGRaAxAkoqK/SEKKms0GK2YCqE1h5xxBEm7CvrSX/SMPEAcNrfbD25FsCQ+RaQLtRRqQlYRaMA+2uvvdYjVLsqc7UHH4Qw9+rVqyrT6p4HBx+EiBIB0UyIaDuAYey8Q+yf0b/bf+OthBQiCsZnSe0afc7vLOGjxBMhw9SWPPjggzNPHQ8lESZ4QBdbbDHTD2kMhx56aGpJkehNX3/9dVN6CoGfuDIjGv6aeZn0QkVAEcgZASWVOQPazu6UVLYT/c67NyqH5HJCHooIcbTqtghOVNHY0OINIje1imZJF2IoCy64YBWnaESzUCi2oedVmyQeOMImXUEiiJpL1lzClfS5D8GLI3VJpDCNLPJ7H+MwjLaE8aPi6v7E1dzl9+7n9t/0gWcRRVkUna+//vqmUg7efvttIYqBAycIKoczo0aNMnmRLjHcbLPNzPuXdcLIqaeG8LbbbmueS94xEFRCc5988sk6z+nNN99srrnxxhvltttuk6uuusr8m/dRFVWb1VPp843QNopAexFQUtle/HO9u5LKXOEsrDNUXJGInzFjhhGzIdRqxx13lFNOOaW7ZiU3Z6Nw0kknCUqcELMxY8bIgQcemNu4kKynziIhsHmKANkBUqQckZc4ldvcJtHGjiCUEHMElWwh8jYOJ/dbW08XIYBVLZ1CCCykC7XNPMwlbM0StDTilUbi+D25sXjyrNiSJYc+c/UhZXEELYm4WcLn02+0j7j78Bk/06ZNM2HaePOymsWJgxQOGJ544gmTX9mMPf/883LkkUeagwves8OGDTNKrm4ONkJYRIrwg1EKhvc8SrTUtcTLSV7n6aef3iMMPSm65IorrpADDjigmaGX8lollaVcFh2UIlCHgJLKCj0QSio7YzEpF4KIBhtZTsfZQBDCRKgoBA8jbI0NByFYe+yxhwklZUPCqfSuu+6ay0TZtFLHjhqY+++/fy59up0gPjR37lxZb731cu+7DB2iGvnAAw8Yz4ANcSvDuPIcAwcfKGSSt9Zus4TNx/vVyLPmXk99QrxIeIUgMj59p5FFH5wsIcrqWYsStCRyRzu8XYT3cjDVqF2U7OUdEu+DS5Y2lIbhAIs8xmZyR8mpxGPI+wp1Zw771MqDgJLK8qyFjkQRSEJASWWFng0llZ27mJQaoeSI3eButdVWwqn5I4880j2pvffe23jG2OjnZVtvvbU54Yew5m0QSjZ7ePI6ZYMaigGehQEDBlQi3CyOULFhZ6ON16ZR2GQe4ZFJfbjj8lkfl7ClkSh+Tz4dpIv82KxeNve6tHvatj5zyaMNOXkoMUO6quxR53vIIVlW45CIMixEjXz44YeZCSr1M6MeyjPPPDNVJTpJHZyxuOHnrCf9cwAJiUat9vzzzze1jKtsSiqrvLo6t6ogoKSyKispwqbPLxGlQnOuylR++9vfyi677GJKi7A5IlzqnHPOqasheeutt8oOO+xQV36k2flToxLSgAc0b2MzhMe1qrUAwYsNJB6ONE+er/CIT0hjXF++nrlGXjbf9W8UxuhDqPIgbo3CI0MPMHj+yVmrqked0M4HH3zQ5PgttdRSvsvc9nZJ/53xrPOdI6yXP/Gk8278+c9/bnISbSgrn9t2tG30Y/vhwI6yHe+9916mgzCbS7nSSivV5VLuu+++sSI7LsiQSqIejjnmmDrsiWixzzRzIqIFQwiIFAYiTXj/kItZZVNSWeXV1blVBQEllVVZSSWVHbeSbH7YJLCRGT58uPF23XLLLebfbADvvPNOwZNo7aWXXjLCD9HPm5k4xbTZjKFkGroZT7tvq8putFN4hPBJyHPv3r0NHEkqk77nPUn5ZmkkKi6MMi+Ch3d88cUXNwqb1guYtvad9HsIBB7nKgsSQbogluedd55ZmqTnkc95J0HAeLbt35MIWSPS5kPq3DZp97Jj4L0ZNb5/jIXnE48jtsACCxjvIN68uL/zOz7n9/ydH7zWRIg8/vjjmd6HiOoQ9UGNYb4z2CWXXCIjRoxIVX2FVA4dOrQh+YQ0I/ZDviY1jDE8lkS2QIarejDCPJVUdtJbVcf6eUVASWWFVl49lZ21mIifEO6KQR5R80NwgpBXyhygnuqqNiIo0r9/f7nmmmuEUNg8jJzALbfc0uQ+ugIStu9mhUfI52JObLDyEB6J6yOUsOVJ3NjgIrLxjW98w2xQm8mRKythYw15PvISs8njuc2zD3v4wfOPV71Za/Q88ry4RMz9e5RgRdtZ0mW9apZkuf9O+jtzmjJliqy22mrGk5fUjj59DLXVJNJmyVqU0LmfR9sk/a7R5/yOsFBLCBE1Q3iJuruML+n7lPfhmYsXIca86yZPntz9MYI7fJZWn9KHVCL2Qz4+Bz3uOxr1bkJiyc2vqimprOrK6ryqhICSygqtppLKzlpMNgfvv/++PPfcc0LODcSEDRFekyJIJSqyhEq5m1VO5SENCM2waWUM48aNq6v55oNqkvAIfWJ4EkLUH5v1siWpRfrMJaQNBII8LDzIhC1X0fDe4LnZZJNNUvPCWjH/RmGRcV62RqTLEjM8eYQyI4wV9bC51ycRP/t5nLfNvQZPdprx7FrCZglT9E/XwxZtm3QtpSwIr6RcRRxZs33a6/kz2hckDs+ezc2MErQiCVsabqzflVdeKWeccUa32m3aNXn/fskllzRRJ+PHj6/rGtEf6kxGP3cbQSr5rhHSCsYQVFTCV1999e5meCJ5R1PyxDUOQ6hFGv087/m1sz8lle1EX++tCPghoKTSD6eOaKWksiOWKXaQTz/9tCm6fdNNN5kSHIS/3nXXXSasyVqz4a8U4p44cWL3yb7dqBKytdNOOxmRCzZFENpQb17SZpJ78rPBBht07uKkjJy1A8tmShqUBZw4wsZniJcgCILysCVZ9nAiLszRx3sWd71vCGRc2KQ9wEjD0j73blgk3nRCCvnuIdgTR+LsZ40In/s79++WjLl9uOTNkjw+s+U/4r5TzZC2n/zkJ0ZFmvB5tWIQ4JmC1I4ePbruBniIeQcSCptkI0eONNEAqPRy0EcY7T/+8Q/505/+ZIgkRq4mtS15j7tG2CwHJFY9vJjZtbdXJZXtxV/vrgj4IKCk0gelDmmjpLJDFipmmGzc2VBSj+zoo482Qj1sAo866qju1tSt3H777XMV6uG+bGTol+LceRvCFRTtptZaFZUnwYs6eXfffbeMHTs2Fb5GXjbrNXNz2Xy8bI3CJpNy4aKetUbhkPTBYQMeFGpzNjIIkRsWGc1ba5Tb5hI51xPnErIkr1ycRy2OwLHpjwuLZF3YsCNcRRhhFY11tPOv4vzKMKdmSGV0/G+88YasssoqJoeS3HcllV/Q/WoZHnIdgyLQAAH9klbo8VBS2bmLScjroEGD5IYbbpDdd9/deCjZxD/00EPdk0JBEEGdPEuK0DlhWdgvf/nL3AGEvNhajn379s29f7dDH/GRqLBIIy+b60lzlSbt3/mT68mZYt1Q7+Xfad62OBIYJz4SB5ZPWKTNZUvLR4uSuDhPnA2B/P3vfy+XXXaZ8Z7bPLY4T1ySl60ZD1uhD43T+X333WcK3yNIpKYIZEGAw5eDDjpIiP5wzSf8Ne5+Q4YMEXLpEeHBCH/l+USwxzUNf82yWnqNIqAI5I2Aksq8EW1jf0oq2wh+wK0JI6Tu3xprrGHq4xE+iVeSDckTTzxhQu8oxTF48GBTuxKSCTEjrArysttuuwXcLb0p/d5xxx3CpjrJ0sRHXKIVJV177rmnGTP1KqPt0qT+G/0+RDkyHQWpU4F0CZcbJhkXFokoCGHLiC0RpuYrUGL7inri4sIiadsu8RGww2vC4QPzLPpwwGettI0iUEYEyINcYoklZNKkSd3DI0+S70yaUI8PqUSoh9q/hMRa492MgNsRRxyhQj1lfCh0TIrA5wgBJZUVWmwllZ2xmIg1QA5RKkS4A+8IRBMRHbeANeIhKBoisEOezZgxY4wIRN5GSZHjjz9ettlmm24vW1Rh0pK7uLDJNPERRIBs+ZSowIiV808Ki4zzttlr4vLUot62RmGRbh5bs+IjlKPYYost5OSTT857ebQ/RUAR6BAE8FBSP5I8ct572KWXXiqHHnpoakmR6BRff/11k6fNYQ4iS5gtKcL/CYRrY0QR8O7RkiId8pDoMBWBCiOgpLJCi6ukskKL2cKpIN5B7ibqnnhOQ8Iio6QwqhgJ8TvllFOMwu3vfve72NpvnRAambYcbCLxNDN/NUVAEfh8IkAOOSJrqEGfeOKJpmQUuerkRVpiCDKI7bzyyivyl7/8pZss3n777UaZd5llljFCPRDUt956y+Skr7DCCqYdh3rrrLOOeY9CXkmR4DCSqBeur7KpUE+VV1fnVhUElFRWZSW7Clp/VqHp6FQqggChvIRsHXbYYRWZkU5DEVAEFIF4BFARRuzp0UcfNYJrhKyi5MphnTVSG6hvyw9GTj2RKM8++6zJ0cbLibgZwm3f+ta36m4EUUUpFqVXQuKJcvnpT39aF+VSxbVRUlnFVdU5VQ0BJZUVWlEllRVaTJ2KIqAIKAKKgCKgCBgElFTqg6AIlB8BJZXlXyPvESqp9IZKGyoCioAioAgoAopAhyCgpLJDFkqH+blGQEllhZZfSWWFFlOnoggoAoqAIqAIKALqqdRnQBHoEASUVHbIQvkMU0mlD0rlbTNv3jxT7JqcGUqLUHbEGjUCzz77bKMgSI4NOTpDhw4t72R0ZIqAIqAIKAKKQE4IqKcyJyC1G0WgQASUVBYIbqu7VlLZasTzvd8JJ5wgv/nNb+Qf//hHHam8/vrrZe+995axY8ca8QbKkUAyH3roIVl//fXzHYT2pggoAoqAIqAIlAwBJZUlWxAdjiIQg4CSygo9FkoqO3cxX3jhBeOZPO+880xNM9dTiWfyO9/5jlx77bXdE9xggw2MQuAdd9zRuZPWkSsCioAioAgoAh4IKKn0AEmbKAJtRkBJZZsXIM/bK6nME83W9kXx6jXXXNOEtG666abdpJJ6ZSuuuKJMmjRJdtxxx+5BXXDBBXLcccfJu+++q7URW7tUejdFQBFQBBSBFiOgpLLFgOvtFIEMCCipzABaWS9RUlnWlWk8rptvvlmOOOIImTlzpjz11FN1pBJP5JAhQ4TaZ+RbWrv33ntlyy237PF5ZyKgo1YEFAFFIBsCmoueDbdOu0pJZaetmI7384iAksoKrbqSys5bzA8++MCQxVNPPVWGDx8uU6ZMqSOV11xzjey7777yxhtvyNJLL909wWnTpsl3v/tdeeSRR4RQWDVFQBFQBD6PCGgu+udj1ZVUfj7WWWfZ2Qgoqezs9asbvZLKzlvME088UfA6Pv744xR3VlLZeUuoI1YESoUA0Q0oRc+YMcOEx3/1q181ofOnnHKKLLroot1jve222+Skk06SF198Ub72ta/JmDFj5MADDyzVXNIGo7noaQhV5/dKKquzljqT6iKgpLJCa6uksrMW85VXXpGVV17Z5Etab+PDDz8s2223nTzwwANGuGfq1Kkm/JXNE4I91jT8tbPWWkdbHgRuuukmufrqq+XJJ5+Ut99+W1ZaaSUZOXKkIVQc7Fjr1DI+zG369Onyve99T5ZYYgl59tlnTSTEOuusI/fcc4+ZHu+ZwYMHy8EHHyx77LGH3H///aZM0Y033ii77rpreRYrZSSai94xS9X0QJVUNg2hdqAIFI6AksrCIW7dDZRUtg7rPO5kQ12T+mJTiOIrQj2TJ0+WHXbYobvphRdeKMcee6y899578pWvfCWP4WgfisDnAoFBgwbJ17/+deO969+/v4kUOOecc+Tkk0823jysamV8fvWrX8khhxxiauAus8wystVWWwm5iITPW6Ns0Z///Gfj4ewE01z0Tlil/MaopDI/LLUnRaAoBJRUFoVsG/pVUtkG0Ju45b///W+ziXONfx999NEyceJEkzOJdwEPJX/HA2Fto402kkUWWURLijSBv14aj8Bf/vIXOffcc+UPf/iD8XKR88ufUetUT96//vUv6devX910IFzUf8Vz+cUvftF856pUxue3v/2t7LLLLjJr1iwZMGCALLzwwoZI//jHP+7G4dZbbzUHV7SBdJfZNBe9zKtTzNiUVBaDq/aqCOSJgJLKPNFsc19KKtu8ADncPirUQ5fXXXed7LPPPjJu3Dgj4sPm99JLLzWhsXhd1NqPAOHJRx55pDz66KNmwz5s2DA588wzO9KLfMsttxg1YjzlKBJ/+umnPUhl1Tx5F198sYwYMcLkIM6dO7cSZXz++9//yscff2w8j4iALb/88sLa8u9VV11V7rzzTtl66627vzwvvfSSCcePft7+b1fPEWguehlXpdgxKaksFl/tXRHIA5J2F7EAAAnaSURBVAEllXmgWJI+lFSWZCGaGEYcqaQ7vELjx4+XV1991XhRzjrrLFPTshOM8MIrrrhC/vjHPwp1Nw8//HD5xS9+0WPo//nPf2Ts2LFy1VVXmbBe8kxp5+aSlnG+eLfYpJObx2aXEMNRo0YZ1d64eZZxDu6YIJF467ADDjhAUBqOeiqr5snj0ObBBx+UOXPmGO9/Fcr4LLvssuZZxCCPhIv26dPHhLwS6fDYY4/J+uuv3730eHAJB0ZxmlDYsprmopd1ZYodl5LKYvHV3hWBPBBQUpkHiiXpQ0llSRZCh1GHwDHHHCN33XWX8XwhSsQGPo5sHXrooSaX7fzzzzeKlQiHQEKfe+65OtXKssH7v//7v2asEP7FF1/cDO+SSy4xni8+I4etUy2OVLIm5PmyluQlWrvgggvkuOOOM96+BRZYoGOmjGjNJptsIuedd54JB61KGR/Eet5//33z/cFr/o1vfMPkjxLW3MmkUnPRO+arletAlVTmCqd2pggUgoCSykJgbU+nSirbg7vetTECrueLXC08rFFSiYeI302YMMEIimBvvfWWKXWAgMrxxx9fWpi///3vGzKJmJI18mX57PLLLzfevk61OFJZFU8ea8Jzx2HHt7/9baOMioe2KqTSfeaefvppWWuttQTl24EDBxrPOgc9CPZY65TwV81F79S3SXPjVlLZHH56tSLQCgSUVLYC5RbdQ0lli4DW22RGIIlUQr4ob/Dmm29K3759u/vfeeedDbnEO1FWW3LJJU3OGuHJruFt3W+//Xp8XtZ5xI0rjlRWhXRBTjbeeGNTRuShhx7q9oZb0lylMj6fffaZ8R6ffvrpRgiMvN+f/OQnctRRR3UvO3Urt99++44Q6ok+q5qL3klvlWxjVVKZDTe9ShFoJQJKKluJdsH3UlJZMMDafdMIJJFKPJFXXnml/P3vf6+7BzmW5JNGP296IDl28OUvf1nOOOMMGT16dF2vq622mskLJRS2U62qpPLDDz8UahwSnkxuIQcA1mx4b5XK+BDyiqgXIl+777678VCioAqZtkYO8FNPPdUxJUXc71QVc9E79Z1R1LiVVBaFrParCOSHgJLK/LBse09KKtu+BDqAFASSSOUPf/hDs8HFO+QapS0Qv0HEp6z2eSOVne7J++STT2SnnXYySr08c4SDRq2Ty/jg3V933XVljTXWkF69egmhr3gl8ag/8cQTRpGYPNLBgwebUHNI5gMPPGAORiCdu+22W1m/ajquzzECSio/x4uvU+8YBJRUdsxSpQ9USWU6RtqieQTeeecdeeONN1I7QhiEDaxrVSSVbNYPOuggQbDHtaqGv3a6Jw8i9atf/coI8+BJdm3ttdc2YaKdXMaHMGzI4csvv2zKwfCdg2gee+yxpratNepSnnTSSfLiiy+a3OUxY8aYMG41RaCMCCipLOOq6JgUgXoElFRW6IlQUlmhxSzxVKiRiWcxzZ5//nlZZZVVvEgl4a+UEomS1U4If0WoZ4klljBqqNYg3uSGVlGohzl2sicPkkVZijibNWuWIWFYJ5fxSftu6u8VgU5DQEllp62YjvfziICSygqtupLKCi1mRacSKtSzyy67GPGeMgv14KGkbujs2bNlscUWMysH8aZESieWFCHXjhBX7KKLLjIeL8q8YJTeoJZhJ3vyKvrV0mkpApVGQEllpZdXJ1cRBJRUVmQhmYaSygotZkWnklZSZOLEiUYFFnv77bdNWN64ceNKXVKEcVKiYeWVVzb5nxScHzVqVGI9zrIv7d/+9jdZYYUVYodJ7h25eJh68sq+kjo+RaA6CCiprM5a6kyqi4CSyuqurc5MESgLAsuLyHdrg5kgIk/CSWr/vtkZ5EQR2UNERonIayJyooh8U0RWFZF3yjKZhHF8W0QuFBGS9N4TkStFZKyIlFdhqOSA6vAUAUVAEVAEFAFFoHMQUFLZOWulI1UEOhWBA0TkioTBu++gBUTkf0RkPxFZWEQeEZEjRaReErZTUdBxKwKKgCKgCCgCioAiUFEElFRWdGF1WoqAIqAIKAKKgCKgCCgCioAioAi0AgElla1AWe+hCCgCioAioAgoAoqAIqAIKAKKQEURUFJZ0YXVaSkCioAioAgoAoqAIqAIKAKKgCLQCgSUVLYCZb2HIqAIKAKKgCKgCCgCioAioAgoAhVFQEllRRdWp6UIKAKKgCKgCCgCioAioAgoAopAKxBQUtkKlPUeioAioAgoAoqAIqAIKAKKgCKgCFQUASWVFV1YnZYioAgoAoqAIqAIKAKKgCKgCCgCrUBASWUrUNZ7KAKKgCKgCCgCioAioAgoAoqAIlBRBJRUVnRhdVqKgCKgCCgCioAioAgoAoqAIqAItAIBJZWtQFnvoQgoAoqAIqAIKAKKgCKgCCgCikBFEVBSWdGF1WkpAoqAIqAIKAKKgCKgCCgCioAi0AoElFS2AmW9hyKgCCgCioAioAgoAoqAIqAIKAIVRUBJZUUXVqelCCgCioAioAgoAoqAIqAIKAKKQCsQUFLZCpT1HoqAIqAIKAKKgCKgCCgCioAioAhUFAEllRVdWJ2WIqAIKAKKgCKgCCgCioAioAgoAq1AQEllK1DWeygCioAioAgoAoqAIqAIKAKKgCJQUQSUVFZ0YXVaioAioAgoAoqAIqAIKAKKgCKgCLQCASWVrUBZ76EIKAKKgCKgCCgCioAioAgoAopARRFQUlnRhdVpKQKKgCKgCCgCioAioAgoAoqAItAKBJRUtgJlvYcioAgoAoqAIqAIKAKKgCKgCCgCFUVASWVFF1anpQgoAoqAIqAIKAKKgCKgCCgCikArEFBS2QqU9R6KgCKgCCgCioAioAgoAoqAIqAIVBQBJZUVXVidliKgCCgCioAioAgoAoqAIqAIKAKtQEBJZStQ1nsoAoqAIqAIKAKKgCKgCCgCioAiUFEElFRWdGF1WoqAIqAIKAKKgCKgCCgCioAioAi0AgElla1AWe+hCCgCioAioAgoAoqAIqAIKAKKQEURUFJZ0YXVaSkCioAioAgoAoqAIqAIKAKKgCLQCgSUVLYCZb2HIqAIKAKKgCKgCCgCioAioAgoAhVFQEllRRdWp6UIKAKKgCKgCCgCioAioAgoAopAKxBQUtkKlPUeioAioAgoAoqAIqAIKAKKgCKgCFQUASWVFV1YnZYioAgoAoqAIqAIKAKKgCKgCCgCrUBASWUrUNZ7KAKKgCKgCCgCioAioAgoAoqAIlBRBJRUVnRhdVqKgCKgCCgCioAioAgoAoqAIqAItAIBJZWtQFnvoQgoAoqAIqAIKAKKgCKgCCgCikBFEVBSWdGF1WkpAoqAIqAIKAKKgCKgCCgCioAi0AoE/n/tK7RI6vKPJwAAAABJRU5ErkJggg==\" width=\"833.6363455677824\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(leaky_relu, \"leaky\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"904.5454349399603\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(e_swish_1, \"e_swish_1\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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scdcaOvxxx/vrveJT3zCyDtsNmgfwr2lIQV6RQEBYK/stNYZgwICwBh2SQAY+S7J/CIU6AQA6R9IIZT61geEIR577LEti8A8/PDDrmAIc+CVajQANDx+yRxAvDx4wOqLwBAWCKwQjsho5wFMA4BAFt61+iIwtKYYO3asW7svPlN0CCihmYRX4r0jDJOCOXgrCYX0eZO+absvAjNt2rTCAJCJyD/E00h4JUVu5p9/fgOSKRiTBgCzriENAAKx5BkS6tlo4AGtz2Ut4nOiOaRAqAoIAEPdGdnViwoIACPfdXkAI99AmZ9agU4AkPw54I1QyuTAU8TDfLsqoAAgFSYBvYUWWmg2m4855hjnAUtWASWvjdw3wCQ5eI0CLx4AyRmkrUXSq+WPB9rShIDieSPMEuBINpNnzYStJgGQXLX77ruvsD6Ad999twOtpKcP+8mH23bbbfutKy0Apg0BbXTz4OlDAwrQEL6bJgQ06xpYa7sQUKqcnnnmmTZ16tTZ8iNT3/Q6UApUSAEBYIU2U0uJXgEBYORbKACMfANlfmoFOgFA8gb33Xdfu+eee+yjH/2ou+bzzz/vcrjwRrUDQLw4X/rSl1wxD6pGJvsAAnB4+vD40WLAV3Yk74yCJYQWzjvvvO6aVIoEwDjeA+CDDz7oqlw2KjLTqggMHjb+cD2qYI4aNcq1PqCaJQOvFhUyac2QBEByz/CU1YdqNtuIdlVAgUk0ZX9o58DwRVH+/ve/5wLAtEVgZsyY4YrHJAu70HphmWWWcQVYyBmcMmWKkZ+JxngHGxWByboG35ajvgjMlVde6fYE7x7XYv9oUUE7jOSgqiv3HdVINaRAryggAOyVndY6Y1BAABjDLrWwUQAY+QbK/NQKeACkgiSwVT986wRery8C8+qrrxr97pZcckkHSIDDL37xC/egDrS1A0DmpAIogIV3iVBPCo5wLl42Wh8Qbomn0A9f7ZHiNBQuIUwRjxThkQCJB8B3333X9c3DNipe8j5VPlljsg0ETegBVnLoqDBJ+wjaDPix8cYbuxYJzEEvvD/96U+uYiiAmgRAKmbSCoIeeLRhoMgKUNoKAIEmALh+4PnDywdcUW2TOSnKQlsHABPgTno203oAgThaOFDkhjYQhJhecskls7WBwHNHLiT6cgw5eRR7wfuI5++zn/2sM5mWGXvttZeDZCCevcM27gNgjj3Isgb2gMqiaM5ekesH3FP19P/+7//6ZMJ2vIAUh0EnYB0PJWDKvUSvRA0p0CsKCAB7Zae1zhgUEADGsEsCwMh3SeYXoUCrNhDMnwSNegDkfaCAUFBy+oAr8v94GE/TCN7bj2cKbyJ5e8ADEEDoJUU/KP1fP04++WTjD/3rCPPkoZ+CJQwPgPydEE48RdiGd6hVI3gPioQYJgdFSAidxOuIZ4nQT+CTCplJAKRKJz0H//GPf7iQ2DSN4OuL0Pjrcg2qoz7wwAMOrG+55RbnYSP8Eo9rfcGWtADI/OQUAt1ozn5SNIX9SzaCB+yPPPJIB99PP/20A0ByENEYD2xy/PWvf3WtKQiTBcQ4DmDFI8rIuoZkI3jCgoE9woDxSCYHlT6BQIraYB8eVYCQtS299NJFfDQ0hxSIQgEBYBTbJCN7RAEBYOQbLQ9g5Bso86VACwXwAAKPEydOlE6BKNAMYgMxT2ZIgWAVEAAGuzUyrAcVEABGvukCwMg3UOZLAQFgVPeAADCq7ZKxASkgAAxoM2RKzysgAIz8FhAARr6BMl8KCACjugcEgFFtl4wNSAEBYECbIVN6XgEBYOS3gAAw8g2U+VJAABjVPSAAjGq7ZGxACggAA9oMmdLzCggAI78FBICRb6DMlwJSQApIASnQAwoIAHtgk7XEaBQQAEazVY0NFQBGvoEyXwpIASkgBaRADyggAOyBTdYSo1FAABjNVgkAI98qmS8FpIAUkAJSoGcVEAD27NZr4QEqIAAMcFOymCQPYBa1dKwUkAJSQApIASnQDQUEgN1QXdeUAo0VEABGfmcIACPfQJkvBaSAFJACUqAHFBAA9sAma4nRKCAAjGarGhsqAIx8A2W+FJACUkAKSIEeUEAA2AObrCVGo4AAMJqtEgBGvlUyXwpIASkgBaRAzyogAOzZrdfCA1RAABjgpmQxSR7ALGrpWCkgBaSAFJACUqAbCggAu6G6rikFGisgAIz8zhAARr6BMl8KSAEpIAWkQA8oIADsgU3WEqNRQAAYzVY1NlQAGPkGynwpIAWkgBSQAj2ggACwBzZZS4xGAQFgNFslAIx8q2S+FJACUkAKSIGeVUAA2LNbr4UHqIAAMMBNyWKSPIBZ1NKxUkAKSAEpIAWkQDcUEAB2Q3VdUwo0VkAAGPmdIQCMfANlvhSQAlJACkiBHlBAANgDm6wlRqOAADCarWpsqAAw8g2U+VJACkgBKSAFekABAWAPbLKWGI0CAsBotkoAGPlWyXwpIAWkgBSQAj2rgACwZ7deCw9QAQFggJuSxSR5ALOopWOlgBSQAlJACkiBbiggAOyG6rqmFGisgAAw8jtDABj5Bsp8KSAFpIAUkAI9oIAAsAc2WUuMRgEBYDRb1dhQAWDkGyjzpYAUkAJSQAr0gAICwB7YZC0xGgUEgNFslQAw8q2S+VJACkgBKSAFelYBAWDPbr0WHqACAsAANyWLSfIAZlFLx0oBKSAFpIAUkALdUEAA2A3VdU0p0FgBAWDkd4YAMPINlPlSQApIASkgBXpAAQFgD2yylhiNAgLAaLaqsaECwMg3UOZLASkgBaSAFOgBBQSAPbDJWmI0CggAo9kqAWDkWyXzpYAUkAJSQAr0rAICwJ7dei08QAUEgAFuShaT5AHMopaOlQJSQApIASkgBbqhgACwG6rrmlKgsQICwMjvDAFg5Bso86WAFJACUkAK9IACAsAe2GQtMRoFBIDRbFVjQwWAkW+gzJcCUkAKSAEp0AMKCAB7YJO1xGgUEABGs1UCwMi3SuZLASkgBaSAFOhZBQSAPbv1WniACggAA9yULCbJA5hFLR0rBaSAFJACUkAKdEMBAWA3VNc1pUBjBQSAkd8ZAsDIN1DmSwEpIAWkgBToAQUEgD2wyVpiNAoIAKPZqsaGCgAj30CZLwWkgBSQAlKgBxQQAPbAJmuJ0SggAIxmqwSAkW+VzJcCUkAKSAEp0LMKCAB7duu18AAVEAAGuClZTJIHMItaOlYKSAEp0FyBDz/8sCvydOu6LLZb155jjjlszjnn7Ireumh3FBAAdkd3XVUKNFJAABj5fSEAjHwDB9j8bj3sdeu63XzArcKaB2oN/jrvvvuuvf/++zZo0KBMn4yi7CxqnqTxzz//vC288MKZ15RJgA4OzrrmN954w15++WVbZZVV+l0VoEs73nnnHZtrrrlswQUXTHuKjquAAgLACmyillAZBdJ/Y1dmydVaiACwWvuZZzUzZ85M/St+2oe9N99803igGzJkSB6TgjrHr/nRRx+1xRZbzIYOHZrLviwPuMkLpNW83qh21+Mh+uabb7bNN998Nk9K3mumEaadXWnmaHbMk08+aa+//rqts846nUwzYOem0eKGG26wNddc0xZddNEBs6vMC73wwgv2xBNP2Mc+9rG+y6TRIWnT22+/bR988IH7fgEENXpDAQFgb+yzVhmHAgLAOPapqZUCwMg3sADzeZgq+oH/oYcecpatvvrqBVgYxhTA0oorrmhLLbVUZoOyPuBmvkCOEwD/a6+91saNG1eZh2jAAgBcd911cygS5inXX3+9rbXWWrbIIouEaWBGq5577jl7+umnbaONNsp45n8O58cL/uDpnX/++d2PTRrVV0AAWP091grjUUDfuvHsVUNLBYCRb2AB5vMgVQYAEoqH56IqAwBcYYUVbOmll67Ektj3a665plIA+PjjjxshhlUCwOuuu84++tGPVgYAn332WePPhhtu2O97J/kd5P/e7LX33nvPeQDx+PIdkzXktxIf4B5chACwBzddSw5WAQFgsFuTzjABYDqdqnxUGQD48MMPG/lYI0eOrIx0t9xyiy2//PK2zDLLZF5TqwdaJkv78Jvl2Hqor78G+zNhwgTniaGYRiMbk9fr1MZ2c2VZW7NjX3vtNXvrrbfcHrWzt509jc73G98OUOqPa2ZvOxt4nzWR6zbPPPM03KN2+9buGs3uk2brL/JezvxBSpywxhpr2AMPPGBbbLGFC8uWF7ATNeM4VwAYxz7Jyt5QQAAY+T4LACPfwALMf/DBB40w0DQP4GkfJqdOndqXo9PJw2+7h/h2D6lp7W31wO6vgdcBUOJBM+tDcwHb1HaK5AOw/3uj15jIvw4EAhb8f7tjk+cl/97uukWc1+4a/v3p06e7Hx4ompLlus2ObbfOgbgG4dT88ECoYzt76iEo7X2QZe+zHNvIXoravPLKK86rmUc/PnuEkPIH2N90001dLuC8887b9jOiA+JWQAAY9/7J+mopIACMfD8FgJFvYAHmA4Dkg/mR9mG71cMmD3g8iCe9ZY0eRrM80LZ78Gz3fp6HzaQmEydOtCWWWMKWXHLJpg+uWa6Rxd40x2a9FXwI6NixY23uuefOenqQxxMCSgGiWIrApBGRPM3Ro0fnLj6U5hoDecxTTz3lqoCut956uS4LAPJ9hUeeH64222wz9yPGQgstJC9gLkXjOUkAGM9eydLqKyAAjHyPBYCRb2AB5pcRAvrYY48ZXsC11167AAvDmOK2225zQLvsssuGYVCHVgDo48ePNwFgh0KWfDp5msBS3uqzJZuXefpOC/UAgNy7eAAnT57sqtjyAwke0vnmmy+zPTohHgUEgPHslSytvgICwMj3WAAY+QYWYH4ZAFhFTwz5chSAEQAWcNOVNAU/PEyZMqVSHkAgfYMNNqhESxW2vdPvBg+AhPveeuutNmLECPeZJDwbL6Caw5f04QpgWgFgAJsgE6RATQEBYOS3ggAw8g0swHx+TaeiXpGj01/5i7SlqLluv/12F/653HLLFTVlV+fxHsBtttnGhdBVYVTR83z11Ve7Qj3ATRVGp3uUBEC88gAfPQXpB0g10MGDB1dBJq2hgQICQN0WUiAcBQSA4exFLksEgLlkq9RJZQAg5dlfffVVl7tUlXHHHXfY4osv7gpyVGFQ1Aa4EACGvZtVA8BHH33U8N75IjBZ1fcAOG3aNOMzOWzYMBf+iSeQUFAqpqo5fFZV4zheABjHPsnK3lBAABj5PgsAI9/AAswvAwA7LfRQwLIKn4KHzcUWW8yGDx9e+NzdmFAA2A3Vs1/zqquuso033tiBTRXGI4884qp30tw+z/AASKjv3Xff7X5kwjtPX8HXX3/dhcrmadWSxxadM7AKCAAHVm9dTQq0UkAAGPn9IQCMfAMLML8MAHzmmWeMcu/kLlVl3Hnnna4ZN83gqzCqCIB4l/AMVan40JVXXulaHSywwAJVuO1c4RaqeI4aNSrXejwAUu313nvvdUVgJk2a5FqzcE/TAmT11VeXFzCXumGfJAAMe39kXW8pIACMfL8FgJFvYAHmlwGAzz77rPGHX+WrMu666y73cFk1ANx6660r00Ot0/DCEO/VqgEgfQ3ff/99W3PNNXPJ7QHwjTfesPvvv9/BMe0gaAuBl5RWLXxGCQtVc/hcEgd7kgAw2K2RYT2ogAAw8k0XAEa+gQWYXwYAPvfcc65MO8UrqjIIN6MU/4orrliJJXkPoAAw7O284oornJfLN4IP29r21tF3FIhbY4012h/c4AjOpXIxAPjAAw/YJpts4o4itJTvnZVWWskVawIGq1LcKJdQFTxJAFjBTdWSolVAABjt1s0yXAAY+QYWYH4ZAEj4J5VAyV2qygAAyS+i2EQVBl4Y8ssEgGHv5uWXX25bbLFFZapbAm145gjTzDM8AL722mv28MMP933H8D124403uiq95OnOO++8LmxWXsA8Kod5jgAwzH2RVb2pgAAw8n0XAEa+gQWYXwYAvvDCC0a5d//rfAFmdn2Ke+65x5XiFwB2fSuaGqmhFMIAACAASURBVFC1EFBgBw9glQCQfD2qdK622mq5biQPgFQZxutHCwg//v3vf7u/EnnAcQAgIKhRDQUEgNXYR62iGgoIACPfRwFg5BtYgPmEAuINKnK89NJLrtgD+TlVGRSc4IGSELMqDO8B3GqrrWy++earwpIcEMyYMSN3i4HQRPAAuOWWW7oed1UY5O0RmrnqqqvmWo4HwFdeecX9yJQMM58wYYKrMApcLrroog40+dFGXsBcUgd3kgAwuC2RQT2sgAAw8s0XAEa+gQWYXwYAvvzyy0axh80226wAC8OYAgAkD2vllVcOw6AOrfjggw+MAiNVA8BOWgx0KGnhp1dxjyZOnOh+cFhllVVy6eUBkO8Y+o0mKw3TGJ48XbyDgCH68aNNVX7gyCVYhU4SAFZoM7WU6BUQAEa+hQLAyDewAPPLAEB+nSfXh+IVVRn33Xef88LkfXANTYcqwkWnPeZC3aMq5Wl26kn3AEiUAYWm1l9//b5tu/XWW12INp7Bj3zkI7b44os7j/Cyyy5rc845Z2jbK3syKiAAzCiYDpcCJSogACxR3IGYWgA4ECqHfY0yAJBf4An1GjNmTNiLz2Bdp56LDJcakEM9AFYpvLBqAFjFQj2d5tJ6AHzxxRddq5n11luv7/NCKwhCS7m3+QHqox/9qGsWv80221SmiM6AfDkEehEBYKAbI7N6UgEBYOTbLgCMfAMLML8MAHz99deNBz3goiqj09yl0HSoIgCSd0pPuLXWWis0uXPZ41t1ADBVaWnQaTsV7lvaQFBoij+jR4/u05YiMLSXGDZsmN15550u/BNPIaHotIUgJ1AjXgUEgPHunSyvngICwMj3VAAY+QYWYH4ZAEiPLhqnk19WldFp9cLQdKhigREAcObMmTZq1KjQ5M5lDxV6x48fb2PHjrW555471xyhncT3wsILL+yatecZHgBpNQPcrbvuun3T3HTTTTZy5EgHgG+++ab7DgL6qBRK+HZVeinm0a0K5wgAq7CLWkNVFBAARr6TAsDIN7AA88sAQB6+7rjjDtdjrioDACSPKG//stB0EACGtiOz21NFAMQzR4VOevXlGR4AafpOqPnaa6/dNw19AAn7pF8ng2tNmTKlLxcZL2BVQDqPdrGfIwCMfQdlf5UUEABGvpsCwMg3sADzywBAHrooyU7oWlVGpw2sQ9Ohij3mquYBJNTxmmuusXHjxlUmfJEfhijOsvzyy+f6SHgAJP+PUHOAz48bbrjB1llnHdf6gYGXkM8tFUHpB0g1ULyAaguRS/qunyQA7PoWyAAp0KeAADDym0EAGPkGFmA+hSaAwCLH1KlTjYIMPLhWZfAgySDHqCrj8ssvr1ST8aoBIOGs1157rW277baVqWLJD0NLL720q8yZZ3gAfOaZZ1yYZzLf81//+pfLCcTTx3jttdeM4k2LLLKIrbnmmq44DO9VJZ8yj34xnyMAjHn3ZHvVFBAARr6jAsDIN7AA88sAwOnTpxv5ODy4VmU8+OCD7gGSB8mqjKoB4MMPP+wKhFQlB7CKAEivPlo08CfP8ABICwh+aEru9XXXXef6AlL8hUGIKPcEOgKGgwcPdp5A3pcXMI/63T1HANhd/XV1KZBUQAAY+f0gAIx8AwswvwwApPcW4VjbbbddARaGMQWN7dFKABjGfjSyomoASEVToIbP0UADCyHCDP/f5N/TvtbofAqzLLPMMrbEEkv0m7/RnI2uCQBS2IXwzrfeeqvf5xFvKeGevtgLzeIff/xxl3OIt5B8Qa4DAAKCGnEpIACMa79kbbUVEABGvr8CwMg3sADzywBAHsyuv/76rjy4FiDJbFPw0AhcUJTDA2DaB9Y8D8555s7zsE4+FlUTyY0qys52tuexM61t5IQBCIT8pT2nmT1l2pl2brTkxxQPNFm0TQtpzeYs43OUnDMJtP7vaV7DXo5baqml3OfRh2TzOgC4ySabuIqfDKqEPvnkk65S6M033+w+u5zjzxtoqC5b06rPLwCs+g5rfTEpIACMabca2CoAjHwDCzD/lVdesSeeeCL1L/1pHqx5COdhvP5BfCAfttPY6eVr5e0oQOK2U6R58GWStMclj210jn9/2rRpzhtCddNm87ebK61NrexIc+12dvA+7Ue49xZbbLE+zbPARRo72q2jnZ1p9WIeQhfJYSOs0Y9W6ynyHklrZzs96t8HxFZccUUHcHkGP1iRX4wXEDD2VXnZd7ylm266ad8PGjSLJ1eQZvGEjFI5lHsDzypQ6H/4yGOHzhl4BQSAA6+5rigFmikgAIz83hAARr6BBZgPqPFglPVBrv6BNPnwyUMaOXN4l1rBRdqHzHYP1VkefPOsk3MIJeOB3Hsc8szTbB0FbGOuKa644gpXIr8q/dEI06WgEfddFUYVQ6lp1r7SSivZkksumWuLAD3CP++//343h/888p1D1AFN3314J8fxh/w/zgMc+cGDIjCAI9VC/fdTLmN00oAqIAAcULl1MSnQUgEBYOQ3iAAw8g0swPwyQkCr2L/skUceceF4ybLzBcjf1SmqCIBVytOsYjElevWtttpqrhVEngHI8UMMnkSKuuDdYwD+VAEdM2ZMX68/ftjCC+ibxb/wwgvGjwSAIxDK+fzRiEMBAWAc+yQre0MBAWDk+ywAjHwDCzCfByqArcjBw9jVV1/t+gBWpeT6o48+ajyQVw0A8Zj4qolF3gPdmKtqhXoI0QV0qtROheJQeO2SYbpZ7hVfBZTQWKp8cv/yHcN3GHNvscUWfT0T6RVIiLtvFk+oNwCKx5t+gXjxaQtBOKlG+AoIAMPfI1nYOwoIACPfawFg5BtYgPllACBemKuuusq23nrrylTbAwB5IPcPkwVI3/Up8AAKALu+DU0NqCIA4qUjRJfKnHmGB0CKMgGA9BQcMWKEa/8B3G255ZZ9YZ3k/9ELMPmjzT333OPykykWAwBSMKYqIdB59IzpHAFgTLslW6uugAAw8h0WAEa+gQWYXwYAMueVV17pHsZ8Rb4CTO3qFI899phNmTLFeQ6qMtgjimbIAxjmjtLn7tZbb7WxY8eGaWAOq8jTo3m7LxCVdQoPgHh78ejR6mHjjTd2OX7kF2611VZ9+cxPPfXUbM3iH3jgAQeAtKGgGA0DL+Dcc8+d1RQdP8AKCAAHWHBdTgq0UEAAGPntIQCMfAMLML8MAOTBDO8S4VhVybGhCAy9xKoGgHhCeACuwqDwEPdzVXo18oPDhAkTXCh1VQaVOvGiL7zwwrmW5AGQvea7hc8k9y+N5SnyAgD6QQuI+mbxACCD3EB6BgJ+VAPFC6i2ELm2ZMBOEgAOmNS6kBRoq4AAsK1EYR8gAAx7fwbCujIAELurVmCEVhl4DnxBiYHYm7KvgQdQAFi2yvnnryIA0quPz9CwYcNyCeMBEJDDc40n8c4773Q/zNx9990u6sAPPrMUbkr+IED1UM4jvJbcPwrSMCcQWZV85VzCRnCSADCCTZKJPaOAADDyrRYARr6BBZhfFgBWLbyQh0nyiSgpX5VBnibhc1XyAOJ99q0BYt8nvFt33HGHy6Wtyhg/frytv/76NnTo0FxL8gAIyDHH8ssvb/fee6+DOQq+EHXgB2HbydYtvE7xGNo/UIX0tttucz0WqQ4KFK688sryAubalYE5SQA4MDrrKlIgjQICwDQqBXyMADDgzRkg08oCwKrBBeFkPGD6svMDtD2lXqZqe0RYYJUAkMb2d911V7+wxlJviAGYnOrAhF4CYXlGsgooYaTLLbecC/O8/fbbXfGXJABSuInqoL5ZPNe77777nPeR8ygkQ1N4ziMElB8OfA/BPLbpnHIVEACWq69mlwJZFBAAZlErwGMFgAFuygCbVBYA8kv/hhtumPtBb4BlaHs5CkpQcEIA2Faqrh3g87uq4gGsIgB2+qOD7wOIJ49Kossuu6y73wBlPKbJEFB6d3L8qquu2ndPUgXUn0flUNpsAKOEkg4fPtz9XbmAXfsIt7xwQAA4yMzmDVMlZ9U7ZvZ2wPbJtAooIACMfBMFgJFvYAHm4zHhQajocc011zhYyhvqVbQ9nc739NNPu8IRhK9VZfAw/rGPfawykF41ACTnFGBJQk3s916noeG0mOH7Ck8eYZwUf2EQxjlp0iQX0uwLT02ePNm9t8oqq/TJRp5g8jyKO/HZXmGFFVxLCUJBKQqjEZ4CgQDgoAXN3poWnjxJi14wM0rcCgLD3qeorRMARr19RrjUh5EvQeZ3qEBZANhpsYcOl1X46Twk8pBJzlBVhgAw7J0k5xTQSYY1hm1xe+s6LQ7lARAwBtj4w/CwvOSSS/blgNIqgtxAcvv8wFPIMcsss4x76b333nMN5JmH4wgHxQvIfzXCUiAQABxiZm9+y8xC/Jlgppn9eNa2kWQ7JawdlDVVUkAAGPluCgAj38ACzC8LADst917A0gqdgqbSzz//fKUAsNN8rEIFLmAyPICE7yVzvgqYtmtT0OicUMeqAGAR7WEANvL68OTh/VtqqaXc/gDL7D/vEXpOTh85oVT2XGmllfr2kKI6yfN4g/YRzOv7CeJBrEr7mq7dvCVcOCQA/J6ZEQca2sDld6wAMLRtqaQ9AsDIt1UAGPkGFmB+WQDYacPnApZW6BTPPvus8YeHy6oMAWDYOwkAUu1yzJgxYRua0jry8QgBJaR10KB8j88eAPHkUcgFbx4Drcj5GzJkiMv7GzlypANCruMbvnMcxWKS5/EaUPjWW2+54wBKfkSgMi7eQ41wFAgJAH8QMABimzyA4dy3VbVEABj5zgoAI9/AAswvCwD/9a9/uf5biy22WAFWdn+K5557zuUKUcGwKgMABGh5aK7CqJoHkKqzrGnzzTevwvYY4ZuEHdPWIm+1TQ+A9P6jaMsSSyzhtEEr2j6stdZaduuttzpPPa1byOkjv8+PCRMmuP8nD9APAJCwz5deesnlxPKdCDjiRdQIR4GQAPDogAHw+wLAcG7aClsiAIx8cwWAkW9gAeaXBYA33nija7KcfNAqwNyuTQEAUgmUB8SqDCq18qBcFQCkCAi5W1UJAaXqLHlsm222WSVuOeCNHx222Wab3E3XPQACbSNGjOj7gQl4o1UL9zOaUSgGTx5gByj6ARwSEpr8YQoopJ8gP/DwOh5CBl7AueeeuxLaV2ERIQEgYZb5fNjl7gQhoISnygNYrs6a3UwAGPldIACMfAMLML8sALzppptc9T3/C30BpnZ1CvL/8CiQJ1SVUUUAJGyPHx6qMKoGgOTncc+NHTs2N1jhRWQemrjz/UJLBwYVegE4qvTS/J32DlQgTgIdx5HvlzyP15gLmOTeoak8P/KQU0g1UMBQbSHC+DSFBIDHBQyA3xEAhnHDVtwKAWDkGywAjHwDCzC/LAD897//7X5p9zk6BZja1SmoAErJ+KoBIA/MVWnVgQewSgCIV4tWBptuumlX7/2iLo5XjvYw48aNy51f5wEQTx6gT/8+Bp9PvPSjR492/49uvEZen+8VyOuAYfI8XmMuKoACk1QXJfQTyCRMddSoUbm9lUXppnlmKRASAJ5oZoMD3Ji3zOzQfAD4DTM72MyoqnSPme3HbyNNlvgVMzu77j0KkNY7RdcwM6TawsxwpU8ys8+a2VMBSieTMiogAMwoWGiHCwBD25Hu2MMv5kUPHrR8UYWi5+7GfHgYHn30Udtkk026cflSrok3RgBYirSFTAoAUtikKvcc3zO0h9l2221zt1kgBJQ/ePLWWGMNW3jhhZ3WwB+f0XXXXdf9P7BJGDoevGQbCCITKBAzbNiwvj1KQuG0adNcoRjAkvxBIJDz8+YsFnIjaJLgAPCHAQPgIdkBcBcz+62Z7c3vIWZ2oJntbGaEUrzU4PYDAH9ae9+/TUuxFxPHUnoXgPy1mZ1Xa0kxEid8kzl1l0emgAAwsg2rN1cAGPkGFmR+GQDIr+rk0vh+WwWZ2rVpeLjkYbwq3hiExBuz3nrryQPYtbuq9YWr9qMDlTapDrzddtvlDqv0AAi04Z3z3msq9BIyu8466/SJCuyRw5cs3AQUUigm6fUmWoGCVR4K8STTV5B2ERRJIt+Q9zW6q0BIHkB67YXqAaRHYcYcQKBvgpntW9thmmA+bWanmdkJTQDwFDP7z68osx/0JzN718y+3N27RlcvSwEBYFnKDtC8AsABEjrwy5QBgOTV8ADFnyqMqoXjVREAaZnAA39VcgCrFnY8Y8YM13QdAMw7CM0kDBRo++hHP9pXwIg+nbSCWHvttfumprjL9OnTHRR6uOP6/D/FYfyoh8K3337bzc8PWBSVoZ8geYMa3VUgJAD8ScAAeNCsbVrWzKYmdowwn0ahPvOa2Qwz+5yZXZo4/twa4H26CQCeZWbPmhmweKeZkXp4fwIg3zQzHKVUsMIt/7iZHV93je7eULp6RwoIADuSr/snCwC7vwchWFAGAPLwtfTSS/fLvwlhrXltqFpBjqoCII2/V1111bzbXMh55NUy/H+Tf8/yGj86UNjE57WlPTfv9Yqw2QvYSAO+Z/CiE7rZ6rhW66THH3nF/MBEuKcHOXR64403nHfPD0I5KeQCNHoN8UDi9abCpx9AIeCYrIaLh5EBrFL8KVlJtJCbRJNkViAkAMQ1FqoHkOS9BuMoM6u1COz37jI1kCO3YdZNP2sAb+TuNep7RCU0fhG5t+Zp/LaZ0ayUEM9nanmEz9fAkqKk15rZ9mZG7ZytzOz6zJuvE4JTQAAY3JZkM0gAmE2vqh5NvkzyoauIdfLwxYOaL6neas6iHpjTPFQ2erjkvHY28HBJmXm8DmmOz2tL2of8vOtI2s562CMeklutv6j1djJPGl3w3FCx0edrtdvT+j3qxL7kuUV8fprNkaxI6f9e1mtp58XWNLbgueNzRGXOtHPXHzd16lRXpIW+f0mQo0XLlClTXFioH/wIxffPww8/7F4nr++6665zXj3y+/ygZymAmIRC+gy++eab9r3vfc+uuOIK92OWRncVCAkATw8YAKnmksEDmAcA62+EeczsgVqu3xFm5uck9++LiYP/YmbTzWzX7t5JunoRCggAi1Cxi3MIALsofkCXJgSKX+eLfGDmYY+HN/8AVz930cBZ9gMz9uJJAJbqH3ZbPcw2ei/tA3Oe4xqd08w+8qbo08iakufluW7aB/q0c2fVlOOBAKqAJqs+pgGTtDa10yiPzf6+bWQnrUfwbJHDVoVWBMAbucG0gcg7gDzvnSM/z4McXjrCPSnw4odv70BhFw+MACBtHgYP/o//phEU3nXXXc7LeNppp7miMFXpZ5pX9xDOCwkAfxEwAO4za7OG1gqvtNu6PCGgjea80Mzeq8EdcwJ6eB1pmegHFUEJCa1GWeN2ylb8fQFg5BssAIx8AwsynwdNgC3Pg3Czh17yZgipSuYApnkYL+Ihuv5BvQiZyC8ix2zMGCJdqjGoyEgYXbIiYswrmzhxovP+dTsEtCgNAXT+ADpVGMAbXjkawecdFIG57777XMVPWrJ4kKNFC0VmksVafM8/Cr4AjYSe0uePqqqDBv2nYj2fAyAbz6IfFH5hYOtZZ51le+yxR16TdV5BCoQEgL8MGAC/NkvvtADIsRSBoWKnjx4lr49WDT9rUgSmfkfnquX//cPMvll7899m9mhdEZg/mxmdKpJewYLuDk0z0AoIAAda8YKvJwAsWNBIpysjBPTuu+92lfZoBVGFQWNoHjy32IK0iGoMHnwpiOFL6ce+KgAQb2ZVCnZQ2IT2BlUBQEIqCa3caivSgPINvPAeJAnb9D9ePPbYY671w+qrr943cbK9A95hiurgDaSSr/d649nnc1APhXj9+DFhs802s7/+9a+2ww475DNYZxWmQEgASAWU//xcUNgSO56Iai57ZQdA2kBQ9OXrNRCkDcTnzYwPE60daBFBwZfDawZ+v9bO4ZFaoRj6B+5oZuvVev1x2E5mdr6ZEZHqcwCpHLqlmd3Y8UI1QdcVEAB2fQs6M0AA2Jl+VTm7DACkoTIFGkaMGFEJmSgLz5q23JJ/v6oxBIBh7yMAiHeenLUqjCI+QwAgf8jbW2qppfo8fhSXIYohWQE22d6B9wBCvus233zzvubuFJUhBDQJhWiNp5KCQnj88ToSWqzRXQVCAsDfBAyAX80OgJxBCwjfCP5uM9u/5hnkvevM7Akzo/8fgyKon6kVe3ndzHCXU+zlrro7BFOARiqSPmRmR5rZZd29i3T1ohQQABalZJfmEQB2SfjALlsGABJqRX7OSivRDzb+wcMrXs1OvBehqcCDL9UP5QEMbWdm2UP+H6GO66+/fpgGZrSqCC868Mf3FZU755xzThcGijdv8uTJLoc5Gf5b396BPEE8hUkABAypDJp8jWWRq8j822+/vVFciL9rdFeBkADwnIABsEZpWUJAu7uxunqUCggAo9y2/xgtAIx8AwsyvwwArFo4HtULKQwhACzopithmqrdc4Qt0n6EapdVGJ3m0fpCTHxfAXdUEyWXD+ij0if5w8nw3/r2Dr6/H7mAvqonQMlxeProIekH+YOMnXbayYWcCgC7fweGBIC/DxgAvzRrqwSA3b9lK22BADDy7RUARr6BBZlfBgBSMCWEnmwFSeRKwlMYYuutty5qyq7PgweQthaUx6/CIEcTIKhKDmDVAJBKnA888IDztuUZHgCpWHzTTTe51g145fEC4t0D4JIRB/XtHfx5FHshrxKo8zBJbm8yzJPwUcJDd911V8P7n6YK6+mnn24nnXSSyzXEs04F0Wb5m+ecc85shWXwZAKpfpCveNhhh9mll17qmtyTT73//vvb3nvvnUe+6M8JCQD/GDAA1iqsCACjv+PDXoAAMOz9aWudALCtRD1xAL+C87BT5Jg0aZJ7oErm5BQ5/0DPBQBSGKKTCoYDbXO761URAKkKufLKK7dbehTv06eRB3/fxDwKo1sYiTfzoYcecoVV8gwPgFT7JJ+PfFy8vuw5eXoAVDLnmNBOwmd9qwh/Hj8SAFN4AT0U4tlPQh6ACRzutddeLgy3HQCef/75tttuu9kZZ5zhKoqecsopduGFF7r1LrHEErMtFwA84IAD3Pt+cA36cvrxta99za655hpXhXSFFVawK6+80v73f//XLrnkEvvUpz6VR8KozwkJAP8UMAB+YdYuCwCjvtvDN14AGP4etbRQABj5BhZkfhkAyC/9PNAkq/IVZG5XpimihH1XDG9xUR6Q11prrUp5AKsEgHi18D7RqqMK46WXXjKKtVBxM8/wADhjxgwjRBNoo7cgodmLLrqoA71k1eH69g6cR24f30m0jaAfIAAITNZ79gkL5b19993XKMbTDgCBPor1/OxnVM4394MaTej3228/58WrHwDggQceaISWNxs0r99ll13siCPorT1rEA788Y9/3I49NtleLY+a8Z0TEgBeFDAAfk4AGN/NHaHFAsAINy1psgAw8g0syPwyAJA+gDwEJftyFWRuV6Ypool1VwxvA4A8ZPLwXIVBCKgAMNydJDQS8CJkM8/wAEjDd5q0+3xcwkAJnaQqKJ4yP/CeJXsFch5efEJQOX/55Zd3bST4e311X8JHAcaDDz7Y2dwKAPEUElZ60UUX2Y47Ug1/1th9990d4F122eyFDwFAvIv0SeV7Ei/vcccd16+RPR5A4JYQ0GWWWcZVK8Xz9/e//71S/UjT3gshAeDFZrZAWsMH8Di6r39WADiAivfupQSAke+9ADDyDSzI/DIAkNAmwrJGjhxZkJXdnYZ8HDwF48aN664hBV4dD2CVAJDKszyIVyUEFPAg9JhejVUYtLQgrBXPW56RBEDaNHho8wWagD/vAfT9/ZLtHby3kIIvwOijjz7qcmDpTVjf35PPBscfeeSRrsBMq0GvRkCOvMEk3B5yyCGuwihex/rBdwmVS7k+e/yjH/3ItbYgd3rZZamab84DCQT+9re/dfmN5Cz+6le/cqGmvThCAkA6mocKgDTgUwhoL35CBnbNAsCB1bvwqwkAC5c0ygnLAEAebniAATCqMABAHvC23XbbKizHrYEHTgC9Kh7AqrUeoWUBocdVAUBAidYWhEvmGR4A+SxSkCkJbYRs0nfUa+XbO5BvSEN3BlrSyxMPIHPh+ePexy6gMDkIHwXMTjjhBAdlRQNg/Xx8B1OdlKIzxxxzjHsbKAT4+O/w4cPd5/Xwww+3P//5zzZ27Ng8EkZ9TkgAiE83VAD8tAAw6vs8FuMFgLHsVBM7BYCRb2BB5pcBgOT6UHSBHLMqDMLHKD2/3XbbVWE5AsAIdhEAxAtFRckqjGeffdb406wyZrs1egBMevL8OYR2AoYAH94yog8ApmR/P4COojF4BRnkJPoiLMnKpN57SN/Cn/70p67SaKuRJwS00Xw777yzs/28885z351Dhw51sPdf//VffYcTNkpO4uWXX95Orsq9HxIA/j1gAKzdLSoCU7lPQFgLEgCGtR+ZrREAZpaskieUAYCEVwFNhDhVYZAPhJdBABjublbNA1i1zxDgQhgoxVLyDA+AgBx7nYQ2cuXIA6SyJ6Ggvr9fsr0DBXUoTuWL0DAfxWQAuKQ3kZw88u0AxF/+8pdGuGm7gVcTsKX1A4M5yDGkiEyjIjD18+GxxBv/iU98wk4++WTnrQQA//GPf7iiL358/etfdzmJVATttRESAP4zYACs3S0CwF77gAzwegWAAyx40ZcTABataJzzlQGAVQtf4xd58nm23377ODe5gdV4SCjSQ0PtKoyqAWDVvOid9jX0AEjOH568ZDsJAHDBBRd0uX3k4QFgeOzJE/RN3PHo4fFL5ulRaZXvqmQjeB8+irfyD3/4g+s52G7QBoKiL2eeeaYDQdpAXHDBBUYxLFo7kLdHnuDxxx/vpjr66KNdLiT5qqyH/oEUeyG01RfOwnZ6J1JZlBBQvn/22WcfB4j8t9dGSACI/zXUENDav1ACwF77gAzwegWAeeSEEAAAIABJREFUAyx40ZcTABataJzzES7FQ0+Ro2ol7D0A4gFsVxK+SB3LnAuPJnlHVQFA8ruAgGQz8DL1K3tu8mjxalUljLrTvoYeAOs9eewD4ESlTLyMABe998jZTfb3o6ciUJ3MQeQ1qsfiNfQVRH34KPYSgkk+YJoBqPlG8OQinnrqqX3XAuaYn+qfjIMOOsj18wNYF154YdfegdYOyZYfvEfOH94+4BUIpCgM51blOyiNrv6YkABwfMAAuM0swQSAWW4uHZtZAQFgZsnCOkEAGNZ+dMuaMgCw04e9bmnR7Lo8iBMWJgAMbWf+Yw8ASCGQZDPwcK1tb1nVCil1+qMQAEi4JgBY78kjB5C+e3PNNZfzugFghG76VhGoTSN6wieTOYh42KjyyXcgoaHk4HENvIfA4hVXXNGT4Zbt786BPyIkALzGzBYceAnaXnGamW0tAGyrkw7oXAEBYOcadnUGAWBX5Q/m4mUAYKfhXsGIUzOEiqZ4AqgC6kPKQrMxqz14AGmKvfjii2c9NcjjqwaAgAnh2VVppdJpWwsPgN6Tl2wnAezhYcObzd+p7knF0WR/P3L6+GEqmYPooRBwXGSRRVwbCT7rhH0CkoRJ03dPo/sKhASA1wUMgFsKALt/s/aABQLAyDdZABj5BhZkfhkAyMPXiy++aOuvv35BVnZ3GrwCNJamDyAPi1UYAsCwdxEvF6HZPicsbGvbW9dpVVMPgHjtmCsZyklLBzy/AKCv7ol2SQAkpJK8PsIt/eBYfqwibJgcUryAfB/Sp4/QUDyL5OZpdF+BkADwXwEDYK2hiUJAu3/LVtoCAWDk2ysAjHwDCzK/DAAkF4f+WnlLvhe0tMKm8QBI/y3CxKowCHNbbbXVKuMBpFz/kCFDKhMCWjUA7LSqqQdAvHb1njyqea6yyirO8+ere+LJSwIgFUj5M3r06L6PbxIKKSTD/UMlUYCSvMJJkya5Yi4a3VcgJAD8d8AAuIk8gN2/WXvAAgFg5JssAIx8AwsyvwwA7LTpc0FLK2waQvHGjx/vGjALAAuTtdCJAEBK5xPGV4VBCCIwQ6GeKoxOcxo9AOK1I8IgGV2Ax44fMwjjZBBuyp9kFVC+k4hKSBZaAQiBQF6jvQT3EK1r8AZSRAbQpBKoRvcVCAkAbw4YADfOB4DfMLODzWwpM7vHzPYzs9ua7PpXzOzsuvdmmtmgxGtUO9q97pgrzKw6ZbS7/5HoqgUCwK7K3/nFBYCda1iFGcoAQB6seHhK5unErBUaXX311bbNNtvYPPPME/NS+mzHA7jqqqu6iolVGFUDQHrWMaoCgJ3mNHoABOLqQzmBNXSioiaD1gp49IBCqoMyOIfw0bXXXrvvduc1PIoUjWGQR8oPPFTdJOSbfMOzz65/1q3CpyW+NYQEgJBRqEVgNswOgLuY2W/NbG8zu9XMDjSznc1sNTN7qcGdAgD+tPa+f/tDM3uxDgCXNLM9Eq8Bia/Hd+fJ4kYKCAAjvy8EgJFvYEHmlwGA/KrOL/DJnlsFmduVaTwAbr311jbvvPN2xYaiL1oEAPJQzvD/Tf4962t55/LnARgLLLCAe+Bvde1W12lkf5q50q47iybACsOHNfY9aaXQPMt1ms2bdY52ulJJl2Pmm2++fvdMlusMGjTIllpqKZfnlwzlpGjLqFGjnAeYQaVQegWSr8uPUBRuwmvI63j4/CBUHdjzr02dOtWFfnIeFUCxmd5+Gt1XICQAvDNgAKwFOGfJAQT6JpjZvrVdntPMnjaz08zshCYAeIqZDWtxV+AB5P0du3/nyIIyFBAAlqHqAM4pABxAsQO+FD3upk+f3vfQnOWBrNnDIw9aeAH5VT7Ng32aY7I+nGc9vtG6/Rw0lsZbQH6QrwKaVae8x7eyqx18tdKVvEYedFlP/XFpbS3rtvZ9zur/y/WavQak854P0c0zR6v5k+81Oi6LrV63ZjbyOp8h9mbYsFnPWcnebwO9tiLWy+eH+2r55Zfvu22yaMa5FGah1Qf3bjKUk4JGePbI4WP4pu+cQ0gwn1uKvRDmmeyrCBTiLUy+BgACgv/85z+d/vT30+i+AiEB4F1mtlD3JZnNgqlmtu6sV9MCIL9mzjCzz5lZstrRuTWA+3SDZeIBPAunupkBi/Dwd8zs/sSxACDw907N60fnjO+Z2asByiaTciggAMwhWkinCABD2o3u2UJFPcLNinjAXGXcOLeQh664wpVTxyOT5SGv1YNmEQ/naeeo14IHSTyalJoHmlo9uNc/3Ge9Zqt9KEIfPz95TvROI2+q1R4V8fCfFVjyfBoI+QOWqpIDeP/99zuYJYyxCsN/x9B6JM/gRxjCyvm+AgJ92CZz0a4BIOR1hm8Vwf0N+FExlHP5oSvZVoPXgD28h36QK0j+JSHfhHuffPLJeczVOQUrEBIAkiQXKgDWApyXNTN40A/CL/lTP4iPBuSoHUNqox8/NLMtzGyjBueQZriKmd1bA81vmxnFR0ea2TO1479QA8vHzWwlMzvOzGhTyLnvF3xraLouKCAA7ILoRV5SAFikmvHORbl0vCdFjBcGJfPAzZZ6++0ipu36HAAgIWEUlSAMrQqDsDkqJ1YlB7CKAAiAkKdZhUFFTX48yQu0ACAhmeT7DR48uF8/v+uvv94VheEHJ4bv78drFIhZeeWVbcaMGUa0Q7KtBs3peT35GvBI+CiN4IHBH/6QZ2GNbisQEgDeFzAArtV4o44ysx80eCsPANZPQ1I8CcvnmdkRTe6TEWb2qJmNNbPx3b6XdP3OFRAAdq5hV2cQAHZV/mAuXiYAJhcZMwx6ANxiiy3cw2cVBg/S9D9bckly9eMfACBFQPDSVmEAIeTLAelVGKyH/Nm8QAsAUo0XzzWhnJtvvnmf5/q6665zLWfmn39+J1Wy6Tt5foSf0iOQ0NFkUR28iUQqJF8j95J80uOPP97lGcoDGMbdFxIATgoYANectV1pPYB5QkAb3RAXmhm/Iu/a4m55uRYGqqTaMD5SHVkhAOxIvu6fLADs/h6EYEGRAPhSzTv2QYuFxQqCl19+uY0ZM6bvITOEvevEBgFgJ+qVfy75bvzYgPeqCqPT9QCAABxhm3juCAH1VT+p2EnBKf/jTLK/H99veAEpEAOAJj2Q9CYEKpNhqR4e999/fxcCyg8LGt1XICQAfChgAKwFjKfNAWRjKQJDYVNaPzDI63vKzEh+bVQEpv5mmKuW//cPM/tmkzsFIGVO8gL/0v27SRZ0qoAAsFMFu3y+ALDLGxDI5csAQL+0ViDIMTHBICGgeB28lyGQ7ctthgAwt3QDciKeLu61qgAg6yFEE69znuEBkJw+WkHgHQUC8c5fe+21tskmm/SFZ9c3fafYC9BIuHMSAAnzZN6kV5K5OR4PIHmLHOMrl+axW+cUo0BIADg5YACsxQtkAUDaQFD05es1EKQNxOfNjGRdWjvQIoI8wcNrO/l9M7vFzB6pFYqhfyBgt56Z4RylQ8aRZnaxmb1QywEkjpq0SSJUG+UiFnOTaJYBU0AAOGBSl3MhAWA5usY2a5kA2A4EfS+lBSPIFbzyyitt00037cszim2f6+0FAEeMGOHK6ldh3Hnnna6gTVVCQDsFptD2lB57FGnhnsszPAD6vD1CNddbbz33eSQEdLPNNutr0VLf9J3vOCqFcn8k20AQ6kmBomSYrYfHk046yeX8fvvb1LjQ6LYCIQEglU1CLQKz4qyNygKAHE8LCN8I/m4z27/mGeS968zsCTOj+ifjJ2b2mVrTePr63VEL7fSucnIkqChKQVJKGD9nZlfW8gOTvQK7fUvp+h0oIADsQLwQThUAhrAL3bfB59YUYcnLtRDQWd3hGg/vFWzWSDdUGAQA8TIsuGCILYCz754AMLtmA3lGp8A0kLamudbdd9/twjDzVmn1AEg1XsI2ATf+S/4eRWDwzhOyyWjU9P322293RWT4EcdXpX3ooYdcpdWkV9LD47HHHmuf/OQnbb/9fGRcmlXqmLIUCAkAoaFZDUfCGlPMrJYBnRUAw1qIrAleAQFg8FvU2kABYOQbWJD5ZQCgN60VCM4q19B8hAaCV111lcszqgoAkhfFw3iVPIA0TR8+fHhBn4zuTgMw0dcur8esu9bPfvVOi/R4AKRwCx49+gnecsstzgsI3JGf63tANmr6TlsNqoPiAcQTyCDEk/DOpMYeHo844gjbZZddbO+99w5Nyp60JyQAJJktVACsddkUAPbkp2TgFi0AHDitS7mSALAUWaObtEgAfK3mAWzU6KceBpP/gLZrQhECDNIXjH5ivtdYdBtdZ7AAMOwd7NRjFtrqaLBOJc68gO4BkMItDMI2gTqatRO2SYVe2kwwGjV9pw0FHkByBqnuiReQ1ygck/RKeng89NBDbY899rA999wzlZSnn366ETZKARqa0p922mmuMmmjcc4557i5kwMQxT4/kv1Ak8fRluLgg4nW660REgDS7C5UAKTaSo4Q0N66mbTajhUQAHYsYXcnEAB2V/9Qrl4GAPq1tQLBRv+ANgPBvqDLLuYKjh8/3vUewytThQEAki+39NJLV2E51ilghCZCpx6z0NaDl44iLHju8gwPgBRlAfoojkMTd/ad97baaqu+0M5GDd6BRWCPthBrrbWWqyDKa3j0k1AKPE6ZMsUOPPBA23fffe3LX/5yW3PPP/9822233eyMM85wPxKdcsopduGFFxohpo36bAKABxxwgHs/CXzJliyAZHL885//dDDK+qviFW4rbOKAkACQnQnxXwFCQGsZ3fIAZrm5dGxmBQSAmSUL6wQBYFj70S1rigJA7/1rtI5GINgqib4eBBtm3Q0wDAKANJYmj6kKQwAY9i5WDQAnTJjgfmxYdtmajyKj/B4AKdxCrp/P26P4zxtvvGFbb71134zkCdY3facNBZ/d9957z/URXHfddc2/loRSisxMnz7d9tlnHzv88MPt85+nIGLrAfTx49DPfkblfHNAutxyy7n8wcMOO2y2kwFAABO7044dd9zRAS/fQ704QgLAlwIGwCVm3RwCwF78kAzgmgWAAyh2GZcSAJahanxzdgKAJ9RCPg97+21rBYBelSQILlx7sV34J++3LLsyQCBIrzHyjaoCgORP4fmokgdw8cUXz+1hCu2TC9hUKafxtttus4985CPuT57hARCvWTJvj/BPcvmSOYCNGrxTVRWvHzmvFECihQSeQl4D1vzw8LjXXnvZUUcdZZ/5DAUPmw96E9Ku46KLLjIgzY/dd9/dAd5ll13WEACZHy1YFyGpxx13nI0cObLhhWhNATife+659sUvfjGPfNGfExIAvhIwAC4mAIz+Xo9hAQLAGHaphY0CwMg3sCDzOwFATPAQ+LUM9gCCHgCTpzWDQcJt3kkzf4kwSK8xvAbDhlHZOv4BAOL5WGaZZeJfDLXI77jDqgSAVVvPrbfe6kAr7/3mAfDBBx/sl7c3bdo0Ay4JCfWevEYN3qmqSg4i0MX7eNPIs/Ov+Q8B8AjUfelLX7If//jHtsMOO7T8fFA1lDmBSopE+XHIIYe46qSsu37gfZ88ebIrSIM38kc/+pH961//ciGpjTyk5P2dcMIJxrUG1X50q8SHNsMiQgJA/LahhoDW/nWSBzDDvaVDsysgAMyuWVBnCACD2o6uGdMpAP5x0CCjKpofaUGQf6iaNYqvB8H6f2xbweB8g2lDZDbzdVoUFTfoNUZxBzwGVRgCwLB3sdOcudBW16nH2QMg3j56//l+jwAgWhEWCoCRH9iowTshteTYAaAAHsDGPMBbEko5lyqjhH6S07ftttsWDoD1E/p2Frvuuqsdc8wxs11v9dVXt3HjxrnCMr06QgLANwHAOcJ7BJ7y4Ycu9lMhoL36KRm4dYd39w/c2itxJQFgJbax40VQFY8Hok5GHghM+tHagWCzX1sbWe0BMLmeImAQAEyWkO9ErxDO7dQjE8IakjZUDZhYD8CSDE8MTfMs9nTad9IDIF4ywrC9tw9PHnA377zz9nm0GzV4J6SWcGcf8owHjvBRqokmw6B5ne9Ewjl/97vfuWbwrUaeENBG8+28886ujcV5553X720a2BPeSlVYfoDq1REUAM49d7gA+J77+VQewF79oAzQugWAAyR0WZcRAJalbFzzFgGAfxs0yN4ys6frQjVbeQMbBVI2A8F2PQNR3MNgIwD0O9IJCBLORfVA30Msrl2e3VoBYNg72GnRlNBWd9NNNznYalQVM42tHgAnTpzYL2+PEEqKuVAZkwqeFGQBAGkJQVioH4TUEl7pK23OnDnTsIliMskqoOQYcu7HP/5xu/jii13j+HaDa9LywXvosBVApYpooyIw9fPhcST/7xOf+ISdfPLJ/d7+yle+YqyZHwR6eQQFgIMGhQuAs9IgBIC9/GEZgLULAAdA5DIvIQAsU9145i4CAK8aNMhmmjkIfJc+XInlN4PAVpl09SC4wDzz9M344btcofmYoxYC2m4HssIgOTo8pFGYowqjigBYJY9Zp0VTQrtHb7zxRltttdVcnmaeAVQBbcAQn0GfK0ehFbyChH/iZeQar776qvMIJtslANSAXhJA8a7RGoLqvn6QY0g46TbbbGO0XmjWyy+5BtpAUPTlzDPPdMfTBuKCCy4w5uKepEUEoabHH3+8O+3oo4+2j33sYw5QsZ/+gZdeeqnLY11zzTX7pqYdBd5JchF7vSF9UAA4bFi4ADirsqwAMM+XjM5JrYAAMLVUYR4oAAxzXwbaqiIA8MaaBxAIZKTxBqYppeJBMAmAXp9mIDiHf8CcNq21lEvVOibVjpr5wAMtjwcAeTijaEQVBgDIQ3TeqoyhaVC1kMmqASCwtcYaa+T+/HgAxNsHRPr79vXXX3egBQDiAXzppZcc1FGZM9ngHT35/ySAkpdIuwigjXxABs3hKbSy2WabGdCaNuySFhC+ETwVRk899VTnjWQQRkrOIu0fGAcddJBdcsklrmk8OcVUFz722GNdkank+OUvf+naRRCqWpXqw3m/N4ICwEUXtSFzzpl3KaWdN+WDD2zoq68KAEtTWBN7BQSAkd8LAsDIN7Ag84sAwHsHDTICTwjDBP6aeQMx2XsE0wCgX+LghAewftn1INgHgMkDG8FgHQAmD28EgzzAUowhrwejoO0qbJqqAQYeHkr8VyVnrmoe2k496IRJkm9HNc9kLh/ePvL28KjR4w8v4EILLeTAyheK4UODnnjckh58ABDYw+PnWzDgTQQGgcJ6j1xhHz5NlFmBoABwySXDBcAXXxQAZr67dEJWBQSAWRUL7HgBYGAb0iVzigBATH+4BoFTEhDI6/XeQAIoP2tmmQDQh3XOSnBvOoDBhgDoz0iCYAsAbASDnYawdWl7m15WABjajvS3p9OqmaGtrtMcWuCOapkUQ8H7B+wzXnnlFdfWwXvb+DseM3Lwkg3eab1AeGgyh5fXgETy/gA+vIaEmAKQeP54PZlHGJqmvWRPUAA4fHi4APjkkwLAXvpgdGmtAsAuCV/UZQWARSkZ9zxFAWASAht5A32BmGQG3Z4ppSOka7bRDAaXXdasXfgnky3Ysr18/8slwj5n3nBDSqvDPgwApPx9o75jYVve2LqqFU3xcJKsUBnjvnib6aNJaGTeNioeAKn4iZfXF3N5+eWXjebtPlcPLyE/1gB/SXjDM0gIavL6vEZYN/31GPydhvFDhgxxFX+feOKJfgViYtY/dtuDAsARI2zIXHMFJ+mU99+3oY89JgAMbmeqZ5AAMPI9FQBGvoEFmV8kAGLSc4MGGdl3QGC9N5D/J68Pr6AfaSBwML33mjV5rwdBANCPViCY9AC2A8Y2eX8xQqEAsKAPUM5p+Nwx/H+Tf+c19scXLWl2TP053pT6uYs6P808jWziPHLr8Lb5H3Oa2djoGsxJCCjeO/oAJou5kPNH7l+ykAvVPSkCs8EGG/TtDlBIFd9kLp1/jRBQ9PYVRAkBHT16tPMkek9jzm3WaQUpEBQArrpquAD48MMCwILuOU3TXAEBYOR3hwAw8g0s0Hyq6xU9nhg0yOUEUpMsmRtITz+qhCYhkGu3AkEHgH60A8EkACYXVQ95jUJAm4Fg2sIvCa/iKxdc0Pdwn+bBOc0xzR6uW4FEM0jAu8HDsH8gTnP9oo7pdB2NzqcdwHzzzece/POAkJ+z1Rpb2Z11TUV/3phvjlpz6vr/tnrPH1v2+VS7xLNGr7tG12r3GsVasBVPINU9fTEmCqk8++yzrpCKH+TuUUGTvEAPnOTwEtaJDX7wGl5JQj7J/aP9A9chFBR4JLy0KlV/y7jfBnLOoABwzTXDBcBJk9gWVQEdyJuzB68lAIx80wWAkW9ggebz6zdV9to9OGcBAB7AeJBacty4ft5A/mXCO/iamfMUpgHBfgDojWwGgu1y+zzkpT2O6+UAwGbbc/khh/R7APYPvq0exFsdk/fhngdyCmCwR36EDBDNbPOvEwY4bNgw5yXKs45WADLQe8P1+EwCOr7oULv1F/h1UMpUV199tfOwAVt5xowZM1xoJ4BPLp8HM7x0/MFj5wdhongMgb1VV13VvUwRGqpsJq/Pa5y34IIL2vTp040wYv4ONFJVlO+wvPbmWaPOaa5AUAA4enS4AHjnnXkA8BtmdrCZkVh7j5ntZ2a3NdmNr5jZ2XXv8QvyoCbHn2FmX6f4rZmdonu8GgoIACPfRwFg5BtYoPkUO0iOZh6ENA/C/kGaX9QJ+eKB6v3hw/uFhfIbPBDInzTewIYA6A2uB8EVVpj1TjNA9OdlyQEc1OzftrpNyDJng2NnXnhhgbvaeqqqVc2sWkhrp43TB+xGSnmhq666ykEV3wd5BkDHd8qLL77oQjl9MRfy93gt2UIBDyDvP/nkk7bJJps4aKQIDWGivt0DNlx33XXO0+dfowAMbSXwitMGgsiIpEc5j906pxgFggLADTawITVPdjGrK2aWKe+9Z0MnTMgKgLuY2W/NbG+K5ZrZgWa2s5mtZmYvNbAMAPxp7X3/NvHsrvxo3djJzI40M5p/niQALGafQ5hFABjCLnRggwCwA/EqdmoZIaCNqv4RFkoeIDjlAZD/vpAIE01K68NCWwKgP8EDnwfA5ESNYDBLDmDy2FmNdhuPLA+3aY9NC59l3ZNpH3Rqx8381a/KsqTlvFWralq1qrNXXnmlbbrppv0ALMuNAgAS5ss+07LBF4Eh/JNQzWS/PnpCUigGzyBeQDypFKHBA5n0ePNaMkx02rRpbn4AcKuttnLhpoSFanRfgaAAcOONwwXAm2/OCoBAH9S4b22XaXBIzbbTzOyEJgCIJ69dIe+P1IByOzP7ew3+5AHs/kepEAsEgIXI2L1JBIDd0z60K1M5LxneWYR9rfp+vVZrGfFK7UJAIHX4moWEpgJAb3Sr0M4kCGbJAWw1ZxII00IdtqY9NjIAbHXvlAmHVQPAqvWdvOKKK2zzzTfvB2BZvmeAMf7wvbLEEku4ip2Mp59+2nntqNrpB95tog/IN6RxPJ5H9OS/ySI0ACAeQkKh/eCHqznnnNN22mknlw/I3zW6r0BQALjVVuEC4LXXZgFAEqZnmNnnzOzSxC6fWwO8TzcBwLPM7Fkz48NBzOl3zOz+xLG8frWZXVbzFj4hAOz+Z6hICwSARarZhbkEgF0QPdBLlgGA7R5gp9W8gb5iaDIkFJl8biCxI4xdkoVgWumYJgQUEMySA9juWG9Pmz6F/czuQQDsW3/d2mf+5CcdfzJo9E1LC3rEVWHw+aFtgS92EvOa+HEJANxyyy37wVaWNXkAxDPKfIRo4p2jAiieQcJC/fD5k+QJ4g0EGOkPiAeSQkEMcp4JAWWeZJgnrSHefvtt22effeyZZ54RAGbZpBKPDQoAx461IfPMU+Jq80095d13bejVcJdRCntqYhZy9BpVelumBnKbmJlzHdbGD81sCzPbqIElG5vZKmZ2b63YzLfNbIyZjTSzZ2rHH25mW5kZ3j/CQwWA+bY02LMEgMFuTTrDBIDpdOqFo8oAQB7UKMDAw1ergTeQsFD+eAj0IaH12UKpILA+BLRZLmDafoEUjUkLgPXFYl5gJU2GALDlfZEVCqsGgK086LF9JwFbhIASVukBLOsaaAJPGChgzBzAPn/I85s6daqNGjWqb8pbbrnFVlllFVcohjYRkydPdvl8SdhjLrx9eCVpA+EH/ReBQOwlDzOZ95zVZh1fnAJBAeD224cLgJdf3kj0o8zsBw3eyAOA9dPw4XnAzM4zsyPMjHK8hHxSlWlWg00BYHEfhEBmEgAGshF5zRAA5lWueueVAYA8RK200kp9uTrtVEv2DwQECQ/1wVf8f3K0BMFGOYCcXA+CafsFci7Htsr988a1qxaaBEIBYLtbomGY7Mxjjml4HgBI3hfN7aswGuXQxrouYIsiMFtvvXXuoipJAKQPIPl95O8BgFQI9SGhaATEUSmUQjB4CwFCwjnHjBnT14aC+YDJ5GucC/Tdc8899t3vftcVl1EbiDDuuqAAcIcdwgXAv/3N/YuV0gOYJwS00Q1B9bL3zGzXWhGZk2stf/2xJNJSZpzcwlqVtjDuK1mRTwEBYD7dgjlLABjMVnTdkDIAkIewFVdcMVMj5UbeQP7V8J7BVBC48sqzDmsWjulBMG2/QA+AyYs3g8F2AJicIwmArTyFFcoB7Ft+WvhNe1xCo5nf/W7XP09FGFAlACR8kzYQ22yzTT9vWxadPADiGaXn37333usiDPD+4d0jXNYPfnwCCGkLwiBPEC9gEvb4ziNKgbDUZJ4frwGLX/ziF+2cc86xL3zhC1nM1LElKRAUAH7uc+EC4EUXsQNZ+gBSBIaWD7R+YPC7K8W5f9akCEz9DgN35P/9w8y+aWaLmtnSdQddYWa/q7WP6F9yvKT7RdOWq4AAsFx9S59dAFi6xNFcoAwA5Fd3fqlfeun6fwvay5L0BlKegTgS7wVsstB1AAAgAElEQVRs6w30AOgv0wwE28Fasil8M1jkGkkYbDdnMwBsJglgKABsf8NwREqdYgFE8tOobLlw2tzXdCp15Sjgbfz48TZ27Ng+D1xWQzwAAsZU83z55Zft1VdfdRU7eW/11VfvmxKIIyeQ9xgAHT9IESbqQ9KBRrx9hKUmwzzxClIN9JBDDjF6SyoENOtOlXN8UAC4yy42ZF6cZ2GNKe+8Y0PPPx+jsgAgbSAo+kKvPkCQNhCfNzM+ULR2oEUEBV/I62N838xuMbNHaoVi6B+4Yy3003WhbzCUAxjWrdKxNQLAjiXs7gQCwO7qH9LVywDATkPy8AYSBkrPwHcSENjWG1gPgF7oehD0oaJJ0Gu0KbzfCgCT5wCDaSEwrXer2XHP+Hz7ku+kjG0gUlnT6dobXSQlALaqvjrzgANSmT8QB1Ghkt523os1ENcs6xp8v1xzzTW27bbb5i6qghcR0AMAqdxJARg8fYsvvriDSt/wnTUAccAzLSAYFHXhWP6fXoAMoJAfqQDA5GB+vIpHHnmkPfzww2VJonkzKhAUAP73f4cLgH/4A8pmAUCOpwWEbwR/t5ntX2vhwHvX1XL46P/HoGLXZ2pN4183szvM7HtmdleLLRUAZrzfQz9cABj6DrWxTwAY+QYWaH4ZAEgpdrx/FGroZLxXA0EgkEIxQGFLb2AzAPRGeBCszxVsBYJpC8ZwDV8w5hXf5KLJ6rsJQVk2pBkAPsIPwImRFhQ5pZtrT3vtZFjp1/lxfGBHlQAQAMOjud122+X2qAF//CEE1FfzBNBee+01l6dH0Rc/OGb06NF9TefJEQT2AEXaRQDVvEa1UEJAkwM7aStx4oknusbzGmEoEBQA7rlnuAD461+zYVkBMIxNlhXRKCAAjGarGhsqAIx8Aws0nwcrKvUVOSi/TrNmCnN0OuYcNMheq4EfENgsJJR6o1ttTJXqFjmA3pi8OYDtPIaNKoY2gsG0IJIFmNJ6wbJsSFqwq7/2gw82v0ratac9jiulXXvaOdvMN7NkKMRjRq6bD2PMsmWhHYu3Dc9apwDID1V493w1Tw92FP5JhoByLTx9CyywgJNi+vTpxg9SfBcR3ol3kP/ecccdtsUWVLv/z0B3QktPPfVUu/tunCHtx+mnn24nnXSSvfDCC27u0047zTbccMOGJ5JXuMcee/R7j6qmQHJyPPDAA3booYc63fB+ktN48cUX2/LLL9/eoAoeERQAfv3r4QLgmWey+wLACn4GQlqSADCk3chhiwAwh2gVPaUMAOThivCsIh9Y3qg1kK8PCWVbeHxKNpxwINiuLx9ewGbHpMkBbASD7VpGeBhMCyKxAmCzzwpgmHbtaY/rAgD2W16t2AivzdyFlJrOBzlzG2ywQV8YY+czdm8GQA1wAwDzDCp58h3lC7ckWzcQ2kkfPx/ayfx4T8kTnH/++d3lgL0777zTVQ3leI5lTgCPufzgNc6l+udZZ53lPITtxvnnn2+77babnXHGGe6ap5xyil144YX20EMPNWyBAwAecMAB7n0/yDPkxzI/6FkIQO6555626667unsAbyT2t2ur087eWN8PCgD/939tSK2fZEh6Tpk504b+/OeYJAAMaWMqaIsAMPJNFQBGvoEFml8GAPLARRn2FZq1ZchpP95APICNIHBWxs9/RltvoLetFSimyQFMgmA7APTm+VzBNLl8aUEorRcsi/ZpPYDMmfb6CWBypkyc2NiitOvOcu0y5qxfT91q8kAhAAgELLTQQll2K8hj8cBRcIUcwDzDA6Av3ILXjhxABi0bCAMF5Ajx9BC38cYb2+DBg90x5PR52HvwwQddtAOh6VQSxZvoh+8NSAP4P/7xj87mdgPoA9R/9jOKJs5qMI+ncb/99rPDDjtsttMBwAMPPNDeaNFWhsqj9Cb83e8onKiBAkEB4P77hwuAp56KXAJAfWxKVUAAWKq85U8uACxf41iuUAYA3nXXXS7XhlYQZQzvDfQhoVzDA2B9pdCmIFgPp81A0IeLtgv/7KRpvBepERCmhZa0AJZlQwYCAOvt8UCYdt2cn3btZczZBgD7lpe49sz/+q+Wu0DbBOCiCgCIB44qnOPGjcty5/Ud6wHQF3NJtm6YOHGivfnmmy7SAPACwMjj83mCTML79913n4M9n/tH24hHHnnEHecHoZbkD9Jb8M9//rPzBrYaeCTxMl500UW2444UQpw1dt99dwd4l1122WynA4B77bWXfeQjH3G2kqt43HHH2ciRI92xvEbYL1VIqWbK9yjfoYcffni/a+QSMuKTggLAQw4JFwB/+EN2WQAY8b0eg+kCwBh2qYWNAsDIN7BA88sAQH6Z5+F1xIgRBVraf6oZgwa5wjD8IUeQrl8zaoekgkBKx9c3iOf8ehCszxdsVzCGOdo1jk9bLTTNXF6WtBCUZUe6AYDeviSsNfMSZl17IADYaAuSUEjjdLxYC2axN8u+DuCxeOAIp6QPYJ7hAdDn/CVbNwCAjClTpji9ACjy5nye4KyPzxsuhNLDHucwJ2DKOX745vCA4ZVXXmlXXEH7subjueeecyBHWGlyHuANG6iEXD8AYXoSUowGMP3Rj37koBP78EqSR0jxLMDy2GOPdVVKL7/8cvvOd77jgLQ+ZzGPnjGeExQAHn64DSnju7bDjZny9ts29PjjmUUA2KGWOr21AgLAyO8QAWDkG1ig+WUAIOFVFGFYaaWVCrS08VQvDBrkQkJ903iOAgSbQiAHAHmJ3mEtQTBvwRhvbiMYzAKAjSCgkaewjIeSUACw2V3kwTDt2rMAVdo5c3gA234oanbOrCtS0va8AA8AdMgJ3nrrrXNZ5wGQUFKgKjkPnj1y5AjbpBUEfRMBqmSeICGi5Nx5SANIsYfiK0lw8yGmhIkyx9///vfCAbB+Qr578UaS63fMMceYh0r+nzBUPz71qU+579Pzzjsvl4axnxQUAB5xRLgAeMwxAsDYb/YI7BcARrBJrUwUAEa+gQWaXwYA8mBGDs7K7doyFLQOQkIp+UDzBWAQ+GvrDay3rZE3EPva5fWlKRjDPGU2jWf+JNw8QeulAkY3AbB+Ta2WUw92zSo4RgaAzZYcExjigSOUsb7nXtq70wMgHjuqCydbN/BDE7nGeP5oDo9njYIzyTxBqnridaOIih94JAG+ZBEYH2LKdxeASBhoq5EnBLTRfDvvvLPLXwTumBPQow/h975He7VZg4qghISmyUtMq2tMxwUFgP/3f+EC4He/KwCM6caO1FYBYKQb580WAEa+gQWaXwYAEmbFL+zJ/lwFmtxwqvkWX9xmTp1qL1P6PeEBbOoNbAan9SDoj0uTA5i272EWsEoLLWmPQ71m3q369g1Z7CzaY9YJADa72er7F7a6KYteT5b9SXtsnY0zN9qo7I9Z5vnpq0dIeH3PvbQTJQGwvnUD8y622GKuOiahmOTSAYXJPMFXXnnFHnvssX6tGR5//HHjTxIUCTHFw8g1aMNwwQUXtDWRPE2K9dD6gQGIko+47777NiwCUz8hhWew+ROf+ISdfPLJ7m0a3RM5kSwCs9NOO7kf1JJewbbGVeiAoADwxBPDBcBDDxUAVui+D3UpAsBQdyalXQLAlEL1wGEUP+BBpMgxadIkV6lvtdVWK3LadHMNGmTT6prGN/IGbvXlL7eez4NgPSi2AkEKy7TL/+OqSa9iyE3j2wFgEhiLBqYyALAZWDXyGBa9nrRQl2XdaW0cNMhmrr12us9PwUcRgolXLW/+mgdAQjfxJI4ZM6bPQv6fFgr0AqQxPD0H8fgl8wTxDD7xxBOuWqcf5NoR6glo+V6lhJjiYQQkn3rqKfv973/fVgnaQFD05cwzz3QgSBsIwJG5sYsWEeQJHj8rN8uOPvpo54kkMgLPKP0DL730Uged9Ppj4HncZZddjP6CPgeQyqEUt0lWLW1rXIUOCAoAf/QjG1KrMBuSxFPeesuGfvvbAsCQNqWitggAI99YAWDkG1ig+WUAIL+g098q2aC5QJPbT1XLC2wVErp4bZZRrUAQCGzmKWwEgsnKoq1AsFlYaSdN47MARlpwaAeAyZ1IW7Qlbc5cFhBKu/a0x3HtVs3sk+vu5nrS7mPa41hXbc9n1ipTtv+wtT4CIKPISRLcsszpAZBcQrx7ybBN2s1QNIU/wN8tt9ziCrwk8wTp6/f000/36xUIAAKF/PBFHuCcc87p2kUAlBRbweazzz47lZm0gPCN4NdZZx3XRB7PIANPJK1wqP7JOOigg+ySSy5xxV7IV1xvvfVcsZd1112337V+85vfOGgkt5Ef0Y466ij79Kc/ncqeKh4UFACeckq4AHjggQLAKn4AAluTADCwDclqjgAwq2LVPb4MAOQXcB7EKHDQrTHf4ME29cMP++UFYgvewPruai0hEABslh/IhEkQbNT3sBEItssr9KIBhGmhJe1xzJ0WCPICYKNN9wVbuglMWTRqdOztt8++sm6uJ+0+lrXnKT/cV736al8OHt8LDP/f5N/r3+P//WsAHqHlSS8YnjM8bEvVPk/0+8PjmARAYOvZZ591sOUHxVZ4nTxAAA2ApJIoIaVU/yQfEK+eRhgKBAWAZ5wRLgDuvbcAMIxbttJWCAAj314BYOQbWKD5ZQAgVff4dd2HNRVobvapEs3jfWVQ3zOQXMHkaAiCSQ9gOxBs1fg+CYJpARDj0jaNzwI3acGhSAD0QntgatfagePTrintcWXMmSWvMK2daY9Lu49dBsDLnn461eeWqAE/+Lv/f3Lr8IThtSNHzo8JEybY8OHDXQ4g4/nnn3f5e3gbKazCAPbwAia9bAAhoaGLL76487IRvgkAAph/+ctfXPi6z+tLZbgOKlWBoADwV7+yIfNTciysMWXGDBv6P/+DUWoDEdbWVM4aAWDkWyoAjHwDCzSfXBTCnxhZf51vdg4PXACg/2W+/tf+tL/++2UmPQGNrtlu/rU32qgvL5DzAcHUENgoBLQZCAKA7YrFZHkY59hmLSPqW0GkhYYs1y8TAOvv4UZAmHZNaY/jmmmPTXtcvQewkZfQrzXtnGmPiwQAr3njjT4PYD3kIU3ytfrbgs82oZ9UyKRQS7J1A9U8aZQOyDH4HgMKKT7lc/s87BGe6QfQh6eQAiz05iNUHegDHmnsTg9TX5SlwK9aTZVTgaAA8OyzwwXAPfYQAOa8x3RaegUEgOm1CvJIAWCQ29IVo2h8TNGD+gcx/1DW7r/J8/yxFFTgV/uhQ/kxsv8DXrv56t9vNH8zT0GrY0esuaaDQBrH0yrCAyAwOJsnkH9IfUN4+gXWN4f3O1UPgkkPYJqG8e0KwLQCwPq7BRBJ2/4hLTgMJAA2AsK0IJT2uIEAwEafYg+Fae1Me1zafcSmtMdm2fOU31jX1Rq1pzy832EAIDl5hHsOGjSoHwBStZNCLlQCZeDFIwwU7x/FVsjt87BHiwg/+L4jp3CttdayJ5980qgUCkhSSOYPf/iDK+By4okn5jFX55SgQFAA+LvfhQuAs/LZ5QEs4R7UlP9RQAAY+d0gAIx8Aws0v4wQ0EcffdSAwORDV4Em557K5wXO26RXYBIER3kITDaMbwSCSQhsFALaDATrW0Y0g8G0TeOb5aI1gsIyYCAttGTJmas/toj+fmntTHtclvX4OVt5CbNAatp97DIAXj91ar8+fFk+wAAg3j9gj++qZA4gRV9oAE8vQAbRDIRxAn5U2iQ0NAl7/rpAH97CUaNGuTnpr0cuIHmBFGABKmnMrhGGAkEB4IUXhguAO+8sAAzjlq20FQLAyLdXABj5BhZofhkASN8tHrDW7lLp+XbyzLfiijbz+eddKCgj2StwNm8gnoB68GsGgq1yAOtBsFnPwHoQTAuALCQtjCThpl0OW1qPUBnAlGY9QGHaa2eBq7RzprHR35Ct5kxCYdprRwKA/5o2ra8yZrvPZv37HgCJVMCbR2VNHwFAywYKTVFRk0HPQcI4l112WXvppZdc5c8k7Pm56QFIURmfo8zcnMv4xS9+4X64OuKII7KaquNLUiAoALzkEhuywAIlrTT/tFOmT7ehn/mMADC/hDozpQICwJRChXqYADDUnRl4u8jVAwKLHDxgEWKVzLspcv4i5ppvrbXsw8mTXTgoIJjsFZgbAskXbBX6mXwvTdN4YLBsAGwnJpCRpiVCWmjJAkxpj62/djNPYcgAmNyHtJ7CSADwhunT+zVib3fLJd/3AEheMXCH187n/OG5I49vWO0+Ia+PME7Aj/f4/gHsyB1MFqTiByoqgPoqxfwdmJx//vmNtg60cTj88MOzmKljS1QgKAD8y1/CBcBPfSoPAH7DzA6mO62Z3WNm+5nZbU228ytmVt8fZSYB5onjf2BmXzCz5WrZFneY2XfN7NYSbxFNPYAKCAAHUOwyLiUALEPVOOcsAwCp1sfD2OjRo4MWpRUEYni/kFCfE9TKG5gsGNMOBNMAIEb4iqEvvNBey7zA1GrmdpDh4TAtAHKttHamPS7NtT0Upjk2CyhmWU8R164PH223P35v0x6X1uPb/m7sO+LGGTP6NWLPcKorTEUIKABIaDmQ5it63njjjS6Pz+cak8vHMQDc5MmTXTuHBRZYoB/scW2O4UcvKov6QU9BwtZ//vOfuwbs3/rWt7KYqWNLVCAoALzyynABcNttswLgLmb2WzOjfwSARiNB4kj5YLzUYEsBwJ/W3vdv09flxcSxX6yd+5iZDab9ZW3Olc3s5RJvE009QAoIAAdI6LIuIwAsS9n45i0DAMm7ocx6svdWqMrMt/nmzsM1c+rUliGhS9cWsGirkND6iqGtIJBj0xSBadQyohkMFglMWcEhee12LR7S2pn2uLRgVQ9rRVTsHGgArP8gJdd+yy3NP2ZdBMB/v/127u8CD4C+n9+0adOch2/BBRe0G264wXn5qNrJ4DuH6APaOvjG8PT4Y1Dp0w/gkHnJH/SDUFHA8Nxzz3V9BPfdd99Qv7J6zq6gAHD8eBuS5ftmgHZryrRpNnSbbbICINA3wcz8zT6nmdGz5TQzO6EJAJ7CT3gZlkW9tTfNbKyZjc9wng4NVAEBYKAbk9YsAWBapap/XBkA+PTTT7tf7HlQi2E4CHzmmdnyArE92TLCr8VBIKPeG9gsB7ARCCZhsRUItusZmITBMoApLTi0unY9EKa1M+1xWR7IWs2ZJw8vJABs9WFLevZagW/RHsB11rGbb701dzQAoEaIJj3+yOubb775XMsGvHfXX3+9A0tgkMH7/Pjkv3coCAMI4iFMwh5houQR0i7CD9pFEBpKsRmgce9ZTbU1AlAgKAC8/vpwAXCLLbIAILXQyHz4nJldmtjmc2uA9+kmAHiWmT1rZsDinWb2HTO7v8ltwjX2N7PvmRkewFcCuJ1kQocKCAA7FLDbpwsAu70D4Vy/DACkWAMPbBtssEE4C01hiS8O48HP5wXyc6f/e0sIHDVq1tvNPH/J1xv1F2wEgu0AMLmuVo3qk8dlAaYiADB5bWAwLdilPS7LetLO6Y9r5VXz60o7Z1o70x7H9dMeWzTYpfg8+UNuue22fo3YM5zq2skQAkpDd9pBjBgxwrWE2HTTTV2eH98xhHkyvJfQRx7QFoJjl1lmmX7hng899JBrFUG1Tz/4ziJ0/ZBDDrGvfe1rtt9+pEJphKBAUAB4003hAuCmm7Jdy9ISM7Fv5Ojxp34sUwO5Tczs5sSbPzQzSHKjBudsbGb8anJvrd3Et81sjJmNNLNnEsfvYGZ/MrP5zex5M9ux5mkM4XaSDR0qIADsUMBuny4A7PYOhHP9MgCQX9N5oCIXJ7ZRnxeI/cSwJCuGJtfUzxvoAZAD2uUANgLA5MQeBrMAYP2xzfoCpoUG7CkaAJkzbbhoWrBizrRrSjtns+MaAWHaOdPamPa4LOvuIgDeOmFC7oJQHgD5TqGgCxU6gToKwfh8P/ICGfzoxJ9k7jF5gryffI1iMngSgUk/fLuIz372s7bJJpvYxRdfHNtXV2XtDQoA77gjXABcb71G98BRZkZhliIAsH6OeczsATM7z8ySZXP5RYbYaxp0/o+ZbV0DykZ5hZW9b6u6MAFg5DsrAIx8Aws0vwwA5Nd6HqhoxhzjqIfAoQsvbB++/np7CEz2DPQLbwaC7SqG+vOzPLi3g0UPhFkAo2wATN4geUNFs4BQWlhLexxAmPbYtLqnPS7LurPcRyV8aGe++26uWT0A8oOSb97uC8JQ5AVYo0E8g+8d3vNFYnjtrrvucg3iN998c9cfkDFp0iQbPHiwa/7uh28X8aUvfcl5FO+9FyeHRggKBAWAd99tQ2o5pyFo422YMnWqDV1nHf43rQcwTwhooyVfSDKEme3aQo/JZvYbMzs+JM1kSz4FBID5dAvmLAFgMFvRdUN4wHo358NZM+MJxaIYw8YbEzES50hCIKGh9sYbDgIZybYRfnXOE+g9gPW5ga1yAFt5Ct0/5/x77uLb2gvZDgD9DB5Y2vUA5PiBBMD6FT6TjCpqs/y00JQW1tIeZ2ZTps0CC8aQZyY1NzStjWmP40ppj40YAPl+AtAoAEMbCL6zaNtAaCihoHjzGHgJqQSa7D96//33u9BRcgaXXHJJdxyvkTc4fPjwvr3yvQG/+tWv2tFHH214AjXCUCAoAJw4MVwAnPXvz1C+klLuHEVgaPng4535InvKzH7WpAhM/bRz1fL//mFm32xxzUfN7HdNPJEpTdVhoSggAAxlJ3LaIQD8f/beBFyK4lzc/25cEBEFVFziBu4LKi5R44bidtG/migqMW6JWwwqGCUuMUZFcEFEUYN6E2O8igJX8cY9imhMwB13fkoE1AQRRQWOmMR4/89bZ+pQ06d7urqne6a6T33Pczycmerq6q+623rr21IqroSH5QGAeoee3fkii0oOg2h3zDgIfOihZZcbVluxVgxgFAgGy0XUAsGkABg1OSYYNhEAv+nWo2qE33qjhlXGFoRswc62XQAAgyqtAkLbMSYBO9s+CwyAgB4eBZRpoO4fQsIWYvb22GMPWXFFjBkiJJ/SbqJ6HoA9Sj7QB8lhSP7y+uuvq8QwG2ywQdt06dqAxx13nFx77bVyyCGEMXlxQQNOAeCsWe4CYGtYQRIApAwESV9Oq4AgZSCOImlupbQDJSJI+KKLYv5SREg1PKuSKIb6gcT34XvKzheun9T8+99K7B8uoNQZpDQEbaKSxbhwm/kxWGrAA6Clolxt5gHQ1Zlp/LjyAECy8ZFqnd35okuniy8WIWsigAYIBiCQ62uXIEaDYBgEcgB9RcUABkGwVr3AIAxmBYDmpJmAUasgfAJgsnWZDAJg2L3UBoW2IGQ7Ttt2MQBojpmpXfdrNtgtxPZ6bNsVHACBPbN4O8Xdp0+frrKA6jqAOo6P2oBayASKtY/viB+kaDzunT169JD1jGeLeEIsjUcffbTccsstsv/++1tMkm/SCA04BYCzZ8uqqxIV7pbg5rxaq0tzEgCkPSUgdCH4GZWsnbpo+1QRmSMi1P9DrhOR71eKxuMOQ5F3Mny+UvkeX+y7K/F+wN+nleQvw30SGLful3pG4wGwHu05cKwHQAcmwZEh5AGA1OMi0x6782UQFo+bnnZaFQRyXVFxgasTI3IPSdAqEgaCcbCmQdCmYLwGwbg+9XgSwE2si6GGwiR9Wra1AUB9SZ9/3vqvHp9Tf7iGWJ5b9WDZ1nQBrXXqMCNvJBDagp1tu4IDIC6aAJqu58e/qQNIhk/9mXYT1VZC5gLY6969u4JHSkIAh6+++qqsscYa8u1vf7ttunRtwMMPP1zuvPNO6devn9Wr66abbpJrrrlGZSDF9XTs2LGqDmGY/O53v5OTTjqp6ivcV4ll1HLiiSeqWoSmHHjggfLoo49ajaeMjZwCwA8+cBcA11+f6U8KgGW8Zfw15agBD4A5KrcRXXsAbISWi3GOPACQmBtcr/baiwzRxRdd13D3a69V9QK1JZArC4NABYBILQjUCWM0uUSpyRJC1OHAok3cXJI+LQHjS5XxW2Tlj2IAjEaW508DgEE1tgNCy3Orfizb1gOAYdOuoNBS79btCg6AuGiSsIpYPgSgowwEiV3YaKKsg47j22qrrdrUCuytvvrqCvimTZumMhOzOUU8IPCoRdcGHDBggNx3330quUyc3HvvvXL88cfLuHHjVL9jxoyRiRMnqv579uzZ7nAA8Oyzz1bfa8ElVccm8hkAiAv97bff3tYGSARiO6o4BYDz5rkLgOuQeNMDYEd9Thp13R4AG6XpnM7jATAnxRaw2zwAcOHChSrOZu/WwrSFF7OuYadBg5ZBIFcWcAntHMwQZ0Ig7bU10MwYmhUEBq2FUTBoCTZq4ixBRAOgOdmRMGh5/iwAMHjz9ZCF9vej5TizBkAGGGYt3GyNkLFbzo8UHABx0aQovC7ojtWMRDC44wFQ66+/vooLNN1E0eOMGTMUjAF7bEqtsMIKKpkMf69tWMyBMgrM9+/fX1nboqx45s0D9FGH8MYbyZkhKjkN46CG4Pnnnx8KgEOGDJHPazzvACDfT55s1ua2v2XL2NIpAPzsM3cBsHWTwFsAy/gQOHRNHgAdmow0Q/EAmEZr5TwmDwAkEQM777ZuVK5rFgAkxbxeFMZCILEYwaLuQWtgWMmIqIWhjhcMKxRvKq+Wu2gQBi3hxhYAGUYYBOrhVcGg5bnzAMCgint3qwGEluNsFACGPSeG96Bsu8U/az9KDYLAt2Yuy4qqB7TxxmG1qOOffF0GYtasWcrat0nlWcCdkxjALbfcUln+KDkDAJLwRVsJ6f3ll1+WddZZR/0QJ8XflHkgAYxpedO1AbEmUjvQzCQaNkqSylBfcNKkSYLbqJYTTjhBAdwDDzwQCoAnn3yycj3luqhNOGLEiLbENhwAAAJ/JLbB6rfvvvvK8OHDlRWzo4pLAPjZZ184C4Ddu8N+HgA76nPSqOv2ANgoTed0Hl5tplEAACAASURBVA+AOSm2gN3mAYAsgKi/tc8++xRQI+2HHFbYvtMpp4jo+LdAcpj/0PXFakEgNZuiMn8GKSWYMCYKBG3iBbk8YNASbrICQFOrK39lZ4VrBAAGZ7sKCC115AoAxj1sUTmJgscl4UTbtvUCIDF6uHluvPHGargkgXn++eeVmzmWQOIAee/wPtNWQtpRNB7g0tY+AJBjgUSKyWvRtQHZ5OHdBVjWEjaE6Jdzm+Vuhg0bJk8//bQ895zOo7GsF1xQuQ6S0VDTcNSoUfLMM88oy6ROSHPPPfcosKRGIVbPCy+8UCWx4VgslB1RXALABQvcBcA11/QA2BGfj0ZfswfARms84/N5AMxYoQXuDrcqdrOzFBY3L774onKnKoOw2CMOEJcvUxQEAlNGhlD1PcCmIS0KAluL9tpBYFTG0GDftgBI5sqvW2P2Vv3q49pTZOtiGGMBNE9iDnuDVaJhsBkAaI6z90bfWN2+HgDj1VQvAOKiSSxc7969K4/NEgV3uJljAeSdAzgRU7fpppu2DYj3ENY+HZNHgioyg5IoxozT47OuXbsqyx/n0pbGqCtLA4DBvkhkA2gOGjRILr/88tBTYdUEep944onSvE/j75bqFi4B4AcfuAuA66/vATDpveXbJ9eAB8DkOnPqCA+ATk1HUweTBwAuXrxY7YDvt99+Tb22rE4+b948VYgaN7OgtINADWtY8WpBoAbAWhDId/QTBYB6MPo8KQDQvJ5QGMwZAIP6DAKhLQTGhVHq89i206o3x7fD9uFAaAuAcVNtnivKOBzUl+kCGne/F9kCOHPmTOncubOyjCG8Y7DUYQEk7g8LGYleVlpppSp4w0rIMdrax/tu6tSpKlbPhDxilrG0AYA862aNwDC9pnEBDetn4MCByrI5fvz4yOlj7LiBnkYm4g4oLgHg7NnuAmCvXh4AO+Dj0fBL9gDYcJVne0IPgNnqs8i95QGAJFlgQVaWWloAIHXITFcvc847DR3a6g6q6/sZheMjITCspl7Eqv+bTTaTb31u4TZpW7TdsADWuncVEDYYAM3xAIMuAWBQVxoIPQDGvwHrtQASo0fs3kYbbaRORjwfccZ77rmn+hs3St47wJK2EvI5G1FY0IBDLZSPALrY0MFiiNAX/ROXx/NuJoiJujo8AnAZpfQDgvsp4Dh48ODQJDDBfshqiiWSzKOjR48OPQ3xx/RJXOChhx4ar+gStnAJAGfN+kK6dnWvDuDixYtkk008AJbw9nfukjwAOjclyQbkATCZvsrcOg8AbGlpUSnaDzjggFKojhpfuJlFASAX2QaBJHfRZqZKbKBSAv8Oi90LgmAIBAKASBwELpQeql2PlYKl6dtPg3YBjZsg02K2wRq1+62VBMY8T1wum7C2psE0bMy2lj3bdpzDti3XE2IcDlWtrWXPtl1RLIBz5z6i9MH7xua32QYYA67I+Kktc7h84ra5++67q/4AQlxC+V7HCfI5iWKICaTwuxaSvABfxOLp8gpkC8V6CNBRxsZsH/WMUAaCpC8Ujuc4ykBMmDBBsFaSYIYSEcQJjhw5UnVx2WWXKejE8ki8IvUDATvGTekKAPbSSy+VI444QgEoMYDEFGLtxEKJC2xHFJcAcOZMdwFwiy08AHbE56PR1+wBsNEaz/h8HgAzVmiBu2NhRVIEm0VZ1OIteCwp2km2ADDZLvh0u3rGETyX7qvevgFaFmykjq/V1w5z5pB3vqpOoFkzMK01UAOgvs2iQFADoG5XCwTTAGDwNg8CYZ4AGDx3EAhtYc22XVIADI4vCghtwc62XVEAcI01WmNNtcVN/w77zGzDuwQ3zm7duikrHq6bCJmGsQqa9fpIqLLaaqtVZfAkSQtxdmYdPQAQwCNjKBCI8L4CsHhnAZPEA9oIJSB0Ifjtt99ebrjhhrZYYbIgY7Gk/h8ydOhQVWOQDSXGs+OOOyrXzr59+6rvyWxKRlFcW/X7hk004gPNjKU24ypTG5cAcMYMdwFw++09AJbpvnf1WjwAujozluPyAGipqA7QjFiWRx5p3Z23keACLuxvIInFFanMbRd6cQtBPTab82fdFwszLA5m4WjGEXVt20yYsMzap2MBTasgAzTNYDvtJPLf/91e/RUKCAIgDcMgMAiAZodBGMwCAIMDNrzsat5KaSyAcfdmxTMwrpn63hYCbdvZXI8GQluws21XFACsxwWURC48g8CfzpZJrVGStZhWed2OUg762cQTATdLAFILLqBY3LCqYbkjeQxWONxCcSklppB3lxc3NOASAL700heyyiruuYAuWbJIdtzRA6Abd2y5R+EBsODz6wGw4BOY4fCxALK4CoOmqM/iTs8C6qmnnlIuoNTuKrp8/PHH8s477wgLS1vpdMUV9hAIACJhEEhs0drrRp7WBMFaAKg70CCYBwB+9NGyYW6zTbSmbIBJH23bNsjTtebJFuxs29mOkTFpsItzGbUFQPq0hcBmJoFhnGkgkPcTGTeJ8SO7pwZA3DSpDWhm5iWOD8vZNtts01Y3D2sfVj7cR7VQpgHr2/vvv69KK1ASAngEGqm7R3bOjlpywfb91sh2LgHgc8+5C4C77OIBsJH3ZUc9lwfAgs+8B8CCT2CGw88jBhCr4pQpU1QSmDIspEgdj7UhCQAyRZ2uv14Et1AkyhIYNF1NmtRuRb9wpXVjy/YBgjYAqG8d2/ptthBEvyYA6vOEgWASYLJtW6ud5ms9Jttrsm1nO0YTAMMeYRMKmwmAjM32/rBtlxYAidVjQwmQo5i7LvL+ySefqMLvWPC0AIC8zwA5Xcgdt1BcLE2XTrKA7rzzziquEMsfcYS4XSIkZGFDrAwbVxn+b6KpXbkEgE8/7S4A7r23B8Cm3qgd5OQeAAs+0R4ACz6BGQ+fBVaWwg76k08+qcpA4FZVdGGxSbyRzjiY5HoUBJp1As0Mobq8Q5CcAhAIAGqpVZvcFljoi/KFWmq5TybpMwwATV1pGEwCTLZtbdsBg7bXZNvO9txxAGjqKgiAtSyGWVsA0wDgG2/EPxWHHZb8PQMAsqFEMhfeJdrih1Wecg2AnBYSuRBbBxjSDtdObe2jxIMWvBNIxkJZCcCPY+bPn68Swxx55JHK3dsDYPx8NqqFSwD45JPuAmD//h4AG3VPduTzeAAs+Ox7ACz4BGY8/KwBkPg/ChfjTlWGWBrczXBBo+ZYGqkJgazsIYgaEGgCIOePgkCdUHTtteNHaQJgLRC0hSD6iANAfR7axWX11G1t4cq2Hf3qtnFumLbXnuTctpY923Zcj+naGbR2mneCrQsox9gAXYKqI2oYaQCQdwkbSljqSMakwQ5g++CDD2Qn44JJ5IKVkPjAFVZYQWX/xNqn4/wYAxZCAJDkMWT9ZHOHrJ0AHwB47LHHquPN+N74p8m3yFMDLgHg449/IV26uBcD2NKySA44wANgnveh77tVAx4AC34neAAs+ARmPPysARDXqscff1z22WefUqQurxcAma5ICKRshEklISAY5toZB4F0WQsEwwDQvK20VdAWgpICoD5XHAjawpVtO1PVwccoCIS2157k3LZgZ9suCIBZvRpsDPeNBEDgDkgjlo9YQDJp/u1vf1OxfFqARMouYNnDGohrJwlftLWPdrybgELcudmcAgipFQhk8nPqqaeqvj0AZnUn1d+PSwD48MPuAuCAAR4A67/bfA9xGvAAGKchx7/3AOj4BDV4eFkDIIuqxx57TEiDzi570QWLABkD995777oupR0EBuFPk0QAAt/7rymRVr8gCIbVlw8DwTgA1BdKu1oJXUyFJLEAhikyDAZt4cq2XS0ANMcEDNoCoG2ftLMFO9t2HQUAgbuePXuqxC2AHRZAfnQJBfRAIhcyhdKOf1NL7913322z9rXq6mshLhB3bqyECCBJkicA8KyzzhKKr3sArOtVl+nBLgHgffe5C4Df/74HwExvPN9ZqAY8ABb8xvAAWPAJzHj4xNiY9e2y6P7RRx9VwMRufNGFmmMkmABo65UqCISsNLkEf6eEwDAAZMxBCLQFQI4129aCwXoB0NSthkFbsLNtlwTWdJ82yV9tz28LdrbtOgoAAnQUeKc4OsXeseSRnInae1peeOEFVXdvzTXXlHnz5qkYQWqcamsf7YA8rIK4c+v4ZFw/iRXkPUjhdWIIPQDW+6bL7niXAHDixC9k5ZXdcwH98stFMnBgKgD8qYicx/8iRORVETlTRJ6PmL0TReT2wHcE9updXnZUhpNLSUR6i8gXIvKEiJwvIn/P7o7wPTVTAx4Am6n9DM7tATADJZaoizwAEAsgu+wkYii6AIC4lOHSmoW0QSAWQJ0d1CSTFJZADscaGAWAetwaBNMCoHn9QRi0BUD6sG1Lu1pxbXo8tgCWBgDNa46CQdvz24KdbbuyA6BOAkMxeDKAAnS4aGLdwzVbZ/tED7hyAokUjAcQqQEI8JnWPt51ZBRlM8dM9EJcIG0vvPBClfHXizsacAkAx493FwAHDUoMgEeLyO9F5HQeHxEZIiIDRWRzEfk45A4AAK+vfK+//j8RmV/5gwFMEpHbKjDZvdJ+ORGp1Dpy577yI0mnAQ+A6fTmzFEeAJ2ZCicGkgcAEgNIogUz+54TF5tiENQWI1tgVgDIEBQEQlAAoFkk3sISqGEv7FJswSpJQhAbWORSbM+dFADN64yCQVsAqxcAgzrXQGh7fluws23XEQAQMCML6JZbbqnKOQBwxPp99dVX0qdPn7YpoQ3xgauvvrr6DPdPEsWY1j6O+ctf/qKeZdPKpwHwuuuuU/17cUcDLgHg7be7C4AnnZQYAIG+F0RkcGW2Kdr7gYiMFZErIwBwDHuNCe4O0vRiUdxQRN5PcJxv6qgGPAA6OjG2w/IAaKupjtEuDwAkCygZ+8z6W0XVJmnhiUEiq2mW0unVV1tdQJNA4K67ynsnj1DDCEsEM2NG6wjjMoHOmtXarlYJCH2tNgBIWwCwUfGCnM+EQVsA4zjbtrbt6JMpjMsqSrskYGfbNgnM296/riSBwQIIAE6bNk223npr6datm8rauWTJEhVfTNF3LbTBStijRw/10eLFiwW3UN5DXbp0UZ9R4w9QDG7mAIC33XabAkas/V7c0YBLAHjbbe4C4CmnKABcj1vfmD1cNMPqr6woIl+KyJEiMtlof0cF8A4LuQOwAP4XYbMiAiy+LCIXisibNe6W/UTk8Uqfi9y5q/xI0mrAA2BazTlynAdARybCkWHkAYDUAaRGF1n7ii6LFi1SC8n+/ftnfimJIbBCGVEQaK5da0GgBkB9QbVAMAkA6v7iQNDWWmjbDhi0Bbas22kADN4cYUBoC3VJYLFZAMgYk+R4SlsGgsQtWO223XZb9T4B7IgJxNXTtADi8qkhkbFpa996662nSkIglJLgWTbjeXVpCDKNDh8+XLmYEkfoxQ0NuASA48Z9IZ07u/f/tKVLF8nppysADMqlIvKrkM8pLgvIfVdEphnfXy0iZDvbJeSY3URkUxF5TUQ42bkiQm2krQkVD2lPbOCfRWSmiBzrxt3kR1GvBjwA1qvBJh/vAbDJE+DY6fMAwClTpqgU7autFvo/Jcc0UHs4LDiJL6KwfR5iDYEBSguDwDDjRRgIBgGwFgimAUBTT2EwaAt2tu04nwa7uJjBRgFgGBB2BACMqiF40UXJC8EDf/wAdyR80R4F/E0ZB7MQPK6bAKF+52DtwypIshfc0fmN5RBrvpnRV5eGIPvniBEjVAkbQNKLGxpwCQBvvNFdABw8OJEFMA0ABm8Ikr68LSLjReTiwJd89z8ViyTZ07z1z43Hqe5ReACsW4XN7cADYHP179rZ8wBAXKpYsHXvThx4sSVvAEQ79UIgfeASGuW9FoTAKAAMA8F6AVD3aYKgLdjZtjMB0LzbwmCwWQDIuIIAWMtt1BYWm2UBBPSSWADTACDun7iBkrmTDSUdU/zaa68JsbkkeNGxfLQhKYz2OiBhDBs3HLPuuuuquEGeZVw8OU6LzgI6Z84cmTx5sioU78UdDbgEgKNGuQuA556bKAYwjQto2E0xkeoqIjLI+BL4m1DJBErcxKfu3E1+JPVqwANgvRps8vEeAJs8AY6dPg8ApNgyLls6HsexS040HKwGWBL233//RMclbRwJgZpuNtmktcsQKtLWwDlzos9qQmAcAJogmBUAmiBoC3a27aIAMAwGXQLAWvdInButPjYvAIyy5JljTgKAHJcUAjUAUrsPa5+O5aNuH/X7zE0m2uywww5tkKitfbh/Uj/wO9/5juDOTU1PSkNo0aUhZs2apax/ZDD24o4GXALAq676QlZayT0X0K++WiQ//3kiAGSCSQJDghZKPyDE9ZGo5caIJDDBm4LsnsT/PSwi51S+1PCHqyhpsxe4cyf5kWShAQ+AWWixiX14AGyi8h08NQsg3KCyFBZjuFHpjHxZ9t3ovogbwuXsgAMOyP3U1hDISIK1Ak8eIS++KLLGGrWHCQjaAiA9AZWaPWv1nATWNKjGuWsm6dMW7OjTJmGLbX/oxLZovK1Vjz6/+ir32y30BEmgLklbTpYWAKnTRzIXXVaGDJ+UZ+FvnQiGTScTErH2kb2XwvHEEPI+ovTDm2++qT7ToktDvP322yoD6IMPPtgcxfuzhmrAJQC84gp3AfCiixIDIGUgSPpyWgUEKQNxlIhsUSntQIkI4gQvqEzML0VkuoiQQoxMoNQPPFxEdhSRt0QE+KMMxA4icohRHoLDF4rIP/0tXnwNeAAs+Bx6ACz4BGY8/DwAEHcs0raTqKHoAgCyMDzwwAMbcimREIjVQhNRRK3ACdu3ZghFaqk+CYiYVsVaIJgE1oKWyigQTNKnLbAF+6wFg7Z9egC0ezTSACAxgAAgcXxk/kSwAPL5/PnzFcwRD4jrpgmJWPtwFcXaRwF5nmMSwlDnb7fdyGfRKjpZDJZB4gPvv/9+q4u56aab5JprrlFJY3A9HTt2rLIyhsnvfvc7Oemkk6q+6tSpkzp3mJx++ulyyy23CGUphgxhXd5xxSUAvPhidwHw8ssTAyA3FSUgdCF40t+eVbEM8t1U9v9EhOyfyHUi8v1K0fjPROQlEfmFiLxS+X4jEZkdcadiDaQ/LwXXgAfAgk+gB8CCT2DGw88DAAEmUrKXIZsesUQAbaMAkOkNhUAKxyMZQKB277MpAxHlVhqEwSSwFtVnEAST9GkLa1F9hoGgbZ8eAO1eSmkAkPcTHgWAHtCEAHEkdQHyiDPecMMNFQACdp07d1ZtiBHU1j5AS5eJwB10V2OydWkI4I8SE/fee2/sxdDm+OOPl3HjxinoHDNmjEycOFGNq2fPnu2OBwDPPvvsqiLzxC6utdZa7doCoJdeeqksWLBAzjvvPA+AZsHG2JnJrQF+n19ccIG7ADhyZCoAzE1hvuNyasADYMHn1QNgwScw4+HnAYC4TFKUOWwxlPHwc++OBSIWiIMOOij3c5knqIJAHRAWtPyltAQG47tqgWCtuELGWyM0MVJfcX1yIDDYSAA0B6v5wANg7Vu+ES6guGiyAYMlD0sfgrsmMEiCF2L3gDCeURMScRGlHZZD5I033lCu7sCgaanTpSEARODwv//7v2Ofc86Hu+mNNxIuJarf9ddfX84880w5//zzQwEQSx5QWkuIa6Rv4hAPPvhgBX/eAvgfLqw5FQAOG/aFdOrkXgzgP/6xSK6+2gNg7IPrG9StARcexrovoiN34AGwI89++2vPAwBZTPXq1UvWjqtIXoCpYMFIfBEWwEZvRrdB4PbbL6tz0GAItIE1pnGVVewn07ZPADAuTlCf1RbWkkAlbY18IZEX6C2AdnOfxgL4j3/8Q7lgk7lzhRUIMxJ56623lKUPyx/xfSR6AfBMSFy4cKFyFdXWPuCLDKDEDZoAqJPF8IxzzG9/+9uaFwOQ0sekSZPk8MMJgWqVE044QQHeAw88EAqAJ598sspECiySrIaSE2a5CT6n1Mxhhx2mrIUbbbSRB0AR3rkurDkVAJ5zjrsAOHq0B0C7t5BvVY8GXHgY6xl/hz/WA2CHvwWqFJAHAE6fPl0tztZZZ53CK1sDIElgSCLRaFEQqAFQU04KCGTcZlxgVJbHoDXQFtZ0YhmGGie2fQZhrRYM5gWA5rVEwaAtANKXbfxls5LAMEZby55tO/qcOVPkzjvtawFSoJ13EwCIRwG1+5ZbjsSDolw7yQgKJL333nvKFRR422uvvZRrKPLJJ5+o2D8sagj9sTGFaKsg/9alIbC68awTe1dL/v73vyuQAzzNWMJhw4YpKySlJ4LCeUlcQ2bkL774QkaNGqXcWrkO4hKRkSNHKjdWxgHzeABs1aJLAHjGGe4C4M03ewCM+/+O/75+DXgArF+HTe3BA2BT1e/cyfMAQBZBuERRf6vowgKUhVmzAPDNNzsJ4X8rL//PVitgEgjcYw+ZsGRA1RRoCKyV5t+EQFtY4yRmdtFaIGjbZy1rXRAGGwGApiJNGPQAWP2UA3th8qMfPdoGYxrKav1mw4VkUiRo6devX9sGDNY+6v1tsMEGChCBMQDPhERi6GbPnl1l7cMllMQxtNOGJYCM/v/whz8owCSZS9YAGOyPdy7XNWjQILn88stV8hlcPl9++eW2d6YHQPcA8LTTvpAVV3TPBfSf/1wkt9ziAbDoa40ijN8DYBFmqcYYPQAWfAIzHn4UALKgMhdn+t/681p/42pFggNcQOPa2ywEg2Np5N8UiibOaJNNNqlyAWUMcdcW/D7ttfbosatgKEgEgQahhEGgTZ03QNAW1oIAqG/TMBC07dPGXVODYKMB0HwMAUAbV1GOKZsFMAr2wl5TY8fOVx9r+Ir6rY/FuofLJda8ffbZp+04snuS/IVNJoT3DRZAExI//vhjFdO3k7FT8OGHHypLnFk/ELdNXEpJ4rLaaqvJtddeW/MNm8YFNKzDgQMHKmvl+PHjVRKZc845p8rDgPcOAMw1UqS+o4pLFsAf/9hdAPzNbzwAdtRnpJHX7QGwkdrO4VweAHNQaoG7ZPH0wQcftMGMCS1ZXVbcgs/l74nNYTEJzJouoIzZDE/J+xq6dt1cunVLAIEhNRtMELSBK+afBb5OQBp3P9SqL2iCoO161naMjIuC9TYQlqRP27ZhFsCosRQZAIObBkniPpmjpC6gwNnzzz+v3k377rtv2+336quvqhqj2n2SUgxAnGkB5DOSquy4I2XKWoW/586dK127dpU+ffqozwBHsnfefffdatPqqquuirvNlVspcYTaWsg7Amvk4MGDQ5PABDsE7oj/GzBggIwePVo+/fRTmTdvXlUzYo6PO+44VT6CjModVVwCwGOPdRcA77rLA2BHfUYaed0eABup7RzO5QEwB6UWuEuy4OFGZQswXGoY/JgwhCsTJSCIAyy6YCF98sknVYIGHV/UrGv69NNO9hBo1g00BqwhkKLxSCUEKfKStIXHBgJtCswDgrYAyKBsIQwA1FILBG37S3JuWxdQxuU6AAJ5trF9eQMgzx4AyDsKC6AWNq14vxCLh1CqhbhjoEqXVgCo+CHhihYsgLiGApYkhyGRDPCFhZ/kL1j5L7vsstjHmzIQJH0hXhAQxII3YcIEVUaC81MigrER14fQJ+ejf85N/cDJkycr18+tttoq9HzeBbRVLS4B4NFHuwuA997rATD2wfUN6taAB8C6VdjcDjwANlf/rp2dgsrsSGcpACA79GUAQPTzxBNPSP/+/duyEGapq6R9WUNgsG6gcSIgUANgHAQGXfxqgaANAHI+ANAmWUwSCDMB0NRpEAabCYCMKyq5S7AOYV5JYGxcf20BkOtJAoFJLYAAIHF8/JguoK+88oqyyOskUwAioIgLpwY+krVgucfdUwsuocT8IcAfQKZjBanpR5KWiy++2OqRpASELgTPOW644Ya2hDO4ogJw1P9Dhg4dKvfdd58qGo/rKlbJ4cOHS9++fSPP5QHQPQA88sgvZIUV3IsB/Ne/FsmkSR4ArR5c36guDXgArEt9zT/YA2Dz58ClEeQBgCzQWOiwiCm6AMd//OMflQuarkPW7GuygsB+/ZaZz0KoZ9jU6uQwUZbAqBivMBBMAoBah3EgaAtsUQCoz6NB0La/JPBpawGsBYDBe+rrr7O9y5JAXZK2eQMgFjM2lIAmAA/BcoaFTZeZoZTDi5UdDWL+qA+ItQ/r3nbbbdemSNw/aUtyKhK/UDeQNnwOwGGlC6vjl+1M+N6SaMAlC+Dhh7sLgJMnewBMcl/5tuk04AEwnd6cOcoDoDNT4cRA8gBAXLRYrFELsOhCfM/jjz+uLBAUn3ZFgMB11/hn63DCsoMGLYApITAuyYcJgmkAMA4EbYEtDgDNeQsJjwydVttzewC0fypsrYC6DAQlHgBAXCvJnIkAeyRH0e6eupQDXge4aVMXkLhmisFj1dOCJXHp0qWqHyyG9EFcL7GBlGbgGf/Zz35mfzG+Ze4acAkADzrIXQB89FEPgLnfjP4E4gGw4DeBB8CCT2DGw88DAEnSwC78xhtvnPFoG9+dqwCIJtogMFgaYpttWhWlCcYkmQDVBC2BHGZaA+MAkPYaAusBwCgQtIWwJACo+4xLGmN7bg+A9s9lUgDEAkjWT4AQix2A98ILLyjvAuIAESCRNiR2YfOJdriA8vk2+lkQUTUDiXcGAIE+fkgkg2vmFVdcIYceeqhK5OLFHQ24BID77ecuAD7xhAdAd+7a8o7EA2DB59YDYMEnMOPh5wGAuFfpGJuMh9vw7lh4UpyZuJ6VkvjGNWik//53p2XlITinhsGMINAGAPWlVmpwx165TRIY7RpqC2FpANAcaBgM2p7bA2DtKTc3BqZNsysGry2AWPHI8In7Na6buH5ivevdu7esUSlqCSRSVJ0C78AhbYgfJDaQxDBaKAzP+46smrh2U2CePrEg/vKXv1R1+U477bTY+9c3aJwGXALAffb5PLKxqQAAIABJREFUQpZf3r0YwK+/XiRPPeUBsHF3Zcc9kwfAgs+9B8CCT2DGw88DACnUjLvkpptumvFom9Pdo48+qlLMA7UuShACF660rhpmj27ftA63DktgEgDUbQ2jS6i6bABQH7j22nYarxcAw2CwTADI9dnuX9i2o8+wGMBaluCkAKjLNFBmAavezjvvrDJ+8m7B5RMBEinyDgBqyx5w+NVXX1Vl2aQGIGCJiyhC9k/6511F7N+PfvQj9ePFHQ24BIC77eYuAE6b5gHQnbu2vCPxAFjwufUAWPAJzHj47IQDgVkKu/ErrLBC20Iry76b0RcWwD333FNWXnnlZpw+9pxvvtlJuWHqQvFvfb6uaHCyhsBdd5VhV/Zod64ZM0RsY+ZMWKwFgUkAULcNZskMDjRLANR921437W2tgLbZPTN+JNUl2YKdbTsNgLauv7RPAoAUXQfQADeSu2CxI8snG0xY8Xr0aL1fafPOO++oJC68y2jXs2dPVa5mCyNIlTbE/JH9EyEeEJgkYRWxf2eddZb88Ic/jH3efIPGacAlANx5Z3cB8IUXPAA27q7suGfyAFjwufcAWPAJzHj4eQAgLlsstMzFV8bDbmh3rgMgypg7t5OK3QMC35q1otJPIgg0CMsEQQAQsYGhMGthGAimAUBzwsNgMA8ANC2AcfGCWQMg15s1BNqCXa12wTnu1i3Zo5gUAD/55BMVu0fxdax8vFfI3EkcH+CG0Ab3TtogFHbHLbRbt25VRdT5nBhCMzb5ueeeU1bBYcOGyQUXXCBHHXVUsgvyrXPVgEsAuMMOX8hyy7nnAvrvf5MoyQNgrjei71xpwANgwW8ED4AFn8CMh58HALJQQ3TWvoyH3PDuyAJKYokuXbo0/NxJTkhSmOnTRQiNqoRH2UFgSLmOMAiMA8Eod9EgBNYLgFonJgjmDYDBeQgCYRkB0Mb9N28ApE7fnDlzVMF1aviRYAoIJOGLLguha/nRBqHUA7GA1Ak0N6F4L+HuSfygFj4jCQwxgJdccol873vfS/LI+bY5a8AlANx2W3cB8LXXPADmfCv67j0AFv8e8ABY/DnM8gryAEB22ul3q622ynKoTeuLOoC77babymzqutx1VydhUR6EwDZXUC4gLCawBgRqK6C+9ihrYBwwaBDMCgBNEGw0AJr3ATBYZAAMFodPcpvnDYAUc6ekAy6gWOpIAENsH0XUV1211RpDG4q800bLs88+q1y2dWF4Psczgc/M+qRYF6kZePfdd6skMIcccojrj3iHGp9LALjZZu4C4DvvpALAn4rIeTiLiMirInKmiDwfcYOdKCK3B74jo9NKxmffF5HTRWRHQtBFpK+IVHxIOtRtW9qL9RbAgk+tB8CCT2DGw88DAIm1IQufmYEv42E3tDsAkPiirl27NvS8aU8GBGpIAwTbuYKGQOA/t9lBVlyysN0psQQGAZBGYRAYB4C68ySAYQuLtItz09Tnt03uYtuOfpcsqVZdVMyibQwgvWXtApokVs92jvIGwPnz56vELhSCR4BBYgJJBqOfRyx4ZhvaUTsQSyCxu8QCIsQOcsyGG27YNlm4jlII/je/+Y389Kc/lQMPPDDtY+ePy0EDLgFg797uAuB77yUGwKNF5PcVYHtORIaIyEAR2Zw9lZCpBACvr3yvv/4/EZlvtD1ORCj++3cRuc0DYA4PRJO79ADY5Amo9/QeAOvVYLmOzwMAWaBRb8uswVVkrT3xxBMqvqgoAIiuH3qoU5sbaBwE/nON1qyhSBgEHnBM++Qwur0JgrYASDvDWFPz1kgCgLqjOBC0BTvbdmEAGHVRAGBcQht9bFIADFrygmOwjQHkOFcAELibN29emyWPjaU//elPygVU1wHke7MN48fah2WQQvA6WQzlaXAbJaOoFjKBUi/woIMOkquvvlrOPBMjiBdXNOASAG644RfyrW+5FwP4zTeLZO7cxAAI9L0gIrrw5bfYXxGRsSJyZcj8A4BjRMQm6ncjEZntAdCVpyi7cXgAzE6XTenJA2BT1O7sSfMAQBZVX375pVp8lUGefPJJZXHQLmdFuSZbCHzvq3Wrir+nhUBbAER/um0cCKYBQHN+6qnvlxcA2t4/NnUVbdro8zUbALFALlhgXweQLKCUfgDkcPnUMmXKFFl77bXbXMxpg6XQbAMA4ipKNmJgEaFYPDBI8XcteCtgIcRSiJWfZ92LOxpwCQDXWstdAJw/XwEgN/ZiY/Z42MIeOLKEfSkiR4rIZKP9HRXAOywCAP9LRP4mIsDiyyJyoYi8GdLWA6A7j1CmI/EAmKk6G9+ZB8DG69zlM37zzTfKXTNLIa6G4srbbbddlt02rS8WnLig6aQTTRtIwhNfe21reYiaSWFE5L05/P9c6obANACoLykKBOsFQN2/CYK2YGfbjnMEXUCjpiqJC6gN3Nm00WPJAwDpu5YbaNDtNAkA4kUA3JH103yX8DySzXOPPfZQCWGI4Qu2wd1zpZVWUi6j1Ack+cuMGTOU1ZBC8VqIV15uueVkn332kdtvv90ngUn4jsm7uUsAuPrq7gLgp58qAAzKpSLyq5DPcfkA5L5LZRbj+6tFZG8RaU2nWy27iQiFfV8TEU52rojsJSJbi8iHgbYeAPN+MJrUvwfAJik+q9N6AMxKk+XoJw8AnD17tsrYt/3225dCSUUFQJQflRSG73RimGee/VYV/GkDSVJL4IsvithOeRQsBkEwKwA0QdAW7GzbeQBc9pjbxBnaQCDvJSyAxPZR6F17E5AI5qmnnlJwRzmHtdZaS0Ge2YbR4O5JGQhqBGK579Wrl7zyyiuq/brrLnN5JgvoiiuuqCyAJI4py6ZVKV68ZB3UAZzNvSD8Pr/o1u0L+Y//cM8F9P/+b5F8/nkiC2AaAAzOwAoiQrrv8SJysQfA5t6gjTq7B8BGaTqn83gAzEmxBe02DwAkbTsLMtMlq6DqUcNmwcm1sKAsotSCQDNuzPCMawPCJBAIACI2EBhnLdQgaAuAnNe27eLFZJE0k9eFz6oHwPi7ffbsb6Rr11YLsq0kAUCse2wmaTdO3ldTp05VmTz5nOeSDKBmG8ah3T0BRax8ZPEFALH+4T6qBVfRzp07qzITfF+W0jW2c+F6O5cAcJVV3AXAJUsSxQCmcQENu1UmkqtKRAZ5AHT9ScpmfB4As9Fj03rxANg01Tt54jwAkKx6FGfWmfucvPAEg2LBiWVAF55OcKgzTeuBQC7CBMF3Pukhg3XqAOMKNQDqj2qBYBwA0gcQaAt1SQHQnJgoGCwCAHIdtm6gWbiAAnxByRMAgTuyeeqEUl9//bU888wzKikTJSH4TYxgS0tLVdZh7e6JtW/atGmy6aabqmyf66+/vrICannzzTdVfU+8FQDFTaJqnDjzJHesgbgEgMsv7y4Afv11IgDkJiIJDCUfdNYjdnHeF5EbI5LABG+85Srxfw+LyDkeADvGc+kBsODz7AGw4BOY8fDzAEAWbSzKzLpcGQ+7od0BgGY2wYaePMOTBZPC0LW2ABpGkVB3UBMCAUAtJggGAZA2URBoA4AcP29ei/Tp08VKC7awiAUwTIIg6AFQJAz4GgWAxCYDbSbc6Syge+21l+C+SV0/4gCXLl1aVXfUdPfUHgnAI5ZDnT2U68BVVAMg5zIzhEbddDfddJNcc801qoA8G0Njx45VFsQw+d3vficnnXRS1VfEI5KgRsuvfvUrueeee5QrK+6obJxdccUVCm47urgEgCLuAmBrWJ76zyLLe4YyECR9Oa0CgpSBOEpEtqiUdqBEBHGCF1T6+6WITBeRWZVEMdQPPLxS8++tShv+x0CKXVxMHxKRY0Tk/1F5tvJjOTTfzFUNeAB0dWYsx+UB0FJRHaRZHgCI2xZp2cmcWQZ5+umnlQVi9dVXL/TlmElhuBCSwyBJIdAEQI7XEBgGgHwfBoFJAFArPQ4E6wVAc3I1DNpCYLOSwDDmrCyACxaYCQTtoJvzJ7UAzp+/VKmaeL6o37yX+AGKgCXtmklcILF6e++9t3z++ecKAtdZZx0VL2i6b1IHEOsf7p589+c//1nFDWIJXEPf+BVXUT5nswqgM62DYQ/7vffeK8cff7yMGzdOAdqYMWNk4sSJynrYs2fPdocAgGeffbb6XgthbeZ5KELPsb1791Yge91116k+yaZswmqhXz4pB+8WAH4mIu7FALYyX3c0nAQA1avbKARPwfazKpZBvpuKV72IUP4BuU5EKPSO/zSKeElEfiEirxhTG1Ysnq+jktGkvCv8Yc3SgAfAZmk+o/N6AMxIkSXpJg8AJHEDP1G74kVTHS5nFLUvOgCid9MVlL91kXdtAbSxBIaBFhAYBYB6vk0QTAOAup8oEMwSAJfdo4tknXXaL+yD97AtAHKcbSZQW7CzbRd0Aa0GvuAV5QeAd975gNUrAMsczxyWuy1IZ0tO+3/8Q8EcWTsR3Dupz4nlbPPNqWHdKi+++GKVuydZQUkIE3yOX331VZUhlHcVmUR1zcCoAQJ9bGzdeCOecqIgFbdS6geef/757Q4DAIcMGaJg1VaoS0jGYeqP9u/f3/awUrZzCwA/cRgA1W5eUgAs5T3jLyo/DXgAzE+3DenZA2BD1FyYk1AHELcqvRvPwPl31N+1du31sbh/YgHEbdKmvT4u+Ds4Btt29Zwz7By4tLIQJVlE1BhsP6/3moLzk3SeWEx/9NEAlbofQ8iDDy4r/2ALgc8+W10yQt/sNgXONQTWA4D6fEEQzAsAgw9zGBC6DICLF39cuYTW+9dO8gHAxYs/l88//w9Vew+J+s17iYLvABm1/DTcYSED+gBAjiXjMO8arHqbbbZZ26W98MILVe6eJKXCLRSXTXMjh8/on1IRlK5ZZZVVItWDJRGX00mTJsnhh+P91ionnHCCArwHHmgPtgDgySefrJLPAIs77LCDjBgxoipe0Twh57jhhhtk+PDhygJoWivt5q1crdwCwL87DIAqs60HwHLd/s5djQdA56Yk2YA8ACbTV9lbs+ihzEEWYmbsBkyor6UXeeZ3/Du48Au2i1sgNvJ7XMPIAAoA2pw36lqjrrGR1w6csxh+771+CgKnT6+GORsIBAARM2uovn9sITALANTn1CDYKAAMPisAYbMBcBnkxT3JthCYHQACfaZ89VX8GIAlErRwrwKBGu6+/PJLee6559osgNoiiBuo6QJKghhcKjVA8T4ilpcYP8pHaHnppZfUe4qYQvrCkhgl1CQE5P7yl7+orKJahg0bJriJM66gAKvvvvuu2gwjU+moUaNUEhuuzSxI/+CDD8oxxxwjXB/XMnny5NK40MfdkbW+dwsAKXfnqgsoNeA9ANZzr/lj4zXgATBeR0638ADo9PQ0fHDstJNlT0NIPb/14AEmisGzq14GIeYIC0QZ4nGIqWJ+cGP7yU86qenBEqhhznQBjXIHNUErLQR+9tnHVq6VJIGxla5d7aAlKglM+HlscyqEJ5ahz65dyYuwTJK6gC5dSi6GWkJJLluJh6/Wnux02Xp91WUggsAXHJktAOKSSSwf0KUBkIQwWPf69evX1i1ABriZSaemT5+u4v1Max+QRryfmVwFV1E2bPbdd1/lCaE3rcK0mQYAg/1wDkB10KBBcvnll7d9zXVhySR78m233aY25QDKsLhC25kuQzu3AHCuwwC4IdPtLYBluOkdvgYPgA5Pjs3QPADaaKnjtGFnHLejLAUrE7veu+++e5bdNq0vYo5YTJZhMWYm6Nl//05C1nudE8MWAoMuoGkgEABE4uLrkgCgSGufXbv2qnmvNBoA09+4rRb0eGk+AMZBn3kNtgCok7cQ40eZBoTNKqAQi50W3DiJm+MzbX3H8samjRnTBwDyviPLJn0iwCSfHXzwwcr6RkbRKEnjAhrW18CBA2X55ZeX8eOpoR0uvG9+9KMfyQUX6CSM8XdBGVu4BYAkwGy9b9wSkjdtwpA8ALo1MaUbjQfAgk+pB8CCT2DGw88DABcsWKCy3u2xxx4Zj7Y53WFhwG0sLkNgc0aX7KzBDK0DB3ZSAGgLgVHWv6QQqAFQjz4KBNMAIH3WgkAPgLb3TC0r4DyjE3trIQclAUBgD9dMvZkE6JG4Zc8992w7P+6UWM5I8KJdPtm04W9ct7UAgDqmUCeVwcqGF8SRRx6pXDRrASD9YD0kYQylHxBcVXErHTx4cGgSmKCmORfjGjBggIwePTpyInjfHHfccUKJiI4sbgEgmVxdBUCVAMkDYEd+WBpw7R4AG6DkPE/hATBP7Rav7zwAkMXYW2+9VbVLXzzNLBsxAEg8ESnliy5hJTqSQGAt618YBKKvsLjAIADSLgwC0wKgnqcwEPQAaHsXB8HOhD6zj2QAaAOBgBUWN94jeBTsuuuuKgELkEbtPnNzCQAEErGqEWuH4Lbdp08flU1TCzGAwBd9ApS0x1WU8wBbuJya8bthWqIMBElfbrnlFgWClIGYMGGCzJw5U20QUSICl9WRI0eqwy+77DI1dgrMkyiG+oHE9xF7uNVWW6kah9T8O/TQQ1XsH+9O6gxSGoI2jLcji1sA+LbDALilB8CO/KA06No9ADZI0XmdxgNgXpotZr95ACALKdKuU6urDII7Wa9evUoDgMQyBUt0mPGAzFmUO2hYAhgT/MIgkEQzQygzbEgYAOqvTRCsFwDDQNADoO1TaRv/SH/JIDDOCqgBkDp/uH1i2cMqBkQBfKZ7Oe8aEjSRrZe4Y8o6kEEUt1Ht6skIiasjeQvHs5lDEhY2d6gzeOqpp6rY2DgApB9KQOhC8JyDrJ06rpDYRIrNk/0TGTp0qNx3332q7+7duyv3UzJ89u3bV33PuX/wgx+oeD/gj5hF4nN/8Ytf+CQwrVliXVhzkvnlC5E3HAbAbTwA2r7WfLvUGnDhYUw9eH+gSvHfWn3Xi9dApeRD1jGAZO7DTctM1FBkZWMl2HDDDdUOfdElqkajtgJyfbXcQWcRBlORWuBnfgcAIiYE1gJA2moIzAoAq+ctyTzaQlB0Epj67hmbOMCsYgBJc29KdEmE9teUDwBirSMxC5tKQB8AiLXNzML52muvKbiiDS6fABiZNim5oMs68L+9p556SvVBPUBgkU0QABArHMXaSZDkBm/Ud8eU6Wi3APBlhwFwB6bdu4CW6eZ38Fo8ADo4KUmG5AEwibbK3zYPCyCLNBIz6GLNRdciu/MUe153XVVrqdASBYBcVNAVlM+ClsCpU6vLP9hAoAZAEwLjAFArGRC0h0Bd7y5uij6rNNgqrqGIlBkAg8AXVEceADhfneSrrzaqqXttAcS6B9QBbLrMAwmmcKvUwmYTljMygVI7Dzgk3g9LGsXkEWLv+AzXUYCSGEHcRemfuEKKuJO52AOgxSPRwCZuAeCLIpLkmWiUosjivZMHwEapuwOfxwNgwSffA2DBJzCH4RM/k6UQp0P8CqnVyyAAIO5ixPYUXQBA4gDNVPjmNUXFA9LGrN0XBX5BF9APKZ0VECyBtgDYeiidqCQHMZIUAM3uomCwTACIHpNY6pIsdmv12wp9ptgCIPF+wB3vKIrA47r517/+ter+nTFjhirRgoVeJ38BCrnHiRtEvv76a2UVJHkMhd+BSDwfsBqyYUWsHomrvLilAbcA8HmHAfA7TJy3ALp1+5ZuNB4ACz6lHgALPoE5DD9rAGRHnfTq/fv3z2G0je+SotLAXxkAkPg/XN2iABDt1guB9KFBMFhoXs/eCSfYwpoGQH7HQaBtn9oCGHUvmTBYVAAMIW91uY0AwPbAlxYAce8E7nDxZCOG7J3cv2YMK5lCgT9+sAASV0fiGOIBqfuHUH+PuEDikrEAUvKB5xrBZf3qq69W1kAvbmnALQB81mEAVBm3PQC6dfuWbjQeAAs+pR4ACz6BOQw/awBcvHixyq63//775zDaxncJzLK4xApYdEkCgFxrMB7wiSeSuYBq988o1dmBYBBmokAwKwA0ZznJnOcRBxgXA0hx6lbIsZO8ABA3NHuxsQDyXgIAya6J+zVeBVj0iNkzi77zOZszWAcBO0AR13bi/UgIg9AX1kHiknWpB9zUgT+Sr5DYhb+9uKUBtwDwaYcBUCVc8wDo1u1butF4ACz4lHoALPgE5jD8rAGQBRqLrQMOOCCH0Ta+yxdffFEtQokDLLrYACDXGJYUpl4X0PQQGGbNCoNAWwDkCuOsgHqmW0KmPApA8wJAIK+WNAsAzRhCEiXaSxwAErOHiyaunMAfcMe9O2fOHAV1ZNPUwvPJs6nrdAKEuKFrd0/aYRUk4QtxyTrOb/78+SojKBk6f/vb3ypw9OKWBtwCwCkOA6AKt/AA6NbtW7rReAAs+JR6ACz4BOYwfBZaWSaHZRced6sDDzwwh9E2vksWmD179lQFn4suLKJJqGEm0Yi6piAE2lr/ghlA48pEcP7alsAod0aONGGsUQAYpbF/B77oHdHwPcvbaEXLdjSzhcB6LYBRiWPyAUCschruiOPjvUJiF9MFFAs9mT9xFUWIcyWezwRA4gfxSjATU+m4wAULFqiyDdQO9OKWBtwCwCcTulA3SpdsUqlwCw+AjVJ5Bz2PB8CCT7wHwIJPYA7DzxoAWWyRcQ8ALENWPSwKLC7LAIDz5s2TuXPnWgHg/vt3km7dlrmBzpjRevPZJIAxYwCDx4T9XRsCawGgfiAAQdcAsN6H1QUAjMsUqq8xGQAuWRLuWqvfF8AZPyR4oQQLGzAImzF4LJh1ALHcUSOQWoGItvZRo69Hjx7qM7wSAEWzNI3ODMqG1W233abqBHpxSwNuAeAjDgPgf3oAdOvWLeVoPAAWfFo9ABZ8AnMYftYAyAJs6tSpygVUx9vkMOxMrZa1xocVgoUkC9F6JUtLa5qxAIDBJBq1+jn66M5tcYAaAG0hkHZmFtCgC2iYS2i4JdAGADlb9wQqqccFNOo0QQtgguGENm0WAM6rjCaJtTAZAC5aFF5SRQMgSVsoBcGz17t37zbrHtk7yWJLMhf9bsGyt+mmm6psoYiO98NtdKutWhP6UEyeZDF77bVXm6a1BZDv7rnnHvnDH/5Q74T54zPWgFsA+KDDAHiIB8CM7z3fXXsNeAAs+F3hAbDgE5jx8Fk8EWdz3HHHtfWcBFLCLHwAJRZAykCQcQ+x6VO3sbEa2vRnqkr3mfQ4+iARBbXIkloA05wreO02fUTpK+xY0t4H0+hrPUWd69hju0pYMhcbSyBedRsZJd/iIPBFSm2JyNixpjXPFgA1uNjU9+uoAIh2g2Cn9RZ8ueQBgMvmtaVlWRxf8Mw6BhBoM+EO92Xq9QF22io4bdo0lR2UTKEIHgh8BiBiKaTsA4mpsCbiFqqF9xRunyNGjFDvKWIEvbilAbcA8AGHAfCwNAD4UxE5T0TWFpFXReRMEWlNjdteThSR2wMfUz/K9D2HDy4VkVNEpJuI/FlEfiIi77p1V/nRpNWAB8C0mnPkOA+AjkyEI8MYNWqUgrW777471YjC4IMdeBZWxNvkaQFMMmAbqIzqj/TwxB316tUrySlTta1nnDYnZCGMG50ZCxV3XJcu/xSRVWXNNb+lmtqAH23MkCpbCNQAyHmWQWBSANRXVAsEOzIA2pa2QI9ZQGC4a24tAOTMvEdw29x8883bXDkBQOJYO3fuLNttt52aaF37j00aRGcC7dq1q0oeQ/ZeksJQU5BC8Fq0pXDy5Mkyfvx4VRCeYvJe3NGAWwA4KeHz0Cg9EgN4JCdLEgN4tIj8XkROFxGyHw0h91clqDrsgQUArw8EXf+fiJg1X34uIhcQ0i0is0XkchHpIyK8iPPIkNUoBfvzVDTgAbDgt4IHwIJPYMbDv/DCC5VL1bhx4zLrmQUYu+n77bdfZn02s6O33npLLQw32WSTZg4jk3PjWod7LgC4/PLLW/eZBgLnzKnu3oTAKJA0AXAZBKYFwFog2JEAMGjhSwJ1SdpqN1C7WMzFi/uqCdKWZ36b/+ZeJf4W65527yR+lcLtCxcuVHX+yAjKZlOfPn1ktdVY/7bG+7HJgeWQd9vOO++sAJDnmGO0aEsh57jjjjtUP2XI9Gv9UBegoVsAOEFEVnZQa1+KyFFJARDoe0FEBlcuiN29D9h3E5ErQy4SABxTseyF6QA2IGD4WhEZVWnAAwkgcuw9DirODymhBjwAJlSYa809ALo2I80dz+mnn64WUSNHjsxsIGmsTJmdPIeOyCiIZW6zzTbLoffGdskC+6mnnlJZFFdZZZVEJ+/SpdXbx9YSiAUw6PJZCwI/+ih8OGPHvmw5zihXRvNwbRUsMwDG6SEJ1Nm2/TSxdWTy5IVW80rJBw13s2fPVi6eWO90XC6ZQUn4gsUP0fF+wB7Wwb59+6qEMjzHu+22W9s5dTF4NqsAxTvvvNNqPL5R4zTgFgCOdxgABzEpZFZabMwOLpr8BAUzN9SI2XCy8eUdFcBT/qQBAeL+iyS7IgIs8lK+UETerLQj5fFfRYRdnUq6MPUNxRP5++zG3TX+THlpwANgXpptUL8eABuk6IKc5uijj1bxNOedRyhAtJg787Sq9TeuVNTXotRAkuPi+s36ezPmrdY4KRRNTBIxR6aVImo8Uf3GnS/uuKTfB69Jj5eFLxZN7Z4b7DdKF4ccsoOIrNUGgGrFYSRzDP5bu4AmgUAsgFH1AuNBMA58zPt7HcsnNKwOYNShzUoCw3XbloFg7LZQF9cW6DMlSb8iCxdu05YlWLs+B38Tp4do10zi/4A/4v2oCbjLLruo0hA77LBD24YG7x/imon3m1kpXkkWXxLImOVPtKUQizgWRWoBenFLA24B4F0OA+CxYRNHPN6vQr4gAxMghzl8mvH91SJCRfldQo5h52RTQuIrrqbniggZlbYm11elL2L+6Nt8EWM2xVUUl1MvBdeAB8CCT6AHwIJPYB3DHzt2rLL0kV1P/2CtI0kCi35ibS69lP9ntAe8Ok5bdWjUQi+LtjwvAAAgAElEQVTN52a8XJLj444Lfo9FAQDUSSa4oLDzRfUbbBs8Pur7rI4LjmvWrFnqWnTdtCS6W2stNo+XxQNyLVEQaLqA2kCg6f6ZDgKTAGAwDi6quLtrABh1jY0CwCD0pQfAuBhAesZyhysoSVr4IYERf+PeiXWPOEAyheLmSZwugrsncbskgAEG+R43UtxHzfqBvPv47rHHHlNF57N0g8/qfdnR+3ELAMmB4qoL6EnqVWxpAUwDgMFbcQUReVtEMIte7AGwYzypHgALPs8eAAs+gXUMnwLJuDph+dE/xx9/vBx77LEqNmbllVduK3WQBAqCQIPFjIUau+1hEFPHJTTlUBaOLCq33Xbbppw/65PWG9No6wo6dWp1BtAgLAbdQYMuoMkhsB4ADGpZA2GzAJBN9c4Jpj5PAKwFffkCIBtV2grIRhWbF3yGOzYunfz7o48+UpZA3l/IZ599Jm+//bZ6p7GxRSKZVVddVWUCBRS1AIdk+KX8A3DJBpmN3HTTTXLNNdeo8wKgHGeCpdkHBeZPOkktztsEl3tK5SDA7C9+8Qt5+OGHVXZTXF2Jnb7yyitl3XXDS2XYjLEsbdwCQDwgXQXAk5ly2yQwaVxAw26piezRiAj+p94FtCwPXY3r8ABY8En2AFjwCcxw+CyO2Em//vrrE2WFjBvC/Pnz29yz4toW4Xvq5gG1xBKVQVho4ga6zTbbpL4cGwgEAJFacX/m99oCGFcqQg+6vUtolgCoz/KvEB1FZYO1dQG1TWrTLAA0k7gkqe9n6wLaGn/Z0mKXJAoAxAJP0iI2lhDeW9q6x3fA3kortUIw7pxmvB+bXjzDAOROO+3UNp8kk8FVfdKkSQq8rr2W/BW15d577xU2zbAWAp1jxoyRiRMnqvPpshRmDwDg2Wefrb7XwqbYWmutpf5kY+nII4+UU045RcEk8Ep7rolENh1d3ALAWxNuyjRq9paKyKmczBYAaUsSGEo+UPoBIa7vfRG5MSIJTPBiqO9E/N/DInIOTjGVJDAkgNEPEi8PXiY+CUyjboWcz+MBMGcF5929B8C8NVyc/gFAsus9+OCDKolCVkKadn7MxVZWfTejH3897bWus4LyTVhSmFmzqo+xgcBaLqB21sA8AJDrCIPAeu7E1nIa8dJIAIzK3JkVALZPumMLgLomIFY6LID8JiMv76/nnntObWbg7ollDaHWJe0ANAQ3UuIESXpkWgC1pZASEJSLwOoWJ/RJHzfeyDpZlAWSzKFnnnmmnH/++e0OBwCHDBmiMpfaChZLLIp4HiStPWp7jqK0cwsAb3IYACnplwgAickj6ctpFRCkDASpRLeoZO6kRARxgpR1QH4pItNFhDc79VZIGnC4iFDM861KG8pA8BCYZSBwm/FlIIrywMWM0wNgwSfSA2DBJzDD4bMwIrkCu+AbbrhhZj2XzWKGRTMYP5SZsprQERYS3EDNmmhphlELAl95ZXFbVkbddxwEfvJJ9SjiLIGV/B7qoD/+kaR0ZQNArswWApO6gNqVaiDe016CFsD4TKs2EKjdQAE+rNdY8jbeeGM1LBLB8Bn3sk4Ug7Wez0y3TKxpJI8BFLUAiiSGuf322xVQXnbZZTUvFUskbqZYDA8/nLVvq5xwwgkK8B54gELh1QIAnnzyyfLtb39bwSLJaig8v/XW5M4IlyeeeEIOOOAA1Seuqx1Z3ALAGxI8j42cNSyAZ3HCJBZA2lMCQheCJ1MnnWAZRPDfoJAP1jvkOhH5fqVoPA/2SyLyCxF5xbhSXQgecySQ+KyInCEi7zRSG/5c+WnAA2B+um1Izx4AG6LmQpyEhRKJQIhl0SnUsxi4rtWlCzVn0Wcz+1iwYIGyKJgp5Js5nnrPres09u/fvy1GM22fwfqA9AO4AYBI8L6qBYFYAOMgUffPbxMA9fj/+MeHLC8lSTH0ZlkAuZQsAVBDn62rJudPAiD0Gw995gTZACDtiZVjw4rnEFfP3r0JOWot+j59+nRlldP32scff6w2bExrH6CHK+hee+2lLIiIjlW+5ZZbVHzvxReTyyJa8AQA5CgbYb4Lhg0bJk8//bSyRgZl2rRpCjLpH3fPUaNGyTPPPKM23ShQHxRiA4FUktbcdRdZJzu2uAWAMJDt89jIeQMAh3LCpADYyEH6c5VAAx4ACz6JHgALPoEZDv+dd95RcWBYhHRJgCy6zyLGLItxZNVHVhazrMZTbz+41E2ZMkUthrXbXNo+w6yA9LVgwbJyVLYQqF1AgxBIf2HWwDAApK0dBHYEAIyy8mUNgNp0mwQWW+84WwDUVkBi6XDl3Khyk2CVo4A7HgzaKojFHtijfqCW999/X6ghSPKYddZpLQHCxg4WxBtuuEElrApz4TSfizQAGHyuANktt9xSBg0aJJdffnnV13x3xBFHqLFTmqKjW/9QjlsASHibqwBIVQYPgGn/P+aPs9OAB0A7PTnbygOgs1PT8IGxO40rE8BmlgqodyDs0rMwo75gGYSd+xkzZsjee1MiqRyCxYK4T11gu56rCoPABQsIFWlNdIHEQWBYEfg4a+Abb7S3GJrXURsEywaAXLntNWUBgAF/3TbFJ4NAWwCke1w4cV3mntUu63xGOQg2MkgEw3ts3rx56gd3Sy0AIJ+RSEaDIaCIuzpWOazh55xDLotoSeMCGtbbwIED1TiIPdQC/B111FHqXczmDLHZXlwDQGJEk7haN2oGySir4k+9BbBRKu+g5/EAWPCJ9wBY8AnMcPikHmfR89JLL2UKgDrjHXUFyyCkjyeGaJ999inD5ahrwG2uV69ebdkI670wEwJF3jO6s4PA119vTYu/5prVC6xa1kAAEAlrY15POAjawhI9ueoCGox5TAJ2SdpqqIuCPlPbtgDYqv+WFsKK7ARIev3116Vbt25tAIjLJC6ZxAUSV9ejRw+VgAq4M7P2YunjOSbuT9cMxPWdtldccYUceuihMngwIVG1hSQwxBbqkhFYJknUwrFxFkR6xvrOOAcMGCCjR49WJ9Pwh6voU0891VafM24sHeF7tyyAVzgMgBdxO3gA7AgPRROv0QNgE5Wfxak9AGahxXL0ceedd8rNN98sTz75ZKYXVG+duUwHk0FnOmaO+lxlkVdffbVqIV3vdXXp8kXFPUoDQDgEcp4wa6AGQL63gcCwpIrJQLBoAGiT4CYJ1Nm2BfpsoY7Zq9W2vc6ffbaXyuaJmL/1v4Oft7S0KPdPbQHk2ST2jng6LHTAFfVOie8zY5Bx/1y6dKlKxIK1kDISGhR/+ctfKpfM004jIWJtoQwESV+IGwQEKQMxYcIEmTlzptpMoUQEcYIjR45UHZFYBvdSksyQ1IX6gZMnT1abbnhIAH+UgXj55ZdVNmZdHoJjgVmd2CZuXGX93i0AJEmQqxZAknR6ACzrc+DKdXkAdGUmUo7DA2BKxZXwMOr/PfbYYyqrXZbyxhtvSJcuXZSFyRUJW2QGF5dRf7OwZJGpMwhGLU718cFzRX1ez5hsxx61sGaBjOBqFrbwTjO23XYjg7gGgBfo3Zj+ZZZAPgxC4OLFrRZALUEI5HMT8LT1L6w8RBQI6tIUs2eTKMZlADRhL0nMkS3Uoc2otmFWvnoAsLaeZ87cuc37APdN0xVd/9v8HAsa9w6JYIhbBggpm0DJGaz0PKNY/4jbJfGKFlwrcRcFsEjAQjtcQnkOsNz9+Mc/blewPerdRQkIXQgeN2piCHXJiX79+ilAJfsnMnToULnvvvtUoq3u3bsr99Phw4e3WSexTEa9J7EG0l9HFrcAkKSXrgLgcG4TbwHsyA9LA67dA2ADlJznKTwA5qldd/smho2YE3bA9Q+uU8Si6BTlP//5z9UCLM3i3wQNCjTjkqUTjKTpLwqyovqKg6ysZsZcjJoLVPrP8u+4hXDwXPr8tmNYsmSJspisscYaSjW2x8W123rrdUVkWdFrWwhcvJgaxD2tIVADIAfYQqBZm3D2bNy5tLRmlIyWvFxAbax6jQDAONfOpACYBK6TuYGaNQHJ5sl9jDWN+FxAcN1111VulsTt9unTp21KiUvmc5LA4P5MFlHuf2oBAmlnnXWW/PCHP8zqNeH7yUgDbgHghQ4D4AgPgBndc76baA14ACz43eEBsOATmHL47I6zK82uuf5hh5ldaXasWdhTzJjv4hb5YRBkQgSJFdilx4XJpq8sASTMahDsPyk8AZ24yWaRNTPl9GV+GFYSrA/acpHlCbp0eUZEuhtdxlsCWwEQqYZAPgmzBi5Z0n7EtUAwWJheH10NgmFaAA7TAuAHEWpdMYG68wBAgC+JtdAGADX02bStvvwkcYBmTUDcI4nrY3Nrzz33VK6fWPUobQMYmrX2iK/jOQYAue8BP6zfgOIZZ5whF110kZCcxYtbGnALAEm00sktBanR/ENESFDjLYAOTk6phuQBsODT6QGw4BOY4fDZ8Qb+LryQnc3sBNhkJ56fsgjFmXEb69w5yYLc3asnHok4wDwym3bp8mDFFdQOAtvXjouHwAULPpfOnak1XC1hEEiLr7+On4toGPwm/uC2Ft+yaNsMAAxa+bIAwDBLXxIAbHX7bWn5gYXOljXBcoc1D+8FAJDEMBSCJ56OjKCUeeB7MwsxJW/Y3CIWD1dQvB903CCF2inJ8L3vfS/ROHzj/DXgFgBSM91VALzGA2D+t2OHP4MHwILfAh4ACz6BGQ6fTHSkP//JT36SYa+i4uUASzOhQaYnaEJnWEvJHkgNsjII2RP/9Kc/qfnPsgak1k2rFRDREBhMa18dExheQDzaJRQA1GIDgnPmtLaOAkRzTtuDYBEBMM6tMy0Axrl3xgFgdaxnGgDECgjEcd9i6dMxffTFv0n2Qgwy9fa0kKQFt3RdKxBoBCTZ0DnuuOPkuuuuk4MPPrgMj3aprsEtABziMACO8QBYqjvfzYvxAOjmvFiPygOgtaqcbEjK8oceeki5PeEChSUnKNS8AuqAFoCFrHVkpWPH3JTddttNTjnlFDnmmGMyvVZ213G10vFlmXbepM6yrJvXpEuoOq12a6V22sorr5zLkOwhcGbl/EEo5OP21kCR9hARBoEm8GkA1BcaB4Iffkh6fh0nWAQATOrWmQQAk94eQQhsP19mj0ktgBwLAAKCJIF5++23VQ1AhOQvr732mqy99tqyxRYkJWoV2hCTTOwfQjkIklXhLkoGULJ67r///kkv1LfPWQNuAeCZDgPgWGbCJ4HJ+X7s6N17ACz4HeABsNgTeMkll6j0/R9++KH85je/aQeAuD6RmY4FEJnqiIkhNTmgN2KEChRXAgCwQLrqqqvkgAMOyFQpzz77rHK/IgawLFLGa8ICqGun5TVPdhCoAZBR2EDg3Ih2EuoWSq9RLqBRIAgAavnXvy5PoB4bF1C6s3UDjXI5DrPwJYG6uLampS/OqhdUD+1rQ595RBoAxN3z66+/FhJOkeCFUgv6vfbMM8+od6RZBgLLIFZBPBPMdhSVBwDvuuuuXNyhE9w4vmmIBtwCwDMcBsCbPQD6Jyh3DXgAzF3F+Z7AA2C++m1U7yR0GTJkSDsAfOSRR+SQQw5RNa60C+a4ceOEDJ8LFixoqysFAPL9xIkTlWtjllI2axm6KaNVk6yJZIDNM1YzPh4Q7X4WcvvVAkEAUEtYu/YguHTpp+qAFVYIuqK29hMEQRMAzcHFw2BeABjn0sko46DOvJKwtrXcO20gUEOfTVs9ln+qf7S0nJjoFaSTwWDxCyYz4r4GDvFw0IK1j8RUun4gnxOrjBsoHhL3339/VftEg/GNc9OAWwB4SoKNm9xUEtIxz9BtfO4tgI1Uewc8lwfAgk+6B8CCT2Bl+FEASFHj//3f/1UuoloogozrE8WG+/btqz7GUkgtLVKokxghSylbvBy6KWNcI3FQWEW0W1yW90AVarSLB+RbE8QoGxHm6hkFgSYA6jO1b6vdQjX8mWOKA8EoANR9RINgFgD4cWAqbMHOth3d67ZxMX16KFFQF2bpiwPAVugzJSkAcizw9vHHHwtZhynKrgVrH1luSQyjC6lzr2MVXH/99dvavfXWW6o+3znnnCP33HNP5htheT1PHalftwDwxw4D4G+4LTwAdqSHownX6gGwCUrP8pQeALPUZvP6igLAU089VebOnasKvGv58ssv1UL/4Ycflv/8z/9UH5P+nAURi6esXTXJmJlnbFkztF7GzKakxseVzsyWmJdu27uCmhBo1g0MgmAYBC6NGGa4NVDk35GXFQaC//rXp5GWwrCOqmEwKQAGYS/sDGnALuqSTeBL0q8JdXHunWEA2B766gVANrHwdKD8g+nFQLwfZR7I8rnBBhuo05DxlrIPfKaFxDC0u+OOO5SHhFk4Pq/nwPebTANuASBWalvX7WTXWV9rnq3feQCsT4n+aAsNeAC0UJLLTTwAujc7559/vorFqyUsasykBvUC4Hvvvacsf4AgBZWzElyzylYzD91gPe3Zs2fVAjIrnTWrH+Aft+AddtihIUMIh8Aw18Y4a6C2AIa141KCIKgLrke1r3YNBQBNibIW6jb/+tdio/kNEboMgl6ShWQSUEvi2pmk3yTj1QBYG/pMRS1ZcoKKS0bM32Gf6Ta8a4hx/uSTT2SnnXZq6w7LHi6gZAPFMkjdTzwiSPiCy7MW3qlYCymD8/jjj1cVjm/IA+FPEqsBtwDwOIcB8E4PgLF3k29QrwY8ANarwSYf7wGwyRMQcnoW4WSlqyW46Wl3JtrV6wJKnMyBBx6oLIC6MHoWmmHhhQtov379VNr1sggWBCymZgxR0a+N+w4roM6g2IjrCYfAt0JcQGtBoOkCagOBGgD1FdYGwSAA6qOiQLAaAHXrX8SoMwlQJQE12tq6dcb1a1r64tw6zcsF/GzbL8uwOnnyMvfMJPci2Ya32WabtnImQB2ZbcmGjMv7qquuqjZwqA/IjxbakRmZWMFbb71VTjrppCSn9W0boAG3APBohwHw3jQA+FMRobjh2hjJRYQ0p89bTCtpw8eLyAMicrjRnp03drLJKkeRVmoB0ee7Fn36JgXQgAfAAkxSrSF6ACz4BFaGH5cEhp1xLFYIi5vzzjtPxcuQCh1hx5tSEaRMzxIASc9OFj5dX67WDn6t7xhjku912+BxSfqodU6yrgLgLDaDferjsjoX/WTZV3B8+m9c6KijRnKMpPqOax81/sMP/4cBB7pGIACIBMHMrgRE+LH6Oa9VwiEKBLFKrlHzRWHCYDgAmoeHwWDWAKihLw7qzHGFtY1y76wFdGGWvlrtw+dk4cJj295F+p3Eb/P9ZH7OlehkMHgxaE8G4v3I7kmJCGoFbr755irWGesf2ZG1kBgGUCRr8umnny6//vWvy/E/hxJdhVsAOJA0Ug5q918iMpFxJYkBhGZ/LyKnE+IuIhQ55AI3F5Fafumk0X1WRN6j6ooBgLDBX0SEwfyssgt1jogcJCJbkefJQcX5ISXUgAfAhApzrbkHQNdmJNl42NUm8x2JXijzQCp/BHdOav7pMhBkdrz66qtVkgPq/LHQYQHEgokf+qAYOIWQWazjhkqR5Hrhhr6zlOCCr56/o45lvOZCM6ydXkyiR5sx2LTR57X9HbUQTnsu5gorCNYTFs9hi+7gOdOeyzxu9dWfMCAwzjoXBmmzRWSZJaf6fgtrzzmi4gPDwNN0S60NguliggDCegEwysqXBgDjYvrQURDo4tw7g+3j3wstLT9K9erQNQG1hwSbWt27d1eWPyz3u+++u7zyyisqHlBvinEi2pEIC/dR3pM6a3KqQfiDctGAWwB4hMMA+D9JARDoe0FEBlcmjuDlD0SEgoJXRkwmsSJY9X4rIntWrHzaAriZiBDMvY2IvGn0+ZGIXCgi/5XLDeI7bagGPAA2VN3Zn8wDYPY6bWSPJ554okpaEBTtdsnnJIHBujd16lSV/GXgwIGy1157KZdMdsT5efTRR4UkCPTH37hKsWtus8Cv1QZQYgcet6o0fYXBUCP1G3Wu//f/WhOVYE0ok0yZMkXFSbF50EhZVh6CbLVBOAuDOP0Z8KfFBgKDgBkHgrXKLUTBoC5jUatvU7smvI2xUHuWbp2cTm/G27pp6iGuZDFW3YS+46Ev2GEaCNQ1AXHn5F0G9GGpZxOM7L29evVS70R+EweohXZA4y677KI2xIBGL25pwC0A/P8cBsA/MHG2FkB2n74UkSNFZLIx4ywscN08LOIuuFREthWR71WyztBWA2Af9lTYixaRvxrHA5VPikiyOi9u3YZ+NBUNeAAs+K3gAbDgE5jR8K+44gq1UMKVNEshqQwJF/bee+8su216XxSbJu18IzJmNvJiqW+46aabVi2MG3X+Ll2GG5YlWwg0AdAGBIMAqI+JgjUAMDpGsPVoEwTDahjSJqr/uPg8DYRps3VGWQDDPLBsANC09MW1D2ZbZT2aTNIAoHYDZcMJCMTahzUPAKRG4Oeffy5YCfFwAAy10I72WAgXL17c8E2QZJrpmK3dAkBC25Z3cCK+JqiDcZHi1sxIhb89P0FZV0T+JiLfFZFpxpdXiwj/494l5Jg9ROQeEdleRHhJsnAwARDf2FkVd9LTKrtMQyvWRAZ3oIOK80NKqAEPgAkV5lpzD4CuzYj9eG666Sbl9om70nbbbSdjx46tqn9l35PIz372MxX/dd111yU5LLYtO+lk4aMGV5mEWopYN3GXLJOQHAOriFkfrZHXlxwCo0JJoqyBAGAtoAuCmmkBjANBNGWTQdc8RxwABrV/WcoC77YhN0lKNoS1jS6x0WqQiJPqshlp6gFyBtMN1Ez4wudschD7TBZls+QN7bAY4h3B5k6ZklbFab0o3zsCgJi+2XlaFkDqngKXiEjQjQOL3a8yAECCxLHunSEij1T6CwIgH+8oIhQk3I5SwyKCrz9uAHBDa/0pL4XWgAfAQk+fSqzRmmvbS6E0cO+998rxxx8v48aNUy5LY8aMkYkTJwquiWZci+1FkfGOXXIKx2cppGR/5513GppZMsvxR/WFCxmWBMC7TAKs4wZHDGmzpDYEMioTxLQFMArOgiBoWgBtQDDKBTTq2M8NtZnF7aO02TmhmlnXBeWWiD409CWJA0xSskG3rQV9emiAcZRbce1aiWkgkOzDuIISy4oHAvX+dEwfng54JvTp06fKzZPEMPzvkIRVHA8MxkmSTTi8K4KZRQFRYq+13HfffeqdzljYPMMqSVIaL60acAQAGQoQmCRw14UpjLIAJnUB5YZ8pQJ1+rr0wwLgERdhun2y88M5FlQsgi+KCBlHvRRcAx4ACz6BHgCLOYFAH8WOb7zxRnUBuD1htTnzzDNVApekcthhhylI4/gsZf78+crtivGWScgCShbVRtXMa5TuqAf55ZdfNt2yWQ2BXH2US6jpAmoDgXFJZoKajrPoBc9pAqDZVxQMBi1zcUlmwgAwOObRgQ9sAFBDnE1bs/uo9lF6AwLjwKr62JYW6q0lE+0GylEAIOVa9MYYzy0ZP4lzNuP8KIXDcYcccoh6BuIAMOkmHAB49tlnq006Lbipmslm7rzzTsG7AHfVU045xQNgYNodAsBkN6T7rUkCQ8kHvQDgIX1fRFhgBJPAAL/BHUL897EMni0i74hIWFaoTUVkZsX6p3xUvRRbAx4Aiz1/3gJYwPnDPYnsk5MmTZLDD19WdueEE05QVqkHHqAcTzLB7emHP/yh+slS/v73vws/ZmHmLPtvVl/+uvLXfDwERrk12rhqBscfdYyux2nTp61BQMOgjVumCYQ28Gdel5nNs32iqFavrKDUA4C1YFl/F9Z/HGSLpIFA3pNkQQYAzYQvQB4JsUjgZBaCf/7555XVkCRZWAjjADDpJlxUqZ6wJ4lNM8bsLYDV2vEAmNt7lzIQvCSI1wMEKQNxlIhsISLzKyUiiBO8IGIEYS6glJHA6gdIkhTmehF5SURIn+qlBBrwAFjwSfQWwOJNIPDBwoVYFrJrahk2bJg8/fTTKtNdUiGW7Ve/+pUMGDAg6aE125fVUlZWy6ZrMZvxEEj5qbAEK1HAhgUwKj6QW9k8TsOfLSyaCWBsMoDq+D8buGQMAKONW6keb1w5B7K31wOAgFtSC6BubwOLy8bW0vKDxO8l4A8IJLaPxEarr75Md2RJJv7PdOGePn26ih3Etf7TTz+tWQ81zSYcAHjyySe3ld/Be2DEiBGy9dZbt7s2D4Dh0+0BMPFjkOQASkDoQvCkYz6r4rJJH1NFZE6N7J1hAMjx9MfLkBcvdQYvj7AOJhmnb+uIBjwAOjIRaYfhATCt5pp3XNYASNwLMTK///3vq4Ayiyssa6xcWWMbcX1jY4E4KLPmXxb3Qj19dOlyg3G4CVcAIBIFXCZcBd0/a4EgfcZZpoLglkUG0FowGGUxjILCOAA0Z0S3vbsBUBeExig9V1tUW1qOlC5ddEkxrILtwcm8Iu0G+uKLLyprn5nwBQsgQnIqMn8i06ZNU66fFIGfN29ezfs/zTuY/t99913ZdtttlYVx1KhR8swzz6jam7x/TfEA6AGwnvelP9ZroBEa8ADYCC3neA4PgDkqN6eu0+w+1xoKAEh9wGeffVZlxstSypot0zVLWVZzxqL5ySefVJkQSVDhkrS3BoYBUS0QjCoBEQWCOgGMjTWPrOe2ovuzyQBqAqGNyyhjSOrGyTE2sPhg4AJruXXWAmjGFwZ9AN+WFkpskZaW74S20znN9G/elbh2brnllu0AkKLvFIMn3g7585//rDIhDxkyRD744IPMATA4YNxNGdegQYPk8ssxjCwTD4AeAC0eBN/Ea6CpGvAA2FT1139yD4D167AZPRB/QsFuSj8gLNxZzAwePDhxEhjKGVD4mwQgZkKCLK6rrPXyylrfkDnHjZjMg6utZpO2P4u7JFkfy6yBhJaEWb+igC0uW2UQBIMZQONAUFsAbVw6NczFJX3RullqKMnmGLM9h8YdYwOA5jzF6c/ZGMIAACAASURBVNJsG2dJpW0wMQz6aZHJk79WFjqs0SbcBUEvLpn1jjvuWHU/T5kyRTbaaCP57LPPhO8QNsAWLVokF1xwgfz1r3/N3AU07C4n3pDrGz9+vAdAi9eAdwG1UJJv4jXQIA14AGyQovM6jQfAvDSbb79koCPpyy233KJAkDIQEyZMkJkzZyaGOHa7gUcSyGRd+0pnvMMFK2uJWgTW+jzNMYw7eByuYm+//bbKJJhFn2n6SHNM2LUEF9bEP5FkCAtJPedIc6ztMYceqstP6bvKBgT/XmkcB3IaBKNKQNBNWB9RLqBhQBhlzYsCtSDQmU9T2DG12ocdmwQAk8Af5woDQG39RA8ryAcfbKeAS7sd699sIGlLHT0Fv6/1t/4OSxsJXShvou9/YgB5b5L5k8007vc//elPCggvu+yyqkydUe+tejfhiFEk/o+469GjqzO3egtguNY9AGb9f1Hfn9dAeg14AEyvOyeO9ADoxDSkGgQlIHQheCw2N9xwQ2y5BWLyLr30UmUx1D8s+EmLvtlmm6nPDj30UNVPcDEehAebxTo1rliIsfiivc0xcZBifp9KccZBSRaUegHKMTq+qHPnzlaL0jTniVrwpukr6hjzmvS/iX/iukiRb7PATjIeM67QPC5JH+aY11jjYmM2o2LhNKxpANSHxIEgQGdjzaOfKPgL3qG6P1t3To4H8GyBjvYa5mwTxvyr3sco4njAz7zOTyRpMheSLRE3t/vuu6eOSQUAqeuHpQ0QBLywchP/xwYV8Lfxxhurz3gX8k7lfRgncZtwJJMhWdfIkSNVV4DlrrvuqmpsstnGeSZPnqxq/m211VaqDa7l77//vsqcfPDBB8s999yj4hfXXntt9dPRxQNgR78D/PW7pAEPgC7NRoqxeABMobQCH8Li/tZbb1ULIf1DnB7JCAA/PmORQixgcFEeBgtRkKI/BzjZwV9nnVaris1CPwoS0pw/7nxpp5JsgeiMZClx6eLTnqNZx+Vptc3rmrp0OdcCBGtZroIwGAS6OBDUFq04V0s9zKSQZlro4sAuiTWPvpICYBi8LotnbGkho3w2AqzxnJExM61LslkTEA8HYJA+99xzT5WMBa8JABMApEYgxd3JHGojtTbh+vXrp9xMyf6JDB06VCj0/tFHH6nNFVxPhw8frrwItIQVi+e7Sy65RGVp7ujiAbCj3wH++l3SgAdAl2YjxVg8AKZQWskOoZ4gSQjIUpe1vPrqq9KtWzdViLlMwiISNzIWeVm7zTZbT0XN3Nqly+kiQqFxLSYofWwBiDTRIFjLohcGg1EJXaKAsBakBQEvCdClSQBT647jukwXzlb4a2n5UcNuU6xxPGP1uJHrmoBYAYFK3D333ntvtSFF1luSsbz22mvK8nb77benKqXTMIV04BN5AOzAk+8v3TkNeAB0bkqSDcgDYDJ9NaM1u9W4C+EqhAXv/vvvryoAj1slO8S33Xabci1iN/vXv/61qn1lIxx39913y0MPPWTTPFEbChmvscYasv766yc6zvXGLmfLrFd3Zahx2N4iaAJgGCAGtQb42FrzAEKbjJ6cgz6TAB3H/LMyuO4WU5s0Pk93WW3Va2k5xeJcjWlCyZW33npLWezSlibRNQGXW245ZQEk4yebN1jvSfhCTO+CBQtU9k9cOwFEL+5pwAOge3PiR9RxNeABsOBz7wHQ/Ql85JFH1IIFl6Hvf//77QDwqquuUnEmd9xxh/Tq1Usuvvhief3119WiCffLOLnyyivVjvddd90V1zTx99TgIs26TrWeuAOHD3jiiSfku9/9roohKpOULcNpKwxqi96qEVMVtLoFgS4OBk2ASuOiWat/DYBZ3GVcV2vJipaWoVl0mHsfbLawCUb9PLOWX5ITazdQNsv4N1a/ffbZRwEl8Mf7j+/IhPyHP/xByBLqxT0NeAB0b078iDquBjwAFnzuPQAWawJZsJgWQBYtwNXPfvYzOffc1jgoFvCUcyCe5Jhjjom9wGHDhgm77MSzZC3U4ML9M+vyElmPM01/uIDutNNO0rVr1zSHO3uMjm/cd999BYtJmaRLlx+ISOfKJUXBoE1NPxPYbBK6mFCYxAKYxKK3LOlKS8ulZZo2lXGXd51OlpLm4rQbKElheC8BgFrYqKIEBAln/vjHP8pjjz2W5hT+mJw14AEwZwX77r0GEmjAA2ACZbnY1AOgi7MSPaYgALJjTQY7XC3JBKqF+Bb+vv7662Mv8JRTTlEJFshSl7UQV0jWuzXXXDPrrpven+v18tIqiIU2xeDLaN1k8T916lQV/6XLAqCnLl1+LCIaCJcEVBfnemkmUYmCSt1lEqALziAF0K+LnFbqeU6fPl25NpYN3CnPQDzxXnvtlTrpEpY/NjfITEySF/SkBddP4A+vCbwtHnwwWPQ+7dPkj8tSAx4As9Sm78troD4NeACsT39NP9oDYNOnINEAggCIKxMxfyQv0Jk26fCoo45S7k3Es8TJEUccobLskaUua6G4Mrv2aV23sh5Plv35a8tSm43ri/gu7snVV49z1Vw2pi5domLivjEGHgd/NAUAl0pLy22ZXzDgru/JJNeW+UBy6JBr0/NGTHFaAQAXL16sXOSBSS3AM26gZMAFosnW6cU9DXgAdG9O/Ig6rgY8ABZ87j0AFmsC8wBAXKEGDhwoJ554YubKKKuVDEWV2bpZ5thNLElYvEnRXzZ58803VcZManqWTd555x3BjXObbbZJfWlYgEmUhZ5IKqMFqyCbaSRAIv6P+nte3NOAB0D35sSPqONqwANgwefeA2CxJjAPF1BcRX/+85/LYYcdlrkyyhonh6LKHN9I6n2S2/Tu3Tvze6LZHeI2jcWnT58+zR5K5uenxhx1PXfbbbfM+252h8TosTGB+25aF1fcQIl3JqaQQvBaSASD5Q9AfOCBB+TOO+9s9uX684dowAOgvy28BtzRgAdAd+Yi1Ug8AKZSW9MOikoCQwIYEsEgLJR69uxplQQG1yosIbfcckuVS1RWF1jWTJnop8xWslmzZilrSz1JN7K6h7Luh3T/xHsR41g2Yc6wuuPe2KlTp7bL4zlHkvxO0lb3neSYJG3pH3jjvuTdphMv2fQRbAP8kygLSKYMBMJnbOhcccUVqmQNtVG9uKcBD4DuzYkfUcfVgAfAgs+9B0D3J3DJkiVq4YP07dtXRo8erTLYEVe3wQYbCGUgKOVgloGgqLFNGQgWRxRqJ/NdPa5VYVrUtfJwtbIpR+H+TFSPsEg1Dm0WyiYgEFOKpYR7IsmxebVNAxhRx2Dl+fDDD9VCHwAIG3OS68hybDbnjWtDzTuzXp5uX8/zpftL8jtJW8ZG+7hjli5dqgq5r7pqa7ylzTHBPjmO99F6663XZknkHUtiGOqtzp07V1lRvbinAQ+A7s2JH1HH1YAHwILPvQdA9yeQrIVmynI94hNOOEFZ+XQh+FtvvVXFt+DadPPNN7eLA6IdRZABM/3DgorkMSSPYGed+KEuXbqoU9S7MGahxqJqu+22k+WXXz4RSGRx/rwX5ugafeEqia7iFub16rOe66n3Lo9bmId97/IxAC7JRPTGhM1YbdpoKEkLJ0mBJmxMc+bMUc/5pptuGgpUaa6j3vsnq+MBNZK14AbKOyWN6JqAHMvzi+A1QWzo+PHjVfwfVmI2xry4pQEPgG7Nhx9Nx9aAB8CCz78HwIJPYILhk/ggzso3YMAAOfXUU9v1mmaBTydk3WORbbuwTbM4bdYxJIxgAamzEqbRUb3H5HXtLLRxcSVVvnaTS3CrOd+UjQlKk2AFLJt8+umnbUlOTEtgWa6T5EvUFqX+aVrRNQGBSO5vXELJDPo///M/ynX2pptuStu1Py5HDXgAzFG5vmuvgYQa8ACYUGGuNfcA6NqM5DceXN9YHLLg0T9k1tt///1V+nMSK1AbDagJc5tKOjKsi9TU6t+/f5VLWtJ+XG1PIgkWkFhayiZYkEjgAwBqK0mZrhGXajYntt566zJdlroWLO94Deyyyy6yyiqrlO76cM+kLiCla9KKrgnIe5BnmP54nu+++27lEYFLvRf3NOAB0L058SPquBrwAFjwufcAWPAJrHP41NY65phjBBDM2lqAFYnECvvuu2+do3TzcKAZ2Xzzzd0cYJ2jKnMGV6y3gMSuu+5ap5bcPPyll15SFk5ihMsmemOJRDdsWKUVNgAAQfpYuHCh2gTDpZ4NnUsvvTRtt/64HDXgATBH5fquvQYSasADYEKFudbcA6BrM7JsPCNHjlQFiWfOnCmdO3dWWQtJ+GICB/WryP5J3AoLmgMPPFDF/6211lpWF0bK84suukiBWtaCW9WMGTNUvE4ZpcyZMpmvMtc5JO0/dd/YnCijiytxgFi1SBpVRuF9haWuHhdePCKwdGsLIM8z2ZC33XZbufjii8uotsJfkwfAwk+hv4ASacADYMEn0wOguxN40EEHKevczjvvrBYqF154oVCfjeyeOlHLT37yE3nooYfUzjXFrQcPHqwWtLhe2sjtt98uv/3tb+Wxxx6zaZ6oDQtQxmsWXE7UgeONsSBh5SxjPTlUX6Qsp0lvFRLy4Ca54447tmWUTNqHy+0XL14sL7zwQmljON9//335+OOPZaeddko9DToZDJ4PvKt4nseOHavKQ1AXNU6IEyRrKLUXSXTFsd/5zndCD+P9fNJJJ1V9R6whG3hadDKv2267TSXz2n333eXXv/51KV3M43Qb9b0HwLSa88d5DWSvAQ+A2eu0oT16AGyouus6GZnpyNSp63xhYcPNi7iVI488UvWNtXDLLbdU1hsb97Zrr71WLYTvvffeusYWdjBlBHAtLWO9Na6XRSgLRxZ/ZZQyxzgyXyS5wYr07W9/u3TTB0zwnuDe7N69e+muD28H3NfJeFxPiRntBspz/MEHH8ioUaNUzPI555xTU2e8L48//ngZN26cirUcM2aMTJw4UbmR8o4OCgB49tlnq++1AJ6mpwbeHXh9mOV8SExjU86ndBMccUEeADvKTPvrLIIGPAAWYZZqjNEDYHEmEBcl4lNYFJDNc8qUKWqxwuLFTFlOhrwhQ4bI0KFDYy8O909qorHTnLWwQ8+uOgukMgp64xrrSUbhsl7KbuFkMQ4obbHFFi5PQ+qx8Z7AdXyTTTZJ3YfLBxLnSAZe3ndpBc8KXEHZrJo3b56MGDFCDj30UOVJUUt4p+GZceONN6pmWBNxRz3zzDPl/PPPDwVA3slY9sKE+5Csprjzn3vuuaoJG3wAIvCIJ4gXVffRrzn9jeA14IgG/MPoyESkHYYHwLSaa+xxLDBYmLCAoGYfguUPtyJ2sU3BDYm6gewoxwkupCRBYOc5a2FBBSSxUCqj+Osr9qxSC5D7M8ptr9hXJ/K3v/1N/ZT1+pg7rq+eDSbtBkpSIDZzLrnkEhk0aJCcdtppkdNPCQlqf06aNEkOP/zwtnbUZeX9TFx1UIC4k08+WVmbOSebRsCmzkL73nvvycYbb6zcrrfffvu2w4mf5u/rr7++6LdjJuP3AJiJGn0nXgOZaMADYCZqbF4nHgCbp/skZwbUHnnkEQV/6623XmYAyM4yLqPnnXdekuFYtS27hazsFs6yx3DqLLVslpTRsEB8Ge8LIKKMpTyw3OHmios5QJZWADpAkhI5F1xwgQK1YLye2TcbB4AcSYSIF9QybNgwNR4K1QcFl/x3331XJZjBsoer6TPPPKPqNfI+py9i/ugbt2QtRx11lLo383DRT6uvZh7nAbCZ2vfn9hqo1oAHwILfER4A3Z9A3JHYVWbB0KtXr7YBZ+ECesABB8ghhxyiFj1ZS9lj5Moe41j2Oo5YYih1QaysTqqU9TPQ7P4AC1xAw+LSmj22LM6PxYzkV717907dHXUTyZqK9Q4XTFw1f/CDH0T2lwYAg50Br2y8YW28/PLLPQBazp4HQEtF+WZeAw3QgAfABig5z1N4AMxTu/X1TVwIMSX333+/StQSLDiuk8CMHz9ejjjiCHUy4pqIabJNAoN7JufQSWTqG3H10WWPISu7hQxAevLJJ1UW13oSbWR5T2XdF9YaYsjWXnvtrLt2oj+SQvEeATb4jST5HdY2yfH6fFkdE+yHdyCWO+YQS1na8dIP5SDYbCMueuDAgZHzl8YFNKwzzsE5eX97F1C7x8UDoJ2efCuvgUZowANgI7Sc4zk8AOao3Dq7PuOMM1ScH9Y/s/YfO94kd0BwDX344YdVooBVV11VwRzCzn+csFgCKslgl0ex9rLXyVu0aJGK2SlancMki3E2Erbaaivp2rVrIeCh1rWFfYcVl7IpZMpMCw9J9JmkbS14su0HiM9KtJtsrd9ZtWHMZl9R/dKOOezRo0ebm2uaMaAn3qvE9A0fPrwqti9Mf8QdEltJ6QeE4zfYYAMFkGFJYIJ9YHUk/m/AgAEyevRode+RBIYEMFghEd4vWG59Ephl2vMAmNXT7PvxGqhfAx4A69dhU3vwANhU9dc8eVRcErX7TjzxRHWsLgTPLrJZCN7GosGig4XT5MmTVWwKfxMrlHQRrReqwd9z585VfbEwimpTa5EbPMZ20Wu2y+pawvrBjQvrA5n6ksJD1tcSpd+o89Rz19sssG0X8ElhIutzEweIq6tOx2/Tv00bm+vPqp/gucy/AQ2KplPvUG8acd40567nnsnz2Ndee01dW9BDIsk5dTIY3IGvu+46Ofjgg2seTkweSV8oHA8Isok2YcIEVYaHe4kSEcQJ6uRal112mXI1xh0XV1PqB/LeJZMpGywISbuuvPLKqjIQXJsvA+EBMMm97Nt6DTRKAx4AG6XpnM7jATAnxTrWLQXZcfdkoWP+mMNkkXjxxRcnGnnYQlJ/pq0PuDlFLVJtFqI2bZqx4AYAiXMkex9WJD2GpFBTawFvu1i30ZFNm+A1cN9gWd5oo42q5jDRTeJwY1z/tBW3jIlgUD0F4UksopNHOTwdqYZGMiZdb1Q/h2k6YgONd+Ctt94q++23X2wXlIDQheDJ1HnDDTe0ZSTt16+femaw3iGU5LnvvvtU0XiszZwHS2Pfvn3bzsNmDVlIOT+QSI3Dm2++WTbbbLPYsXSUBt4C2FFm2l9nETTgAbAIs1RjjB4ACz6BlsNncQOssEDSPyx+2b2moDKLfOK8cIOycb2yWSwDD2TnqydBg+XlNaUZsUBk/aMWYz0Lz6YM3vKkZa+Vh4WMZEpkkixrIhieb4re16oLqi3FlrdFaDP64Ce4yWTzN/MQ1s6mP2r54Tb54x//WLnKm/1E9Rs8F+ehn/+/vfsMlata/wC8NGIHw0VNLFHsEXs3KmqiEiPWKIlf7OWDKCqWaMD4xRIU1NhiRfxkLFixhGA3ERE1FiwgdlQUu/hBLH/e/WfC5DgnZ+bsc2b2mv1skHsvZ/bMu553jnf/ztp7rfgD2N13352mT59ehsK5oyQgAI4SrLclMAwBAXAYaFU6RQCsUje6W0v81TyeQ4nFTEYjwMTtS82zR90d3eh/WswAPvnkk2natGlptdVWG/0P7NInNIeBZ555Jr355ptp9uzZHX16u2EgLrrbuciPC/ZOXtfuhX+8b9x2N3Xq1OJ3YajPabxvO7XEa5rrGOyceE0777ei2lqd3/js+G7GLGfMVMexokC2ojoaP1tRWOvoS9L04viDUvw7aMyYMcv9karxx6rGz5v/gNX83+N7FH/kitm1ga8d7D1bvXfcbnnnnXcWKyM7qicgAFavJyqqr4AAmHnvBcBqNnD+/Pkp/onlyeOIi9M5c+YUYSOOxrN/CxYsWO7Zv8azTO2MKhb4OOqoo1Ks1tlqRq/srECEhrjtNBZWGHhh2eoiciQuhAe+b7sX1oNdFA+8iG9+XVx0xhjjn3gGaagL64FhoMysx1BBpZ1Zl6Fs4uex+EvcYhcXy83v2c657XwHW71msIv8dsPAUOcP/HnsARe3KU+YMOE/4aHVew0VKAae0xxIms9tFVRW9PPBxtU4p1VIip9Fr2J2LG4vjBmyxvu0GsdQQatxTicGQ722+TbnxvehnTsMmr87ixYtKrayieeOOz13uN9T53VfQADsvrlPJDCYgACY+XdDAKxmA5944onir+GxsEFcwN17773F8ybxl/wIg7H6Z8w+xTMmcdtmrD4XF3aLFy9ue0DxfnHLUzxjUmZ2YrBzY/YvFpWJ516Gewx1MT/YRe+KLsKHes/BgkarC9no0yGHHFLMdLbzvgPDwFAX752GnqEutocKEc0/j1pjljg2uI6VZptnaEbycwaGnoHfleFc0HdyTiz6EQulxEJK/XrEapXxXNoOO+zQl0OMP8bEc3PrrrtuX47PoP5fQAD0TSBQHQEBsDq9GFYlAuCw2HpyUqzYGaEt9uxbb731ii0iGvv3xepzsddXu/v/xQDefvvtZSuANl/4d3pxP9jrZ86cmU499dR0xBFHtAxHMeuyotDU6iK+kwv74c4mdNLcMI/FHeI/+/GI50Rj1dlzzz23b2dW4lbe+C4O57vVjz03JgJVFRAAq9oZddVRQADMvOsCYPUbGLcKPvjgg8Wy4zEDGDNqsfBIPLs3duzYZQOIzZDPO++8FS720M3RxvNjscR5YxuIbn62zyJAgACB/hIQAPurn0aTt4AAmHf/4ta/fzMfQt+W/+6776ZJkyYVz/utvfbaxYxfbBwc/3nKKacUz/41H7Gi5+TJk4v9pBwECBAgQKCfBATAfuqmseQuIABm3kEBsLoNjG0GYuuGuA3voYceSnfddVex7cDSpUsFwOq2TWUECBAgMAoCAuAooHpLAsMUEACHCVeV0wTAqnRi6Dpic+JYyj2ercvhFtChR+QVBAgQIECgPQEBsD0nryLQDQEBsBvKo/gZAuAo4o7wW0+ZMqV4nm7evHnFIjCxauGxxx5bfEps2D1x4sSOFoEZ4fK8HQECBAgQGDUBAXDUaL0xgY4FBMCOyap1ggBYrX40qrn00kuLPf8i8P3222/Fc3/xbN/ChQuLbQdiG4hYmj+2gYgtCM4555zi1CVLllRzQKoiQIAAAQIlBATAEnhOJTDCAgLgCIN2++0EwG6Lt/d5sXHzs88+m7755ptin78dd9wxzZo1qwh/cTQ2go9ZwFgMZurUqenWW29N48ePb+8DvIoAAQIECGQkIABm1Cyl9r2AAJh5iwXAzBuofAIECBAgUAMBAbAGTTbEbAQEwGxa1bpQATDzBiqfAAECBAjUQEAArEGTDTEbAQEwm1YJgJm3SvkECBAgQKC2AgJgbVtv4BUUEAAr2JROSjID2IlW9187d+7cFAvCnHvuuemGG24oCmg8/7dgwYLlnv8bN25c9wv0iQQIECBAoAsCAmAXkH0EgTYFBMA2oar6MgGwqp1J6fXXX08zZswoVvmcPHnysgAYK4A++eSTxQqgsUDM2WefnVZeeeW0ePHi6g5GZQQIECBAoISAAFgCz6kERlhAABxh0G6/nQDYbfH2Pu/3339Pu+66a7Gy5xVXXJF23nnnIgD+8ssvxR6AsS3EcccdV7zZhx9+mLbddlt7ALZH61UECBAgkKGAAJhh05TctwICYOatFQCr2cCTTjop/e9//0vXX399OvDAA5cFwOeeey4ddNBB6aeffkpjx45dVvymm26azjvvvHT++edXc0CqIkCAAAECJQQEwBJ4TiUwwgIC4AiDdvvtBMBuiw/9efFs35VXXlncArr66qsvFwBj5u+UU04pnv1rPvbcc8/iNtHYLN5BgAABAgT6TUAA7LeOGk/OAgJgzt1LKQmA1Wrgl19+mXbfffe0aNGiYvP3OJpnAAXAavVLNQQIECDQHQEBsDvOPoVAOwICYDtKFX6NAFit5jz66KPpmGOOSWPGjFlW2N9//51WWmmlYqGXhQsXpoMPPtgtoNVqm2oIEOiBgFWSe4Dew48UAHuI76MJDBAQADP/SgiA1Wrgb7/9lj7//PPliopbPidOnJhmzZqVJkyYUCwCc99996Vjjz22eN1HH31U/PzVV19Ne++9d7UGpBoCFRG4+uqr08MPP1wsmrTGGmukffbZp7hleptttllWYe5brMyfPz/FP5999lkxpu222y7NmTMnTZs2rfjfuY+v+atkleSK/GJ1sQwBsIvYPorAEAICYOZfEQGw+g1svgU0qo1tIJ566qliG4jYIuKcc84pBrFkyZLqD6aPKrzlllvStddem7799tu00047pZtuuinFs5i5Hi+99FIxnjfeeCN988036ZFHHklHH330suH8+++/6fLLL0933nln+vnnn9O+++5bhI2tttoqiyEfeuih6fjjj0977LFH+uuvv9Ls2bPTe++9l95///201lprFWPIfYuVJ554orh7IHoS/br33nuLnr711ltFGMx9fI0vmlWSs/iVG/EiBcARJ/WGBIYtIAAOm64aJwqA1ejDiqoYGAAbf8WPWcBYDGbq1KnFdhHjx4+vzGBiEZvYq3Dp0qVp1VVXLQLDwOOLL74oLkiff/75tPbaa6dY+TRmaVZZZZXKjGOwQu6///504oknpttuuy3ttddexRYdDz74YDEbu/7661e+/lYFPv3008VekrvttluaPn36fwJgzJZFfyJUbLbZZumyyy5L7777bhGgYrGi3I7vv/++6NWLL76Y9t9//77dYiVWE44QGNvG9MsWMlZJzu23bWTqFQBHxtG7EBgJAQFwJBR7+B4CYA/x+/ijY6Yotqn46quv0t133/2fABjPNcbehhFa4+I0ZpwiUJ1xxhnpqquuqrxMhL6YSbr55puLWv/555/i9tyYjb3kkksqX/9QBcYzp80zgDGbtOGGG6YLLrggXXjhhcXpsSfluHHjipnomFnL7fj444+LmbIIsdtvv33qty1W4ncs/igRYSlmAGOmuh+2kLFKcm6/aSNXrwA4cpbeiUBZAQGwrGCPzxcAe9yAPv/4CAexP+HAGcCYbTr88MPT0qKzKwAAC6lJREFU119/XYSIOGI2LZ5zjJmZmDWs6vHnn3+mNddcMz300EPL3SIZF9oxzscee6yqpbdd18AA+Mknn6QtttiiCBIR3BvHAQccUPzvefPmtf3eVXhhBPYjjzyy6Ncrr7xSlNQvK+xGoJ00aVLxvF/MrMe4DjvssL4Yn1WSq/Db07saBMDe2ftkAgMFBMDMvxMCYOYNrHj5gwXAWJji8ccfL24RbRyffvpp2nzzzdObb76Zdtlll8qOLELrRhttVDxzGRfajePiiy8ubid87bXXKlt7u4UNDIAx1njmL8a+wQYbLHubGTNmFCvUxi2xOR1x63H8ESLC38Ybb9xXATD+QBG3V8cMbfyR4q677iq+l/G7lvseolZJzum3bORrFQBH3tQ7EhiugAA4XLmKnCcAVqQRfVrGYAHwzDPPLFY7jW0tGscff/xRLMYRC9w0Vi2sIosAmHcAPPvss4tZ2lj0Jp5lbBz9dgtoY1yxbUzM3s6cOTP7W0CtklzFfyN2ryYBsHvWPonAUAIC4FBCFf+5AFjxBlWovHi2LRYCWdHxwQcfFFtSNI5+DIBuAc3zFtB4jjGe0YxnG1944YX/rF4aM2b9uMXKlClT0iabbFLcptuP47NKcoX+T2KUSxEARxnY2xPoQEAA7ACrii8VAKvYlWrWFM/m/fDDDyssLm7hbH5+rx9vAQ2AWAQmtnyIrR/iiGfK4iI7Zpf6eRGYWAAmFoKJ49dffy1W0cxlEZizzjqreA4uZv+a9/5bZ511in0B48h9i5VLL720mD2P72LMlsV44482MdN+yCGHZD++Vv/yyXGV5Gr+G776VQmA1e+RCusjIABm3msBMPMGVrz8oRaBidU/G9sm3HHHHemiiy5K3333XVpttdUqPbJ45i0Wfbn99tuLIBjbQDzwwAPFJuONRW0qPYAWxcXearEyZhzxDOZ1112XJk+enGIbgQgUESTmzp273DYQ77zzTjbbQMSziq2Oe+65J5188snFj3LYYmVF36vTTjstPfvss8WquhFsd9xxx2JhpQh//TC+3H6n1DuyAgLgyHp6NwJlBATAMnoVOFcArEAT+rCEWITixx9/LBZ6iW0eXn755WKUW265ZbEyYWMbiNha4JprrimWqD/hhBPS6aefnsU2EDGW2AKisRF8rIR54403FjODuR5xW2QEvoFHBN0I8o2N4COox+qZ++23X7H/5NZbb53rkNVNgEBGAgJgRs1Sat8LCICZt1gAzLyBFS0/ZlRiw/CBR2z6HrdsxRGLwMQtdxE8YvGXCBoxw5TDRvAVZVcWAQIE+lZAAOzb1hpYhgICYIZNay5ZAMy8gconQIAAAQI1EBAAa9BkQ8xGQADMplWtCxUAM2+g8gkQIECAQA0EBMAaNNkQsxEQALNplQCYeauUT4AAAQIEaisgANa29QZeQQEBsIJN6aQkM4CdaHktAQIECBAg0AsBAbAX6j6TQGsBATDzb4YAmHkDlU+AAAECBGogIADWoMmGmI2AAJhNq1oXKgBm3kDlEyBAgACBGggIgDVosiFmIyAAZtMqATDzVimfAAECBAjUVkAArG3rDbyCAgJgBZvSSUlmADvR8loCBAgQIECgFwICYC/UfSaB1gICYObfDAEw8wYqnwABAgQI1EBAAKxBkw0xGwEBMJtWtS5UAMy8gconQIAAAQI1EBAAa9BkQ8xGQADMplUCYOatUj4BAgQIEKitgABY29YbeAUFBMAKNqWTkswAdqLltQQIECBAgEAvBATAXqj7TAKtBQTAzL8ZAmDmDVQ+AQIECBCogYAAWIMmG2I2AgJgNq1qXagAmHkDlU+AAAECBGogIADWoMmGmI2AAJhNqwTAzFulfAIECBAgUFsBAbC2rTfwCgoIgBVsSiclmQHsRMtrCRAgQIAAgV4ICIC9UPeZBFoLCICZfzMEwMwbqHwCBAgQIFADAQGwBk02xGwEBMBsWtW6UAEw8wYqnwABAgQI1EBAAKxBkw0xGwEBMJtWCYCZt0r5BAgQIECgtgICYG1bb+AVFBAAK9iUTkoyA9iJltcSIECAAAECvRAQAHuh7jMJtBYQADP/ZgiAmTdQ+QQIECBAoAYCAmANmmyI2QgIgNm0qnWhAmDmDVQ+AQIECBCogYAAWIMmG2I2AgJgNq0SADNvlfIJECBAgEBtBQTA2rbewCsoIABWsCmdlGQGsBMtryVAgAABAgR6ISAA9kLdZxJoLSAAZv7NEAAzb6DyCRAgQIBADQQEwBo02RCzERAAs2lV60IFwMwbqHwCBAgQIFADAQGwBk02xGwEBMBsWiUAZt4q5RMgQIAAgdoKCIC1bb2BV1BAAKxgUzopyQxgJ1peS4AAAQIECPRCQADshbrPJNBaQADM/JshAGbeQOUTIECAAIEaCAiANWiyIWYjIABm06rWhQqAmTdQ+QQIECBAoAYCAmANmmyI2QgIgNm0SqEECBAgQIAAAQIECBAoJyAAlvNzNgECBAgQIECAAAECBLIREACzaZVCCRAgQIAAAQIECBAgUE5AACzn52wCBAgQIECAAAECBAhkIyAAZtMqhRIgQIAAAQIECBAgQKCcgABYzs/ZBAgQIECAAAECBAgQyEZAAMymVQolQIAAAQIECBAgQIBAOQEBsJyfswkQIECAAAECBAgQIJCNgACYTasUSoAAAQIECBAgQIAAgXICAmA5P2cTIECAAAECBAgQIEAgGwEBMJtWKZQAAQIECBAgQIAAAQLlBATAcn7OJkCAAAECBAgQIECAQDYCAmA2rVIoAQIECBAgQIAAAQIEygkIgOX8nE2AAAECBAgQIECAAIFsBATAbFqlUAIECBAgQIAAAQIECJQTEADL+TmbAAECBAgQIECAAAEC2QgIgNm0SqEECBAgQIAAAQIECBAoJyAAlvNzNgECBAgQIECAAAECBLIREACzaZVCCRAgQIAAAQIECBAgUE5AACzn52wCBAgQIECAAAECBAhkIyAAZtMqhRIgQIAAAQIECBAgQKCcgABYzs/ZBAgQIECAAAECBAgQyEZAAMymVQolQIAAAQIECBAgQIBAOQEBsJyfswkQIECAAAECBAgQIJCNgACYTasUSoAAAQIECBAgQIAAgXICAmA5P2cTIECAAAECBAgQIEAgGwEBMJtWKZQAAQIECBAgQIAAAQLlBATAcn7OJkCAAAECBAgQIECAQDYCAmA2rVIoAQIECBAgQIAAAQIEygkIgOX8nE2AAAECBAgQIECAAIFsBATAbFqlUAIECBAgQIAAAQIECJQTEADL+TmbAAECBAgQIECAAAEC2QgIgNm0SqEECBAgQIAAAQIECBAoJyAAlvNzNgECBAgQIECAAAECBLIREACzaZVCCRAgQIAAAQIECBAgUE5AACzn52wCBAgQIECAAAECBAhkIyAAZtMqhRIgQIAAAQIECBAgQKCcgABYzs/ZBAgQIECAAAECBAgQyEZAAMymVQolQIAAAQIECBAgQIBAOQEBsJyfswkQIECAAAECBAgQIJCNgACYTasUSoAAAQIECBAgQIAAgXICAmA5P2cTIECAAAECBAgQIEAgGwEBMJtWKZQAAQIECBAgQIAAAQLlBATAcn7OJkCAAAECBAgQIECAQDYCAmA2rVIoAQIECBAgQIAAAQIEygkIgOX8nE2AAAECBAgQIECAAIFsBATAbFqlUAIECBAgQIAAAQIECJQTEADL+TmbAAECBAgQIECAAAEC2QgIgNm0SqEECBAgQIAAAQIECBAoJyAAlvNzNgECBAgQIECAAAECBLIREACzaZVCCRAgQIAAAQIECBAgUE5AACzn52wCBAgQIECAAAECBAhkIyAAZtMqhRIgQIAAAQIECBAgQKCcgABYzs/ZBAgQIECAAAECBAgQyEZAAMymVQolQIAAAQIECBAgQIBAOQEBsJyfswkQIECAAAECBAgQIJCNgACYTasUSoAAAQIECBAgQIAAgXIC/webav0OgUsjoAAAAABJRU5ErkJggg==\" 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(\"elu\", \"Elu\", x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"971\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(51, 51) (51, 51) (51, 51)\n"
-     ]
-    }
-   ],
-   "source": [
-    "show_1(\"selu\", \"selu\", x, lims=(0.495, 0.58))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/e_swish.PNG b/e_swish.PNG
index 7b6b94a..b84c05e 100644
Binary files a/e_swish.PNG and b/e_swish.PNG differ
diff --git a/e_swish_paper.pdf b/e_swish_paper.pdf
new file mode 100755
index 0000000..1f7375e
Binary files /dev/null and b/e_swish_paper.pdf differ
diff --git a/mnist/deep_nets/.ipynb_checkpoints/mnist_deep_nets-checkpoint.ipynb b/mnist/deep_nets/.ipynb_checkpoints/mnist_deep_nets-checkpoint.ipynb
deleted file mode 100644
index 879749d..0000000
--- a/mnist/deep_nets/.ipynb_checkpoints/mnist_deep_nets-checkpoint.ipynb
+++ /dev/null
@@ -1,3766 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Repeating MNIST first experiment of swish paper. \"\"\"\n",
-    "\n",
-    "import gc # Garbage collector\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "%matplotlib notebook\n",
-    "\n",
-    "np.random.seed(2)\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "from keras.utils.np_utils import to_categorical # convert to one-hot-encoding\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Dropout, Flatten, Activation, BatchNormalization\n",
-    "from keras.optimizers import SGD\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
-    "\n",
-    "# For adding new activation function\n",
-    "from keras import backend as K\n",
-    "from keras.datasets import mnist\n",
-    "from keras.utils.generic_utils import get_custom_objects\n",
-    "from keras.utils import np_utils"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X_train original shape (60000, 28, 28)\n",
-      "y_train original shape (60000,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()\n",
-    "print(\"X_train original shape\", X_train.shape)\n",
-    "print(\"y_train original shape\", Y_train.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Normalization\n",
-    "nb_classes = 10\n",
-    "# Normalize the data\n",
-    "X_train = X_train / 255.0\n",
-    "X_test = X_test / 255.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Encode labels to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])\n",
-    "Y_train = np_utils.to_categorical(Y_train, nb_classes)\n",
-    "Y_test = np_utils.to_categorical(Y_test, nb_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Set the random seed\n",
-    "random_seed = 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Split the train and the validation set for the fitting\n",
-    "X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(54000, 784) (6000, 784) (10000, 784)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Reshape image in 3 dimensions (height = 28px, width = 28px , canal = 1)\n",
-    "X_train = X_train.reshape(-1,784)\n",
-    "X_val = X_val.reshape(-1,784)\n",
-    "X_test = X_test.reshape(-1,784)\n",
-    "# test = test.values.reshape(-1,28,28,1)\n",
-    "print(X_train.shape, X_val.shape, X_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "def e_swish_2(x):\n",
-    "    sigmoid = K.sigmoid(x)\n",
-    "    return K.maximum(x*sigmoid, x*(2-sigmoid))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Set the CNN model \n",
-    "# my CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out\n",
-    "def create(act, n):\n",
-    "    model = Sequential()\n",
-    "    model.add(Dense(512, input_shape=(784,)))\n",
-    "    model.add(Activation(act))\n",
-    "    for i in range(n-1):\n",
-    "        if i%2 == 0:\n",
-    "            model.add(BatchNormalization())\n",
-    "        model.add(Dense(512))\n",
-    "        model.add(Activation(act))\n",
-    "        model.add(Dropout(0.3))\n",
-    "        \n",
-    "    model.add(Dense(nb_classes))\n",
-    "    model.add(Activation(\"softmax\"))\n",
-    "    \n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def plot(arr, names = [\"relu\", \"e_swish_2\", \"swish\"]):\n",
-    "    fig, ax = plt.subplots()\n",
-    "    for item in arr:\n",
-    "        ax.plot(np.array(item))\n",
-    "    \n",
-    "    plt.grid()\n",
-    "    plt.legend(names[:len(arr)], loc='upper right')\n",
-    "    ax.set_ylim(0,1)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " Starting round with 23 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 74s 1ms/step - loss: 1.8138 - acc: 0.3087 - val_loss: 1.1260 - val_acc: 0.5507\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.2893 - acc: 0.5202 - val_loss: 0.9207 - val_acc: 0.6202\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.0644 - acc: 0.6229 - val_loss: 0.5850 - val_acc: 0.7962\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.8988 - acc: 0.7164 - val_loss: 0.4861 - val_acc: 0.8677\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.7803 - acc: 0.7758 - val_loss: 0.3949 - val_acc: 0.8998\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.7047 - acc: 0.8082 - val_loss: 0.3282 - val_acc: 0.9175\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.6350 - acc: 0.8374 - val_loss: 0.3050 - val_acc: 0.9350\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5737 - acc: 0.8583 - val_loss: 0.2914 - val_acc: 0.9350\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.5354 - acc: 0.8728 - val_loss: 0.2899 - val_acc: 0.9353\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4996 - acc: 0.8849 - val_loss: 0.2261 - val_acc: 0.9500\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4711 - acc: 0.8925 - val_loss: 0.1973 - val_acc: 0.9552\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4436 - acc: 0.8996 - val_loss: 0.2120 - val_acc: 0.9542\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4255 - acc: 0.9039 - val_loss: 0.1910 - val_acc: 0.9587\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3896 - acc: 0.9135 - val_loss: 0.1816 - val_acc: 0.9607\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3799 - acc: 0.9154 - val_loss: 0.1784 - val_acc: 0.9622\n",
-      "10000/10000 [==============================] - 3s 269us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 74s 1ms/step - loss: 1.8127 - acc: 0.3074 - val_loss: 1.3107 - val_acc: 0.4187\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.4308 - acc: 0.4223 - val_loss: 1.0700 - val_acc: 0.5315\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.2363 - acc: 0.5301 - val_loss: 0.8384 - val_acc: 0.6692\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.0572 - acc: 0.6320 - val_loss: 0.6474 - val_acc: 0.7562\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.9042 - acc: 0.7116 - val_loss: 0.5228 - val_acc: 0.8303\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.7645 - acc: 0.7876 - val_loss: 0.3467 - val_acc: 0.9162\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.6375 - acc: 0.8412 - val_loss: 0.2950 - val_acc: 0.9327\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5902 - acc: 0.8589 - val_loss: 0.2737 - val_acc: 0.9385\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5359 - acc: 0.8741 - val_loss: 0.2423 - val_acc: 0.9450\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4854 - acc: 0.8887 - val_loss: 0.2335 - val_acc: 0.9480\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4661 - acc: 0.8951 - val_loss: 0.2555 - val_acc: 0.9428\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4263 - acc: 0.9038 - val_loss: 0.2108 - val_acc: 0.9528\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4121 - acc: 0.9078 - val_loss: 0.1952 - val_acc: 0.9577\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3795 - acc: 0.9153 - val_loss: 0.1928 - val_acc: 0.9575\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3760 - acc: 0.9171 - val_loss: 0.1806 - val_acc: 0.9592\n",
-      "10000/10000 [==============================] - 3s 273us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 75s 1ms/step - loss: 1.7887 - acc: 0.3216 - val_loss: 1.1843 - val_acc: 0.5382\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.3035 - acc: 0.5196 - val_loss: 0.7345 - val_acc: 0.7475\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.0250 - acc: 0.6629 - val_loss: 0.5340 - val_acc: 0.7988\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.8569 - acc: 0.7424 - val_loss: 0.4266 - val_acc: 0.8577\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.7270 - acc: 0.7926 - val_loss: 0.3304 - val_acc: 0.9098\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.6308 - acc: 0.8376 - val_loss: 0.2981 - val_acc: 0.9323\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5508 - acc: 0.8681 - val_loss: 0.2565 - val_acc: 0.9405\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5030 - acc: 0.8828 - val_loss: 0.2424 - val_acc: 0.9455\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4592 - acc: 0.8956 - val_loss: 0.2017 - val_acc: 0.9535\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4238 - acc: 0.9049 - val_loss: 0.1809 - val_acc: 0.9597\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3871 - acc: 0.9153 - val_loss: 0.1930 - val_acc: 0.9628\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3656 - acc: 0.9210 - val_loss: 0.1650 - val_acc: 0.9645\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3505 - acc: 0.9248 - val_loss: 0.1512 - val_acc: 0.9670\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3241 - acc: 0.9287 - val_loss: 0.1535 - val_acc: 0.9658\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3223 - acc: 0.9316 - val_loss: 0.1555 - val_acc: 0.9667\n",
-      "10000/10000 [==============================] - 3s 277us/step\n",
-      "0.9717\n",
-      "\n",
-      " \n",
-      " Starting round with 26 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 86s 2ms/step - loss: 2.0965 - acc: 0.2386 - val_loss: 1.6202 - val_acc: 0.3768\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.7012 - acc: 0.3495 - val_loss: 1.4475 - val_acc: 0.4187\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.5591 - acc: 0.3974 - val_loss: 1.2574 - val_acc: 0.5033\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.4484 - acc: 0.4457 - val_loss: 1.1232 - val_acc: 0.5427\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.3466 - acc: 0.4886 - val_loss: 0.9860 - val_acc: 0.6398\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.2584 - acc: 0.5271 - val_loss: 0.9340 - val_acc: 0.6635\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.1640 - acc: 0.5663 - val_loss: 0.8329 - val_acc: 0.6995\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.1019 - acc: 0.5921 - val_loss: 0.7778 - val_acc: 0.7352\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.0371 - acc: 0.6234 - val_loss: 0.7590 - val_acc: 0.7663\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.9852 - acc: 0.6538 - val_loss: 0.6538 - val_acc: 0.8298\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.9471 - acc: 0.6815 - val_loss: 0.6097 - val_acc: 0.8428\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8885 - acc: 0.7088 - val_loss: 0.5574 - val_acc: 0.8697\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8460 - acc: 0.7373 - val_loss: 0.4696 - val_acc: 0.8858\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.7891 - acc: 0.7652 - val_loss: 0.4418 - val_acc: 0.8923\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.7483 - acc: 0.7843 - val_loss: 0.4252 - val_acc: 0.9003\n",
-      "10000/10000 [==============================] - 3s 296us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 86s 2ms/step - loss: 1.9982 - acc: 0.2780 - val_loss: 1.4930 - val_acc: 0.3850\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 1.6130 - acc: 0.3946 - val_loss: 1.3057 - val_acc: 0.4868\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.4823 - acc: 0.4450 - val_loss: 1.1480 - val_acc: 0.5847\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 1.3685 - acc: 0.4872 - val_loss: 1.0389 - val_acc: 0.6068\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 1.2939 - acc: 0.5173 - val_loss: 0.9993 - val_acc: 0.6492\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.2344 - acc: 0.5405 - val_loss: 0.9296 - val_acc: 0.6480\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.1697 - acc: 0.5709 - val_loss: 0.8562 - val_acc: 0.7013\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.1067 - acc: 0.5964 - val_loss: 0.7633 - val_acc: 0.7223\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 1.0381 - acc: 0.6321 - val_loss: 0.6844 - val_acc: 0.7543\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.9666 - acc: 0.6657 - val_loss: 0.6309 - val_acc: 0.7805\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.9099 - acc: 0.6927 - val_loss: 0.5844 - val_acc: 0.7948\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8645 - acc: 0.7087 - val_loss: 0.5682 - val_acc: 0.8060\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8228 - acc: 0.7250 - val_loss: 0.5175 - val_acc: 0.8213\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8015 - acc: 0.7332 - val_loss: 0.5245 - val_acc: 0.8323\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 0.7605 - acc: 0.7484 - val_loss: 0.4922 - val_acc: 0.8293\n",
-      "10000/10000 [==============================] - 3s 301us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.9193 - acc: 0.2708 - val_loss: 1.3611 - val_acc: 0.4457\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.5362 - acc: 0.3966 - val_loss: 1.1773 - val_acc: 0.5177\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.3826 - acc: 0.4704 - val_loss: 0.9958 - val_acc: 0.6235\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.2693 - acc: 0.5365 - val_loss: 0.9077 - val_acc: 0.6522\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.1785 - acc: 0.5718 - val_loss: 0.8301 - val_acc: 0.7077\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.1156 - acc: 0.6007 - val_loss: 0.7792 - val_acc: 0.7192\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.0420 - acc: 0.6306 - val_loss: 0.7153 - val_acc: 0.7145\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 0.9859 - acc: 0.6512 - val_loss: 0.6334 - val_acc: 0.7340\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.9308 - acc: 0.6704 - val_loss: 0.6165 - val_acc: 0.7670\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.8900 - acc: 0.6859 - val_loss: 0.6492 - val_acc: 0.7450\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.8732 - acc: 0.6962 - val_loss: 0.5701 - val_acc: 0.7725\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.8173 - acc: 0.7156 - val_loss: 0.5401 - val_acc: 0.7613\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.7909 - acc: 0.7352 - val_loss: 0.5304 - val_acc: 0.8087\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.7601 - acc: 0.7490 - val_loss: 0.5166 - val_acc: 0.8232\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.7245 - acc: 0.7640 - val_loss: 0.4621 - val_acc: 0.8750\n",
-      "10000/10000 [==============================] - 3s 318us/step\n",
-      "0.9253\n",
-      "\n",
-      " \n",
-      " Starting round with 29 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 103s 2ms/step - loss: 2.1394 - acc: 0.1811 - val_loss: 1.7070 - val_acc: 0.3285\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 91s 2ms/step - loss: 1.7998 - acc: 0.3149 - val_loss: 1.5464 - val_acc: 0.3957\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.6298 - acc: 0.3833 - val_loss: 1.2759 - val_acc: 0.4550\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.4793 - acc: 0.4264 - val_loss: 1.1568 - val_acc: 0.5247\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.3912 - acc: 0.4651 - val_loss: 1.0731 - val_acc: 0.5627\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.3086 - acc: 0.5055 - val_loss: 1.0157 - val_acc: 0.5457\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.2459 - acc: 0.5271 - val_loss: 0.9286 - val_acc: 0.6388\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1952 - acc: 0.5484 - val_loss: 0.9310 - val_acc: 0.6375\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1534 - acc: 0.5686 - val_loss: 0.8297 - val_acc: 0.6802\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1198 - acc: 0.5861 - val_loss: 0.7934 - val_acc: 0.7277\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 1.0700 - acc: 0.6063 - val_loss: 0.7759 - val_acc: 0.6990\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.0440 - acc: 0.6205 - val_loss: 0.7147 - val_acc: 0.7422\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 1.0073 - acc: 0.6439 - val_loss: 0.6642 - val_acc: 0.7405\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.9668 - acc: 0.6679 - val_loss: 0.6584 - val_acc: 0.7730\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.9263 - acc: 0.6883 - val_loss: 0.5509 - val_acc: 0.8540\n",
-      "10000/10000 [==============================] - 3s 334us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 102s 2ms/step - loss: 2.1911 - acc: 0.1815 - val_loss: 1.9103 - val_acc: 0.2967\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.8623 - acc: 0.3161 - val_loss: 1.4190 - val_acc: 0.4310\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.6369 - acc: 0.3754 - val_loss: 1.3025 - val_acc: 0.4440\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.5305 - acc: 0.4017 - val_loss: 1.1705 - val_acc: 0.5273\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.4550 - acc: 0.4201 - val_loss: 1.1449 - val_acc: 0.5030\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.3927 - acc: 0.4374 - val_loss: 1.0832 - val_acc: 0.5417\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.3294 - acc: 0.4742 - val_loss: 0.9845 - val_acc: 0.5847\n",
-      "Epoch 8/15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54000/54000 [==============================] - 86s 2ms/step - loss: 1.2646 - acc: 0.5022 - val_loss: 1.0034 - val_acc: 0.5597\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.2213 - acc: 0.5196 - val_loss: 0.9305 - val_acc: 0.5847\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.1738 - acc: 0.5393 - val_loss: 0.9307 - val_acc: 0.6253: 1.1738 - a\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.1366 - acc: 0.5599 - val_loss: 0.8411 - val_acc: 0.6435\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.0811 - acc: 0.5784 - val_loss: 0.7895 - val_acc: 0.6858\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 86s 2ms/step - loss: 1.0426 - acc: 0.5961 - val_loss: 0.7910 - val_acc: 0.6593\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.0739 - acc: 0.5937 - val_loss: 0.7843 - val_acc: 0.6813\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.0118 - acc: 0.6188 - val_loss: 0.7808 - val_acc: 0.7212\n",
-      "10000/10000 [==============================] - 3s 333us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 102s 2ms/step - loss: 2.1707 - acc: 0.1879 - val_loss: 1.8979 - val_acc: 0.2793\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.9157 - acc: 0.2715 - val_loss: 1.6330 - val_acc: 0.3645\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 1.7204 - acc: 0.3492 - val_loss: 1.3882 - val_acc: 0.4198\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.5528 - acc: 0.3921 - val_loss: 1.2597 - val_acc: 0.4192\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.4544 - acc: 0.4181 - val_loss: 1.1931 - val_acc: 0.4315\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.3751 - acc: 0.4313 - val_loss: 1.1190 - val_acc: 0.4732\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.3259 - acc: 0.4407 - val_loss: 1.0745 - val_acc: 0.4770\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.2873 - acc: 0.4480 - val_loss: 1.0574 - val_acc: 0.4987\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.2566 - acc: 0.4563 - val_loss: 1.0469 - val_acc: 0.4938\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.2376 - acc: 0.4596 - val_loss: 1.0235 - val_acc: 0.5013\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.2063 - acc: 0.4659 - val_loss: 1.0285 - val_acc: 0.5103\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1835 - acc: 0.4759 - val_loss: 0.9935 - val_acc: 0.5475\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1776 - acc: 0.4825 - val_loss: 1.0194 - val_acc: 0.5100\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1348 - acc: 0.5128 - val_loss: 0.9473 - val_acc: 0.6028\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.0958 - acc: 0.5578 - val_loss: 0.8550 - val_acc: 0.6808\n",
-      "10000/10000 [==============================] - 3s 335us/step\n",
-      "0.8639\n",
-      "\n",
-      " \n",
-      " Starting round with 32 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 115s 2ms/step - loss: 2.2819 - acc: 0.1619 - val_loss: 2.0311 - val_acc: 0.2308\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 2.0998 - acc: 0.2201 - val_loss: 1.9076 - val_acc: 0.2703\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 2.0102 - acc: 0.2480 - val_loss: 1.8020 - val_acc: 0.2720\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.9118 - acc: 0.2798 - val_loss: 1.6791 - val_acc: 0.3537\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.8303 - acc: 0.3080 - val_loss: 1.5650 - val_acc: 0.4035\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.7538 - acc: 0.3427 - val_loss: 1.4796 - val_acc: 0.4688\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.6694 - acc: 0.3717 - val_loss: 1.3897 - val_acc: 0.5032\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.5914 - acc: 0.4072 - val_loss: 1.2499 - val_acc: 0.5707\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 102s 2ms/step - loss: 1.5251 - acc: 0.4347 - val_loss: 1.1472 - val_acc: 0.5955\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.4724 - acc: 0.4477 - val_loss: 1.1315 - val_acc: 0.5867\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.4302 - acc: 0.4660 - val_loss: 1.0692 - val_acc: 0.6055\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.3794 - acc: 0.4866 - val_loss: 1.0372 - val_acc: 0.6585\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.3485 - acc: 0.4999 - val_loss: 1.0167 - val_acc: 0.6445\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.3117 - acc: 0.5146 - val_loss: 0.9546 - val_acc: 0.6678\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.2927 - acc: 0.5249 - val_loss: 1.0052 - val_acc: 0.6493\n",
-      "10000/10000 [==============================] - 4s 375us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 117s 2ms/step - loss: 2.3233 - acc: 0.1530 - val_loss: 2.0805 - val_acc: 0.2280\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 2.1543 - acc: 0.2000 - val_loss: 2.0418 - val_acc: 0.2420\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 2.1086 - acc: 0.2196 - val_loss: 2.0094 - val_acc: 0.2450\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 2.0575 - acc: 0.2346 - val_loss: 1.9317 - val_acc: 0.2675\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.9923 - acc: 0.2468 - val_loss: 1.8413 - val_acc: 0.2805\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.9421 - acc: 0.2562 - val_loss: 1.7853 - val_acc: 0.3082\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.9036 - acc: 0.2664 - val_loss: 1.7303 - val_acc: 0.3182\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 1.8634 - acc: 0.2826 - val_loss: 1.6881 - val_acc: 0.3362\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.8381 - acc: 0.2965 - val_loss: 1.6552 - val_acc: 0.3648\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.7947 - acc: 0.3189 - val_loss: 1.5889 - val_acc: 0.4140\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.7464 - acc: 0.3355 - val_loss: 1.5312 - val_acc: 0.4028\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.7009 - acc: 0.3528 - val_loss: 1.4869 - val_acc: 0.4340\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.6573 - acc: 0.3658 - val_loss: 1.4087 - val_acc: 0.4515\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.6122 - acc: 0.3799 - val_loss: 1.4059 - val_acc: 0.4772\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.5790 - acc: 0.3910 - val_loss: 1.3455 - val_acc: 0.4585\n",
-      "10000/10000 [==============================] - 4s 389us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 2.3440 - acc: 0.1451 - val_loss: 2.1683 - val_acc: 0.1923\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 2.1421 - acc: 0.1992 - val_loss: 2.0372 - val_acc: 0.2317\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 2.0463 - acc: 0.2369 - val_loss: 1.8817 - val_acc: 0.2813\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.9537 - acc: 0.2649 - val_loss: 1.7453 - val_acc: 0.3308\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.8740 - acc: 0.2913 - val_loss: 1.6371 - val_acc: 0.3460\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.7921 - acc: 0.3177 - val_loss: 1.5252 - val_acc: 0.3503\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.7234 - acc: 0.3306 - val_loss: 1.4995 - val_acc: 0.4037\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.6706 - acc: 0.3464 - val_loss: 1.4149 - val_acc: 0.4100\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 1.6176 - acc: 0.3560 - val_loss: 1.3720 - val_acc: 0.4208\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 1.5825 - acc: 0.3633 - val_loss: 1.3573 - val_acc: 0.4270\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 1.5512 - acc: 0.3689 - val_loss: 1.3447 - val_acc: 0.4137\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.5246 - acc: 0.3770 - val_loss: 1.3284 - val_acc: 0.4473\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.4861 - acc: 0.3842 - val_loss: 1.3025 - val_acc: 0.4897\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.4673 - acc: 0.3964 - val_loss: 1.2838 - val_acc: 0.4440\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.4327 - acc: 0.4040 - val_loss: 1.2364 - val_acc: 0.5045\n",
-      "10000/10000 [==============================] - 4s 377us/step\n",
-      "0.6807\n",
-      "\n",
-      " \n",
-      " Starting round with 35 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 129s 2ms/step - loss: 2.3206 - acc: 0.1087 - val_loss: 2.4899 - val_acc: 0.1103\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.2866 - acc: 0.1236 - val_loss: 2.4299 - val_acc: 0.1445\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.2670 - acc: 0.1369 - val_loss: 2.2762 - val_acc: 0.1548\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.2126 - acc: 0.1527 - val_loss: 2.1650 - val_acc: 0.1565\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.1497 - acc: 0.1638 - val_loss: 2.1472 - val_acc: 0.1928\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.0887 - acc: 0.1785 - val_loss: 2.1148 - val_acc: 0.2087\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 105s 2ms/step - loss: 2.0379 - acc: 0.1933 - val_loss: 1.9941 - val_acc: 0.2178\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.9753 - acc: 0.2207 - val_loss: 1.8358 - val_acc: 0.2743\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.9218 - acc: 0.2387 - val_loss: 1.8030 - val_acc: 0.2912\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 105s 2ms/step - loss: 1.8802 - acc: 0.2486 - val_loss: 1.7470 - val_acc: 0.2938\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.8531 - acc: 0.2619 - val_loss: 1.7311 - val_acc: 0.2942\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 105s 2ms/step - loss: 1.8284 - acc: 0.2679 - val_loss: 1.7019 - val_acc: 0.2927\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 105s 2ms/step - loss: 1.8134 - acc: 0.2752 - val_loss: 1.6679 - val_acc: 0.3235\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 105s 2ms/step - loss: 1.7885 - acc: 0.2861 - val_loss: 1.6755 - val_acc: 0.3318\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.7678 - acc: 0.2904 - val_loss: 1.6069 - val_acc: 0.3257\n",
-      "10000/10000 [==============================] - 4s 407us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 2.3044 - acc: 0.1234 - val_loss: 2.1907 - val_acc: 0.1985\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.1771 - acc: 0.1711 - val_loss: 1.9751 - val_acc: 0.2460\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.0610 - acc: 0.1982 - val_loss: 1.9094 - val_acc: 0.2515\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.0053 - acc: 0.2098 - val_loss: 1.9039 - val_acc: 0.2578\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.9649 - acc: 0.2273 - val_loss: 1.8167 - val_acc: 0.2627\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.9153 - acc: 0.2446 - val_loss: 1.7334 - val_acc: 0.3058\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.8718 - acc: 0.2654 - val_loss: 1.6716 - val_acc: 0.3475\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.8341 - acc: 0.2759 - val_loss: 1.6270 - val_acc: 0.3480\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.8006 - acc: 0.2867 - val_loss: 1.6032 - val_acc: 0.3518\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 111s 2ms/step - loss: 1.7556 - acc: 0.3045 - val_loss: 1.5421 - val_acc: 0.3590\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 111s 2ms/step - loss: 1.7121 - acc: 0.3175 - val_loss: 1.5208 - val_acc: 0.3545\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.6728 - acc: 0.3298 - val_loss: 1.4790 - val_acc: 0.3820\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.6411 - acc: 0.3360 - val_loss: 1.4205 - val_acc: 0.4005\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.6008 - acc: 0.3490 - val_loss: 1.4059 - val_acc: 0.3960\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.5652 - acc: 0.3568 - val_loss: 1.3918 - val_acc: 0.4108\n",
-      "10000/10000 [==============================] - 4s 435us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 136s 3ms/step - loss: 2.2477 - acc: 0.1526 - val_loss: 2.1112 - val_acc: 0.1608\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 2.1402 - acc: 0.1908 - val_loss: 2.0318 - val_acc: 0.2263\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 2.0994 - acc: 0.2024 - val_loss: 2.0039 - val_acc: 0.2495\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 2.0617 - acc: 0.2170 - val_loss: 1.9338 - val_acc: 0.2687\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.0225 - acc: 0.2309 - val_loss: 1.8295 - val_acc: 0.2763\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.9737 - acc: 0.2467 - val_loss: 1.7477 - val_acc: 0.3147\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.9305 - acc: 0.2583 - val_loss: 1.7199 - val_acc: 0.3173\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.8883 - acc: 0.2686 - val_loss: 1.7210 - val_acc: 0.3217\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.8561 - acc: 0.2765 - val_loss: 1.6704 - val_acc: 0.3455\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.8218 - acc: 0.2840 - val_loss: 1.6118 - val_acc: 0.3823\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.7898 - acc: 0.2917 - val_loss: 1.6146 - val_acc: 0.3540\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.7658 - acc: 0.2969 - val_loss: 1.5531 - val_acc: 0.3830\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.7373 - acc: 0.3104 - val_loss: 1.5237 - val_acc: 0.3972\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.6989 - acc: 0.3224 - val_loss: 1.4617 - val_acc: 0.4075\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.6567 - acc: 0.3301 - val_loss: 1.4072 - val_acc: 0.3852\n",
-      "10000/10000 [==============================] - 5s 451us/step\n",
-      "0.5834\n",
-      "\n",
-      " \n"
-     ]
-    }
-   ],
-   "source": [
-    "act = \"relu\"\n",
-    "\n",
-    "logs_relu = []\n",
-    "record_relu = []\n",
-    "for n in range(23,42,3):\n",
-    "    ensembler = 0\n",
-    "    logger = [n]\n",
-    "    print(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    for i in range(3):\n",
-    "        # Garbage collector\n",
-    "        gc.collect()\n",
-    "        # Set optimizer\n",
-    "        opt = SGD(lr=0.01, momentum=0.9)\n",
-    "        # Set callbacks (learning rate reducer and early stopping)\n",
-    "        learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.35, min_lr=0.00001)\n",
-    "        early_stop = EarlyStopping(monitor='val_acc', patience=5, verbose = 1)\n",
-    "        # Common params \n",
-    "        epochs = 15\n",
-    "        batch_size = 128\n",
-    "        # Create and compile the model\n",
-    "        model = create(act, n)\n",
-    "        # Compile the model\n",
-    "        model.compile(optimizer = opt , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
-    "        # Train the model\n",
-    "        history = model.fit(X_train,Y_train, epochs = epochs, validation_data = (X_val,Y_val),\n",
-    "                            verbose = 1 , callbacks=[learning_rate_reduction, early_stop])\n",
-    "        \n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, history, learning_rate_reduction, early_stop, opt\n",
-    "     \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    # Save the ensembled accuracy and the three models accuracy\n",
-    "    record_relu.append([n, ensembled])\n",
-    "    logs_relu.append(logger)\n",
-    "    del ensembler, ensembled\n",
-    "    \n",
-    "print(\"\\n \\n \\n\")\n",
-    "print(\"Logs Relu: \", logs_relu)\n",
-    "print(\"Record Relu: \", record_relu)\n",
-    "\n",
-    "    \n",
-    "plot([[x[1] for x in logs_relu]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " Starting round with 38 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 2.4120 - acc: 0.1019 - val_loss: 2.3258 - val_acc: 0.1003\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 2.3133 - acc: 0.1030 - val_loss: 2.3261 - val_acc: 0.1017\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 2.3052 - acc: 0.1075 - val_loss: 2.3299 - val_acc: 0.1698\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 2.3037 - acc: 0.1086 - val_loss: 2.3284 - val_acc: 0.1152\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 2.3031 - acc: 0.1089 - val_loss: 2.3024 - val_acc: 0.1152\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3026 - acc: 0.1092\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.1091 - val_loss: 2.3253 - val_acc: 0.1152\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 111s 2ms/step - loss: 2.3019 - acc: 0.1111 - val_loss: 2.3021 - val_acc: 0.1152\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3018 - acc: 0.1110- ETA: 0s - loss: 2.3018 - acc:\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 2.3018 - acc: 0.1110 - val_loss: 2.3015 - val_acc: 0.1152\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 4s 415us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.4086 - acc: 0.1023 - val_loss: 2.2859 - val_acc: 0.1628\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 2.3116 - acc: 0.1029 - val_loss: 2.3057 - val_acc: 0.1025\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 2.3042 - acc: 0.1063 - val_loss: 2.3049 - val_acc: 0.1152\n",
-      "Epoch 4/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3032 - acc: 0.1069\n",
-      "Epoch 00004: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 2.3032 - acc: 0.1069 - val_loss: 2.3040 - val_acc: 0.1152\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 2.3015 - acc: 0.1106 - val_loss: 2.3051 - val_acc: 0.1152\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3011 - acc: 0.1110\n",
-      "Epoch 00006: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3011 - acc: 0.1110 - val_loss: 2.3040 - val_acc: 0.1152\n",
-      "Epoch 00006: early stopping\n",
-      "10000/10000 [==============================] - 4s 407us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 2.4091 - acc: 0.1003 - val_loss: 2.3121 - val_acc: 0.1152\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3123 - acc: 0.1044 - val_loss: 2.3200 - val_acc: 0.0948\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3054 - acc: 0.1064 - val_loss: 2.3158 - val_acc: 0.1017\n",
-      "Epoch 4/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3037 - acc: 0.1080\n",
-      "Epoch 00004: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3037 - acc: 0.1081 - val_loss: 2.3027 - val_acc: 0.1152\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3025 - acc: 0.1103 - val_loss: 2.3013 - val_acc: 0.1152\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3022 - acc: 0.1102\n",
-      "Epoch 00006: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3022 - acc: 0.1102 - val_loss: 2.3014 - val_acc: 0.1152\n",
-      "Epoch 00006: early stopping\n",
-      "10000/10000 [==============================] - 4s 413us/step\n",
-      "0.1135\n",
-      "\n",
-      " \n",
-      " Starting round with 41 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 2.3259 - acc: 0.1055 - val_loss: 2.3119 - val_acc: 0.1130\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2907 - acc: 0.1246 - val_loss: 2.3211 - val_acc: 0.1240\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2762 - acc: 0.1367 - val_loss: 2.2901 - val_acc: 0.1302\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2599 - acc: 0.1438 - val_loss: 2.2951 - val_acc: 0.1328\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2461 - acc: 0.1506 - val_loss: 2.2627 - val_acc: 0.1438\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2330 - acc: 0.1601 - val_loss: 2.2464 - val_acc: 0.1593\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2165 - acc: 0.1650 - val_loss: 2.2179 - val_acc: 0.1683\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2041 - acc: 0.1720 - val_loss: 2.2431 - val_acc: 0.1515\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.1926 - acc: 0.1760 - val_loss: 2.2141 - val_acc: 0.1523\n",
-      "Epoch 10/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.1845 - acc: 0.1790\n",
-      "Epoch 00010: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 2.1845 - acc: 0.1790 - val_loss: 2.2457 - val_acc: 0.1527\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.1694 - acc: 0.1823 - val_loss: 2.2217 - val_acc: 0.1550\n",
-      "Epoch 12/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.1666 - acc: 0.1845\n",
-      "Epoch 00012: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.1667 - acc: 0.1845 - val_loss: 2.2127 - val_acc: 0.1552\n",
-      "Epoch 00012: early stopping\n",
-      "10000/10000 [==============================] - 4s 436us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 2.3263 - acc: 0.1022 - val_loss: 2.3221 - val_acc: 0.1238\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.3040 - acc: 0.1080 - val_loss: 2.3017 - val_acc: 0.1152\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 2.3030 - acc: 0.1084 - val_loss: 2.3238 - val_acc: 0.1150\n",
-      "Epoch 4/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3029 - acc: 0.1096\n",
-      "Epoch 00004: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 123s 2ms/step - loss: 2.3029 - acc: 0.1096 - val_loss: 2.3158 - val_acc: 0.1152\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 2.3020 - acc: 0.1098 - val_loss: 2.3016 - val_acc: 0.1152\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3018 - acc: 0.1114\n",
-      "Epoch 00006: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 2.3018 - acc: 0.1114 - val_loss: 2.3017 - val_acc: 0.1152\n",
-      "Epoch 00006: early stopping\n",
-      "10000/10000 [==============================] - 4s 449us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 2.3264 - acc: 0.1049 - val_loss: 2.3082 - val_acc: 0.1545\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 2.3039 - acc: 0.1068 - val_loss: 2.3100 - val_acc: 0.1015\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 2.3030 - acc: 0.1082 - val_loss: 2.3080 - val_acc: 0.1498\n",
-      "Epoch 4/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3026 - acc: 0.1092\n",
-      "Epoch 00004: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 123s 2ms/step - loss: 2.3026 - acc: 0.1091 - val_loss: 2.3029 - val_acc: 0.1152\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.3017 - acc: 0.1115 - val_loss: 2.3003 - val_acc: 0.1152\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3018 - acc: 0.1110\n",
-      "Epoch 00006: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.3018 - acc: 0.1110 - val_loss: 2.3084 - val_acc: 0.1153\n",
-      "Epoch 00006: early stopping\n",
-      "10000/10000 [==============================] - 4s 448us/step\n",
-      "0.1572\n",
-      "\n",
-      " \n",
-      " \n",
-      "\n",
-      "Logs Relu:  [[23, 0.9717], [26, 0.9253], [29, 0.8639], [32, 0.6807], [35, 0.5834], [38, 0.1135, 0.1135, 0.1135], [41, 0.15679999999999999, 0.1135, 0.11360000000000001]]\n",
-      "Record Relu:  [[38, 0.1135], [41, 0.1572]]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div id='08ab580f-d6bf-478d-841f-292a711362d2'></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "act = \"relu\"\n",
-    "logs_relu = []\n",
-    "record_relu = [[23, 0.9717], [26, 0.9253], [29, 0.8639], [32, 0.6807], [35, 0.5834]]\n",
-    "logs.info(\"Relu 38-42\")\n",
-    "\n",
-    "for n in range(38,42,3):\n",
-    "    ensembler = 0\n",
-    "    logger = [n]\n",
-    "    print(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    logs.info(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    for i in range(3):\n",
-    "        # Garbage collector\n",
-    "        gc.collect()\n",
-    "        # Set optimizer\n",
-    "        opt = SGD(lr=0.01, momentum=0.9)\n",
-    "        # Set callbacks (learning rate reducer and early stopping)\n",
-    "        learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.35, min_lr=0.00001)\n",
-    "        early_stop = EarlyStopping(monitor='val_acc', patience=5, verbose = 1)\n",
-    "        # Common params \n",
-    "        epochs = 15\n",
-    "        batch_size = 128\n",
-    "        # Create and compile the model\n",
-    "        model = create(act, n)\n",
-    "        # Compile the model\n",
-    "        model.compile(optimizer = opt , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
-    "        # Train the model\n",
-    "        history = model.fit(X_train,Y_train, epochs = epochs, validation_data = (X_val,Y_val),\n",
-    "                            verbose = 1 , callbacks=[learning_rate_reduction, early_stop])\n",
-    "        \n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        logs.info(\"Accuracy \"+str(i)+\": \"+str(logger[-1]))\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, history, learning_rate_reduction, early_stop, opt\n",
-    "     \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    # Save the ensembled accuracy and the three models accuracy\n",
-    "    record_relu.append([n, ensembled])\n",
-    "    logs_relu.append(logger)\n",
-    "    del ensembler, ensembled\n",
-    "    logs.info(\"Ensembled accuracy: \"+str(record_relu[-1]))\n",
-    "    logs.info(\"Logs: \"+str(logs_relu[-1]))\n",
-    "    \n",
-    "logs.info(\"\\n \\n \\n\")\n",
-    "logs.info(\"Logs e_swish_2: \"+str(logs_relu))\n",
-    "logs.info(\"Record e_swish_2: \"+str(record_relu))\n",
-    "print(\"\\n \\n \\n\")\n",
-    "print(\"Logs Relu: \", logs_relu)\n",
-    "print(\"Record Relu: \", record_relu)\n",
-    "\n",
-    "    \n",
-    "plot([[x[1] for x in logs_relu]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " Starting round with 23 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 93s 2ms/step - loss: 1.2903 - acc: 0.5598 - val_loss: 0.5266 - val_acc: 0.8643\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.7332 - acc: 0.8116 - val_loss: 0.3981 - val_acc: 0.9258\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.5698 - acc: 0.8682 - val_loss: 0.3337 - val_acc: 0.9413\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.5351 - acc: 0.8743 - val_loss: 0.2820 - val_acc: 0.9455\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.4442 - acc: 0.9004 - val_loss: 0.2380 - val_acc: 0.9552\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3893 - acc: 0.9156 - val_loss: 0.1961 - val_acc: 0.9615\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3781 - acc: 0.9180 - val_loss: 0.1998 - val_acc: 0.9595\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3472 - acc: 0.9259 - val_loss: 0.2048 - val_acc: 0.9578\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3230 - acc: 0.9314 - val_loss: 0.1770 - val_acc: 0.9640\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2996 - acc: 0.9354 - val_loss: 0.1700 - val_acc: 0.9648\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2923 - acc: 0.9380 - val_loss: 0.1478 - val_acc: 0.9692\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2877 - acc: 0.9384 - val_loss: 0.1692 - val_acc: 0.9632\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2969 - acc: 0.9372 - val_loss: 0.1549 - val_acc: 0.9668\n",
-      "Epoch 14/15\n",
-      "53952/54000 [============================>.] - ETA: 0s - loss: 0.2717 - acc: 0.9426\n",
-      "Epoch 00014: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 0.2719 - acc: 0.9425 - val_loss: 0.1458 - val_acc: 0.9680\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2317 - acc: 0.9505 - val_loss: 0.1225 - val_acc: 0.9750\n",
-      "10000/10000 [==============================] - 3s 318us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 95s 2ms/step - loss: 1.3368 - acc: 0.5298 - val_loss: 0.6997 - val_acc: 0.7782\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8322 - acc: 0.7399 - val_loss: 0.3765 - val_acc: 0.9127\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.6585 - acc: 0.8353 - val_loss: 0.4307 - val_acc: 0.9123\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.5430 - acc: 0.8768 - val_loss: 0.3025 - val_acc: 0.9378\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.4769 - acc: 0.8942 - val_loss: 0.2706 - val_acc: 0.9472\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.4280 - acc: 0.9071 - val_loss: 0.2532 - val_acc: 0.9495\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3962 - acc: 0.9141 - val_loss: 0.2364 - val_acc: 0.9498\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3929 - acc: 0.9151 - val_loss: 0.2046 - val_acc: 0.9573\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3630 - acc: 0.9218 - val_loss: 0.2008 - val_acc: 0.9588\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3389 - acc: 0.9276 - val_loss: 0.1788 - val_acc: 0.9630\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3388 - acc: 0.9274 - val_loss: 0.1864 - val_acc: 0.9628\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3213 - acc: 0.9316 - val_loss: 0.1782 - val_acc: 0.9640\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3102 - acc: 0.9346 - val_loss: 0.1795 - val_acc: 0.9617\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2859 - acc: 0.9395 - val_loss: 0.1659 - val_acc: 0.9632\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2834 - acc: 0.9396 - val_loss: 0.1687 - val_acc: 0.9652\n",
-      "10000/10000 [==============================] - 3s 325us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.3235 - acc: 0.5417 - val_loss: 0.5877 - val_acc: 0.8402\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.7954 - acc: 0.7903 - val_loss: 0.4051 - val_acc: 0.9170\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.6145 - acc: 0.8560 - val_loss: 0.3445 - val_acc: 0.9297\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.5024 - acc: 0.8860 - val_loss: 0.2990 - val_acc: 0.9415\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.4556 - acc: 0.8982 - val_loss: 0.2554 - val_acc: 0.9507\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.4144 - acc: 0.9110 - val_loss: 0.2393 - val_acc: 0.9548\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3797 - acc: 0.9199 - val_loss: 0.2002 - val_acc: 0.9593\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3436 - acc: 0.9271 - val_loss: 0.2377 - val_acc: 0.9517\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3417 - acc: 0.9274 - val_loss: 0.1979 - val_acc: 0.9588\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3456 - acc: 0.9265 - val_loss: 0.1898 - val_acc: 0.9618\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3275 - acc: 0.9303 - val_loss: 0.1840 - val_acc: 0.9603\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3073 - acc: 0.9358 - val_loss: 0.1880 - val_acc: 0.9608\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3108 - acc: 0.9354 - val_loss: 0.1806 - val_acc: 0.9632\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3202 - acc: 0.9310 - val_loss: 0.1576 - val_acc: 0.9660\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2858 - acc: 0.9391 - val_loss: 0.1532 - val_acc: 0.9660\n",
-      "10000/10000 [==============================] - 3s 328us/step\n",
-      "0.9753\n",
-      "\n",
-      " \n",
-      " Starting round with 26 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.5673 - acc: 0.4335 - val_loss: 0.7896 - val_acc: 0.6845\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.9276 - acc: 0.6857 - val_loss: 0.4729 - val_acc: 0.8413\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.7154 - acc: 0.7725 - val_loss: 0.3519 - val_acc: 0.8563\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.6203 - acc: 0.8007 - val_loss: 0.3422 - val_acc: 0.8622\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.5716 - acc: 0.8253 - val_loss: 0.2549 - val_acc: 0.9338\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.4990 - acc: 0.8676 - val_loss: 0.2405 - val_acc: 0.9478\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.4466 - acc: 0.8913 - val_loss: 0.2097 - val_acc: 0.9503\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.4351 - acc: 0.9020 - val_loss: 0.2219 - val_acc: 0.9510\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.4013 - acc: 0.9108 - val_loss: 0.1917 - val_acc: 0.9575\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.3795 - acc: 0.9172 - val_loss: 0.1928 - val_acc: 0.9590\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.3974 - acc: 0.9140 - val_loss: 0.2479 - val_acc: 0.9457\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.3917 - acc: 0.9172 - val_loss: 0.2042 - val_acc: 0.9552\n",
-      "Epoch 13/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.3714 - acc: 0.9203\n",
-      "Epoch 00013: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3715 - acc: 0.9203 - val_loss: 0.1892 - val_acc: 0.9573\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.3142 - acc: 0.9330 - val_loss: 0.1749 - val_acc: 0.9617\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.2933 - acc: 0.9387 - val_loss: 0.1498 - val_acc: 0.9682\n",
-      "10000/10000 [==============================] - 4s 360us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 1.5995 - acc: 0.4336 - val_loss: 0.7574 - val_acc: 0.7243\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.9983 - acc: 0.6611 - val_loss: 0.5428 - val_acc: 0.8122\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.8308 - acc: 0.7368 - val_loss: 0.4433 - val_acc: 0.8485\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.7018 - acc: 0.7885 - val_loss: 0.3912 - val_acc: 0.8737\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6285 - acc: 0.8279 - val_loss: 0.2913 - val_acc: 0.9257\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5638 - acc: 0.8603 - val_loss: 0.2796 - val_acc: 0.9390\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5235 - acc: 0.8778 - val_loss: 0.2847 - val_acc: 0.9418\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4916 - acc: 0.8899 - val_loss: 0.2695 - val_acc: 0.9438\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5001 - acc: 0.8894 - val_loss: 0.2593 - val_acc: 0.9460\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4815 - acc: 0.8941 - val_loss: 0.2649 - val_acc: 0.9477\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4885 - acc: 0.8932 - val_loss: 0.2576 - val_acc: 0.9460\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4662 - acc: 0.8987 - val_loss: 0.2510 - val_acc: 0.9445\n",
-      "Epoch 13/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.4709 - acc: 0.8934\n",
-      "Epoch 00013: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.4711 - acc: 0.8934 - val_loss: 0.3116 - val_acc: 0.9257\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3899 - acc: 0.9152 - val_loss: 0.2052 - val_acc: 0.9560\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3601 - acc: 0.9224 - val_loss: 0.1810 - val_acc: 0.9602\n",
-      "10000/10000 [==============================] - 4s 371us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 1.5404 - acc: 0.4676 - val_loss: 0.6398 - val_acc: 0.7622\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.9269 - acc: 0.7018 - val_loss: 0.4404 - val_acc: 0.8725\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.7742 - acc: 0.7798 - val_loss: 0.3526 - val_acc: 0.9220\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6699 - acc: 0.8283 - val_loss: 0.3119 - val_acc: 0.9323\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5861 - acc: 0.8622 - val_loss: 0.2952 - val_acc: 0.9393\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5304 - acc: 0.8795 - val_loss: 0.2374 - val_acc: 0.9473\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4811 - acc: 0.8949 - val_loss: 0.2446 - val_acc: 0.9508\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4536 - acc: 0.9024 - val_loss: 0.2363 - val_acc: 0.9508\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4603 - acc: 0.9000 - val_loss: 0.2370 - val_acc: 0.9523\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4500 - acc: 0.9035 - val_loss: 0.2244 - val_acc: 0.9482\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4363 - acc: 0.9072 - val_loss: 0.2535 - val_acc: 0.9472\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4126 - acc: 0.9138 - val_loss: 0.2210 - val_acc: 0.9545\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4213 - acc: 0.9091 - val_loss: 0.2211 - val_acc: 0.9523\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4092 - acc: 0.9124 - val_loss: 0.2487 - val_acc: 0.9412\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4185 - acc: 0.9054 - val_loss: 0.1929 - val_acc: 0.9575\n",
-      "10000/10000 [==============================] - 4s 373us/step\n",
-      "0.9682\n",
-      "\n",
-      " \n",
-      " Starting round with 29 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 125s 2ms/step - loss: 1.7071 - acc: 0.3479 - val_loss: 1.1007 - val_acc: 0.5922\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.2199 - acc: 0.5364 - val_loss: 0.8073 - val_acc: 0.6645\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.0574 - acc: 0.6106 - val_loss: 0.7609 - val_acc: 0.6707\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.9805 - acc: 0.6459 - val_loss: 0.6772 - val_acc: 0.7540\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.8971 - acc: 0.7074 - val_loss: 0.5414 - val_acc: 0.8302\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.8134 - acc: 0.7523 - val_loss: 0.4932 - val_acc: 0.8455\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.7929 - acc: 0.7609 - val_loss: 0.4777 - val_acc: 0.8490\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.7059 - acc: 0.7959 - val_loss: 0.4188 - val_acc: 0.8830\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.6506 - acc: 0.8164 - val_loss: 0.4513 - val_acc: 0.8968\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.6722 - acc: 0.8186 - val_loss: 0.4270 - val_acc: 0.9033\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.6654 - acc: 0.8302 - val_loss: 0.4671 - val_acc: 0.8952\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.6525 - acc: 0.8418 - val_loss: 0.3712 - val_acc: 0.9273\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.7426 - acc: 0.8036 - val_loss: 0.4182 - val_acc: 0.9150\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.6193 - acc: 0.8561 - val_loss: 0.3991 - val_acc: 0.9168\n",
-      "Epoch 15/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.5671 - acc: 0.8730\n",
-      "Epoch 00015: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5674 - acc: 0.8730 - val_loss: 0.4288 - val_acc: 0.9108\n",
-      "10000/10000 [==============================] - 4s 411us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 128s 2ms/step - loss: 1.7032 - acc: 0.3491 - val_loss: 1.0651 - val_acc: 0.5462\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.2657 - acc: 0.5430 - val_loss: 0.8338 - val_acc: 0.6930\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.0799 - acc: 0.6306 - val_loss: 0.6866 - val_acc: 0.7850\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.9554 - acc: 0.7049 - val_loss: 0.6452 - val_acc: 0.7932\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.8421 - acc: 0.7467 - val_loss: 0.5023 - val_acc: 0.8502\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.7537 - acc: 0.7876 - val_loss: 0.4633 - val_acc: 0.8952\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.7391 - acc: 0.8039 - val_loss: 0.4733 - val_acc: 0.8892\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.6776 - acc: 0.8312 - val_loss: 0.3940 - val_acc: 0.9142\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.6372 - acc: 0.8457 - val_loss: 0.3853 - val_acc: 0.9140\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.6439 - acc: 0.8404 - val_loss: 0.3843 - val_acc: 0.9120\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5752 - acc: 0.8636 - val_loss: 0.3249 - val_acc: 0.9297\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5619 - acc: 0.8685 - val_loss: 0.3389 - val_acc: 0.9278\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5407 - acc: 0.8761 - val_loss: 0.3321 - val_acc: 0.9335\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5364 - acc: 0.8792 - val_loss: 0.2902 - val_acc: 0.9420\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5300 - acc: 0.8812 - val_loss: 0.3135 - val_acc: 0.9343\n",
-      "10000/10000 [==============================] - 4s 413us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.7511 - acc: 0.3531 - val_loss: 1.0859 - val_acc: 0.5925\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.2537 - acc: 0.5366 - val_loss: 0.7937 - val_acc: 0.7057\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.1296 - acc: 0.6047 - val_loss: 0.7689 - val_acc: 0.7322\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.9447 - acc: 0.6873 - val_loss: 0.6124 - val_acc: 0.8298\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.8373 - acc: 0.7405 - val_loss: 0.6432 - val_acc: 0.7938\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.7866 - acc: 0.7666 - val_loss: 0.5132 - val_acc: 0.8417\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.7097 - acc: 0.8064 - val_loss: 0.4293 - val_acc: 0.9033\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.6807 - acc: 0.8250 - val_loss: 0.4248 - val_acc: 0.8937\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5987 - acc: 0.8555 - val_loss: 0.3695 - val_acc: 0.9268\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5670 - acc: 0.8687 - val_loss: 0.3742 - val_acc: 0.9270\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5529 - acc: 0.8755 - val_loss: 0.3453 - val_acc: 0.9297\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5176 - acc: 0.8869 - val_loss: 0.3104 - val_acc: 0.9403\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5041 - acc: 0.8895 - val_loss: 0.2793 - val_acc: 0.9450\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.4991 - acc: 0.8926 - val_loss: 0.3289 - val_acc: 0.9367\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5276 - acc: 0.8801 - val_loss: 0.3473 - val_acc: 0.9150\n",
-      "10000/10000 [==============================] - 4s 414us/step\n",
-      "0.9487\n",
-      "\n",
-      " \n",
-      " Starting round with 32 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.9114 - acc: 0.3287 - val_loss: 1.0782 - val_acc: 0.5248\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.3285 - acc: 0.5000 - val_loss: 0.8163 - val_acc: 0.7090\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.0871 - acc: 0.6346 - val_loss: 0.6391 - val_acc: 0.8023\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9359 - acc: 0.7077 - val_loss: 0.5163 - val_acc: 0.8348\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8530 - acc: 0.7449 - val_loss: 0.5007 - val_acc: 0.8397\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8437 - acc: 0.7479 - val_loss: 0.5794 - val_acc: 0.8065\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7872 - acc: 0.7739 - val_loss: 0.4864 - val_acc: 0.8482\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7491 - acc: 0.8013 - val_loss: 0.4151 - val_acc: 0.9117\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7216 - acc: 0.8195 - val_loss: 0.4321 - val_acc: 0.9100\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7159 - acc: 0.8261 - val_loss: 0.3941 - val_acc: 0.9150\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.6765 - acc: 0.8399 - val_loss: 0.4581 - val_acc: 0.9032\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.6417 - acc: 0.8512 - val_loss: 0.3826 - val_acc: 0.9192\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.6181 - acc: 0.8592 - val_loss: 0.3900 - val_acc: 0.9117\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.6075 - acc: 0.8624 - val_loss: 0.3767 - val_acc: 0.9210\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.5851 - acc: 0.8699 - val_loss: 0.3257 - val_acc: 0.9280\n",
-      "10000/10000 [==============================] - 5s 462us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 147s 3ms/step - loss: 1.9871 - acc: 0.2745 - val_loss: 1.3103 - val_acc: 0.4328\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.4983 - acc: 0.3921 - val_loss: 1.0637 - val_acc: 0.5253\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.3033 - acc: 0.4845 - val_loss: 0.9561 - val_acc: 0.6200\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.1638 - acc: 0.5490 - val_loss: 0.7583 - val_acc: 0.6685\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.0699 - acc: 0.5985 - val_loss: 0.7410 - val_acc: 0.6893\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9972 - acc: 0.6368 - val_loss: 0.7251 - val_acc: 0.7398\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9696 - acc: 0.6501 - val_loss: 0.6773 - val_acc: 0.7707\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9590 - acc: 0.6703 - val_loss: 0.5999 - val_acc: 0.8000\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8897 - acc: 0.6971 - val_loss: 0.5252 - val_acc: 0.8132\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8946 - acc: 0.7020 - val_loss: 0.5692 - val_acc: 0.7993\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8502 - acc: 0.7255 - val_loss: 0.5427 - val_acc: 0.8577\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9123 - acc: 0.7194 - val_loss: 0.5642 - val_acc: 0.8687\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8218 - acc: 0.7776 - val_loss: 0.5610 - val_acc: 0.8540\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7931 - acc: 0.7869 - val_loss: 0.4648 - val_acc: 0.8937\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7438 - acc: 0.8147 - val_loss: 0.4293 - val_acc: 0.9127\n",
-      "10000/10000 [==============================] - 5s 473us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 150s 3ms/step - loss: 2.0049 - acc: 0.2959 - val_loss: 1.1653 - val_acc: 0.5100\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.3759 - acc: 0.4866 - val_loss: 0.9161 - val_acc: 0.6593\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.1377 - acc: 0.6023 - val_loss: 0.6965 - val_acc: 0.6802\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.0046 - acc: 0.6598 - val_loss: 0.6351 - val_acc: 0.8063\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.9659 - acc: 0.6837 - val_loss: 0.5769 - val_acc: 0.8448\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.9054 - acc: 0.7126 - val_loss: 0.5800 - val_acc: 0.8742\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.9106 - acc: 0.7277 - val_loss: 0.6486 - val_acc: 0.8108\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.9420 - acc: 0.7255 - val_loss: 0.5459 - val_acc: 0.8825\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.8688 - acc: 0.7617 - val_loss: 0.4899 - val_acc: 0.8965\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.9490 - acc: 0.7181 - val_loss: 0.5401 - val_acc: 0.8685\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.0452 - acc: 0.7000 - val_loss: 0.9759 - val_acc: 0.6903\n",
-      "Epoch 12/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.0366 - acc: 0.6875\n",
-      "Epoch 00012: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 1.0366 - acc: 0.6875 - val_loss: 0.5733 - val_acc: 0.8563\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.8278 - acc: 0.7783 - val_loss: 0.4968 - val_acc: 0.8817\n",
-      "Epoch 14/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.7798 - acc: 0.7934\n",
-      "Epoch 00014: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 0.7798 - acc: 0.7934 - val_loss: 0.4661 - val_acc: 0.8932\n",
-      "Epoch 00014: early stopping\n",
-      "10000/10000 [==============================] - 5s 492us/step\n",
-      "0.9353\n",
-      "\n",
-      " \n",
-      " Starting round with 35 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 166s 3ms/step - loss: 1.8817 - acc: 0.2907 - val_loss: 1.3766 - val_acc: 0.4052\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.5171 - acc: 0.3766 - val_loss: 1.1853 - val_acc: 0.4432\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.3701 - acc: 0.4216 - val_loss: 1.0662 - val_acc: 0.5020\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.2757 - acc: 0.4589 - val_loss: 0.9843 - val_acc: 0.5588\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.1974 - acc: 0.5225 - val_loss: 0.8493 - val_acc: 0.6873\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.1157 - acc: 0.5792 - val_loss: 0.7882 - val_acc: 0.6950\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.0612 - acc: 0.6159 - val_loss: 0.7465 - val_acc: 0.7507\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.0261 - acc: 0.6407 - val_loss: 0.7154 - val_acc: 0.7590\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.9783 - acc: 0.6627 - val_loss: 0.6583 - val_acc: 0.7667\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.9259 - acc: 0.6891 - val_loss: 0.6500 - val_acc: 0.7973\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.9176 - acc: 0.7047 - val_loss: 0.5920 - val_acc: 0.8072\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8735 - acc: 0.7190 - val_loss: 0.5742 - val_acc: 0.8115\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8889 - acc: 0.7182 - val_loss: 0.5518 - val_acc: 0.8177\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8427 - acc: 0.7373 - val_loss: 0.5792 - val_acc: 0.8197\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 0.8566 - acc: 0.7359 - val_loss: 0.5899 - val_acc: 0.8133\n",
-      "10000/10000 [==============================] - 5s 503us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 167s 3ms/step - loss: 1.9686 - acc: 0.2741 - val_loss: 1.5565 - val_acc: 0.3598\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.6416 - acc: 0.3579 - val_loss: 1.3158 - val_acc: 0.4473\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.4688 - acc: 0.3975 - val_loss: 1.1632 - val_acc: 0.5185\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3861 - acc: 0.4400 - val_loss: 1.1238 - val_acc: 0.5400\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3061 - acc: 0.4776 - val_loss: 1.1610 - val_acc: 0.5375\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3005 - acc: 0.4849 - val_loss: 1.1697 - val_acc: 0.5092\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3911 - acc: 0.4544\n",
-      "Epoch 00007: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 123s 2ms/step - loss: 1.3911 - acc: 0.4544 - val_loss: 1.4663 - val_acc: 0.4287\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.4326 - acc: 0.4124 - val_loss: 1.1558 - val_acc: 0.4772\n",
-      "Epoch 9/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3263 - acc: 0.4504\n",
-      "Epoch 00009: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3263 - acc: 0.4504 - val_loss: 1.1032 - val_acc: 0.5148\n",
-      "Epoch 00009: early stopping\n",
-      "10000/10000 [==============================] - 5s 523us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 169s 3ms/step - loss: 2.0334 - acc: 0.2620 - val_loss: 1.4756 - val_acc: 0.4202\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.6287 - acc: 0.3809 - val_loss: 1.2871 - val_acc: 0.4365\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.4206 - acc: 0.4572 - val_loss: 1.0214 - val_acc: 0.5918\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 1.2511 - acc: 0.5315 - val_loss: 0.8830 - val_acc: 0.6645\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 124s 2ms/step - loss: 1.1446 - acc: 0.5866 - val_loss: 0.7577 - val_acc: 0.7247\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 1.0565 - acc: 0.6263 - val_loss: 0.8152 - val_acc: 0.7155\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 1.0038 - acc: 0.6516 - val_loss: 0.7053 - val_acc: 0.7535\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 0.9585 - acc: 0.6691 - val_loss: 0.6777 - val_acc: 0.7585\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 0.9324 - acc: 0.6875 - val_loss: 0.6359 - val_acc: 0.8027\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 0.9141 - acc: 0.6970 - val_loss: 0.6855 - val_acc: 0.7748\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8894 - acc: 0.7119 - val_loss: 0.5328 - val_acc: 0.8687\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8866 - acc: 0.7262 - val_loss: 0.5668 - val_acc: 0.8628\n",
-      "Epoch 13/15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8543 - acc: 0.7458 - val_loss: 0.4832 - val_acc: 0.8860\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8199 - acc: 0.7600 - val_loss: 0.4405 - val_acc: 0.8902\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.7756 - acc: 0.7800 - val_loss: 0.4634 - val_acc: 0.8792\n",
-      "10000/10000 [==============================] - 5s 532us/step\n",
-      "0.9047\n",
-      "\n",
-      " \n",
-      " Starting round with 38 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 185s 3ms/step - loss: 2.1707 - acc: 0.2288 - val_loss: 1.6790 - val_acc: 0.3395\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.7943 - acc: 0.3041 - val_loss: 1.5162 - val_acc: 0.3598\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.6937 - acc: 0.3266 - val_loss: 1.4352 - val_acc: 0.3845\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.6118 - acc: 0.3547 - val_loss: 1.3481 - val_acc: 0.4338\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.5401 - acc: 0.3851 - val_loss: 1.2724 - val_acc: 0.5430\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.4788 - acc: 0.4233 - val_loss: 1.1929 - val_acc: 0.5643\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.4243 - acc: 0.4564 - val_loss: 1.1247 - val_acc: 0.5597\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3806 - acc: 0.4794 - val_loss: 1.1098 - val_acc: 0.5223\n",
-      "Epoch 9/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3260 - acc: 0.5024\n",
-      "Epoch 00009: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 135s 3ms/step - loss: 1.3263 - acc: 0.5024 - val_loss: 1.0262 - val_acc: 0.5640\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.2577 - acc: 0.5270 - val_loss: 0.9948 - val_acc: 0.6127\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.2416 - acc: 0.5288 - val_loss: 0.9728 - val_acc: 0.6295\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.2159 - acc: 0.5347 - val_loss: 0.9414 - val_acc: 0.6397\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.2125 - acc: 0.5366 - val_loss: 0.9200 - val_acc: 0.6498\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.1954 - acc: 0.5449 - val_loss: 0.9357 - val_acc: 0.6308\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.1780 - acc: 0.5506 - val_loss: 0.9616 - val_acc: 0.6328\n",
-      "10000/10000 [==============================] - 6s 588us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 189s 4ms/step - loss: 2.1811 - acc: 0.2547 - val_loss: 1.6650 - val_acc: 0.3240\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.7349 - acc: 0.3540 - val_loss: 1.3707 - val_acc: 0.4325\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.5966 - acc: 0.3908 - val_loss: 1.2346 - val_acc: 0.5418\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.5036 - acc: 0.4427 - val_loss: 1.1743 - val_acc: 0.5540\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.4461 - acc: 0.4683 - val_loss: 1.1445 - val_acc: 0.5588\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.4014 - acc: 0.4851 - val_loss: 1.0923 - val_acc: 0.5688\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3144 - acc: 0.5194 - val_loss: 1.0352 - val_acc: 0.5887\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3292 - acc: 0.5093 - val_loss: 1.1284 - val_acc: 0.5797\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3589 - acc: 0.4944 - val_loss: 1.2168 - val_acc: 0.5353\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3536 - acc: 0.5052 - val_loss: 1.0393 - val_acc: 0.5893\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.2319 - acc: 0.5496 - val_loss: 0.9331 - val_acc: 0.6348\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.1583 - acc: 0.5808 - val_loss: 0.8718 - val_acc: 0.6893\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.1220 - acc: 0.5996 - val_loss: 0.8853 - val_acc: 0.7110\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.0888 - acc: 0.6184 - val_loss: 0.8457 - val_acc: 0.7417\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.0620 - acc: 0.6386 - val_loss: 0.7581 - val_acc: 0.7455\n",
-      "10000/10000 [==============================] - 6s 584us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 191s 4ms/step - loss: 2.2452 - acc: 0.2322 - val_loss: 1.7780 - val_acc: 0.3285\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.8257 - acc: 0.3326 - val_loss: 1.5314 - val_acc: 0.3725\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.6694 - acc: 0.3712 - val_loss: 1.4225 - val_acc: 0.4387\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.5744 - acc: 0.4029 - val_loss: 1.3102 - val_acc: 0.4620\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.5090 - acc: 0.4257 - val_loss: 1.3193 - val_acc: 0.5253\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.5002 - acc: 0.4366 - val_loss: 1.3018 - val_acc: 0.5515\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.4277 - acc: 0.4562 - val_loss: 1.1518 - val_acc: 0.5810\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.3620 - acc: 0.4876 - val_loss: 0.9979 - val_acc: 0.6633\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2760 - acc: 0.5181 - val_loss: 0.9547 - val_acc: 0.6838\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2222 - acc: 0.5566 - val_loss: 0.9894 - val_acc: 0.6838\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 129s 2ms/step - loss: 1.2004 - acc: 0.5690 - val_loss: 0.9171 - val_acc: 0.6498\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 129s 2ms/step - loss: 1.1313 - acc: 0.5980 - val_loss: 0.8827 - val_acc: 0.7215\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 129s 2ms/step - loss: 1.1302 - acc: 0.6056 - val_loss: 0.9869 - val_acc: 0.7092\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.1402 - acc: 0.6108 - val_loss: 0.9228 - val_acc: 0.7290\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 129s 2ms/step - loss: 1.1421 - acc: 0.6149 - val_loss: 0.8296 - val_acc: 0.7472\n",
-      "10000/10000 [==============================] - 6s 583us/step\n",
-      "0.7936\n",
-      "\n",
-      " \n"
-     ]
-    }
-   ],
-   "source": [
-    "act = e_swish_2\n",
-    "logs.info(\"\\n \\n \\n\"+\"\\n \\n \\n\")\n",
-    "logs.info(\"ESWISH 23-42\")\n",
-    "\n",
-    "logs_e_swish_2 = []\n",
-    "record_e_swish_2 = []\n",
-    "for n in range(23,42,3):\n",
-    "    ensembler = 0\n",
-    "    logger = [n]\n",
-    "    logs.info(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    print(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    for i in range(3):\n",
-    "        # Garbage collector\n",
-    "        gc.collect()\n",
-    "        # Set optimizer\n",
-    "        opt = SGD(lr=0.01, momentum=0.9)\n",
-    "        # Set callbacks (learning rate reducer and early stopping)\n",
-    "        learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.35, min_lr=0.00001)\n",
-    "        early_stop = EarlyStopping(monitor='val_acc', patience=5, verbose = 1)\n",
-    "        # Common params \n",
-    "        epochs = 15\n",
-    "        batch_size = 128\n",
-    "        # Create and compile the model\n",
-    "        model = create(act, n)\n",
-    "        # Compile the model\n",
-    "        model.compile(optimizer = opt , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
-    "        # Train the model\n",
-    "        history = model.fit(X_train,Y_train, epochs = epochs, validation_data = (X_val,Y_val),\n",
-    "                            verbose = 1 , callbacks=[learning_rate_reduction, early_stop])\n",
-    "        \n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        logs.info(\"Accuracy \"+str(i)+\": \"+str(logger[-1]))\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, history, learning_rate_reduction, early_stop, opt\n",
-    "     \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    # Save the ensembled accuracy and the three models accuracy\n",
-    "    record_e_swish_2.append([n, ensembled])\n",
-    "    logs_e_swish_2.append(logger)\n",
-    "    del ensembler, ensembled\n",
-    "    logs.info(\"Ensembled accuracy: \"+str(record_e_swish_2[-1]))\n",
-    "    logs.info(\"Logs: +\"+str(logs_e_swish_2[-1]))\n",
-    "    \n",
-    "logs.info(\"\\n \\n \\n\")\n",
-    "logs.info(\"Logs e_swish_2: \"+str(logs_e_swish_2))\n",
-    "logs.info(\"Record e_swish_2: \"+str(record_e_swish_2))\n",
-    "    \n",
-    "print(\"\\n \\n \\n\")\n",
-    "print(\"Logs e_swish_2: \", logs_e_swish_2)\n",
-    "print(\"Record e_swish_2: \", record_e_swish_2)\n",
-    "    \n",
-    "plot([[x[1] for x in logs_relu], [x[1] for x in logs_e_swish_2]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " Starting round with 41 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 140s 3ms/step - loss: 2.3038 - acc: 0.1406 - val_loss: 2.0706 - val_acc: 0.1923\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 2.0379 - acc: 0.2082 - val_loss: 1.6829 - val_acc: 0.3590\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.8362 - acc: 0.2873 - val_loss: 1.5304 - val_acc: 0.3847\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.7062 - acc: 0.3286 - val_loss: 1.3722 - val_acc: 0.4520\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.6300 - acc: 0.3511 - val_loss: 1.3249 - val_acc: 0.4560\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.5921 - acc: 0.3683 - val_loss: 1.2773 - val_acc: 0.4715\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.5210 - acc: 0.3947 - val_loss: 1.2199 - val_acc: 0.5048\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.4763 - acc: 0.4110 - val_loss: 1.1553 - val_acc: 0.5087\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.4386 - acc: 0.4236 - val_loss: 1.1733 - val_acc: 0.5128\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.4142 - acc: 0.4379 - val_loss: 1.1209 - val_acc: 0.5393\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.3972 - acc: 0.4476 - val_loss: 1.2173 - val_acc: 0.5260\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.3803 - acc: 0.4607 - val_loss: 1.1212 - val_acc: 0.5545\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.3952 - acc: 0.4548 - val_loss: 1.0860 - val_acc: 0.5848\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.3740 - acc: 0.4694 - val_loss: 1.1527 - val_acc: 0.5573\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.3548 - acc: 0.4726 - val_loss: 1.0085 - val_acc: 0.5955\n",
-      "10000/10000 [==============================] - 5s 501us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 141s 3ms/step - loss: 2.1643 - acc: 0.2034 - val_loss: 1.8464 - val_acc: 0.3268\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.8489 - acc: 0.3126 - val_loss: 1.5335 - val_acc: 0.4038\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.6655 - acc: 0.3639 - val_loss: 1.3698 - val_acc: 0.4458\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 135s 3ms/step - loss: 1.5496 - acc: 0.3935 - val_loss: 1.2659 - val_acc: 0.4668\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 135s 3ms/step - loss: 1.4852 - acc: 0.4126 - val_loss: 1.2291 - val_acc: 0.4860\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.4302 - acc: 0.4237 - val_loss: 1.1775 - val_acc: 0.5048\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.3814 - acc: 0.4394 - val_loss: 1.1432 - val_acc: 0.4968\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3527 - acc: 0.4472 - val_loss: 1.1228 - val_acc: 0.5063\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3153 - acc: 0.4539 - val_loss: 1.1165 - val_acc: 0.4855\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3111 - acc: 0.4544 - val_loss: 1.1080 - val_acc: 0.5113\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.2839 - acc: 0.4612 - val_loss: 1.0990 - val_acc: 0.5185\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2570 - acc: 0.4708 - val_loss: 1.0749 - val_acc: 0.5145\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2470 - acc: 0.4744 - val_loss: 1.0383 - val_acc: 0.5495\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2396 - acc: 0.4871 - val_loss: 1.0218 - val_acc: 0.5642\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2041 - acc: 0.5043 - val_loss: 1.0405 - val_acc: 0.5592\n",
-      "10000/10000 [==============================] - 5s 497us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 2.3649 - acc: 0.1016 - val_loss: 2.2993 - val_acc: 0.1103\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 2.3057 - acc: 0.1184 - val_loss: 2.2853 - val_acc: 0.1530\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 2.2611 - acc: 0.1518 - val_loss: 2.1381 - val_acc: 0.2008\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 135s 2ms/step - loss: 2.1879 - acc: 0.1809 - val_loss: 2.0533 - val_acc: 0.2235\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 135s 3ms/step - loss: 2.1205 - acc: 0.2000 - val_loss: 1.9775 - val_acc: 0.2372\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 135s 2ms/step - loss: 2.0563 - acc: 0.2273 - val_loss: 1.8490 - val_acc: 0.2965\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 135s 2ms/step - loss: 1.9572 - acc: 0.2624 - val_loss: 1.6662 - val_acc: 0.3758\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.8586 - acc: 0.2899 - val_loss: 1.6010 - val_acc: 0.3888\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.7828 - acc: 0.3044 - val_loss: 1.5161 - val_acc: 0.3938\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.7462 - acc: 0.3124 - val_loss: 1.5090 - val_acc: 0.4008\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.7028 - acc: 0.3230 - val_loss: 1.4188 - val_acc: 0.4102\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.6612 - acc: 0.3310 - val_loss: 1.3798 - val_acc: 0.4378\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.6263 - acc: 0.3470 - val_loss: 1.3298 - val_acc: 0.4813\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.5953 - acc: 0.3544 - val_loss: 1.2829 - val_acc: 0.4737\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.5675 - acc: 0.3654 - val_loss: 1.2745 - val_acc: 0.4887\n",
-      "10000/10000 [==============================] - 5s 507us/step\n",
-      "0.679\n",
-      "\n",
-      " \n",
-      " Starting round with 44 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 156s 3ms/step - loss: 2.4722 - acc: 0.1218 - val_loss: 2.1897 - val_acc: 0.1830\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 2.2008 - acc: 0.1718 - val_loss: 2.0837 - val_acc: 0.2348\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 2.1017 - acc: 0.2015 - val_loss: 1.8868 - val_acc: 0.2635\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 2.0335 - acc: 0.2138 - val_loss: 1.8411 - val_acc: 0.2822\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.9797 - acc: 0.2257 - val_loss: 1.7637 - val_acc: 0.2833\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.9390 - acc: 0.2358 - val_loss: 1.7439 - val_acc: 0.2773\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.9051 - acc: 0.2455 - val_loss: 1.7005 - val_acc: 0.3055\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.8740 - acc: 0.2576 - val_loss: 1.6443 - val_acc: 0.3317\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 1.8369 - acc: 0.2670 - val_loss: 1.6188 - val_acc: 0.3180\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 141s 3ms/step - loss: 1.8176 - acc: 0.2744 - val_loss: 1.5991 - val_acc: 0.3493\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 1.7845 - acc: 0.2829 - val_loss: 1.5969 - val_acc: 0.3543\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.7652 - acc: 0.2927 - val_loss: 1.6418 - val_acc: 0.3443\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 1.7439 - acc: 0.2987 - val_loss: 1.6365 - val_acc: 0.3762\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.7236 - acc: 0.3065 - val_loss: 1.8872 - val_acc: 0.3463\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 1.6968 - acc: 0.3172 - val_loss: 2.0396 - val_acc: 0.3560\n",
-      "10000/10000 [==============================] - 5s 546us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 159s 3ms/step - loss: 2.4932 - acc: 0.1228 - val_loss: 2.2272 - val_acc: 0.0997\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 2.2148 - acc: 0.1710 - val_loss: 2.1071 - val_acc: 0.1945\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 2.1358 - acc: 0.1880 - val_loss: 2.0248 - val_acc: 0.2258\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 2.0682 - acc: 0.1977 - val_loss: 1.9193 - val_acc: 0.2300\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.9878 - acc: 0.2080 - val_loss: 1.8347 - val_acc: 0.2695\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.9259 - acc: 0.2240 - val_loss: 1.7532 - val_acc: 0.2583\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 147s 3ms/step - loss: 1.8701 - acc: 0.2450 - val_loss: 1.6683 - val_acc: 0.3253\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.8058 - acc: 0.2732 - val_loss: 1.5864 - val_acc: 0.3107\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.7350 - acc: 0.2986 - val_loss: 1.4601 - val_acc: 0.4045\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.6781 - acc: 0.3231 - val_loss: 1.4073 - val_acc: 0.4257\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.6187 - acc: 0.3464 - val_loss: 1.3308 - val_acc: 0.4595\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.5733 - acc: 0.3644 - val_loss: 1.2976 - val_acc: 0.4560\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.5448 - acc: 0.3859 - val_loss: 1.2761 - val_acc: 0.4857\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.5032 - acc: 0.4020 - val_loss: 1.2401 - val_acc: 0.4782\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.4867 - acc: 0.4064 - val_loss: 1.2073 - val_acc: 0.5113\n",
-      "10000/10000 [==============================] - 6s 572us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 168s 3ms/step - loss: 2.4258 - acc: 0.1513 - val_loss: 2.0415 - val_acc: 0.2923\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 147s 3ms/step - loss: 2.0972 - acc: 0.2365 - val_loss: 1.8824 - val_acc: 0.3028\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.9928 - acc: 0.2632 - val_loss: 1.7859 - val_acc: 0.3117\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 147s 3ms/step - loss: 1.9189 - acc: 0.2836 - val_loss: 1.6812 - val_acc: 0.3143\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 147s 3ms/step - loss: 1.8580 - acc: 0.2909 - val_loss: 1.6030 - val_acc: 0.3592\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.8107 - acc: 0.2989 - val_loss: 1.5806 - val_acc: 0.3843\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.7838 - acc: 0.3065 - val_loss: 1.5491 - val_acc: 0.3670\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.7441 - acc: 0.3194 - val_loss: 1.5258 - val_acc: 0.3755\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.7246 - acc: 0.3226 - val_loss: 1.4924 - val_acc: 0.3973\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.6938 - acc: 0.3303 - val_loss: 1.4606 - val_acc: 0.4048\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.6746 - acc: 0.3353 - val_loss: 1.4463 - val_acc: 0.4312\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.6353 - acc: 0.3461 - val_loss: 1.5158 - val_acc: 0.4285\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.6273 - acc: 0.3492 - val_loss: 1.4647 - val_acc: 0.4062\n",
-      "Epoch 14/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.5953 - acc: 0.3600\n",
-      "Epoch 00014: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.5954 - acc: 0.3600 - val_loss: 1.7459 - val_acc: 0.3537\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.5694 - acc: 0.3670 - val_loss: 1.6717 - val_acc: 0.3930\n",
-      "10000/10000 [==============================] - 6s 570us/step\n",
-      "0.6189\n",
-      "\n",
-      " \n"
-     ]
-    }
-   ],
-   "source": [
-    "act = e_swish_2\n",
-    "# logs.info(\"\\n \\n \\n\"+\"\\n \\n \\n\")\n",
-    "# logs.info(\"ESWISH 41-42\")\n",
-    "\n",
-    "logs_e_swish_2 = []\n",
-    "record_e_swish_2 = []\n",
-    "for n in range(41,45,3):\n",
-    "    ensembler = 0\n",
-    "    logger = [n]\n",
-    "    logs.info(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    print(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    for i in range(3):\n",
-    "        # Garbage collector\n",
-    "        gc.collect()\n",
-    "        # Set optimizer\n",
-    "        opt = SGD(lr=0.01, momentum=0.9)\n",
-    "        # Set callbacks (learning rate reducer and early stopping)\n",
-    "        learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.35, min_lr=0.00001)\n",
-    "        early_stop = EarlyStopping(monitor='val_acc', patience=5, verbose = 1)\n",
-    "        # Common params \n",
-    "        epochs = 15\n",
-    "        batch_size = 128\n",
-    "        # Create and compile the model\n",
-    "        model = create(act, n)\n",
-    "        # Compile the model\n",
-    "        model.compile(optimizer = opt , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
-    "        # Train the model\n",
-    "        history = model.fit(X_train,Y_train, epochs = epochs, validation_data = (X_val,Y_val),\n",
-    "                            verbose = 1 , callbacks=[learning_rate_reduction, early_stop])\n",
-    "        \n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        logs.info(\"Accuracy \"+str(i)+\": \"+str(logger[-1]))\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, history, learning_rate_reduction, early_stop, opt\n",
-    "     \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    # Save the ensembled accuracy and the three models accuracy\n",
-    "    record_e_swish_2.append([n, ensembled])\n",
-    "    logs_e_swish_2.append(logger)\n",
-    "    del ensembler, ensembled\n",
-    "    logs.info(\"Ensembled accuracy: \"+str(record_e_swish_2[-1]))\n",
-    "    logs.info(\"Logs: +\"+str(logs_e_swish_2[-1]))\n",
-    "    \n",
-    "# logs.info(\"\\n \\n \\n\")\n",
-    "# logs.info(\"Logs e_swish_2: \"+str(logs_e_swish_2))\n",
-    "# logs.info(\"Record e_swish_2: \"+str(record_e_swish_2))\n",
-    "    \n",
-    "# print(\"\\n \\n \\n\")\n",
-    "# print(\"Logs e_swish_2: \", logs_e_swish_2)\n",
-    "# print(\"Record e_swish_2: \", record_e_swish_2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " \n",
-      "\n",
-      "Logs e_swish_2:  [[41, 0.6018, 0.55249999999999999, 0.48520000000000002], [44, 0.35160000000000002, 0.51910000000000001, 0.3891]]\n",
-      "Record e_swish_2:  [[41, 0.679], [44, 0.6189]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "logs.info(\"\\n \\n \\n\")\n",
-    "logs.info(\"Logs e_swish_2: \"+str(logs_e_swish_2))\n",
-    "logs.info(\"Record e_swish_2: \"+str(record_e_swish_2))\n",
-    "    \n",
-    "print(\"\\n \\n \\n\")\n",
-    "print(\"Logs e_swish_2: \", logs_e_swish_2)\n",
-    "print(\"Record e_swish_2: \", record_e_swish_2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " Starting round with 23 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 75s 1ms/step - loss: 1.0462 - acc: 0.6580 - val_loss: 0.3307 - val_acc: 0.9118\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.5330 - acc: 0.8714 - val_loss: 0.2276 - val_acc: 0.9472\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.4287 - acc: 0.9022 - val_loss: 0.1684 - val_acc: 0.9613\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.3613 - acc: 0.9221 - val_loss: 0.1670 - val_acc: 0.9608\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.3216 - acc: 0.9314 - val_loss: 0.1519 - val_acc: 0.9670\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.2884 - acc: 0.9379 - val_loss: 0.1291 - val_acc: 0.9715\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.2596 - acc: 0.9458 - val_loss: 0.1389 - val_acc: 0.9715\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.2422 - acc: 0.9500 - val_loss: 0.1757 - val_acc: 0.9612\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.2268 - acc: 0.9536 - val_loss: 0.1385 - val_acc: 0.9718\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2123 - acc: 0.9558 - val_loss: 0.1149 - val_acc: 0.9743\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2037 - acc: 0.9576 - val_loss: 0.1129 - val_acc: 0.9763\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1941 - acc: 0.9597 - val_loss: 0.1262 - val_acc: 0.9742\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1700 - acc: 0.9645 - val_loss: 0.1021 - val_acc: 0.9785\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1528 - acc: 0.9687 - val_loss: 0.1124 - val_acc: 0.9755\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1551 - acc: 0.9676 - val_loss: 0.1046 - val_acc: 0.9792\n",
-      "10000/10000 [==============================] - 3s 271us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 74s 1ms/step - loss: 1.0848 - acc: 0.6401 - val_loss: 0.3346 - val_acc: 0.9087\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5590 - acc: 0.8658 - val_loss: 0.2396 - val_acc: 0.9430\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4316 - acc: 0.9016 - val_loss: 0.1891 - val_acc: 0.9577\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3681 - acc: 0.9191 - val_loss: 0.1858 - val_acc: 0.9587\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3237 - acc: 0.9297 - val_loss: 0.1484 - val_acc: 0.9667\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2876 - acc: 0.9386 - val_loss: 0.1372 - val_acc: 0.9700\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2553 - acc: 0.9474 - val_loss: 0.1242 - val_acc: 0.9712\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2466 - acc: 0.9472 - val_loss: 0.1260 - val_acc: 0.9725\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2218 - acc: 0.9525 - val_loss: 0.1293 - val_acc: 0.9718\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2024 - acc: 0.9577 - val_loss: 0.1140 - val_acc: 0.9765\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1992 - acc: 0.9583 - val_loss: 0.1137 - val_acc: 0.9758\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1747 - acc: 0.9629 - val_loss: 0.1164 - val_acc: 0.9753\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1708 - acc: 0.9648 - val_loss: 0.1061 - val_acc: 0.9793\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1496 - acc: 0.9692 - val_loss: 0.1053 - val_acc: 0.9780\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.1469 - acc: 0.9689 - val_loss: 0.1068 - val_acc: 0.9755\n",
-      "10000/10000 [==============================] - 3s 283us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 1.0590 - acc: 0.6477 - val_loss: 0.3277 - val_acc: 0.9195\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.5580 - acc: 0.8642 - val_loss: 0.2144 - val_acc: 0.9465\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.4428 - acc: 0.9006 - val_loss: 0.1872 - val_acc: 0.9598\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.3763 - acc: 0.9181 - val_loss: 0.1592 - val_acc: 0.9643\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.3234 - acc: 0.9317 - val_loss: 0.1634 - val_acc: 0.9642\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.2859 - acc: 0.9408 - val_loss: 0.1407 - val_acc: 0.9715\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.2669 - acc: 0.9454 - val_loss: 0.1502 - val_acc: 0.9683\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.2393 - acc: 0.9504 - val_loss: 0.1428 - val_acc: 0.9710\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2172 - acc: 0.9560 - val_loss: 0.0996 - val_acc: 0.9797\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2084 - acc: 0.9576 - val_loss: 0.1304 - val_acc: 0.9718\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1996 - acc: 0.9596 - val_loss: 0.1134 - val_acc: 0.9747\n",
-      "Epoch 12/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.1857 - acc: 0.9625\n",
-      "Epoch 00012: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.1857 - acc: 0.9625 - val_loss: 0.1020 - val_acc: 0.9782\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1462 - acc: 0.9704 - val_loss: 0.0933 - val_acc: 0.9802\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1300 - acc: 0.9736 - val_loss: 0.0879 - val_acc: 0.9812\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1194 - acc: 0.9759 - val_loss: 0.0825 - val_acc: 0.9825\n",
-      "10000/10000 [==============================] - 3s 276us/step\n",
-      "0.9855\n",
-      "\n",
-      " \n",
-      " Starting round with 26 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.2490 - acc: 0.5542 - val_loss: 0.4325 - val_acc: 0.8537\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.6548 - acc: 0.8299 - val_loss: 0.2678 - val_acc: 0.9382\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.5192 - acc: 0.8824 - val_loss: 0.2092 - val_acc: 0.9517\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.4420 - acc: 0.9041 - val_loss: 0.1997 - val_acc: 0.9590\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.4057 - acc: 0.9157 - val_loss: 0.2428 - val_acc: 0.9472\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.3756 - acc: 0.9239 - val_loss: 0.1895 - val_acc: 0.9640\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.3706 - acc: 0.9248 - val_loss: 0.1953 - val_acc: 0.9637\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.3515 - acc: 0.9290 - val_loss: 0.1774 - val_acc: 0.9633\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.3338 - acc: 0.9331 - val_loss: 0.1650 - val_acc: 0.9678\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.3426 - acc: 0.9309 - val_loss: 0.2129 - val_acc: 0.9535\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3134 - acc: 0.9362 - val_loss: 0.1761 - val_acc: 0.9643\n",
-      "Epoch 12/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.2990 - acc: 0.9406\n",
-      "Epoch 00012: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.2989 - acc: 0.9406 - val_loss: 0.1630 - val_acc: 0.9670\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.2366 - acc: 0.9543 - val_loss: 0.1267 - val_acc: 0.9748\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.2169 - acc: 0.9576 - val_loss: 0.1236 - val_acc: 0.9753\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.2102 - acc: 0.9583 - val_loss: 0.1224 - val_acc: 0.9735\n",
-      "10000/10000 [==============================] - 3s 307us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1890 - acc: 0.6101 - val_loss: 0.4019 - val_acc: 0.8927\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.6857 - acc: 0.8298 - val_loss: 0.3092 - val_acc: 0.9278\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.5405 - acc: 0.8786 - val_loss: 0.2252 - val_acc: 0.9482\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.4762 - acc: 0.8965 - val_loss: 0.2401 - val_acc: 0.9485\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.4240 - acc: 0.9110 - val_loss: 0.1962 - val_acc: 0.9605\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3875 - acc: 0.9204 - val_loss: 0.2166 - val_acc: 0.9610\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3769 - acc: 0.9232 - val_loss: 0.2108 - val_acc: 0.9602\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3395 - acc: 0.9329 - val_loss: 0.2464 - val_acc: 0.9537\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3381 - acc: 0.9324 - val_loss: 0.1907 - val_acc: 0.9642\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3253 - acc: 0.9354 - val_loss: 0.1884 - val_acc: 0.9633\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3285 - acc: 0.9323 - val_loss: 0.1961 - val_acc: 0.9580\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3050 - acc: 0.9380 - val_loss: 0.1651 - val_acc: 0.9655\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3143 - acc: 0.9348 - val_loss: 0.2064 - val_acc: 0.9610\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3158 - acc: 0.9321 - val_loss: 0.1939 - val_acc: 0.9590\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.2707 - acc: 0.9445 - val_loss: 0.1648 - val_acc: 0.9678\n",
-      "10000/10000 [==============================] - 3s 314us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.1940 - acc: 0.6039 - val_loss: 0.3872 - val_acc: 0.8897\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.6540 - acc: 0.8391 - val_loss: 0.2873 - val_acc: 0.9380\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.5352 - acc: 0.8794 - val_loss: 0.3035 - val_acc: 0.9317\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.4714 - acc: 0.8997 - val_loss: 0.2409 - val_acc: 0.9545\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.4089 - acc: 0.9153 - val_loss: 0.1996 - val_acc: 0.9580\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.3868 - acc: 0.9214 - val_loss: 0.1948 - val_acc: 0.9638\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3657 - acc: 0.9263 - val_loss: 0.2138 - val_acc: 0.9577\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3293 - acc: 0.9335 - val_loss: 0.1745 - val_acc: 0.9645\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3264 - acc: 0.9338 - val_loss: 0.2172 - val_acc: 0.9553\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3376 - acc: 0.9316 - val_loss: 0.1952 - val_acc: 0.9612\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.3225 - acc: 0.9353 - val_loss: 0.1570 - val_acc: 0.9685\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.2957 - acc: 0.9406 - val_loss: 0.1497 - val_acc: 0.9715\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3136 - acc: 0.9364 - val_loss: 0.1942 - val_acc: 0.9608\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.3112 - acc: 0.9361 - val_loss: 0.1779 - val_acc: 0.9585\n",
-      "Epoch 15/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.2768 - acc: 0.9434\n",
-      "Epoch 00015: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.2767 - acc: 0.9434 - val_loss: 0.1525 - val_acc: 0.9708\n",
-      "10000/10000 [==============================] - 3s 317us/step\n",
-      "0.9771\n",
-      "\n",
-      " \n",
-      " Starting round with 29 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 1.3225 - acc: 0.5355 - val_loss: 0.5337 - val_acc: 0.8157\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.8112 - acc: 0.7537 - val_loss: 0.5735 - val_acc: 0.8160\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.7753 - acc: 0.7722 - val_loss: 0.4473 - val_acc: 0.8498\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6826 - acc: 0.8001 - val_loss: 0.4817 - val_acc: 0.8433\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6085 - acc: 0.8288 - val_loss: 0.3433 - val_acc: 0.9257\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5617 - acc: 0.8680 - val_loss: 0.3742 - val_acc: 0.9268\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5616 - acc: 0.8724 - val_loss: 0.3079 - val_acc: 0.9345\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5409 - acc: 0.8753 - val_loss: 0.2611 - val_acc: 0.9470\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4860 - acc: 0.8881 - val_loss: 0.4424 - val_acc: 0.8508\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5590 - acc: 0.8661 - val_loss: 0.3038 - val_acc: 0.9370\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4588 - acc: 0.9015 - val_loss: 0.2402 - val_acc: 0.9510\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4126 - acc: 0.9139 - val_loss: 0.2511 - val_acc: 0.9548\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4064 - acc: 0.9142 - val_loss: 0.2267 - val_acc: 0.9540\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3625 - acc: 0.9263 - val_loss: 0.2242 - val_acc: 0.9577\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3594 - acc: 0.9275 - val_loss: 0.2169 - val_acc: 0.9572\n",
-      "10000/10000 [==============================] - 3s 343us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 104s 2ms/step - loss: 1.3435 - acc: 0.5201 - val_loss: 0.5460 - val_acc: 0.8197\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.8232 - acc: 0.7808 - val_loss: 0.4104 - val_acc: 0.9142\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6608 - acc: 0.8515 - val_loss: 0.4109 - val_acc: 0.9330\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6235 - acc: 0.8671 - val_loss: 0.3474 - val_acc: 0.9322\n",
-      "Epoch 5/15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6429 - acc: 0.8598 - val_loss: 0.3507 - val_acc: 0.9335\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5708 - acc: 0.8796 - val_loss: 0.3894 - val_acc: 0.9255\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5816 - acc: 0.8753 - val_loss: 0.3646 - val_acc: 0.9255\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.5460 - acc: 0.8831\n",
-      "Epoch 00008: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.5461 - acc: 0.8830 - val_loss: 0.4538 - val_acc: 0.8997\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4725 - acc: 0.9004 - val_loss: 0.2715 - val_acc: 0.9467\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3936 - acc: 0.9213 - val_loss: 0.2557 - val_acc: 0.9520\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3709 - acc: 0.9257 - val_loss: 0.2531 - val_acc: 0.9535\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3769 - acc: 0.9254 - val_loss: 0.2538 - val_acc: 0.9507\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3562 - acc: 0.9279 - val_loss: 0.2429 - val_acc: 0.9538\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3624 - acc: 0.9267 - val_loss: 0.2194 - val_acc: 0.9578\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3453 - acc: 0.9302 - val_loss: 0.2308 - val_acc: 0.9558\n",
-      "10000/10000 [==============================] - 4s 351us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.3008 - acc: 0.5256 - val_loss: 0.6076 - val_acc: 0.7525\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.7937 - acc: 0.7669 - val_loss: 0.3975 - val_acc: 0.9140\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6215 - acc: 0.8592 - val_loss: 0.3837 - val_acc: 0.9143\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5682 - acc: 0.8782 - val_loss: 0.3325 - val_acc: 0.9337\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5566 - acc: 0.8819 - val_loss: 0.4199 - val_acc: 0.9168\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5224 - acc: 0.8923 - val_loss: 0.3893 - val_acc: 0.9218\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.5096 - acc: 0.8951\n",
-      "Epoch 00007: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.5096 - acc: 0.8951 - val_loss: 0.3445 - val_acc: 0.9335\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.4072 - acc: 0.9181 - val_loss: 0.2410 - val_acc: 0.9565\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3790 - acc: 0.9260 - val_loss: 0.2448 - val_acc: 0.9563\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3594 - acc: 0.9295 - val_loss: 0.2294 - val_acc: 0.9588\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3453 - acc: 0.9327 - val_loss: 0.2089 - val_acc: 0.9612\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3452 - acc: 0.9323 - val_loss: 0.2249 - val_acc: 0.9602\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3397 - acc: 0.9325 - val_loss: 0.1959 - val_acc: 0.9627\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3250 - acc: 0.9370 - val_loss: 0.2008 - val_acc: 0.9615\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3280 - acc: 0.9360 - val_loss: 0.2631 - val_acc: 0.9517\n",
-      "10000/10000 [==============================] - 3s 348us/step\n",
-      "0.9682\n",
-      "\n",
-      " \n",
-      " Starting round with 32 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 117s 2ms/step - loss: 1.4588 - acc: 0.4800 - val_loss: 0.6421 - val_acc: 0.7875\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.9221 - acc: 0.7286 - val_loss: 0.4728 - val_acc: 0.8930\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.7770 - acc: 0.8130 - val_loss: 0.4685 - val_acc: 0.9063\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.7695 - acc: 0.8220 - val_loss: 0.5612 - val_acc: 0.8928\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.7615 - acc: 0.8266 - val_loss: 0.6018 - val_acc: 0.8718\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.8600 - acc: 0.7820\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 0.8601 - acc: 0.7819 - val_loss: 0.5869 - val_acc: 0.8772\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.6590 - acc: 0.8564 - val_loss: 0.4357 - val_acc: 0.9220\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5772 - acc: 0.8807 - val_loss: 0.3983 - val_acc: 0.9297\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5606 - acc: 0.8849 - val_loss: 0.3682 - val_acc: 0.9378\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5564 - acc: 0.8850 - val_loss: 0.3801 - val_acc: 0.9310\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5763 - acc: 0.8810 - val_loss: 0.3988 - val_acc: 0.9272\n",
-      "Epoch 12/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.5641 - acc: 0.8848\n",
-      "Epoch 00012: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5641 - acc: 0.8848 - val_loss: 0.3706 - val_acc: 0.9317\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5199 - acc: 0.8950 - val_loss: 0.3030 - val_acc: 0.9473\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.4801 - acc: 0.9061 - val_loss: 0.3034 - val_acc: 0.9475\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.4639 - acc: 0.9110 - val_loss: 0.2993 - val_acc: 0.9487\n",
-      "10000/10000 [==============================] - 4s 384us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.4843 - acc: 0.4473 - val_loss: 0.8980 - val_acc: 0.5863\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.1097 - acc: 0.5789 - val_loss: 0.8486 - val_acc: 0.6410\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0654 - acc: 0.6075 - val_loss: 0.8248 - val_acc: 0.6543\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0015 - acc: 0.6234 - val_loss: 0.8657 - val_acc: 0.6528\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0896 - acc: 0.5949 - val_loss: 0.8762 - val_acc: 0.6488\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.1444 - acc: 0.5831\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.1444 - acc: 0.5831 - val_loss: 0.9288 - val_acc: 0.6448\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0179 - acc: 0.6196 - val_loss: 0.9207 - val_acc: 0.6423\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.0050 - acc: 0.6163\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0051 - acc: 0.6162 - val_loss: 0.8832 - val_acc: 0.6390\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 4s 391us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 1.5189 - acc: 0.4200 - val_loss: 0.9912 - val_acc: 0.5223\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.1643 - acc: 0.5662 - val_loss: 0.8155 - val_acc: 0.6682\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0341 - acc: 0.6336 - val_loss: 0.8102 - val_acc: 0.6978\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0125 - acc: 0.6587 - val_loss: 0.8712 - val_acc: 0.6883\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.1190 - acc: 0.6071 - val_loss: 0.9296 - val_acc: 0.6360\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.1996 - acc: 0.5569\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.1999 - acc: 0.5568 - val_loss: 1.1061 - val_acc: 0.5765\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.1309 - acc: 0.5802 - val_loss: 0.8848 - val_acc: 0.6453\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.0254 - acc: 0.6074\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0253 - acc: 0.6074 - val_loss: 0.8628 - val_acc: 0.6537\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 4s 393us/step\n",
-      "0.9467\n",
-      "\n",
-      " \n",
-      " Starting round with 35 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.5391 - acc: 0.4384 - val_loss: 0.9788 - val_acc: 0.6467\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.2433 - acc: 0.5430 - val_loss: 1.0747 - val_acc: 0.5442\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.2490 - acc: 0.5047 - val_loss: 0.9983 - val_acc: 0.5487\n",
-      "Epoch 4/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.2731 - acc: 0.5015\n",
-      "Epoch 00004: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.2733 - acc: 0.5015 - val_loss: 1.1191 - val_acc: 0.5350\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.1767 - acc: 0.5241 - val_loss: 0.9141 - val_acc: 0.5667\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.0793 - acc: 0.5399\n",
-      "Epoch 00006: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.0795 - acc: 0.5399 - val_loss: 0.9632 - val_acc: 0.5673\n",
-      "Epoch 00006: early stopping\n",
-      "10000/10000 [==============================] - 4s 425us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.5595 - acc: 0.3954 - val_loss: 1.0558 - val_acc: 0.5485\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2787 - acc: 0.5113 - val_loss: 1.0723 - val_acc: 0.5482\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2771 - acc: 0.5061 - val_loss: 1.1703 - val_acc: 0.5423\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2655 - acc: 0.5109 - val_loss: 1.1720 - val_acc: 0.5563\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2786 - acc: 0.5004 - val_loss: 1.1071 - val_acc: 0.5335\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.3999 - acc: 0.4567 - val_loss: 1.2591 - val_acc: 0.5238\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3343 - acc: 0.4844\n",
-      "Epoch 00007: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.3342 - acc: 0.4844 - val_loss: 1.0914 - val_acc: 0.5445\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2104 - acc: 0.5201 - val_loss: 1.0993 - val_acc: 0.5520\n",
-      "Epoch 9/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.2007 - acc: 0.5226\n",
-      "Epoch 00009: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2006 - acc: 0.5226 - val_loss: 1.0523 - val_acc: 0.5500\n",
-      "Epoch 00009: early stopping\n",
-      "10000/10000 [==============================] - 4s 430us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 137s 3ms/step - loss: 1.4622 - acc: 0.4822 - val_loss: 0.7312 - val_acc: 0.7602\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 0.9891 - acc: 0.6910 - val_loss: 0.6174 - val_acc: 0.8362\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 0.9441 - acc: 0.7258 - val_loss: 0.6156 - val_acc: 0.8278\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 0.9273 - acc: 0.7308 - val_loss: 0.8065 - val_acc: 0.7023\n",
-      "Epoch 5/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.0004 - acc: 0.7096\n",
-      "Epoch 00005: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.0004 - acc: 0.7096 - val_loss: 0.7710 - val_acc: 0.7937\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9437 - acc: 0.6983 - val_loss: 0.7320 - val_acc: 0.7602\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.8303 - acc: 0.7557\n",
-      "Epoch 00007: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8305 - acc: 0.7556 - val_loss: 0.5952 - val_acc: 0.8298\n",
-      "Epoch 00007: early stopping\n",
-      "10000/10000 [==============================] - 4s 440us/step\n",
-      "0.8342\n",
-      "\n",
-      " \n",
-      " Starting round with 38 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 149s 3ms/step - loss: 1.6836 - acc: 0.3679 - val_loss: 1.0321 - val_acc: 0.5525\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2798 - acc: 0.5245 - val_loss: 1.2246 - val_acc: 0.5232\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2359 - acc: 0.5473 - val_loss: 1.0268 - val_acc: 0.6307\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.3096 - acc: 0.5240 - val_loss: 1.3133 - val_acc: 0.5585\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.3253 - acc: 0.5264 - val_loss: 1.1199 - val_acc: 0.6213\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.2908 - acc: 0.5359\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.2908 - acc: 0.5359 - val_loss: 1.0758 - val_acc: 0.6092\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.1847 - acc: 0.5702 - val_loss: 1.0181 - val_acc: 0.6180\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.1602 - acc: 0.5795\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.1603 - acc: 0.5796 - val_loss: 0.9631 - val_acc: 0.6223\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 5s 475us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 151s 3ms/step - loss: 1.6601 - acc: 0.3901 - val_loss: 1.1790 - val_acc: 0.5700\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2898 - acc: 0.5134 - val_loss: 1.3619 - val_acc: 0.4463\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2958 - acc: 0.5339 - val_loss: 1.0186 - val_acc: 0.6143\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2962 - acc: 0.5406 - val_loss: 1.0778 - val_acc: 0.6033\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.3240 - acc: 0.5334 - val_loss: 1.1436 - val_acc: 0.5823\n",
-      "Epoch 6/15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.4624 - acc: 0.4843\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 1.4623 - acc: 0.4843 - val_loss: 1.2412 - val_acc: 0.5240\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3307 - acc: 0.5085 - val_loss: 1.0924 - val_acc: 0.5707\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.2730 - acc: 0.5207\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2730 - acc: 0.5207 - val_loss: 1.1310 - val_acc: 0.5563\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 5s 483us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 155s 3ms/step - loss: 1.6491 - acc: 0.3853 - val_loss: 1.0922 - val_acc: 0.5350\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.2936 - acc: 0.5155 - val_loss: 1.1264 - val_acc: 0.5693\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.2647 - acc: 0.5244 - val_loss: 1.2683 - val_acc: 0.4367\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.2983 - acc: 0.5229 - val_loss: 1.0166 - val_acc: 0.6105\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2830 - acc: 0.5345 - val_loss: 1.1234 - val_acc: 0.5955\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.4061 - acc: 0.5150 - val_loss: 1.1489 - val_acc: 0.5778\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3552 - acc: 0.5372\n",
-      "Epoch 00007: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 1.3553 - acc: 0.5372 - val_loss: 1.1802 - val_acc: 0.5625\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3149 - acc: 0.5201 - val_loss: 1.0453 - val_acc: 0.5953\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.2243 - acc: 0.5628 - val_loss: 0.9760 - val_acc: 0.6693\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.1691 - acc: 0.6044 - val_loss: 0.9804 - val_acc: 0.6820\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.1175 - acc: 0.6196 - val_loss: 0.9022 - val_acc: 0.7180\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.2511 - acc: 0.5563 - val_loss: 1.0307 - val_acc: 0.6378\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2222 - acc: 0.5583 - val_loss: 0.9987 - val_acc: 0.5960\n",
-      "Epoch 14/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.2075 - acc: 0.5758\n",
-      "Epoch 00014: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2075 - acc: 0.5758 - val_loss: 1.0013 - val_acc: 0.6497\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.1483 - acc: 0.6078 - val_loss: 0.9116 - val_acc: 0.6747\n",
-      "10000/10000 [==============================] - 5s 484us/step\n",
-      "0.7813\n",
-      "\n",
-      " \n",
-      " Starting round with 41 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 166s 3ms/step - loss: 1.7162 - acc: 0.3313 - val_loss: 1.3392 - val_acc: 0.4265\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5130 - acc: 0.4142 - val_loss: 1.3409 - val_acc: 0.4567\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5507 - acc: 0.4148 - val_loss: 1.4844 - val_acc: 0.4730\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5896 - acc: 0.4080 - val_loss: 1.6586 - val_acc: 0.3682\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5646 - acc: 0.4067 - val_loss: 1.3938 - val_acc: 0.4395\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.5245 - acc: 0.3933\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.5245 - acc: 0.3932 - val_loss: 1.3765 - val_acc: 0.4067\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.4536 - acc: 0.4247 - val_loss: 1.3601 - val_acc: 0.4433\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.4542 - acc: 0.4113 - val_loss: 1.3286 - val_acc: 0.4810\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5170 - acc: 0.4105 - val_loss: 1.5308 - val_acc: 0.3923\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5700 - acc: 0.3897 - val_loss: 1.3754 - val_acc: 0.4338\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.4620 - acc: 0.4209 - val_loss: 1.2307 - val_acc: 0.4978\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.3998 - acc: 0.4419 - val_loss: 1.2595 - val_acc: 0.4745\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.4055 - acc: 0.4387 - val_loss: 1.2098 - val_acc: 0.5053\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.3603 - acc: 0.4565 - val_loss: 1.1731 - val_acc: 0.4768\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.3883 - acc: 0.4560 - val_loss: 1.2640 - val_acc: 0.4840\n",
-      "10000/10000 [==============================] - 5s 526us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 169s 3ms/step - loss: 1.9265 - acc: 0.2779 - val_loss: 1.6844 - val_acc: 0.3985\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.5785 - acc: 0.3987 - val_loss: 1.3644 - val_acc: 0.4360\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.5925 - acc: 0.3627 - val_loss: 1.4525 - val_acc: 0.3750\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.7081 - acc: 0.3271 - val_loss: 1.5706 - val_acc: 0.3308\n",
-      "Epoch 5/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.6317 - acc: 0.3527\n",
-      "Epoch 00005: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.6318 - acc: 0.3526 - val_loss: 1.5858 - val_acc: 0.3957\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.5363 - acc: 0.3693 - val_loss: 1.4134 - val_acc: 0.4028\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.5147 - acc: 0.3731\n",
-      "Epoch 00007: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.5148 - acc: 0.3731 - val_loss: 1.4634 - val_acc: 0.4002\n",
-      "Epoch 00007: early stopping\n",
-      "10000/10000 [==============================] - 5s 535us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 174s 3ms/step - loss: 1.7023 - acc: 0.3497 - val_loss: 1.1769 - val_acc: 0.5008\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.3519 - acc: 0.5148 - val_loss: 1.0354 - val_acc: 0.5532\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.4692 - acc: 0.4766 - val_loss: 1.4430 - val_acc: 0.4168\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.5971 - acc: 0.4095 - val_loss: 1.4734 - val_acc: 0.4135\n",
-      "Epoch 5/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.5374 - acc: 0.4192\n",
-      "Epoch 00005: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.5374 - acc: 0.4192 - val_loss: 1.3311 - val_acc: 0.4423\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.4046 - acc: 0.4509 - val_loss: 1.2628 - val_acc: 0.4838\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3967 - acc: 0.4618\n",
-      "Epoch 00007: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.3966 - acc: 0.4618 - val_loss: 1.3484 - val_acc: 0.4568\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 00007: early stopping\n",
-      "10000/10000 [==============================] - 6s 553us/step\n",
-      "0.6553\n",
-      "\n",
-      " \n",
-      " Starting round with 44 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 189s 3ms/step - loss: 1.7660 - acc: 0.3470 - val_loss: 1.1964 - val_acc: 0.4793\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.4717 - acc: 0.4286 - val_loss: 1.4418 - val_acc: 0.3628\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.5745 - acc: 0.3834 - val_loss: 1.3689 - val_acc: 0.4892\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.4541 - acc: 0.4387 - val_loss: 1.3224 - val_acc: 0.4720\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.6842 - acc: 0.3848 - val_loss: 1.7230 - val_acc: 0.3873\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.6507 - acc: 0.3629\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 1.6508 - acc: 0.3629 - val_loss: 1.3781 - val_acc: 0.4370\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.5033 - acc: 0.3959 - val_loss: 1.3533 - val_acc: 0.4427\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.4819 - acc: 0.4020\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.4818 - acc: 0.4019 - val_loss: 1.3030 - val_acc: 0.4490\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 6s 611us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 192s 4ms/step - loss: 1.8588 - acc: 0.3186 - val_loss: 1.5942 - val_acc: 0.4332\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.6491 - acc: 0.3971 - val_loss: 1.3702 - val_acc: 0.4440\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.5632 - acc: 0.3989 - val_loss: 1.4778 - val_acc: 0.4072\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.6650 - acc: 0.3712 - val_loss: 1.7220 - val_acc: 0.3457\n",
-      "Epoch 5/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.8096 - acc: 0.3554\n",
-      "Epoch 00005: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 1.8097 - acc: 0.3554 - val_loss: 1.7808 - val_acc: 0.3617\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.6158 - acc: 0.4158 - val_loss: 1.3793 - val_acc: 0.4612\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.5035 - acc: 0.4369 - val_loss: 1.3492 - val_acc: 0.4722\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.4500 - acc: 0.4412 - val_loss: 1.3339 - val_acc: 0.4812\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.4565 - acc: 0.4446 - val_loss: 1.3018 - val_acc: 0.4552\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.4262 - acc: 0.4391 - val_loss: 1.3035 - val_acc: 0.4622\n",
-      "Epoch 11/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.4460 - acc: 0.4227\n",
-      "Epoch 00011: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.4461 - acc: 0.4227 - val_loss: 1.3582 - val_acc: 0.4480\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.4534 - acc: 0.4221 - val_loss: 1.3006 - val_acc: 0.4597\n",
-      "Epoch 13/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.4599 - acc: 0.4159\n",
-      "Epoch 00013: reducing learning rate to 0.0004287500050850212.\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 1.4601 - acc: 0.4158 - val_loss: 1.3578 - val_acc: 0.4443\n",
-      "Epoch 00013: early stopping\n",
-      "10000/10000 [==============================] - 6s 613us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 198s 4ms/step - loss: 1.8603 - acc: 0.3091 - val_loss: 1.3816 - val_acc: 0.4453\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 1.4916 - acc: 0.4156 - val_loss: 1.1812 - val_acc: 0.5210\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 1.4895 - acc: 0.4398 - val_loss: 1.7455 - val_acc: 0.4738\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.6391 - acc: 0.3839 - val_loss: 1.5984 - val_acc: 0.3573\n",
-      "Epoch 5/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.6155 - acc: 0.3716\n",
-      "Epoch 00005: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.6154 - acc: 0.3716 - val_loss: 1.5327 - val_acc: 0.4082\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.5438 - acc: 0.3925 - val_loss: 1.3389 - val_acc: 0.4248\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.5070 - acc: 0.3935\n",
-      "Epoch 00007: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 140s 3ms/step - loss: 1.5070 - acc: 0.3935 - val_loss: 1.3567 - val_acc: 0.4632\n",
-      "Epoch 00007: early stopping\n",
-      "10000/10000 [==============================] - 6s 612us/step\n",
-      "0.5968\n",
-      "\n",
-      " \n",
-      " \n",
-      "\n",
-      "Logs swish:  [[23, 0.97789999999999999, 0.98109999999999997, 0.98380000000000001], [26, 0.9748, 0.9667, 0.9718], [29, 0.95650000000000002, 0.95889999999999997, 0.95240000000000002], [32, 0.9506, 0.65339999999999998, 0.65449999999999997], [35, 0.57069999999999999, 0.55630000000000002, 0.8357], [38, 0.62029999999999996, 0.55879999999999996, 0.68069999999999997], [41, 0.4733, 0.39119999999999999, 0.45540000000000003], [44, 0.4461, 0.44290000000000002, 0.45960000000000001]]\n",
-      "Record swish:  [[23, 0.9855], [26, 0.9771], [29, 0.9682], [32, 0.9467], [35, 0.8342], [38, 0.7813], [41, 0.6553], [44, 0.5968]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "act = swish\n",
-    "logs.info(\"\\n \\n \\n\"+\"\\n \\n \\n\")\n",
-    "logs.info(\"SWISH 23-42\")\n",
-    "\n",
-    "logs_swish = []\n",
-    "record_swish = []\n",
-    "for n in range(23,45,3):\n",
-    "    ensembler = 0\n",
-    "    logger = [n]\n",
-    "    logs.info(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    print(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    for i in range(3):\n",
-    "        # Garbage collector\n",
-    "        gc.collect()\n",
-    "        # Set optimizer\n",
-    "        opt = SGD(lr=0.01, momentum=0.9)\n",
-    "        # Set callbacks (learning rate reducer and early stopping)\n",
-    "        learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.35, min_lr=0.00001)\n",
-    "        early_stop = EarlyStopping(monitor='val_acc', patience=5, verbose = 1)\n",
-    "        # Common params \n",
-    "        epochs = 15\n",
-    "        batch_size = 128\n",
-    "        # Create and compile the model\n",
-    "        model = create(act, n)\n",
-    "        # Compile the model\n",
-    "        model.compile(optimizer = opt , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
-    "        # Train the model\n",
-    "        history = model.fit(X_train,Y_train, epochs = epochs, validation_data = (X_val,Y_val),\n",
-    "                            verbose = 1 , callbacks=[learning_rate_reduction, early_stop])\n",
-    "        \n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        logs.info(\"Accuracy \"+str(i)+\": \"+str(logger[-1]))\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, history, learning_rate_reduction, early_stop, opt\n",
-    "     \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    # Save the ensembled accuracy and the three models accuracy\n",
-    "    record_swish.append([n, ensembled])\n",
-    "    logs_swish.append(logger)\n",
-    "    del ensembler, ensembled\n",
-    "    logs.info(\"Ensembled accuracy: \"+str(record_swish[-1]))\n",
-    "    logs.info(\"Logs: +\"+str(logs_swish[-1]))\n",
-    "    \n",
-    "logs.info(\"\\n \\n \\n\")\n",
-    "logs.info(\"Logs swish: \"+str(logs_swish))\n",
-    "logs.info(\"Record swish: \"+str(record_swish))\n",
-    "    \n",
-    "print(\"\\n \\n \\n\")\n",
-    "print(\"Logs swish: \", logs_swish)\n",
-    "print(\"Record swish: \", record_swish)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/mnist/deep_nets/deep_nets.png b/mnist/deep_nets/deep_nets.png
deleted file mode 100644
index b55b25b..0000000
Binary files a/mnist/deep_nets/deep_nets.png and /dev/null differ
diff --git a/mnist/deep_nets/log.txt b/mnist/deep_nets/log.txt
deleted file mode 100644
index 4ab2804..0000000
--- a/mnist/deep_nets/log.txt
+++ /dev/null
@@ -1,181 +0,0 @@
-INFO 2017-12-14 07:31:44,260 - Relu 38-42
-INFO 2017-12-14 07:31:44,261 - 
- 
- Starting round with 38 layers
-INFO 2017-12-14 07:47:05,125 - Accuracy 0: 0.1135
-INFO 2017-12-14 07:58:47,179 - Accuracy 1: 0.1135
-INFO 2017-12-14 08:10:23,486 - Accuracy 2: 0.1135
-INFO 2017-12-14 08:10:27,767 - Ensembled accuracy: [38, 0.1135]
-INFO 2017-12-14 08:10:27,768 - Logs: [38, 0.1135, 0.1135, 0.1135]
-INFO 2017-12-14 08:10:27,768 - 
- 
- Starting round with 41 layers
-INFO 2017-12-14 08:34:51,712 - Accuracy 0: 0.1568
-INFO 2017-12-14 08:47:30,148 - Accuracy 1: 0.1135
-INFO 2017-12-14 09:00:11,053 - Accuracy 2: 0.1136
-INFO 2017-12-14 09:00:16,281 - Ensembled accuracy: [41, 0.1572]
-INFO 2017-12-14 09:00:16,281 - Logs: [41, 0.15679999999999999, 0.1135, 0.11360000000000001]
-INFO 2017-12-14 09:00:16,288 - 
- 
- 
-
-INFO 2017-12-14 09:00:16,288 - Logs e_swish_2: [[38, 0.1135, 0.1135, 0.1135], [41, 0.15679999999999999, 0.1135, 0.11360000000000001]]
-INFO 2017-12-14 09:00:16,288 - Record e_swish_2: [[23, 0.9717], [26, 0.9253], [29, 0.8639], [32, 0.6807], [35, 0.5834], [38, 0.1135], [41, 0.1572]]
-INFO 2017-12-14 09:00:16,419 - 
- 
- 
-
- 
- 
-
-INFO 2017-12-14 09:00:16,419 - ESWISH 23-42
-INFO 2017-12-14 09:00:16,419 - 
- 
- Starting round with 23 layers
-INFO 2017-12-14 09:19:52,508 - Accuracy 0: 0.9752
-INFO 2017-12-14 09:39:36,562 - Accuracy 1: 0.9645
-INFO 2017-12-14 09:59:26,861 - Accuracy 2: 0.9654
-INFO 2017-12-14 09:59:31,233 - Ensembled accuracy: [23, 0.9753]
-INFO 2017-12-14 09:59:31,233 - Logs: +[23, 0.97519999999999996, 0.96450000000000002, 0.96540000000000004]
-INFO 2017-12-14 09:59:31,233 - 
- 
- Starting round with 26 layers
-INFO 2017-12-14 10:22:06,588 - Accuracy 0: 0.9681
-INFO 2017-12-14 10:44:56,685 - Accuracy 1: 0.9601
-INFO 2017-12-14 11:07:51,230 - Accuracy 2: 0.9573
-INFO 2017-12-14 11:07:56,751 - Ensembled accuracy: [26, 0.9682]
-INFO 2017-12-14 11:07:56,752 - Logs: +[26, 0.96809999999999996, 0.96009999999999995, 0.95730000000000004]
-INFO 2017-12-14 11:07:56,752 - 
- 
- Starting round with 29 layers
-INFO 2017-12-14 11:32:57,350 - Accuracy 0: 0.9099
-INFO 2017-12-14 11:58:12,808 - Accuracy 1: 0.9362
-INFO 2017-12-14 12:23:33,347 - Accuracy 2: 0.9196
-INFO 2017-12-14 12:23:40,098 - Ensembled accuracy: [29, 0.9487]
-INFO 2017-12-14 12:23:40,098 - Logs: +[29, 0.90990000000000004, 0.93620000000000003, 0.91959999999999997]
-INFO 2017-12-14 12:23:40,098 - 
- 
- Starting round with 32 layers
-INFO 2017-12-14 12:51:38,005 - Accuracy 0: 0.9296
-INFO 2017-12-14 13:19:50,848 - Accuracy 1: 0.9171
-INFO 2017-12-14 13:46:31,957 - Accuracy 2: 0.9001
-INFO 2017-12-14 13:46:41,065 - Ensembled accuracy: [32, 0.9353]
-INFO 2017-12-14 13:46:41,065 - Logs: +[32, 0.92959999999999998, 0.91710000000000003, 0.90010000000000001]
-INFO 2017-12-14 13:46:41,065 - 
- 
- Starting round with 35 layers
-INFO 2017-12-14 14:17:40,030 - Accuracy 0: 0.8245
-INFO 2017-12-14 14:36:46,036 - Accuracy 1: 0.5038
-INFO 2017-12-14 15:08:11,004 - Accuracy 2: 0.8862
-INFO 2017-12-14 15:08:21,653 - Ensembled accuracy: [35, 0.9047]
-INFO 2017-12-14 15:08:21,653 - Logs: +[35, 0.82450000000000001, 0.50380000000000003, 0.88619999999999999]
-INFO 2017-12-14 15:08:21,654 - 
- 
- Starting round with 38 layers
-INFO 2017-12-14 15:42:40,128 - Accuracy 0: 0.6255
-INFO 2017-12-14 16:16:51,912 - Accuracy 1: 0.74
-INFO 2017-12-14 16:51:03,378 - Accuracy 2: 0.7552
-INFO 2017-12-14 16:51:17,416 - Ensembled accuracy: [38, 0.7936]
-INFO 2017-12-14 16:51:17,416 - Logs: +[38, 0.62549999999999994, 0.73999999999999999, 0.75519999999999998]
-INFO 2017-12-14 16:51:17,417 - 
- 
- INFO 2017-12-14 19:37:49,830 - 
- 
- Starting round with 41 layers
-INFO 2017-12-14 20:11:21,765 - Accuracy 0: 0.6018
-INFO 2017-12-14 20:44:49,211 - Accuracy 1: 0.5525
-INFO 2017-12-14 21:18:38,985 - Accuracy 2: 0.4852
-INFO 2017-12-14 21:18:44,346 - Ensembled accuracy: [41, 0.679]
-INFO 2017-12-14 21:18:44,346 - Logs: +[41, 0.6018, 0.55249999999999999, 0.48520000000000002]
-INFO 2017-12-14 21:18:44,346 - 
- 
- Starting round with 44 layers
-INFO 2017-12-14 21:55:06,008 - Accuracy 0: 0.3516
-INFO 2017-12-14 22:31:59,412 - Accuracy 1: 0.5191
-INFO 2017-12-14 23:09:03,930 - Accuracy 2: 0.3891
-INFO 2017-12-14 23:09:10,566 - Ensembled accuracy: [44, 0.6189]
-INFO 2017-12-14 23:09:10,566 - Logs: +[44, 0.35160000000000002, 0.51910000000000001, 0.3891]
-INFO 2017-12-14 23:09:10,566 - 
-
-
-
-INFO 2017-12-14 23:09:55,576 - Logs e_swish_2: [[41, 0.6018, 0.55249999999999999, 0.48520000000000002], [44, 0.35160000000000002, 0.51910000000000001, 0.3891]]
-INFO 2017-12-14 23:09:55,576 - Record e_swish_2: [[23, 0.9753], [26, 0.9682], [29, 0.9487], [32, 0.9353], [35, 0.9047], [38, 0.7936], [41, 0.679], [44, 0.6189]]
-INFO 2017-12-14 23:35:41,622 - 
- 
- 
-
- 
- 
-
-INFO 2017-12-14 23:35:41,622 - SWISH 23-42
-INFO 2017-12-14 23:35:41,623 - 
- 
- Starting round with 23 layers
-INFO 2017-12-14 23:53:33,389 - Accuracy 0: 0.9779
-INFO 2017-12-15 00:11:19,368 - Accuracy 1: 0.9811
-INFO 2017-12-15 00:29:19,715 - Accuracy 2: 0.9838
-INFO 2017-12-15 00:29:22,437 - Ensembled accuracy: [23, 0.9855]
-INFO 2017-12-15 00:29:22,437 - Logs: +[23, 0.97789999999999999, 0.98109999999999997, 0.98380000000000001]
-INFO 2017-12-15 00:29:22,437 - 
- 
- Starting round with 26 layers
-INFO 2017-12-15 00:49:41,318 - Accuracy 0: 0.9748
-INFO 2017-12-15 01:09:46,174 - Accuracy 1: 0.9667
-INFO 2017-12-15 01:30:00,164 - Accuracy 2: 0.9718
-INFO 2017-12-15 01:30:03,634 - Ensembled accuracy: [26, 0.9771]
-INFO 2017-12-15 01:30:03,634 - Logs: +[26, 0.9748, 0.9667, 0.9718]
-INFO 2017-12-15 01:30:03,634 - 
- 
- Starting round with 29 layers
-INFO 2017-12-15 01:52:35,653 - Accuracy 0: 0.9565
-INFO 2017-12-15 02:15:26,155 - Accuracy 1: 0.9589
-INFO 2017-12-15 02:38:21,269 - Accuracy 2: 0.9524
-INFO 2017-12-15 02:38:25,536 - Ensembled accuracy: [29, 0.9682]
-INFO 2017-12-15 02:38:25,536 - Logs: +[29, 0.95650000000000002, 0.95889999999999997, 0.95240000000000002]
-INFO 2017-12-15 02:38:25,536 - 
- 
- Starting round with 32 layers
-INFO 2017-12-15 03:03:33,028 - Accuracy 0: 0.9506
-INFO 2017-12-15 03:17:20,992 - Accuracy 1: 0.6534
-INFO 2017-12-15 03:31:12,608 - Accuracy 2: 0.6545
-INFO 2017-12-15 03:31:17,905 - Ensembled accuracy: [32, 0.9467]
-INFO 2017-12-15 03:31:17,905 - Logs: +[32, 0.9506, 0.65339999999999998, 0.65449999999999997]
-INFO 2017-12-15 03:31:17,905 - 
- 
- Starting round with 35 layers
-INFO 2017-12-15 03:42:39,244 - Accuracy 0: 0.5707
-INFO 2017-12-15 03:59:34,298 - Accuracy 1: 0.5563
-INFO 2017-12-15 04:13:01,300 - Accuracy 2: 0.8357
-INFO 2017-12-15 04:13:07,873 - Ensembled accuracy: [35, 0.8342]
-INFO 2017-12-15 04:13:07,873 - Logs: +[35, 0.57069999999999999, 0.55630000000000002, 0.8357]
-INFO 2017-12-15 04:13:07,873 - 
- 
- Starting round with 38 layers
-INFO 2017-12-15 04:29:35,789 - Accuracy 0: 0.6203
-INFO 2017-12-15 04:46:15,954 - Accuracy 1: 0.5588
-INFO 2017-12-15 05:16:52,579 - Accuracy 2: 0.6807
-INFO 2017-12-15 05:17:00,536 - Ensembled accuracy: [38, 0.7813]
-INFO 2017-12-15 05:17:00,537 - Logs: +[38, 0.62029999999999996, 0.55879999999999996, 0.68069999999999997]
-INFO 2017-12-15 05:17:00,537 - 
- 
- Starting round with 41 layers
-INFO 2017-12-15 05:49:32,623 - Accuracy 0: 0.4733
-INFO 2017-12-15 06:05:28,154 - Accuracy 1: 0.3912
-INFO 2017-12-15 06:21:32,736 - Accuracy 2: 0.4554
-INFO 2017-12-15 06:21:42,734 - Ensembled accuracy: [41, 0.6553]
-INFO 2017-12-15 06:21:42,735 - Logs: +[41, 0.4733, 0.39119999999999999, 0.45540000000000003]
-INFO 2017-12-15 06:21:42,735 - 
- 
- Starting round with 44 layers
-INFO 2017-12-15 06:41:21,236 - Accuracy 0: 0.4461
-INFO 2017-12-15 07:12:52,364 - Accuracy 1: 0.4429
-INFO 2017-12-15 07:30:45,313 - Accuracy 2: 0.4596
-INFO 2017-12-15 07:30:57,684 - Ensembled accuracy: [44, 0.5968]
-INFO 2017-12-15 07:30:57,685 - Logs: +[44, 0.4461, 0.44290000000000002, 0.45960000000000001]
-INFO 2017-12-15 07:30:57,693 - 
- 
- 
-
-INFO 2017-12-15 07:30:57,694 - Logs swish: [[23, 0.97789999999999999, 0.98109999999999997, 0.98380000000000001], [26, 0.9748, 0.9667, 0.9718], [29, 0.95650000000000002, 0.95889999999999997, 0.95240000000000002], [32, 0.9506, 0.65339999999999998, 0.65449999999999997], [35, 0.57069999999999999, 0.55630000000000002, 0.8357], [38, 0.62029999999999996, 0.55879999999999996, 0.68069999999999997], [41, 0.4733, 0.39119999999999999, 0.45540000000000003], [44, 0.4461, 0.44290000000000002, 0.45960000000000001]]
-INFO 2017-12-15 07:30:57,694 - Record swish: [[23, 0.9855], [26, 0.9771], [29, 0.9682], [32, 0.9467], [35, 0.8342], [38, 0.7813], [41, 0.6553], [44, 0.5968]]
diff --git a/mnist/deep_nets/mnist_deep_nets.ipynb b/mnist/deep_nets/mnist_deep_nets.ipynb
deleted file mode 100644
index 879749d..0000000
--- a/mnist/deep_nets/mnist_deep_nets.ipynb
+++ /dev/null
@@ -1,3766 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\" Repeating MNIST first experiment of swish paper. \"\"\"\n",
-    "\n",
-    "import gc # Garbage collector\n",
-    "import logging\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"swish_first_exp_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()\n",
-    "\n",
-    "%matplotlib notebook\n",
-    "\n",
-    "np.random.seed(2)\n",
-    "\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "from keras.utils.np_utils import to_categorical # convert to one-hot-encoding\n",
-    "from keras.models import Sequential\n",
-    "from keras.layers import Dense, Dropout, Flatten, Activation, BatchNormalization\n",
-    "from keras.optimizers import SGD\n",
-    "from keras.preprocessing.image import ImageDataGenerator\n",
-    "from keras.callbacks import ReduceLROnPlateau, EarlyStopping\n",
-    "\n",
-    "# For adding new activation function\n",
-    "from keras import backend as K\n",
-    "from keras.datasets import mnist\n",
-    "from keras.utils.generic_utils import get_custom_objects\n",
-    "from keras.utils import np_utils"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X_train original shape (60000, 28, 28)\n",
-      "y_train original shape (60000,)\n"
-     ]
-    }
-   ],
-   "source": [
-    "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()\n",
-    "print(\"X_train original shape\", X_train.shape)\n",
-    "print(\"y_train original shape\", Y_train.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Normalization\n",
-    "nb_classes = 10\n",
-    "# Normalize the data\n",
-    "X_train = X_train / 255.0\n",
-    "X_test = X_test / 255.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Encode labels to one hot vectors (ex : 2 -> [0,0,1,0,0,0,0,0,0,0])\n",
-    "Y_train = np_utils.to_categorical(Y_train, nb_classes)\n",
-    "Y_test = np_utils.to_categorical(Y_test, nb_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Set the random seed\n",
-    "random_seed = 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Split the train and the validation set for the fitting\n",
-    "X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=random_seed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(54000, 784) (6000, 784) (10000, 784)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Reshape image in 3 dimensions (height = 28px, width = 28px , canal = 1)\n",
-    "X_train = X_train.reshape(-1,784)\n",
-    "X_val = X_val.reshape(-1,784)\n",
-    "X_test = X_test.reshape(-1,784)\n",
-    "# test = test.values.reshape(-1,28,28,1)\n",
-    "print(X_train.shape, X_val.shape, X_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "def e_swish_2(x):\n",
-    "    sigmoid = K.sigmoid(x)\n",
-    "    return K.maximum(x*sigmoid, x*(2-sigmoid))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Set the CNN model \n",
-    "# my CNN architechture is In -> [[Conv2D->relu]*2 -> MaxPool2D -> Dropout]*2 -> Flatten -> Dense -> Dropout -> Out\n",
-    "def create(act, n):\n",
-    "    model = Sequential()\n",
-    "    model.add(Dense(512, input_shape=(784,)))\n",
-    "    model.add(Activation(act))\n",
-    "    for i in range(n-1):\n",
-    "        if i%2 == 0:\n",
-    "            model.add(BatchNormalization())\n",
-    "        model.add(Dense(512))\n",
-    "        model.add(Activation(act))\n",
-    "        model.add(Dropout(0.3))\n",
-    "        \n",
-    "    model.add(Dense(nb_classes))\n",
-    "    model.add(Activation(\"softmax\"))\n",
-    "    \n",
-    "    return model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def plot(arr, names = [\"relu\", \"e_swish_2\", \"swish\"]):\n",
-    "    fig, ax = plt.subplots()\n",
-    "    for item in arr:\n",
-    "        ax.plot(np.array(item))\n",
-    "    \n",
-    "    plt.grid()\n",
-    "    plt.legend(names[:len(arr)], loc='upper right')\n",
-    "    ax.set_ylim(0,1)\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " Starting round with 23 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 74s 1ms/step - loss: 1.8138 - acc: 0.3087 - val_loss: 1.1260 - val_acc: 0.5507\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.2893 - acc: 0.5202 - val_loss: 0.9207 - val_acc: 0.6202\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.0644 - acc: 0.6229 - val_loss: 0.5850 - val_acc: 0.7962\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.8988 - acc: 0.7164 - val_loss: 0.4861 - val_acc: 0.8677\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.7803 - acc: 0.7758 - val_loss: 0.3949 - val_acc: 0.8998\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.7047 - acc: 0.8082 - val_loss: 0.3282 - val_acc: 0.9175\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.6350 - acc: 0.8374 - val_loss: 0.3050 - val_acc: 0.9350\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5737 - acc: 0.8583 - val_loss: 0.2914 - val_acc: 0.9350\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.5354 - acc: 0.8728 - val_loss: 0.2899 - val_acc: 0.9353\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4996 - acc: 0.8849 - val_loss: 0.2261 - val_acc: 0.9500\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4711 - acc: 0.8925 - val_loss: 0.1973 - val_acc: 0.9552\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4436 - acc: 0.8996 - val_loss: 0.2120 - val_acc: 0.9542\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4255 - acc: 0.9039 - val_loss: 0.1910 - val_acc: 0.9587\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3896 - acc: 0.9135 - val_loss: 0.1816 - val_acc: 0.9607\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3799 - acc: 0.9154 - val_loss: 0.1784 - val_acc: 0.9622\n",
-      "10000/10000 [==============================] - 3s 269us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 74s 1ms/step - loss: 1.8127 - acc: 0.3074 - val_loss: 1.3107 - val_acc: 0.4187\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.4308 - acc: 0.4223 - val_loss: 1.0700 - val_acc: 0.5315\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.2363 - acc: 0.5301 - val_loss: 0.8384 - val_acc: 0.6692\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.0572 - acc: 0.6320 - val_loss: 0.6474 - val_acc: 0.7562\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.9042 - acc: 0.7116 - val_loss: 0.5228 - val_acc: 0.8303\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.7645 - acc: 0.7876 - val_loss: 0.3467 - val_acc: 0.9162\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.6375 - acc: 0.8412 - val_loss: 0.2950 - val_acc: 0.9327\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5902 - acc: 0.8589 - val_loss: 0.2737 - val_acc: 0.9385\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5359 - acc: 0.8741 - val_loss: 0.2423 - val_acc: 0.9450\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4854 - acc: 0.8887 - val_loss: 0.2335 - val_acc: 0.9480\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4661 - acc: 0.8951 - val_loss: 0.2555 - val_acc: 0.9428\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4263 - acc: 0.9038 - val_loss: 0.2108 - val_acc: 0.9528\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4121 - acc: 0.9078 - val_loss: 0.1952 - val_acc: 0.9577\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3795 - acc: 0.9153 - val_loss: 0.1928 - val_acc: 0.9575\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3760 - acc: 0.9171 - val_loss: 0.1806 - val_acc: 0.9592\n",
-      "10000/10000 [==============================] - 3s 273us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 75s 1ms/step - loss: 1.7887 - acc: 0.3216 - val_loss: 1.1843 - val_acc: 0.5382\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.3035 - acc: 0.5196 - val_loss: 0.7345 - val_acc: 0.7475\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 1.0250 - acc: 0.6629 - val_loss: 0.5340 - val_acc: 0.7988\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.8569 - acc: 0.7424 - val_loss: 0.4266 - val_acc: 0.8577\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.7270 - acc: 0.7926 - val_loss: 0.3304 - val_acc: 0.9098\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.6308 - acc: 0.8376 - val_loss: 0.2981 - val_acc: 0.9323\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5508 - acc: 0.8681 - val_loss: 0.2565 - val_acc: 0.9405\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5030 - acc: 0.8828 - val_loss: 0.2424 - val_acc: 0.9455\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4592 - acc: 0.8956 - val_loss: 0.2017 - val_acc: 0.9535\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4238 - acc: 0.9049 - val_loss: 0.1809 - val_acc: 0.9597\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3871 - acc: 0.9153 - val_loss: 0.1930 - val_acc: 0.9628\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3656 - acc: 0.9210 - val_loss: 0.1650 - val_acc: 0.9645\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3505 - acc: 0.9248 - val_loss: 0.1512 - val_acc: 0.9670\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3241 - acc: 0.9287 - val_loss: 0.1535 - val_acc: 0.9658\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3223 - acc: 0.9316 - val_loss: 0.1555 - val_acc: 0.9667\n",
-      "10000/10000 [==============================] - 3s 277us/step\n",
-      "0.9717\n",
-      "\n",
-      " \n",
-      " Starting round with 26 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 86s 2ms/step - loss: 2.0965 - acc: 0.2386 - val_loss: 1.6202 - val_acc: 0.3768\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.7012 - acc: 0.3495 - val_loss: 1.4475 - val_acc: 0.4187\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.5591 - acc: 0.3974 - val_loss: 1.2574 - val_acc: 0.5033\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.4484 - acc: 0.4457 - val_loss: 1.1232 - val_acc: 0.5427\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.3466 - acc: 0.4886 - val_loss: 0.9860 - val_acc: 0.6398\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.2584 - acc: 0.5271 - val_loss: 0.9340 - val_acc: 0.6635\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.1640 - acc: 0.5663 - val_loss: 0.8329 - val_acc: 0.6995\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.1019 - acc: 0.5921 - val_loss: 0.7778 - val_acc: 0.7352\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 1.0371 - acc: 0.6234 - val_loss: 0.7590 - val_acc: 0.7663\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.9852 - acc: 0.6538 - val_loss: 0.6538 - val_acc: 0.8298\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.9471 - acc: 0.6815 - val_loss: 0.6097 - val_acc: 0.8428\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8885 - acc: 0.7088 - val_loss: 0.5574 - val_acc: 0.8697\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8460 - acc: 0.7373 - val_loss: 0.4696 - val_acc: 0.8858\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.7891 - acc: 0.7652 - val_loss: 0.4418 - val_acc: 0.8923\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.7483 - acc: 0.7843 - val_loss: 0.4252 - val_acc: 0.9003\n",
-      "10000/10000 [==============================] - 3s 296us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 86s 2ms/step - loss: 1.9982 - acc: 0.2780 - val_loss: 1.4930 - val_acc: 0.3850\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 1.6130 - acc: 0.3946 - val_loss: 1.3057 - val_acc: 0.4868\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.4823 - acc: 0.4450 - val_loss: 1.1480 - val_acc: 0.5847\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 1.3685 - acc: 0.4872 - val_loss: 1.0389 - val_acc: 0.6068\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 1.2939 - acc: 0.5173 - val_loss: 0.9993 - val_acc: 0.6492\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.2344 - acc: 0.5405 - val_loss: 0.9296 - val_acc: 0.6480\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.1697 - acc: 0.5709 - val_loss: 0.8562 - val_acc: 0.7013\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.1067 - acc: 0.5964 - val_loss: 0.7633 - val_acc: 0.7223\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 1.0381 - acc: 0.6321 - val_loss: 0.6844 - val_acc: 0.7543\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.9666 - acc: 0.6657 - val_loss: 0.6309 - val_acc: 0.7805\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.9099 - acc: 0.6927 - val_loss: 0.5844 - val_acc: 0.7948\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8645 - acc: 0.7087 - val_loss: 0.5682 - val_acc: 0.8060\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8228 - acc: 0.7250 - val_loss: 0.5175 - val_acc: 0.8213\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8015 - acc: 0.7332 - val_loss: 0.5245 - val_acc: 0.8323\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 0.7605 - acc: 0.7484 - val_loss: 0.4922 - val_acc: 0.8293\n",
-      "10000/10000 [==============================] - 3s 301us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.9193 - acc: 0.2708 - val_loss: 1.3611 - val_acc: 0.4457\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.5362 - acc: 0.3966 - val_loss: 1.1773 - val_acc: 0.5177\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.3826 - acc: 0.4704 - val_loss: 0.9958 - val_acc: 0.6235\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.2693 - acc: 0.5365 - val_loss: 0.9077 - val_acc: 0.6522\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.1785 - acc: 0.5718 - val_loss: 0.8301 - val_acc: 0.7077\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.1156 - acc: 0.6007 - val_loss: 0.7792 - val_acc: 0.7192\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 1.0420 - acc: 0.6306 - val_loss: 0.7153 - val_acc: 0.7145\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 0.9859 - acc: 0.6512 - val_loss: 0.6334 - val_acc: 0.7340\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.9308 - acc: 0.6704 - val_loss: 0.6165 - val_acc: 0.7670\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.8900 - acc: 0.6859 - val_loss: 0.6492 - val_acc: 0.7450\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.8732 - acc: 0.6962 - val_loss: 0.5701 - val_acc: 0.7725\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.8173 - acc: 0.7156 - val_loss: 0.5401 - val_acc: 0.7613\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.7909 - acc: 0.7352 - val_loss: 0.5304 - val_acc: 0.8087\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.7601 - acc: 0.7490 - val_loss: 0.5166 - val_acc: 0.8232\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.7245 - acc: 0.7640 - val_loss: 0.4621 - val_acc: 0.8750\n",
-      "10000/10000 [==============================] - 3s 318us/step\n",
-      "0.9253\n",
-      "\n",
-      " \n",
-      " Starting round with 29 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 103s 2ms/step - loss: 2.1394 - acc: 0.1811 - val_loss: 1.7070 - val_acc: 0.3285\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 91s 2ms/step - loss: 1.7998 - acc: 0.3149 - val_loss: 1.5464 - val_acc: 0.3957\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.6298 - acc: 0.3833 - val_loss: 1.2759 - val_acc: 0.4550\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.4793 - acc: 0.4264 - val_loss: 1.1568 - val_acc: 0.5247\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.3912 - acc: 0.4651 - val_loss: 1.0731 - val_acc: 0.5627\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.3086 - acc: 0.5055 - val_loss: 1.0157 - val_acc: 0.5457\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.2459 - acc: 0.5271 - val_loss: 0.9286 - val_acc: 0.6388\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1952 - acc: 0.5484 - val_loss: 0.9310 - val_acc: 0.6375\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1534 - acc: 0.5686 - val_loss: 0.8297 - val_acc: 0.6802\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1198 - acc: 0.5861 - val_loss: 0.7934 - val_acc: 0.7277\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 1.0700 - acc: 0.6063 - val_loss: 0.7759 - val_acc: 0.6990\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.0440 - acc: 0.6205 - val_loss: 0.7147 - val_acc: 0.7422\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 1.0073 - acc: 0.6439 - val_loss: 0.6642 - val_acc: 0.7405\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.9668 - acc: 0.6679 - val_loss: 0.6584 - val_acc: 0.7730\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.9263 - acc: 0.6883 - val_loss: 0.5509 - val_acc: 0.8540\n",
-      "10000/10000 [==============================] - 3s 334us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 102s 2ms/step - loss: 2.1911 - acc: 0.1815 - val_loss: 1.9103 - val_acc: 0.2967\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.8623 - acc: 0.3161 - val_loss: 1.4190 - val_acc: 0.4310\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.6369 - acc: 0.3754 - val_loss: 1.3025 - val_acc: 0.4440\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.5305 - acc: 0.4017 - val_loss: 1.1705 - val_acc: 0.5273\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.4550 - acc: 0.4201 - val_loss: 1.1449 - val_acc: 0.5030\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.3927 - acc: 0.4374 - val_loss: 1.0832 - val_acc: 0.5417\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.3294 - acc: 0.4742 - val_loss: 0.9845 - val_acc: 0.5847\n",
-      "Epoch 8/15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54000/54000 [==============================] - 86s 2ms/step - loss: 1.2646 - acc: 0.5022 - val_loss: 1.0034 - val_acc: 0.5597\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.2213 - acc: 0.5196 - val_loss: 0.9305 - val_acc: 0.5847\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.1738 - acc: 0.5393 - val_loss: 0.9307 - val_acc: 0.6253: 1.1738 - a\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.1366 - acc: 0.5599 - val_loss: 0.8411 - val_acc: 0.6435\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.0811 - acc: 0.5784 - val_loss: 0.7895 - val_acc: 0.6858\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 86s 2ms/step - loss: 1.0426 - acc: 0.5961 - val_loss: 0.7910 - val_acc: 0.6593\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.0739 - acc: 0.5937 - val_loss: 0.7843 - val_acc: 0.6813\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.0118 - acc: 0.6188 - val_loss: 0.7808 - val_acc: 0.7212\n",
-      "10000/10000 [==============================] - 3s 333us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 102s 2ms/step - loss: 2.1707 - acc: 0.1879 - val_loss: 1.8979 - val_acc: 0.2793\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 87s 2ms/step - loss: 1.9157 - acc: 0.2715 - val_loss: 1.6330 - val_acc: 0.3645\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 1.7204 - acc: 0.3492 - val_loss: 1.3882 - val_acc: 0.4198\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.5528 - acc: 0.3921 - val_loss: 1.2597 - val_acc: 0.4192\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.4544 - acc: 0.4181 - val_loss: 1.1931 - val_acc: 0.4315\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.3751 - acc: 0.4313 - val_loss: 1.1190 - val_acc: 0.4732\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.3259 - acc: 0.4407 - val_loss: 1.0745 - val_acc: 0.4770\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.2873 - acc: 0.4480 - val_loss: 1.0574 - val_acc: 0.4987\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.2566 - acc: 0.4563 - val_loss: 1.0469 - val_acc: 0.4938\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.2376 - acc: 0.4596 - val_loss: 1.0235 - val_acc: 0.5013\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.2063 - acc: 0.4659 - val_loss: 1.0285 - val_acc: 0.5103\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1835 - acc: 0.4759 - val_loss: 0.9935 - val_acc: 0.5475\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1776 - acc: 0.4825 - val_loss: 1.0194 - val_acc: 0.5100\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1348 - acc: 0.5128 - val_loss: 0.9473 - val_acc: 0.6028\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.0958 - acc: 0.5578 - val_loss: 0.8550 - val_acc: 0.6808\n",
-      "10000/10000 [==============================] - 3s 335us/step\n",
-      "0.8639\n",
-      "\n",
-      " \n",
-      " Starting round with 32 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 115s 2ms/step - loss: 2.2819 - acc: 0.1619 - val_loss: 2.0311 - val_acc: 0.2308\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 2.0998 - acc: 0.2201 - val_loss: 1.9076 - val_acc: 0.2703\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 2.0102 - acc: 0.2480 - val_loss: 1.8020 - val_acc: 0.2720\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.9118 - acc: 0.2798 - val_loss: 1.6791 - val_acc: 0.3537\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.8303 - acc: 0.3080 - val_loss: 1.5650 - val_acc: 0.4035\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.7538 - acc: 0.3427 - val_loss: 1.4796 - val_acc: 0.4688\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.6694 - acc: 0.3717 - val_loss: 1.3897 - val_acc: 0.5032\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.5914 - acc: 0.4072 - val_loss: 1.2499 - val_acc: 0.5707\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 102s 2ms/step - loss: 1.5251 - acc: 0.4347 - val_loss: 1.1472 - val_acc: 0.5955\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.4724 - acc: 0.4477 - val_loss: 1.1315 - val_acc: 0.5867\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.4302 - acc: 0.4660 - val_loss: 1.0692 - val_acc: 0.6055\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.3794 - acc: 0.4866 - val_loss: 1.0372 - val_acc: 0.6585\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.3485 - acc: 0.4999 - val_loss: 1.0167 - val_acc: 0.6445\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.3117 - acc: 0.5146 - val_loss: 0.9546 - val_acc: 0.6678\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.2927 - acc: 0.5249 - val_loss: 1.0052 - val_acc: 0.6493\n",
-      "10000/10000 [==============================] - 4s 375us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 117s 2ms/step - loss: 2.3233 - acc: 0.1530 - val_loss: 2.0805 - val_acc: 0.2280\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 2.1543 - acc: 0.2000 - val_loss: 2.0418 - val_acc: 0.2420\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 2.1086 - acc: 0.2196 - val_loss: 2.0094 - val_acc: 0.2450\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 2.0575 - acc: 0.2346 - val_loss: 1.9317 - val_acc: 0.2675\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.9923 - acc: 0.2468 - val_loss: 1.8413 - val_acc: 0.2805\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.9421 - acc: 0.2562 - val_loss: 1.7853 - val_acc: 0.3082\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.9036 - acc: 0.2664 - val_loss: 1.7303 - val_acc: 0.3182\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 1.8634 - acc: 0.2826 - val_loss: 1.6881 - val_acc: 0.3362\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.8381 - acc: 0.2965 - val_loss: 1.6552 - val_acc: 0.3648\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.7947 - acc: 0.3189 - val_loss: 1.5889 - val_acc: 0.4140\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.7464 - acc: 0.3355 - val_loss: 1.5312 - val_acc: 0.4028\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.7009 - acc: 0.3528 - val_loss: 1.4869 - val_acc: 0.4340\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.6573 - acc: 0.3658 - val_loss: 1.4087 - val_acc: 0.4515\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.6122 - acc: 0.3799 - val_loss: 1.4059 - val_acc: 0.4772\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.5790 - acc: 0.3910 - val_loss: 1.3455 - val_acc: 0.4585\n",
-      "10000/10000 [==============================] - 4s 389us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 2.3440 - acc: 0.1451 - val_loss: 2.1683 - val_acc: 0.1923\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 2.1421 - acc: 0.1992 - val_loss: 2.0372 - val_acc: 0.2317\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 2.0463 - acc: 0.2369 - val_loss: 1.8817 - val_acc: 0.2813\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.9537 - acc: 0.2649 - val_loss: 1.7453 - val_acc: 0.3308\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.8740 - acc: 0.2913 - val_loss: 1.6371 - val_acc: 0.3460\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.7921 - acc: 0.3177 - val_loss: 1.5252 - val_acc: 0.3503\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.7234 - acc: 0.3306 - val_loss: 1.4995 - val_acc: 0.4037\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.6706 - acc: 0.3464 - val_loss: 1.4149 - val_acc: 0.4100\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 1.6176 - acc: 0.3560 - val_loss: 1.3720 - val_acc: 0.4208\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 1.5825 - acc: 0.3633 - val_loss: 1.3573 - val_acc: 0.4270\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 1.5512 - acc: 0.3689 - val_loss: 1.3447 - val_acc: 0.4137\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.5246 - acc: 0.3770 - val_loss: 1.3284 - val_acc: 0.4473\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.4861 - acc: 0.3842 - val_loss: 1.3025 - val_acc: 0.4897\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.4673 - acc: 0.3964 - val_loss: 1.2838 - val_acc: 0.4440\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.4327 - acc: 0.4040 - val_loss: 1.2364 - val_acc: 0.5045\n",
-      "10000/10000 [==============================] - 4s 377us/step\n",
-      "0.6807\n",
-      "\n",
-      " \n",
-      " Starting round with 35 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 129s 2ms/step - loss: 2.3206 - acc: 0.1087 - val_loss: 2.4899 - val_acc: 0.1103\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.2866 - acc: 0.1236 - val_loss: 2.4299 - val_acc: 0.1445\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.2670 - acc: 0.1369 - val_loss: 2.2762 - val_acc: 0.1548\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.2126 - acc: 0.1527 - val_loss: 2.1650 - val_acc: 0.1565\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.1497 - acc: 0.1638 - val_loss: 2.1472 - val_acc: 0.1928\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.0887 - acc: 0.1785 - val_loss: 2.1148 - val_acc: 0.2087\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 105s 2ms/step - loss: 2.0379 - acc: 0.1933 - val_loss: 1.9941 - val_acc: 0.2178\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.9753 - acc: 0.2207 - val_loss: 1.8358 - val_acc: 0.2743\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.9218 - acc: 0.2387 - val_loss: 1.8030 - val_acc: 0.2912\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 105s 2ms/step - loss: 1.8802 - acc: 0.2486 - val_loss: 1.7470 - val_acc: 0.2938\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.8531 - acc: 0.2619 - val_loss: 1.7311 - val_acc: 0.2942\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 105s 2ms/step - loss: 1.8284 - acc: 0.2679 - val_loss: 1.7019 - val_acc: 0.2927\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 105s 2ms/step - loss: 1.8134 - acc: 0.2752 - val_loss: 1.6679 - val_acc: 0.3235\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 105s 2ms/step - loss: 1.7885 - acc: 0.2861 - val_loss: 1.6755 - val_acc: 0.3318\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.7678 - acc: 0.2904 - val_loss: 1.6069 - val_acc: 0.3257\n",
-      "10000/10000 [==============================] - 4s 407us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 2.3044 - acc: 0.1234 - val_loss: 2.1907 - val_acc: 0.1985\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.1771 - acc: 0.1711 - val_loss: 1.9751 - val_acc: 0.2460\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.0610 - acc: 0.1982 - val_loss: 1.9094 - val_acc: 0.2515\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.0053 - acc: 0.2098 - val_loss: 1.9039 - val_acc: 0.2578\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.9649 - acc: 0.2273 - val_loss: 1.8167 - val_acc: 0.2627\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.9153 - acc: 0.2446 - val_loss: 1.7334 - val_acc: 0.3058\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 1.8718 - acc: 0.2654 - val_loss: 1.6716 - val_acc: 0.3475\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.8341 - acc: 0.2759 - val_loss: 1.6270 - val_acc: 0.3480\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.8006 - acc: 0.2867 - val_loss: 1.6032 - val_acc: 0.3518\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 111s 2ms/step - loss: 1.7556 - acc: 0.3045 - val_loss: 1.5421 - val_acc: 0.3590\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 111s 2ms/step - loss: 1.7121 - acc: 0.3175 - val_loss: 1.5208 - val_acc: 0.3545\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.6728 - acc: 0.3298 - val_loss: 1.4790 - val_acc: 0.3820\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.6411 - acc: 0.3360 - val_loss: 1.4205 - val_acc: 0.4005\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.6008 - acc: 0.3490 - val_loss: 1.4059 - val_acc: 0.3960\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.5652 - acc: 0.3568 - val_loss: 1.3918 - val_acc: 0.4108\n",
-      "10000/10000 [==============================] - 4s 435us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 136s 3ms/step - loss: 2.2477 - acc: 0.1526 - val_loss: 2.1112 - val_acc: 0.1608\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 2.1402 - acc: 0.1908 - val_loss: 2.0318 - val_acc: 0.2263\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 2.0994 - acc: 0.2024 - val_loss: 2.0039 - val_acc: 0.2495\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 2.0617 - acc: 0.2170 - val_loss: 1.9338 - val_acc: 0.2687\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 106s 2ms/step - loss: 2.0225 - acc: 0.2309 - val_loss: 1.8295 - val_acc: 0.2763\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.9737 - acc: 0.2467 - val_loss: 1.7477 - val_acc: 0.3147\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.9305 - acc: 0.2583 - val_loss: 1.7199 - val_acc: 0.3173\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.8883 - acc: 0.2686 - val_loss: 1.7210 - val_acc: 0.3217\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.8561 - acc: 0.2765 - val_loss: 1.6704 - val_acc: 0.3455\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.8218 - acc: 0.2840 - val_loss: 1.6118 - val_acc: 0.3823\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.7898 - acc: 0.2917 - val_loss: 1.6146 - val_acc: 0.3540\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.7658 - acc: 0.2969 - val_loss: 1.5531 - val_acc: 0.3830\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.7373 - acc: 0.3104 - val_loss: 1.5237 - val_acc: 0.3972\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.6989 - acc: 0.3224 - val_loss: 1.4617 - val_acc: 0.4075\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.6567 - acc: 0.3301 - val_loss: 1.4072 - val_acc: 0.3852\n",
-      "10000/10000 [==============================] - 5s 451us/step\n",
-      "0.5834\n",
-      "\n",
-      " \n"
-     ]
-    }
-   ],
-   "source": [
-    "act = \"relu\"\n",
-    "\n",
-    "logs_relu = []\n",
-    "record_relu = []\n",
-    "for n in range(23,42,3):\n",
-    "    ensembler = 0\n",
-    "    logger = [n]\n",
-    "    print(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    for i in range(3):\n",
-    "        # Garbage collector\n",
-    "        gc.collect()\n",
-    "        # Set optimizer\n",
-    "        opt = SGD(lr=0.01, momentum=0.9)\n",
-    "        # Set callbacks (learning rate reducer and early stopping)\n",
-    "        learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.35, min_lr=0.00001)\n",
-    "        early_stop = EarlyStopping(monitor='val_acc', patience=5, verbose = 1)\n",
-    "        # Common params \n",
-    "        epochs = 15\n",
-    "        batch_size = 128\n",
-    "        # Create and compile the model\n",
-    "        model = create(act, n)\n",
-    "        # Compile the model\n",
-    "        model.compile(optimizer = opt , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
-    "        # Train the model\n",
-    "        history = model.fit(X_train,Y_train, epochs = epochs, validation_data = (X_val,Y_val),\n",
-    "                            verbose = 1 , callbacks=[learning_rate_reduction, early_stop])\n",
-    "        \n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, history, learning_rate_reduction, early_stop, opt\n",
-    "     \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    # Save the ensembled accuracy and the three models accuracy\n",
-    "    record_relu.append([n, ensembled])\n",
-    "    logs_relu.append(logger)\n",
-    "    del ensembler, ensembled\n",
-    "    \n",
-    "print(\"\\n \\n \\n\")\n",
-    "print(\"Logs Relu: \", logs_relu)\n",
-    "print(\"Record Relu: \", record_relu)\n",
-    "\n",
-    "    \n",
-    "plot([[x[1] for x in logs_relu]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " Starting round with 38 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 2.4120 - acc: 0.1019 - val_loss: 2.3258 - val_acc: 0.1003\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 2.3133 - acc: 0.1030 - val_loss: 2.3261 - val_acc: 0.1017\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 2.3052 - acc: 0.1075 - val_loss: 2.3299 - val_acc: 0.1698\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 2.3037 - acc: 0.1086 - val_loss: 2.3284 - val_acc: 0.1152\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 2.3031 - acc: 0.1089 - val_loss: 2.3024 - val_acc: 0.1152\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3026 - acc: 0.1092\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3026 - acc: 0.1091 - val_loss: 2.3253 - val_acc: 0.1152\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 111s 2ms/step - loss: 2.3019 - acc: 0.1111 - val_loss: 2.3021 - val_acc: 0.1152\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3018 - acc: 0.1110- ETA: 0s - loss: 2.3018 - acc:\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 2.3018 - acc: 0.1110 - val_loss: 2.3015 - val_acc: 0.1152\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 4s 415us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.4086 - acc: 0.1023 - val_loss: 2.2859 - val_acc: 0.1628\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 2.3116 - acc: 0.1029 - val_loss: 2.3057 - val_acc: 0.1025\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 2.3042 - acc: 0.1063 - val_loss: 2.3049 - val_acc: 0.1152\n",
-      "Epoch 4/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3032 - acc: 0.1069\n",
-      "Epoch 00004: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 2.3032 - acc: 0.1069 - val_loss: 2.3040 - val_acc: 0.1152\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 2.3015 - acc: 0.1106 - val_loss: 2.3051 - val_acc: 0.1152\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3011 - acc: 0.1110\n",
-      "Epoch 00006: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3011 - acc: 0.1110 - val_loss: 2.3040 - val_acc: 0.1152\n",
-      "Epoch 00006: early stopping\n",
-      "10000/10000 [==============================] - 4s 407us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 2.4091 - acc: 0.1003 - val_loss: 2.3121 - val_acc: 0.1152\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3123 - acc: 0.1044 - val_loss: 2.3200 - val_acc: 0.0948\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3054 - acc: 0.1064 - val_loss: 2.3158 - val_acc: 0.1017\n",
-      "Epoch 4/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3037 - acc: 0.1080\n",
-      "Epoch 00004: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3037 - acc: 0.1081 - val_loss: 2.3027 - val_acc: 0.1152\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3025 - acc: 0.1103 - val_loss: 2.3013 - val_acc: 0.1152\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3022 - acc: 0.1102\n",
-      "Epoch 00006: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 2.3022 - acc: 0.1102 - val_loss: 2.3014 - val_acc: 0.1152\n",
-      "Epoch 00006: early stopping\n",
-      "10000/10000 [==============================] - 4s 413us/step\n",
-      "0.1135\n",
-      "\n",
-      " \n",
-      " Starting round with 41 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 2.3259 - acc: 0.1055 - val_loss: 2.3119 - val_acc: 0.1130\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2907 - acc: 0.1246 - val_loss: 2.3211 - val_acc: 0.1240\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2762 - acc: 0.1367 - val_loss: 2.2901 - val_acc: 0.1302\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2599 - acc: 0.1438 - val_loss: 2.2951 - val_acc: 0.1328\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2461 - acc: 0.1506 - val_loss: 2.2627 - val_acc: 0.1438\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2330 - acc: 0.1601 - val_loss: 2.2464 - val_acc: 0.1593\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2165 - acc: 0.1650 - val_loss: 2.2179 - val_acc: 0.1683\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.2041 - acc: 0.1720 - val_loss: 2.2431 - val_acc: 0.1515\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.1926 - acc: 0.1760 - val_loss: 2.2141 - val_acc: 0.1523\n",
-      "Epoch 10/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.1845 - acc: 0.1790\n",
-      "Epoch 00010: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 2.1845 - acc: 0.1790 - val_loss: 2.2457 - val_acc: 0.1527\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.1694 - acc: 0.1823 - val_loss: 2.2217 - val_acc: 0.1550\n",
-      "Epoch 12/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.1666 - acc: 0.1845\n",
-      "Epoch 00012: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.1667 - acc: 0.1845 - val_loss: 2.2127 - val_acc: 0.1552\n",
-      "Epoch 00012: early stopping\n",
-      "10000/10000 [==============================] - 4s 436us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 2.3263 - acc: 0.1022 - val_loss: 2.3221 - val_acc: 0.1238\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.3040 - acc: 0.1080 - val_loss: 2.3017 - val_acc: 0.1152\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 2.3030 - acc: 0.1084 - val_loss: 2.3238 - val_acc: 0.1150\n",
-      "Epoch 4/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3029 - acc: 0.1096\n",
-      "Epoch 00004: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 123s 2ms/step - loss: 2.3029 - acc: 0.1096 - val_loss: 2.3158 - val_acc: 0.1152\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 2.3020 - acc: 0.1098 - val_loss: 2.3016 - val_acc: 0.1152\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3018 - acc: 0.1114\n",
-      "Epoch 00006: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 2.3018 - acc: 0.1114 - val_loss: 2.3017 - val_acc: 0.1152\n",
-      "Epoch 00006: early stopping\n",
-      "10000/10000 [==============================] - 4s 449us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 2.3264 - acc: 0.1049 - val_loss: 2.3082 - val_acc: 0.1545\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 2.3039 - acc: 0.1068 - val_loss: 2.3100 - val_acc: 0.1015\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 2.3030 - acc: 0.1082 - val_loss: 2.3080 - val_acc: 0.1498\n",
-      "Epoch 4/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3026 - acc: 0.1092\n",
-      "Epoch 00004: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 123s 2ms/step - loss: 2.3026 - acc: 0.1091 - val_loss: 2.3029 - val_acc: 0.1152\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.3017 - acc: 0.1115 - val_loss: 2.3003 - val_acc: 0.1152\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 2.3018 - acc: 0.1110\n",
-      "Epoch 00006: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 2.3018 - acc: 0.1110 - val_loss: 2.3084 - val_acc: 0.1153\n",
-      "Epoch 00006: early stopping\n",
-      "10000/10000 [==============================] - 4s 448us/step\n",
-      "0.1572\n",
-      "\n",
-      " \n",
-      " \n",
-      "\n",
-      "Logs Relu:  [[23, 0.9717], [26, 0.9253], [29, 0.8639], [32, 0.6807], [35, 0.5834], [38, 0.1135, 0.1135, 0.1135], [41, 0.15679999999999999, 0.1135, 0.11360000000000001]]\n",
-      "Record Relu:  [[38, 0.1135], [41, 0.1572]]\n"
-     ]
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div id='08ab580f-d6bf-478d-841f-292a711362d2'></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "act = \"relu\"\n",
-    "logs_relu = []\n",
-    "record_relu = [[23, 0.9717], [26, 0.9253], [29, 0.8639], [32, 0.6807], [35, 0.5834]]\n",
-    "logs.info(\"Relu 38-42\")\n",
-    "\n",
-    "for n in range(38,42,3):\n",
-    "    ensembler = 0\n",
-    "    logger = [n]\n",
-    "    print(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    logs.info(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    for i in range(3):\n",
-    "        # Garbage collector\n",
-    "        gc.collect()\n",
-    "        # Set optimizer\n",
-    "        opt = SGD(lr=0.01, momentum=0.9)\n",
-    "        # Set callbacks (learning rate reducer and early stopping)\n",
-    "        learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.35, min_lr=0.00001)\n",
-    "        early_stop = EarlyStopping(monitor='val_acc', patience=5, verbose = 1)\n",
-    "        # Common params \n",
-    "        epochs = 15\n",
-    "        batch_size = 128\n",
-    "        # Create and compile the model\n",
-    "        model = create(act, n)\n",
-    "        # Compile the model\n",
-    "        model.compile(optimizer = opt , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
-    "        # Train the model\n",
-    "        history = model.fit(X_train,Y_train, epochs = epochs, validation_data = (X_val,Y_val),\n",
-    "                            verbose = 1 , callbacks=[learning_rate_reduction, early_stop])\n",
-    "        \n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        logs.info(\"Accuracy \"+str(i)+\": \"+str(logger[-1]))\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, history, learning_rate_reduction, early_stop, opt\n",
-    "     \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    # Save the ensembled accuracy and the three models accuracy\n",
-    "    record_relu.append([n, ensembled])\n",
-    "    logs_relu.append(logger)\n",
-    "    del ensembler, ensembled\n",
-    "    logs.info(\"Ensembled accuracy: \"+str(record_relu[-1]))\n",
-    "    logs.info(\"Logs: \"+str(logs_relu[-1]))\n",
-    "    \n",
-    "logs.info(\"\\n \\n \\n\")\n",
-    "logs.info(\"Logs e_swish_2: \"+str(logs_relu))\n",
-    "logs.info(\"Record e_swish_2: \"+str(record_relu))\n",
-    "print(\"\\n \\n \\n\")\n",
-    "print(\"Logs Relu: \", logs_relu)\n",
-    "print(\"Record Relu: \", record_relu)\n",
-    "\n",
-    "    \n",
-    "plot([[x[1] for x in logs_relu]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " Starting round with 23 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 93s 2ms/step - loss: 1.2903 - acc: 0.5598 - val_loss: 0.5266 - val_acc: 0.8643\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.7332 - acc: 0.8116 - val_loss: 0.3981 - val_acc: 0.9258\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.5698 - acc: 0.8682 - val_loss: 0.3337 - val_acc: 0.9413\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.5351 - acc: 0.8743 - val_loss: 0.2820 - val_acc: 0.9455\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.4442 - acc: 0.9004 - val_loss: 0.2380 - val_acc: 0.9552\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3893 - acc: 0.9156 - val_loss: 0.1961 - val_acc: 0.9615\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3781 - acc: 0.9180 - val_loss: 0.1998 - val_acc: 0.9595\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3472 - acc: 0.9259 - val_loss: 0.2048 - val_acc: 0.9578\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3230 - acc: 0.9314 - val_loss: 0.1770 - val_acc: 0.9640\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2996 - acc: 0.9354 - val_loss: 0.1700 - val_acc: 0.9648\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2923 - acc: 0.9380 - val_loss: 0.1478 - val_acc: 0.9692\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2877 - acc: 0.9384 - val_loss: 0.1692 - val_acc: 0.9632\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2969 - acc: 0.9372 - val_loss: 0.1549 - val_acc: 0.9668\n",
-      "Epoch 14/15\n",
-      "53952/54000 [============================>.] - ETA: 0s - loss: 0.2717 - acc: 0.9426\n",
-      "Epoch 00014: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 78s 1ms/step - loss: 0.2719 - acc: 0.9425 - val_loss: 0.1458 - val_acc: 0.9680\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2317 - acc: 0.9505 - val_loss: 0.1225 - val_acc: 0.9750\n",
-      "10000/10000 [==============================] - 3s 318us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 95s 2ms/step - loss: 1.3368 - acc: 0.5298 - val_loss: 0.6997 - val_acc: 0.7782\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.8322 - acc: 0.7399 - val_loss: 0.3765 - val_acc: 0.9127\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.6585 - acc: 0.8353 - val_loss: 0.4307 - val_acc: 0.9123\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.5430 - acc: 0.8768 - val_loss: 0.3025 - val_acc: 0.9378\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.4769 - acc: 0.8942 - val_loss: 0.2706 - val_acc: 0.9472\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.4280 - acc: 0.9071 - val_loss: 0.2532 - val_acc: 0.9495\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3962 - acc: 0.9141 - val_loss: 0.2364 - val_acc: 0.9498\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3929 - acc: 0.9151 - val_loss: 0.2046 - val_acc: 0.9573\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3630 - acc: 0.9218 - val_loss: 0.2008 - val_acc: 0.9588\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3389 - acc: 0.9276 - val_loss: 0.1788 - val_acc: 0.9630\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3388 - acc: 0.9274 - val_loss: 0.1864 - val_acc: 0.9628\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3213 - acc: 0.9316 - val_loss: 0.1782 - val_acc: 0.9640\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3102 - acc: 0.9346 - val_loss: 0.1795 - val_acc: 0.9617\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2859 - acc: 0.9395 - val_loss: 0.1659 - val_acc: 0.9632\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2834 - acc: 0.9396 - val_loss: 0.1687 - val_acc: 0.9652\n",
-      "10000/10000 [==============================] - 3s 325us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.3235 - acc: 0.5417 - val_loss: 0.5877 - val_acc: 0.8402\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.7954 - acc: 0.7903 - val_loss: 0.4051 - val_acc: 0.9170\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.6145 - acc: 0.8560 - val_loss: 0.3445 - val_acc: 0.9297\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.5024 - acc: 0.8860 - val_loss: 0.2990 - val_acc: 0.9415\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.4556 - acc: 0.8982 - val_loss: 0.2554 - val_acc: 0.9507\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.4144 - acc: 0.9110 - val_loss: 0.2393 - val_acc: 0.9548\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3797 - acc: 0.9199 - val_loss: 0.2002 - val_acc: 0.9593\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3436 - acc: 0.9271 - val_loss: 0.2377 - val_acc: 0.9517\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3417 - acc: 0.9274 - val_loss: 0.1979 - val_acc: 0.9588\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3456 - acc: 0.9265 - val_loss: 0.1898 - val_acc: 0.9618\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3275 - acc: 0.9303 - val_loss: 0.1840 - val_acc: 0.9603\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3073 - acc: 0.9358 - val_loss: 0.1880 - val_acc: 0.9608\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3108 - acc: 0.9354 - val_loss: 0.1806 - val_acc: 0.9632\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.3202 - acc: 0.9310 - val_loss: 0.1576 - val_acc: 0.9660\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 0.2858 - acc: 0.9391 - val_loss: 0.1532 - val_acc: 0.9660\n",
-      "10000/10000 [==============================] - 3s 328us/step\n",
-      "0.9753\n",
-      "\n",
-      " \n",
-      " Starting round with 26 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.5673 - acc: 0.4335 - val_loss: 0.7896 - val_acc: 0.6845\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.9276 - acc: 0.6857 - val_loss: 0.4729 - val_acc: 0.8413\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.7154 - acc: 0.7725 - val_loss: 0.3519 - val_acc: 0.8563\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.6203 - acc: 0.8007 - val_loss: 0.3422 - val_acc: 0.8622\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.5716 - acc: 0.8253 - val_loss: 0.2549 - val_acc: 0.9338\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.4990 - acc: 0.8676 - val_loss: 0.2405 - val_acc: 0.9478\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.4466 - acc: 0.8913 - val_loss: 0.2097 - val_acc: 0.9503\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.4351 - acc: 0.9020 - val_loss: 0.2219 - val_acc: 0.9510\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.4013 - acc: 0.9108 - val_loss: 0.1917 - val_acc: 0.9575\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.3795 - acc: 0.9172 - val_loss: 0.1928 - val_acc: 0.9590\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.3974 - acc: 0.9140 - val_loss: 0.2479 - val_acc: 0.9457\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.3917 - acc: 0.9172 - val_loss: 0.2042 - val_acc: 0.9552\n",
-      "Epoch 13/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.3714 - acc: 0.9203\n",
-      "Epoch 00013: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3715 - acc: 0.9203 - val_loss: 0.1892 - val_acc: 0.9573\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.3142 - acc: 0.9330 - val_loss: 0.1749 - val_acc: 0.9617\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 0.2933 - acc: 0.9387 - val_loss: 0.1498 - val_acc: 0.9682\n",
-      "10000/10000 [==============================] - 4s 360us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 1.5995 - acc: 0.4336 - val_loss: 0.7574 - val_acc: 0.7243\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.9983 - acc: 0.6611 - val_loss: 0.5428 - val_acc: 0.8122\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.8308 - acc: 0.7368 - val_loss: 0.4433 - val_acc: 0.8485\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.7018 - acc: 0.7885 - val_loss: 0.3912 - val_acc: 0.8737\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6285 - acc: 0.8279 - val_loss: 0.2913 - val_acc: 0.9257\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5638 - acc: 0.8603 - val_loss: 0.2796 - val_acc: 0.9390\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5235 - acc: 0.8778 - val_loss: 0.2847 - val_acc: 0.9418\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4916 - acc: 0.8899 - val_loss: 0.2695 - val_acc: 0.9438\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5001 - acc: 0.8894 - val_loss: 0.2593 - val_acc: 0.9460\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4815 - acc: 0.8941 - val_loss: 0.2649 - val_acc: 0.9477\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4885 - acc: 0.8932 - val_loss: 0.2576 - val_acc: 0.9460\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4662 - acc: 0.8987 - val_loss: 0.2510 - val_acc: 0.9445\n",
-      "Epoch 13/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.4709 - acc: 0.8934\n",
-      "Epoch 00013: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.4711 - acc: 0.8934 - val_loss: 0.3116 - val_acc: 0.9257\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3899 - acc: 0.9152 - val_loss: 0.2052 - val_acc: 0.9560\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3601 - acc: 0.9224 - val_loss: 0.1810 - val_acc: 0.9602\n",
-      "10000/10000 [==============================] - 4s 371us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 114s 2ms/step - loss: 1.5404 - acc: 0.4676 - val_loss: 0.6398 - val_acc: 0.7622\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.9269 - acc: 0.7018 - val_loss: 0.4404 - val_acc: 0.8725\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.7742 - acc: 0.7798 - val_loss: 0.3526 - val_acc: 0.9220\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6699 - acc: 0.8283 - val_loss: 0.3119 - val_acc: 0.9323\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5861 - acc: 0.8622 - val_loss: 0.2952 - val_acc: 0.9393\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5304 - acc: 0.8795 - val_loss: 0.2374 - val_acc: 0.9473\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4811 - acc: 0.8949 - val_loss: 0.2446 - val_acc: 0.9508\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4536 - acc: 0.9024 - val_loss: 0.2363 - val_acc: 0.9508\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4603 - acc: 0.9000 - val_loss: 0.2370 - val_acc: 0.9523\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4500 - acc: 0.9035 - val_loss: 0.2244 - val_acc: 0.9482\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4363 - acc: 0.9072 - val_loss: 0.2535 - val_acc: 0.9472\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4126 - acc: 0.9138 - val_loss: 0.2210 - val_acc: 0.9545\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4213 - acc: 0.9091 - val_loss: 0.2211 - val_acc: 0.9523\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4092 - acc: 0.9124 - val_loss: 0.2487 - val_acc: 0.9412\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4185 - acc: 0.9054 - val_loss: 0.1929 - val_acc: 0.9575\n",
-      "10000/10000 [==============================] - 4s 373us/step\n",
-      "0.9682\n",
-      "\n",
-      " \n",
-      " Starting round with 29 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 125s 2ms/step - loss: 1.7071 - acc: 0.3479 - val_loss: 1.1007 - val_acc: 0.5922\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.2199 - acc: 0.5364 - val_loss: 0.8073 - val_acc: 0.6645\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 1.0574 - acc: 0.6106 - val_loss: 0.7609 - val_acc: 0.6707\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.9805 - acc: 0.6459 - val_loss: 0.6772 - val_acc: 0.7540\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.8971 - acc: 0.7074 - val_loss: 0.5414 - val_acc: 0.8302\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.8134 - acc: 0.7523 - val_loss: 0.4932 - val_acc: 0.8455\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.7929 - acc: 0.7609 - val_loss: 0.4777 - val_acc: 0.8490\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.7059 - acc: 0.7959 - val_loss: 0.4188 - val_acc: 0.8830\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.6506 - acc: 0.8164 - val_loss: 0.4513 - val_acc: 0.8968\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.6722 - acc: 0.8186 - val_loss: 0.4270 - val_acc: 0.9033\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.6654 - acc: 0.8302 - val_loss: 0.4671 - val_acc: 0.8952\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.6525 - acc: 0.8418 - val_loss: 0.3712 - val_acc: 0.9273\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.7426 - acc: 0.8036 - val_loss: 0.4182 - val_acc: 0.9150\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 97s 2ms/step - loss: 0.6193 - acc: 0.8561 - val_loss: 0.3991 - val_acc: 0.9168\n",
-      "Epoch 15/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.5671 - acc: 0.8730\n",
-      "Epoch 00015: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5674 - acc: 0.8730 - val_loss: 0.4288 - val_acc: 0.9108\n",
-      "10000/10000 [==============================] - 4s 411us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 128s 2ms/step - loss: 1.7032 - acc: 0.3491 - val_loss: 1.0651 - val_acc: 0.5462\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.2657 - acc: 0.5430 - val_loss: 0.8338 - val_acc: 0.6930\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.0799 - acc: 0.6306 - val_loss: 0.6866 - val_acc: 0.7850\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.9554 - acc: 0.7049 - val_loss: 0.6452 - val_acc: 0.7932\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.8421 - acc: 0.7467 - val_loss: 0.5023 - val_acc: 0.8502\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.7537 - acc: 0.7876 - val_loss: 0.4633 - val_acc: 0.8952\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.7391 - acc: 0.8039 - val_loss: 0.4733 - val_acc: 0.8892\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.6776 - acc: 0.8312 - val_loss: 0.3940 - val_acc: 0.9142\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.6372 - acc: 0.8457 - val_loss: 0.3853 - val_acc: 0.9140\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.6439 - acc: 0.8404 - val_loss: 0.3843 - val_acc: 0.9120\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5752 - acc: 0.8636 - val_loss: 0.3249 - val_acc: 0.9297\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5619 - acc: 0.8685 - val_loss: 0.3389 - val_acc: 0.9278\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5407 - acc: 0.8761 - val_loss: 0.3321 - val_acc: 0.9335\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5364 - acc: 0.8792 - val_loss: 0.2902 - val_acc: 0.9420\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5300 - acc: 0.8812 - val_loss: 0.3135 - val_acc: 0.9343\n",
-      "10000/10000 [==============================] - 4s 413us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.7511 - acc: 0.3531 - val_loss: 1.0859 - val_acc: 0.5925\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.2537 - acc: 0.5366 - val_loss: 0.7937 - val_acc: 0.7057\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 1.1296 - acc: 0.6047 - val_loss: 0.7689 - val_acc: 0.7322\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.9447 - acc: 0.6873 - val_loss: 0.6124 - val_acc: 0.8298\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.8373 - acc: 0.7405 - val_loss: 0.6432 - val_acc: 0.7938\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.7866 - acc: 0.7666 - val_loss: 0.5132 - val_acc: 0.8417\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.7097 - acc: 0.8064 - val_loss: 0.4293 - val_acc: 0.9033\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.6807 - acc: 0.8250 - val_loss: 0.4248 - val_acc: 0.8937\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5987 - acc: 0.8555 - val_loss: 0.3695 - val_acc: 0.9268\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5670 - acc: 0.8687 - val_loss: 0.3742 - val_acc: 0.9270\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5529 - acc: 0.8755 - val_loss: 0.3453 - val_acc: 0.9297\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5176 - acc: 0.8869 - val_loss: 0.3104 - val_acc: 0.9403\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5041 - acc: 0.8895 - val_loss: 0.2793 - val_acc: 0.9450\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.4991 - acc: 0.8926 - val_loss: 0.3289 - val_acc: 0.9367\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.5276 - acc: 0.8801 - val_loss: 0.3473 - val_acc: 0.9150\n",
-      "10000/10000 [==============================] - 4s 414us/step\n",
-      "0.9487\n",
-      "\n",
-      " \n",
-      " Starting round with 32 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.9114 - acc: 0.3287 - val_loss: 1.0782 - val_acc: 0.5248\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.3285 - acc: 0.5000 - val_loss: 0.8163 - val_acc: 0.7090\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.0871 - acc: 0.6346 - val_loss: 0.6391 - val_acc: 0.8023\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9359 - acc: 0.7077 - val_loss: 0.5163 - val_acc: 0.8348\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8530 - acc: 0.7449 - val_loss: 0.5007 - val_acc: 0.8397\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8437 - acc: 0.7479 - val_loss: 0.5794 - val_acc: 0.8065\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7872 - acc: 0.7739 - val_loss: 0.4864 - val_acc: 0.8482\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7491 - acc: 0.8013 - val_loss: 0.4151 - val_acc: 0.9117\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7216 - acc: 0.8195 - val_loss: 0.4321 - val_acc: 0.9100\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7159 - acc: 0.8261 - val_loss: 0.3941 - val_acc: 0.9150\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.6765 - acc: 0.8399 - val_loss: 0.4581 - val_acc: 0.9032\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.6417 - acc: 0.8512 - val_loss: 0.3826 - val_acc: 0.9192\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.6181 - acc: 0.8592 - val_loss: 0.3900 - val_acc: 0.9117\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.6075 - acc: 0.8624 - val_loss: 0.3767 - val_acc: 0.9210\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.5851 - acc: 0.8699 - val_loss: 0.3257 - val_acc: 0.9280\n",
-      "10000/10000 [==============================] - 5s 462us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 147s 3ms/step - loss: 1.9871 - acc: 0.2745 - val_loss: 1.3103 - val_acc: 0.4328\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.4983 - acc: 0.3921 - val_loss: 1.0637 - val_acc: 0.5253\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.3033 - acc: 0.4845 - val_loss: 0.9561 - val_acc: 0.6200\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.1638 - acc: 0.5490 - val_loss: 0.7583 - val_acc: 0.6685\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.0699 - acc: 0.5985 - val_loss: 0.7410 - val_acc: 0.6893\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9972 - acc: 0.6368 - val_loss: 0.7251 - val_acc: 0.7398\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9696 - acc: 0.6501 - val_loss: 0.6773 - val_acc: 0.7707\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9590 - acc: 0.6703 - val_loss: 0.5999 - val_acc: 0.8000\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8897 - acc: 0.6971 - val_loss: 0.5252 - val_acc: 0.8132\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8946 - acc: 0.7020 - val_loss: 0.5692 - val_acc: 0.7993\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8502 - acc: 0.7255 - val_loss: 0.5427 - val_acc: 0.8577\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9123 - acc: 0.7194 - val_loss: 0.5642 - val_acc: 0.8687\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8218 - acc: 0.7776 - val_loss: 0.5610 - val_acc: 0.8540\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7931 - acc: 0.7869 - val_loss: 0.4648 - val_acc: 0.8937\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.7438 - acc: 0.8147 - val_loss: 0.4293 - val_acc: 0.9127\n",
-      "10000/10000 [==============================] - 5s 473us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 150s 3ms/step - loss: 2.0049 - acc: 0.2959 - val_loss: 1.1653 - val_acc: 0.5100\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.3759 - acc: 0.4866 - val_loss: 0.9161 - val_acc: 0.6593\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.1377 - acc: 0.6023 - val_loss: 0.6965 - val_acc: 0.6802\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.0046 - acc: 0.6598 - val_loss: 0.6351 - val_acc: 0.8063\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.9659 - acc: 0.6837 - val_loss: 0.5769 - val_acc: 0.8448\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.9054 - acc: 0.7126 - val_loss: 0.5800 - val_acc: 0.8742\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.9106 - acc: 0.7277 - val_loss: 0.6486 - val_acc: 0.8108\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.9420 - acc: 0.7255 - val_loss: 0.5459 - val_acc: 0.8825\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.8688 - acc: 0.7617 - val_loss: 0.4899 - val_acc: 0.8965\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.9490 - acc: 0.7181 - val_loss: 0.5401 - val_acc: 0.8685\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.0452 - acc: 0.7000 - val_loss: 0.9759 - val_acc: 0.6903\n",
-      "Epoch 12/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.0366 - acc: 0.6875\n",
-      "Epoch 00012: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 113s 2ms/step - loss: 1.0366 - acc: 0.6875 - val_loss: 0.5733 - val_acc: 0.8563\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 0.8278 - acc: 0.7783 - val_loss: 0.4968 - val_acc: 0.8817\n",
-      "Epoch 14/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.7798 - acc: 0.7934\n",
-      "Epoch 00014: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 112s 2ms/step - loss: 0.7798 - acc: 0.7934 - val_loss: 0.4661 - val_acc: 0.8932\n",
-      "Epoch 00014: early stopping\n",
-      "10000/10000 [==============================] - 5s 492us/step\n",
-      "0.9353\n",
-      "\n",
-      " \n",
-      " Starting round with 35 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 166s 3ms/step - loss: 1.8817 - acc: 0.2907 - val_loss: 1.3766 - val_acc: 0.4052\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.5171 - acc: 0.3766 - val_loss: 1.1853 - val_acc: 0.4432\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.3701 - acc: 0.4216 - val_loss: 1.0662 - val_acc: 0.5020\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.2757 - acc: 0.4589 - val_loss: 0.9843 - val_acc: 0.5588\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.1974 - acc: 0.5225 - val_loss: 0.8493 - val_acc: 0.6873\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.1157 - acc: 0.5792 - val_loss: 0.7882 - val_acc: 0.6950\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.0612 - acc: 0.6159 - val_loss: 0.7465 - val_acc: 0.7507\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.0261 - acc: 0.6407 - val_loss: 0.7154 - val_acc: 0.7590\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.9783 - acc: 0.6627 - val_loss: 0.6583 - val_acc: 0.7667\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.9259 - acc: 0.6891 - val_loss: 0.6500 - val_acc: 0.7973\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.9176 - acc: 0.7047 - val_loss: 0.5920 - val_acc: 0.8072\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8735 - acc: 0.7190 - val_loss: 0.5742 - val_acc: 0.8115\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8889 - acc: 0.7182 - val_loss: 0.5518 - val_acc: 0.8177\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8427 - acc: 0.7373 - val_loss: 0.5792 - val_acc: 0.8197\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 0.8566 - acc: 0.7359 - val_loss: 0.5899 - val_acc: 0.8133\n",
-      "10000/10000 [==============================] - 5s 503us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 167s 3ms/step - loss: 1.9686 - acc: 0.2741 - val_loss: 1.5565 - val_acc: 0.3598\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.6416 - acc: 0.3579 - val_loss: 1.3158 - val_acc: 0.4473\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.4688 - acc: 0.3975 - val_loss: 1.1632 - val_acc: 0.5185\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3861 - acc: 0.4400 - val_loss: 1.1238 - val_acc: 0.5400\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3061 - acc: 0.4776 - val_loss: 1.1610 - val_acc: 0.5375\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3005 - acc: 0.4849 - val_loss: 1.1697 - val_acc: 0.5092\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3911 - acc: 0.4544\n",
-      "Epoch 00007: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 123s 2ms/step - loss: 1.3911 - acc: 0.4544 - val_loss: 1.4663 - val_acc: 0.4287\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.4326 - acc: 0.4124 - val_loss: 1.1558 - val_acc: 0.4772\n",
-      "Epoch 9/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3263 - acc: 0.4504\n",
-      "Epoch 00009: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3263 - acc: 0.4504 - val_loss: 1.1032 - val_acc: 0.5148\n",
-      "Epoch 00009: early stopping\n",
-      "10000/10000 [==============================] - 5s 523us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 169s 3ms/step - loss: 2.0334 - acc: 0.2620 - val_loss: 1.4756 - val_acc: 0.4202\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.6287 - acc: 0.3809 - val_loss: 1.2871 - val_acc: 0.4365\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.4206 - acc: 0.4572 - val_loss: 1.0214 - val_acc: 0.5918\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 1.2511 - acc: 0.5315 - val_loss: 0.8830 - val_acc: 0.6645\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 124s 2ms/step - loss: 1.1446 - acc: 0.5866 - val_loss: 0.7577 - val_acc: 0.7247\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 1.0565 - acc: 0.6263 - val_loss: 0.8152 - val_acc: 0.7155\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 1.0038 - acc: 0.6516 - val_loss: 0.7053 - val_acc: 0.7535\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 0.9585 - acc: 0.6691 - val_loss: 0.6777 - val_acc: 0.7585\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 122s 2ms/step - loss: 0.9324 - acc: 0.6875 - val_loss: 0.6359 - val_acc: 0.8027\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 0.9141 - acc: 0.6970 - val_loss: 0.6855 - val_acc: 0.7748\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8894 - acc: 0.7119 - val_loss: 0.5328 - val_acc: 0.8687\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8866 - acc: 0.7262 - val_loss: 0.5668 - val_acc: 0.8628\n",
-      "Epoch 13/15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8543 - acc: 0.7458 - val_loss: 0.4832 - val_acc: 0.8860\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.8199 - acc: 0.7600 - val_loss: 0.4405 - val_acc: 0.8902\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 0.7756 - acc: 0.7800 - val_loss: 0.4634 - val_acc: 0.8792\n",
-      "10000/10000 [==============================] - 5s 532us/step\n",
-      "0.9047\n",
-      "\n",
-      " \n",
-      " Starting round with 38 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 185s 3ms/step - loss: 2.1707 - acc: 0.2288 - val_loss: 1.6790 - val_acc: 0.3395\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.7943 - acc: 0.3041 - val_loss: 1.5162 - val_acc: 0.3598\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.6937 - acc: 0.3266 - val_loss: 1.4352 - val_acc: 0.3845\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.6118 - acc: 0.3547 - val_loss: 1.3481 - val_acc: 0.4338\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.5401 - acc: 0.3851 - val_loss: 1.2724 - val_acc: 0.5430\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.4788 - acc: 0.4233 - val_loss: 1.1929 - val_acc: 0.5643\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.4243 - acc: 0.4564 - val_loss: 1.1247 - val_acc: 0.5597\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3806 - acc: 0.4794 - val_loss: 1.1098 - val_acc: 0.5223\n",
-      "Epoch 9/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3260 - acc: 0.5024\n",
-      "Epoch 00009: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 135s 3ms/step - loss: 1.3263 - acc: 0.5024 - val_loss: 1.0262 - val_acc: 0.5640\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.2577 - acc: 0.5270 - val_loss: 0.9948 - val_acc: 0.6127\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.2416 - acc: 0.5288 - val_loss: 0.9728 - val_acc: 0.6295\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.2159 - acc: 0.5347 - val_loss: 0.9414 - val_acc: 0.6397\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.2125 - acc: 0.5366 - val_loss: 0.9200 - val_acc: 0.6498\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.1954 - acc: 0.5449 - val_loss: 0.9357 - val_acc: 0.6308\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.1780 - acc: 0.5506 - val_loss: 0.9616 - val_acc: 0.6328\n",
-      "10000/10000 [==============================] - 6s 588us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 189s 4ms/step - loss: 2.1811 - acc: 0.2547 - val_loss: 1.6650 - val_acc: 0.3240\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.7349 - acc: 0.3540 - val_loss: 1.3707 - val_acc: 0.4325\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.5966 - acc: 0.3908 - val_loss: 1.2346 - val_acc: 0.5418\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.5036 - acc: 0.4427 - val_loss: 1.1743 - val_acc: 0.5540\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.4461 - acc: 0.4683 - val_loss: 1.1445 - val_acc: 0.5588\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.4014 - acc: 0.4851 - val_loss: 1.0923 - val_acc: 0.5688\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3144 - acc: 0.5194 - val_loss: 1.0352 - val_acc: 0.5887\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3292 - acc: 0.5093 - val_loss: 1.1284 - val_acc: 0.5797\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3589 - acc: 0.4944 - val_loss: 1.2168 - val_acc: 0.5353\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3536 - acc: 0.5052 - val_loss: 1.0393 - val_acc: 0.5893\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.2319 - acc: 0.5496 - val_loss: 0.9331 - val_acc: 0.6348\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.1583 - acc: 0.5808 - val_loss: 0.8718 - val_acc: 0.6893\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.1220 - acc: 0.5996 - val_loss: 0.8853 - val_acc: 0.7110\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.0888 - acc: 0.6184 - val_loss: 0.8457 - val_acc: 0.7417\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.0620 - acc: 0.6386 - val_loss: 0.7581 - val_acc: 0.7455\n",
-      "10000/10000 [==============================] - 6s 584us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 191s 4ms/step - loss: 2.2452 - acc: 0.2322 - val_loss: 1.7780 - val_acc: 0.3285\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.8257 - acc: 0.3326 - val_loss: 1.5314 - val_acc: 0.3725\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.6694 - acc: 0.3712 - val_loss: 1.4225 - val_acc: 0.4387\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.5744 - acc: 0.4029 - val_loss: 1.3102 - val_acc: 0.4620\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.5090 - acc: 0.4257 - val_loss: 1.3193 - val_acc: 0.5253\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.5002 - acc: 0.4366 - val_loss: 1.3018 - val_acc: 0.5515\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.4277 - acc: 0.4562 - val_loss: 1.1518 - val_acc: 0.5810\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.3620 - acc: 0.4876 - val_loss: 0.9979 - val_acc: 0.6633\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2760 - acc: 0.5181 - val_loss: 0.9547 - val_acc: 0.6838\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2222 - acc: 0.5566 - val_loss: 0.9894 - val_acc: 0.6838\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 129s 2ms/step - loss: 1.2004 - acc: 0.5690 - val_loss: 0.9171 - val_acc: 0.6498\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 129s 2ms/step - loss: 1.1313 - acc: 0.5980 - val_loss: 0.8827 - val_acc: 0.7215\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 129s 2ms/step - loss: 1.1302 - acc: 0.6056 - val_loss: 0.9869 - val_acc: 0.7092\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.1402 - acc: 0.6108 - val_loss: 0.9228 - val_acc: 0.7290\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 129s 2ms/step - loss: 1.1421 - acc: 0.6149 - val_loss: 0.8296 - val_acc: 0.7472\n",
-      "10000/10000 [==============================] - 6s 583us/step\n",
-      "0.7936\n",
-      "\n",
-      " \n"
-     ]
-    }
-   ],
-   "source": [
-    "act = e_swish_2\n",
-    "logs.info(\"\\n \\n \\n\"+\"\\n \\n \\n\")\n",
-    "logs.info(\"ESWISH 23-42\")\n",
-    "\n",
-    "logs_e_swish_2 = []\n",
-    "record_e_swish_2 = []\n",
-    "for n in range(23,42,3):\n",
-    "    ensembler = 0\n",
-    "    logger = [n]\n",
-    "    logs.info(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    print(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    for i in range(3):\n",
-    "        # Garbage collector\n",
-    "        gc.collect()\n",
-    "        # Set optimizer\n",
-    "        opt = SGD(lr=0.01, momentum=0.9)\n",
-    "        # Set callbacks (learning rate reducer and early stopping)\n",
-    "        learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.35, min_lr=0.00001)\n",
-    "        early_stop = EarlyStopping(monitor='val_acc', patience=5, verbose = 1)\n",
-    "        # Common params \n",
-    "        epochs = 15\n",
-    "        batch_size = 128\n",
-    "        # Create and compile the model\n",
-    "        model = create(act, n)\n",
-    "        # Compile the model\n",
-    "        model.compile(optimizer = opt , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
-    "        # Train the model\n",
-    "        history = model.fit(X_train,Y_train, epochs = epochs, validation_data = (X_val,Y_val),\n",
-    "                            verbose = 1 , callbacks=[learning_rate_reduction, early_stop])\n",
-    "        \n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        logs.info(\"Accuracy \"+str(i)+\": \"+str(logger[-1]))\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, history, learning_rate_reduction, early_stop, opt\n",
-    "     \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    # Save the ensembled accuracy and the three models accuracy\n",
-    "    record_e_swish_2.append([n, ensembled])\n",
-    "    logs_e_swish_2.append(logger)\n",
-    "    del ensembler, ensembled\n",
-    "    logs.info(\"Ensembled accuracy: \"+str(record_e_swish_2[-1]))\n",
-    "    logs.info(\"Logs: +\"+str(logs_e_swish_2[-1]))\n",
-    "    \n",
-    "logs.info(\"\\n \\n \\n\")\n",
-    "logs.info(\"Logs e_swish_2: \"+str(logs_e_swish_2))\n",
-    "logs.info(\"Record e_swish_2: \"+str(record_e_swish_2))\n",
-    "    \n",
-    "print(\"\\n \\n \\n\")\n",
-    "print(\"Logs e_swish_2: \", logs_e_swish_2)\n",
-    "print(\"Record e_swish_2: \", record_e_swish_2)\n",
-    "    \n",
-    "plot([[x[1] for x in logs_relu], [x[1] for x in logs_e_swish_2]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " Starting round with 41 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 140s 3ms/step - loss: 2.3038 - acc: 0.1406 - val_loss: 2.0706 - val_acc: 0.1923\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 2.0379 - acc: 0.2082 - val_loss: 1.6829 - val_acc: 0.3590\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.8362 - acc: 0.2873 - val_loss: 1.5304 - val_acc: 0.3847\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.7062 - acc: 0.3286 - val_loss: 1.3722 - val_acc: 0.4520\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.6300 - acc: 0.3511 - val_loss: 1.3249 - val_acc: 0.4560\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.5921 - acc: 0.3683 - val_loss: 1.2773 - val_acc: 0.4715\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.5210 - acc: 0.3947 - val_loss: 1.2199 - val_acc: 0.5048\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.4763 - acc: 0.4110 - val_loss: 1.1553 - val_acc: 0.5087\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.4386 - acc: 0.4236 - val_loss: 1.1733 - val_acc: 0.5128\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.4142 - acc: 0.4379 - val_loss: 1.1209 - val_acc: 0.5393\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.3972 - acc: 0.4476 - val_loss: 1.2173 - val_acc: 0.5260\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.3803 - acc: 0.4607 - val_loss: 1.1212 - val_acc: 0.5545\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.3952 - acc: 0.4548 - val_loss: 1.0860 - val_acc: 0.5848\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.3740 - acc: 0.4694 - val_loss: 1.1527 - val_acc: 0.5573\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.3548 - acc: 0.4726 - val_loss: 1.0085 - val_acc: 0.5955\n",
-      "10000/10000 [==============================] - 5s 501us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 141s 3ms/step - loss: 2.1643 - acc: 0.2034 - val_loss: 1.8464 - val_acc: 0.3268\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.8489 - acc: 0.3126 - val_loss: 1.5335 - val_acc: 0.4038\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.6655 - acc: 0.3639 - val_loss: 1.3698 - val_acc: 0.4458\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 135s 3ms/step - loss: 1.5496 - acc: 0.3935 - val_loss: 1.2659 - val_acc: 0.4668\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 135s 3ms/step - loss: 1.4852 - acc: 0.4126 - val_loss: 1.2291 - val_acc: 0.4860\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.4302 - acc: 0.4237 - val_loss: 1.1775 - val_acc: 0.5048\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 133s 2ms/step - loss: 1.3814 - acc: 0.4394 - val_loss: 1.1432 - val_acc: 0.4968\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3527 - acc: 0.4472 - val_loss: 1.1228 - val_acc: 0.5063\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3153 - acc: 0.4539 - val_loss: 1.1165 - val_acc: 0.4855\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.3111 - acc: 0.4544 - val_loss: 1.1080 - val_acc: 0.5113\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.2839 - acc: 0.4612 - val_loss: 1.0990 - val_acc: 0.5185\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2570 - acc: 0.4708 - val_loss: 1.0749 - val_acc: 0.5145\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2470 - acc: 0.4744 - val_loss: 1.0383 - val_acc: 0.5495\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2396 - acc: 0.4871 - val_loss: 1.0218 - val_acc: 0.5642\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.2041 - acc: 0.5043 - val_loss: 1.0405 - val_acc: 0.5592\n",
-      "10000/10000 [==============================] - 5s 497us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 2.3649 - acc: 0.1016 - val_loss: 2.2993 - val_acc: 0.1103\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 2.3057 - acc: 0.1184 - val_loss: 2.2853 - val_acc: 0.1530\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 2.2611 - acc: 0.1518 - val_loss: 2.1381 - val_acc: 0.2008\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 135s 2ms/step - loss: 2.1879 - acc: 0.1809 - val_loss: 2.0533 - val_acc: 0.2235\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 135s 3ms/step - loss: 2.1205 - acc: 0.2000 - val_loss: 1.9775 - val_acc: 0.2372\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 135s 2ms/step - loss: 2.0563 - acc: 0.2273 - val_loss: 1.8490 - val_acc: 0.2965\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 135s 2ms/step - loss: 1.9572 - acc: 0.2624 - val_loss: 1.6662 - val_acc: 0.3758\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.8586 - acc: 0.2899 - val_loss: 1.6010 - val_acc: 0.3888\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.7828 - acc: 0.3044 - val_loss: 1.5161 - val_acc: 0.3938\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.7462 - acc: 0.3124 - val_loss: 1.5090 - val_acc: 0.4008\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.7028 - acc: 0.3230 - val_loss: 1.4188 - val_acc: 0.4102\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.6612 - acc: 0.3310 - val_loss: 1.3798 - val_acc: 0.4378\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.6263 - acc: 0.3470 - val_loss: 1.3298 - val_acc: 0.4813\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.5953 - acc: 0.3544 - val_loss: 1.2829 - val_acc: 0.4737\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.5675 - acc: 0.3654 - val_loss: 1.2745 - val_acc: 0.4887\n",
-      "10000/10000 [==============================] - 5s 507us/step\n",
-      "0.679\n",
-      "\n",
-      " \n",
-      " Starting round with 44 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 156s 3ms/step - loss: 2.4722 - acc: 0.1218 - val_loss: 2.1897 - val_acc: 0.1830\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 2.2008 - acc: 0.1718 - val_loss: 2.0837 - val_acc: 0.2348\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 2.1017 - acc: 0.2015 - val_loss: 1.8868 - val_acc: 0.2635\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 2.0335 - acc: 0.2138 - val_loss: 1.8411 - val_acc: 0.2822\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.9797 - acc: 0.2257 - val_loss: 1.7637 - val_acc: 0.2833\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.9390 - acc: 0.2358 - val_loss: 1.7439 - val_acc: 0.2773\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.9051 - acc: 0.2455 - val_loss: 1.7005 - val_acc: 0.3055\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.8740 - acc: 0.2576 - val_loss: 1.6443 - val_acc: 0.3317\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 1.8369 - acc: 0.2670 - val_loss: 1.6188 - val_acc: 0.3180\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 141s 3ms/step - loss: 1.8176 - acc: 0.2744 - val_loss: 1.5991 - val_acc: 0.3493\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 1.7845 - acc: 0.2829 - val_loss: 1.5969 - val_acc: 0.3543\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.7652 - acc: 0.2927 - val_loss: 1.6418 - val_acc: 0.3443\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 1.7439 - acc: 0.2987 - val_loss: 1.6365 - val_acc: 0.3762\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.7236 - acc: 0.3065 - val_loss: 1.8872 - val_acc: 0.3463\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 1.6968 - acc: 0.3172 - val_loss: 2.0396 - val_acc: 0.3560\n",
-      "10000/10000 [==============================] - 5s 546us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 159s 3ms/step - loss: 2.4932 - acc: 0.1228 - val_loss: 2.2272 - val_acc: 0.0997\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 2.2148 - acc: 0.1710 - val_loss: 2.1071 - val_acc: 0.1945\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 2.1358 - acc: 0.1880 - val_loss: 2.0248 - val_acc: 0.2258\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 2.0682 - acc: 0.1977 - val_loss: 1.9193 - val_acc: 0.2300\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.9878 - acc: 0.2080 - val_loss: 1.8347 - val_acc: 0.2695\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.9259 - acc: 0.2240 - val_loss: 1.7532 - val_acc: 0.2583\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 147s 3ms/step - loss: 1.8701 - acc: 0.2450 - val_loss: 1.6683 - val_acc: 0.3253\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.8058 - acc: 0.2732 - val_loss: 1.5864 - val_acc: 0.3107\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.7350 - acc: 0.2986 - val_loss: 1.4601 - val_acc: 0.4045\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.6781 - acc: 0.3231 - val_loss: 1.4073 - val_acc: 0.4257\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 145s 3ms/step - loss: 1.6187 - acc: 0.3464 - val_loss: 1.3308 - val_acc: 0.4595\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.5733 - acc: 0.3644 - val_loss: 1.2976 - val_acc: 0.4560\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.5448 - acc: 0.3859 - val_loss: 1.2761 - val_acc: 0.4857\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.5032 - acc: 0.4020 - val_loss: 1.2401 - val_acc: 0.4782\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.4867 - acc: 0.4064 - val_loss: 1.2073 - val_acc: 0.5113\n",
-      "10000/10000 [==============================] - 6s 572us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 168s 3ms/step - loss: 2.4258 - acc: 0.1513 - val_loss: 2.0415 - val_acc: 0.2923\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 147s 3ms/step - loss: 2.0972 - acc: 0.2365 - val_loss: 1.8824 - val_acc: 0.3028\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.9928 - acc: 0.2632 - val_loss: 1.7859 - val_acc: 0.3117\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 147s 3ms/step - loss: 1.9189 - acc: 0.2836 - val_loss: 1.6812 - val_acc: 0.3143\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 147s 3ms/step - loss: 1.8580 - acc: 0.2909 - val_loss: 1.6030 - val_acc: 0.3592\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.8107 - acc: 0.2989 - val_loss: 1.5806 - val_acc: 0.3843\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.7838 - acc: 0.3065 - val_loss: 1.5491 - val_acc: 0.3670\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.7441 - acc: 0.3194 - val_loss: 1.5258 - val_acc: 0.3755\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.7246 - acc: 0.3226 - val_loss: 1.4924 - val_acc: 0.3973\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.6938 - acc: 0.3303 - val_loss: 1.4606 - val_acc: 0.4048\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.6746 - acc: 0.3353 - val_loss: 1.4463 - val_acc: 0.4312\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.6353 - acc: 0.3461 - val_loss: 1.5158 - val_acc: 0.4285\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.6273 - acc: 0.3492 - val_loss: 1.4647 - val_acc: 0.4062\n",
-      "Epoch 14/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.5953 - acc: 0.3600\n",
-      "Epoch 00014: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.5954 - acc: 0.3600 - val_loss: 1.7459 - val_acc: 0.3537\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 146s 3ms/step - loss: 1.5694 - acc: 0.3670 - val_loss: 1.6717 - val_acc: 0.3930\n",
-      "10000/10000 [==============================] - 6s 570us/step\n",
-      "0.6189\n",
-      "\n",
-      " \n"
-     ]
-    }
-   ],
-   "source": [
-    "act = e_swish_2\n",
-    "# logs.info(\"\\n \\n \\n\"+\"\\n \\n \\n\")\n",
-    "# logs.info(\"ESWISH 41-42\")\n",
-    "\n",
-    "logs_e_swish_2 = []\n",
-    "record_e_swish_2 = []\n",
-    "for n in range(41,45,3):\n",
-    "    ensembler = 0\n",
-    "    logger = [n]\n",
-    "    logs.info(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    print(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    for i in range(3):\n",
-    "        # Garbage collector\n",
-    "        gc.collect()\n",
-    "        # Set optimizer\n",
-    "        opt = SGD(lr=0.01, momentum=0.9)\n",
-    "        # Set callbacks (learning rate reducer and early stopping)\n",
-    "        learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.35, min_lr=0.00001)\n",
-    "        early_stop = EarlyStopping(monitor='val_acc', patience=5, verbose = 1)\n",
-    "        # Common params \n",
-    "        epochs = 15\n",
-    "        batch_size = 128\n",
-    "        # Create and compile the model\n",
-    "        model = create(act, n)\n",
-    "        # Compile the model\n",
-    "        model.compile(optimizer = opt , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
-    "        # Train the model\n",
-    "        history = model.fit(X_train,Y_train, epochs = epochs, validation_data = (X_val,Y_val),\n",
-    "                            verbose = 1 , callbacks=[learning_rate_reduction, early_stop])\n",
-    "        \n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        logs.info(\"Accuracy \"+str(i)+\": \"+str(logger[-1]))\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, history, learning_rate_reduction, early_stop, opt\n",
-    "     \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    # Save the ensembled accuracy and the three models accuracy\n",
-    "    record_e_swish_2.append([n, ensembled])\n",
-    "    logs_e_swish_2.append(logger)\n",
-    "    del ensembler, ensembled\n",
-    "    logs.info(\"Ensembled accuracy: \"+str(record_e_swish_2[-1]))\n",
-    "    logs.info(\"Logs: +\"+str(logs_e_swish_2[-1]))\n",
-    "    \n",
-    "# logs.info(\"\\n \\n \\n\")\n",
-    "# logs.info(\"Logs e_swish_2: \"+str(logs_e_swish_2))\n",
-    "# logs.info(\"Record e_swish_2: \"+str(record_e_swish_2))\n",
-    "    \n",
-    "# print(\"\\n \\n \\n\")\n",
-    "# print(\"Logs e_swish_2: \", logs_e_swish_2)\n",
-    "# print(\"Record e_swish_2: \", record_e_swish_2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " \n",
-      "\n",
-      "Logs e_swish_2:  [[41, 0.6018, 0.55249999999999999, 0.48520000000000002], [44, 0.35160000000000002, 0.51910000000000001, 0.3891]]\n",
-      "Record e_swish_2:  [[41, 0.679], [44, 0.6189]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "logs.info(\"\\n \\n \\n\")\n",
-    "logs.info(\"Logs e_swish_2: \"+str(logs_e_swish_2))\n",
-    "logs.info(\"Record e_swish_2: \"+str(record_e_swish_2))\n",
-    "    \n",
-    "print(\"\\n \\n \\n\")\n",
-    "print(\"Logs e_swish_2: \", logs_e_swish_2)\n",
-    "print(\"Record e_swish_2: \", record_e_swish_2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " \n",
-      " Starting round with 23 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 75s 1ms/step - loss: 1.0462 - acc: 0.6580 - val_loss: 0.3307 - val_acc: 0.9118\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.5330 - acc: 0.8714 - val_loss: 0.2276 - val_acc: 0.9472\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.4287 - acc: 0.9022 - val_loss: 0.1684 - val_acc: 0.9613\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.3613 - acc: 0.9221 - val_loss: 0.1670 - val_acc: 0.9608\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.3216 - acc: 0.9314 - val_loss: 0.1519 - val_acc: 0.9670\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.2884 - acc: 0.9379 - val_loss: 0.1291 - val_acc: 0.9715\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.2596 - acc: 0.9458 - val_loss: 0.1389 - val_acc: 0.9715\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.2422 - acc: 0.9500 - val_loss: 0.1757 - val_acc: 0.9612\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.2268 - acc: 0.9536 - val_loss: 0.1385 - val_acc: 0.9718\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2123 - acc: 0.9558 - val_loss: 0.1149 - val_acc: 0.9743\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2037 - acc: 0.9576 - val_loss: 0.1129 - val_acc: 0.9763\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1941 - acc: 0.9597 - val_loss: 0.1262 - val_acc: 0.9742\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1700 - acc: 0.9645 - val_loss: 0.1021 - val_acc: 0.9785\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1528 - acc: 0.9687 - val_loss: 0.1124 - val_acc: 0.9755\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1551 - acc: 0.9676 - val_loss: 0.1046 - val_acc: 0.9792\n",
-      "10000/10000 [==============================] - 3s 271us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 74s 1ms/step - loss: 1.0848 - acc: 0.6401 - val_loss: 0.3346 - val_acc: 0.9087\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.5590 - acc: 0.8658 - val_loss: 0.2396 - val_acc: 0.9430\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.4316 - acc: 0.9016 - val_loss: 0.1891 - val_acc: 0.9577\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3681 - acc: 0.9191 - val_loss: 0.1858 - val_acc: 0.9587\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.3237 - acc: 0.9297 - val_loss: 0.1484 - val_acc: 0.9667\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2876 - acc: 0.9386 - val_loss: 0.1372 - val_acc: 0.9700\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2553 - acc: 0.9474 - val_loss: 0.1242 - val_acc: 0.9712\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2466 - acc: 0.9472 - val_loss: 0.1260 - val_acc: 0.9725\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2218 - acc: 0.9525 - val_loss: 0.1293 - val_acc: 0.9718\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2024 - acc: 0.9577 - val_loss: 0.1140 - val_acc: 0.9765\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1992 - acc: 0.9583 - val_loss: 0.1137 - val_acc: 0.9758\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1747 - acc: 0.9629 - val_loss: 0.1164 - val_acc: 0.9753\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1708 - acc: 0.9648 - val_loss: 0.1061 - val_acc: 0.9793\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1496 - acc: 0.9692 - val_loss: 0.1053 - val_acc: 0.9780\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.1469 - acc: 0.9689 - val_loss: 0.1068 - val_acc: 0.9755\n",
-      "10000/10000 [==============================] - 3s 283us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 77s 1ms/step - loss: 1.0590 - acc: 0.6477 - val_loss: 0.3277 - val_acc: 0.9195\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.5580 - acc: 0.8642 - val_loss: 0.2144 - val_acc: 0.9465\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.4428 - acc: 0.9006 - val_loss: 0.1872 - val_acc: 0.9598\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.3763 - acc: 0.9181 - val_loss: 0.1592 - val_acc: 0.9643\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.3234 - acc: 0.9317 - val_loss: 0.1634 - val_acc: 0.9642\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.2859 - acc: 0.9408 - val_loss: 0.1407 - val_acc: 0.9715\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.2669 - acc: 0.9454 - val_loss: 0.1502 - val_acc: 0.9683\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 72s 1ms/step - loss: 0.2393 - acc: 0.9504 - val_loss: 0.1428 - val_acc: 0.9710\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2172 - acc: 0.9560 - val_loss: 0.0996 - val_acc: 0.9797\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.2084 - acc: 0.9576 - val_loss: 0.1304 - val_acc: 0.9718\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1996 - acc: 0.9596 - val_loss: 0.1134 - val_acc: 0.9747\n",
-      "Epoch 12/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.1857 - acc: 0.9625\n",
-      "Epoch 00012: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 71s 1ms/step - loss: 0.1857 - acc: 0.9625 - val_loss: 0.1020 - val_acc: 0.9782\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1462 - acc: 0.9704 - val_loss: 0.0933 - val_acc: 0.9802\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1300 - acc: 0.9736 - val_loss: 0.0879 - val_acc: 0.9812\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 70s 1ms/step - loss: 0.1194 - acc: 0.9759 - val_loss: 0.0825 - val_acc: 0.9825\n",
-      "10000/10000 [==============================] - 3s 276us/step\n",
-      "0.9855\n",
-      "\n",
-      " \n",
-      " Starting round with 26 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.2490 - acc: 0.5542 - val_loss: 0.4325 - val_acc: 0.8537\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.6548 - acc: 0.8299 - val_loss: 0.2678 - val_acc: 0.9382\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.5192 - acc: 0.8824 - val_loss: 0.2092 - val_acc: 0.9517\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.4420 - acc: 0.9041 - val_loss: 0.1997 - val_acc: 0.9590\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.4057 - acc: 0.9157 - val_loss: 0.2428 - val_acc: 0.9472\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.3756 - acc: 0.9239 - val_loss: 0.1895 - val_acc: 0.9640\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.3706 - acc: 0.9248 - val_loss: 0.1953 - val_acc: 0.9637\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.3515 - acc: 0.9290 - val_loss: 0.1774 - val_acc: 0.9633\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.3338 - acc: 0.9331 - val_loss: 0.1650 - val_acc: 0.9678\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 81s 1ms/step - loss: 0.3426 - acc: 0.9309 - val_loss: 0.2129 - val_acc: 0.9535\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3134 - acc: 0.9362 - val_loss: 0.1761 - val_acc: 0.9643\n",
-      "Epoch 12/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.2990 - acc: 0.9406\n",
-      "Epoch 00012: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.2989 - acc: 0.9406 - val_loss: 0.1630 - val_acc: 0.9670\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.2366 - acc: 0.9543 - val_loss: 0.1267 - val_acc: 0.9748\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.2169 - acc: 0.9576 - val_loss: 0.1236 - val_acc: 0.9753\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.2102 - acc: 0.9583 - val_loss: 0.1224 - val_acc: 0.9735\n",
-      "10000/10000 [==============================] - 3s 307us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 88s 2ms/step - loss: 1.1890 - acc: 0.6101 - val_loss: 0.4019 - val_acc: 0.8927\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.6857 - acc: 0.8298 - val_loss: 0.3092 - val_acc: 0.9278\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.5405 - acc: 0.8786 - val_loss: 0.2252 - val_acc: 0.9482\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.4762 - acc: 0.8965 - val_loss: 0.2401 - val_acc: 0.9485\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.4240 - acc: 0.9110 - val_loss: 0.1962 - val_acc: 0.9605\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3875 - acc: 0.9204 - val_loss: 0.2166 - val_acc: 0.9610\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3769 - acc: 0.9232 - val_loss: 0.2108 - val_acc: 0.9602\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3395 - acc: 0.9329 - val_loss: 0.2464 - val_acc: 0.9537\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3381 - acc: 0.9324 - val_loss: 0.1907 - val_acc: 0.9642\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3253 - acc: 0.9354 - val_loss: 0.1884 - val_acc: 0.9633\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3285 - acc: 0.9323 - val_loss: 0.1961 - val_acc: 0.9580\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3050 - acc: 0.9380 - val_loss: 0.1651 - val_acc: 0.9655\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3143 - acc: 0.9348 - val_loss: 0.2064 - val_acc: 0.9610\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3158 - acc: 0.9321 - val_loss: 0.1939 - val_acc: 0.9590\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.2707 - acc: 0.9445 - val_loss: 0.1648 - val_acc: 0.9678\n",
-      "10000/10000 [==============================] - 3s 314us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 1.1940 - acc: 0.6039 - val_loss: 0.3872 - val_acc: 0.8897\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.6540 - acc: 0.8391 - val_loss: 0.2873 - val_acc: 0.9380\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.5352 - acc: 0.8794 - val_loss: 0.3035 - val_acc: 0.9317\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.4714 - acc: 0.8997 - val_loss: 0.2409 - val_acc: 0.9545\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.4089 - acc: 0.9153 - val_loss: 0.1996 - val_acc: 0.9580\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.3868 - acc: 0.9214 - val_loss: 0.1948 - val_acc: 0.9638\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3657 - acc: 0.9263 - val_loss: 0.2138 - val_acc: 0.9577\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3293 - acc: 0.9335 - val_loss: 0.1745 - val_acc: 0.9645\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3264 - acc: 0.9338 - val_loss: 0.2172 - val_acc: 0.9553\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3376 - acc: 0.9316 - val_loss: 0.1952 - val_acc: 0.9612\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.3225 - acc: 0.9353 - val_loss: 0.1570 - val_acc: 0.9685\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.2957 - acc: 0.9406 - val_loss: 0.1497 - val_acc: 0.9715\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 79s 1ms/step - loss: 0.3136 - acc: 0.9364 - val_loss: 0.1942 - val_acc: 0.9608\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.3112 - acc: 0.9361 - val_loss: 0.1779 - val_acc: 0.9585\n",
-      "Epoch 15/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.2768 - acc: 0.9434\n",
-      "Epoch 00015: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 80s 1ms/step - loss: 0.2767 - acc: 0.9434 - val_loss: 0.1525 - val_acc: 0.9708\n",
-      "10000/10000 [==============================] - 3s 317us/step\n",
-      "0.9771\n",
-      "\n",
-      " \n",
-      " Starting round with 29 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 101s 2ms/step - loss: 1.3225 - acc: 0.5355 - val_loss: 0.5337 - val_acc: 0.8157\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.8112 - acc: 0.7537 - val_loss: 0.5735 - val_acc: 0.8160\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.7753 - acc: 0.7722 - val_loss: 0.4473 - val_acc: 0.8498\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6826 - acc: 0.8001 - val_loss: 0.4817 - val_acc: 0.8433\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6085 - acc: 0.8288 - val_loss: 0.3433 - val_acc: 0.9257\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5617 - acc: 0.8680 - val_loss: 0.3742 - val_acc: 0.9268\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5616 - acc: 0.8724 - val_loss: 0.3079 - val_acc: 0.9345\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5409 - acc: 0.8753 - val_loss: 0.2611 - val_acc: 0.9470\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4860 - acc: 0.8881 - val_loss: 0.4424 - val_acc: 0.8508\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5590 - acc: 0.8661 - val_loss: 0.3038 - val_acc: 0.9370\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4588 - acc: 0.9015 - val_loss: 0.2402 - val_acc: 0.9510\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4126 - acc: 0.9139 - val_loss: 0.2511 - val_acc: 0.9548\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4064 - acc: 0.9142 - val_loss: 0.2267 - val_acc: 0.9540\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3625 - acc: 0.9263 - val_loss: 0.2242 - val_acc: 0.9577\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3594 - acc: 0.9275 - val_loss: 0.2169 - val_acc: 0.9572\n",
-      "10000/10000 [==============================] - 3s 343us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 104s 2ms/step - loss: 1.3435 - acc: 0.5201 - val_loss: 0.5460 - val_acc: 0.8197\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.8232 - acc: 0.7808 - val_loss: 0.4104 - val_acc: 0.9142\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6608 - acc: 0.8515 - val_loss: 0.4109 - val_acc: 0.9330\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6235 - acc: 0.8671 - val_loss: 0.3474 - val_acc: 0.9322\n",
-      "Epoch 5/15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6429 - acc: 0.8598 - val_loss: 0.3507 - val_acc: 0.9335\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5708 - acc: 0.8796 - val_loss: 0.3894 - val_acc: 0.9255\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5816 - acc: 0.8753 - val_loss: 0.3646 - val_acc: 0.9255\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.5460 - acc: 0.8831\n",
-      "Epoch 00008: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.5461 - acc: 0.8830 - val_loss: 0.4538 - val_acc: 0.8997\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.4725 - acc: 0.9004 - val_loss: 0.2715 - val_acc: 0.9467\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3936 - acc: 0.9213 - val_loss: 0.2557 - val_acc: 0.9520\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3709 - acc: 0.9257 - val_loss: 0.2531 - val_acc: 0.9535\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3769 - acc: 0.9254 - val_loss: 0.2538 - val_acc: 0.9507\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3562 - acc: 0.9279 - val_loss: 0.2429 - val_acc: 0.9538\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3624 - acc: 0.9267 - val_loss: 0.2194 - val_acc: 0.9578\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3453 - acc: 0.9302 - val_loss: 0.2308 - val_acc: 0.9558\n",
-      "10000/10000 [==============================] - 4s 351us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.3008 - acc: 0.5256 - val_loss: 0.6076 - val_acc: 0.7525\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.7937 - acc: 0.7669 - val_loss: 0.3975 - val_acc: 0.9140\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.6215 - acc: 0.8592 - val_loss: 0.3837 - val_acc: 0.9143\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5682 - acc: 0.8782 - val_loss: 0.3325 - val_acc: 0.9337\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5566 - acc: 0.8819 - val_loss: 0.4199 - val_acc: 0.9168\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.5224 - acc: 0.8923 - val_loss: 0.3893 - val_acc: 0.9218\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.5096 - acc: 0.8951\n",
-      "Epoch 00007: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.5096 - acc: 0.8951 - val_loss: 0.3445 - val_acc: 0.9335\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.4072 - acc: 0.9181 - val_loss: 0.2410 - val_acc: 0.9565\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3790 - acc: 0.9260 - val_loss: 0.2448 - val_acc: 0.9563\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3594 - acc: 0.9295 - val_loss: 0.2294 - val_acc: 0.9588\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3453 - acc: 0.9327 - val_loss: 0.2089 - val_acc: 0.9612\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3452 - acc: 0.9323 - val_loss: 0.2249 - val_acc: 0.9602\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3397 - acc: 0.9325 - val_loss: 0.1959 - val_acc: 0.9627\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 89s 2ms/step - loss: 0.3250 - acc: 0.9370 - val_loss: 0.2008 - val_acc: 0.9615\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 90s 2ms/step - loss: 0.3280 - acc: 0.9360 - val_loss: 0.2631 - val_acc: 0.9517\n",
-      "10000/10000 [==============================] - 3s 348us/step\n",
-      "0.9682\n",
-      "\n",
-      " \n",
-      " Starting round with 32 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 117s 2ms/step - loss: 1.4588 - acc: 0.4800 - val_loss: 0.6421 - val_acc: 0.7875\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.9221 - acc: 0.7286 - val_loss: 0.4728 - val_acc: 0.8930\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.7770 - acc: 0.8130 - val_loss: 0.4685 - val_acc: 0.9063\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.7695 - acc: 0.8220 - val_loss: 0.5612 - val_acc: 0.8928\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.7615 - acc: 0.8266 - val_loss: 0.6018 - val_acc: 0.8718\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.8600 - acc: 0.7820\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 0.8601 - acc: 0.7819 - val_loss: 0.5869 - val_acc: 0.8772\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.6590 - acc: 0.8564 - val_loss: 0.4357 - val_acc: 0.9220\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5772 - acc: 0.8807 - val_loss: 0.3983 - val_acc: 0.9297\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5606 - acc: 0.8849 - val_loss: 0.3682 - val_acc: 0.9378\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5564 - acc: 0.8850 - val_loss: 0.3801 - val_acc: 0.9310\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5763 - acc: 0.8810 - val_loss: 0.3988 - val_acc: 0.9272\n",
-      "Epoch 12/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.5641 - acc: 0.8848\n",
-      "Epoch 00012: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5641 - acc: 0.8848 - val_loss: 0.3706 - val_acc: 0.9317\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.5199 - acc: 0.8950 - val_loss: 0.3030 - val_acc: 0.9473\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 98s 2ms/step - loss: 0.4801 - acc: 0.9061 - val_loss: 0.3034 - val_acc: 0.9475\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 0.4639 - acc: 0.9110 - val_loss: 0.2993 - val_acc: 0.9487\n",
-      "10000/10000 [==============================] - 4s 384us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.4843 - acc: 0.4473 - val_loss: 0.8980 - val_acc: 0.5863\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.1097 - acc: 0.5789 - val_loss: 0.8486 - val_acc: 0.6410\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0654 - acc: 0.6075 - val_loss: 0.8248 - val_acc: 0.6543\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0015 - acc: 0.6234 - val_loss: 0.8657 - val_acc: 0.6528\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0896 - acc: 0.5949 - val_loss: 0.8762 - val_acc: 0.6488\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.1444 - acc: 0.5831\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.1444 - acc: 0.5831 - val_loss: 0.9288 - val_acc: 0.6448\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0179 - acc: 0.6196 - val_loss: 0.9207 - val_acc: 0.6423\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.0050 - acc: 0.6163\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0051 - acc: 0.6162 - val_loss: 0.8832 - val_acc: 0.6390\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 4s 391us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 1.5189 - acc: 0.4200 - val_loss: 0.9912 - val_acc: 0.5223\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.1643 - acc: 0.5662 - val_loss: 0.8155 - val_acc: 0.6682\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0341 - acc: 0.6336 - val_loss: 0.8102 - val_acc: 0.6978\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0125 - acc: 0.6587 - val_loss: 0.8712 - val_acc: 0.6883\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.1190 - acc: 0.6071 - val_loss: 0.9296 - val_acc: 0.6360\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.1996 - acc: 0.5569\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 100s 2ms/step - loss: 1.1999 - acc: 0.5568 - val_loss: 1.1061 - val_acc: 0.5765\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.1309 - acc: 0.5802 - val_loss: 0.8848 - val_acc: 0.6453\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.0254 - acc: 0.6074\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 99s 2ms/step - loss: 1.0253 - acc: 0.6074 - val_loss: 0.8628 - val_acc: 0.6537\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 4s 393us/step\n",
-      "0.9467\n",
-      "\n",
-      " \n",
-      " Starting round with 35 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 132s 2ms/step - loss: 1.5391 - acc: 0.4384 - val_loss: 0.9788 - val_acc: 0.6467\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.2433 - acc: 0.5430 - val_loss: 1.0747 - val_acc: 0.5442\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 107s 2ms/step - loss: 1.2490 - acc: 0.5047 - val_loss: 0.9983 - val_acc: 0.5487\n",
-      "Epoch 4/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.2731 - acc: 0.5015\n",
-      "Epoch 00004: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.2733 - acc: 0.5015 - val_loss: 1.1191 - val_acc: 0.5350\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.1767 - acc: 0.5241 - val_loss: 0.9141 - val_acc: 0.5667\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.0793 - acc: 0.5399\n",
-      "Epoch 00006: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.0795 - acc: 0.5399 - val_loss: 0.9632 - val_acc: 0.5673\n",
-      "Epoch 00006: early stopping\n",
-      "10000/10000 [==============================] - 4s 425us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 134s 2ms/step - loss: 1.5595 - acc: 0.3954 - val_loss: 1.0558 - val_acc: 0.5485\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2787 - acc: 0.5113 - val_loss: 1.0723 - val_acc: 0.5482\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2771 - acc: 0.5061 - val_loss: 1.1703 - val_acc: 0.5423\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2655 - acc: 0.5109 - val_loss: 1.1720 - val_acc: 0.5563\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2786 - acc: 0.5004 - val_loss: 1.1071 - val_acc: 0.5335\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.3999 - acc: 0.4567 - val_loss: 1.2591 - val_acc: 0.5238\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3343 - acc: 0.4844\n",
-      "Epoch 00007: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 1.3342 - acc: 0.4844 - val_loss: 1.0914 - val_acc: 0.5445\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2104 - acc: 0.5201 - val_loss: 1.0993 - val_acc: 0.5520\n",
-      "Epoch 9/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.2007 - acc: 0.5226\n",
-      "Epoch 00009: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 1.2006 - acc: 0.5226 - val_loss: 1.0523 - val_acc: 0.5500\n",
-      "Epoch 00009: early stopping\n",
-      "10000/10000 [==============================] - 4s 430us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 137s 3ms/step - loss: 1.4622 - acc: 0.4822 - val_loss: 0.7312 - val_acc: 0.7602\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 0.9891 - acc: 0.6910 - val_loss: 0.6174 - val_acc: 0.8362\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 0.9441 - acc: 0.7258 - val_loss: 0.6156 - val_acc: 0.8278\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 108s 2ms/step - loss: 0.9273 - acc: 0.7308 - val_loss: 0.8065 - val_acc: 0.7023\n",
-      "Epoch 5/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.0004 - acc: 0.7096\n",
-      "Epoch 00005: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 110s 2ms/step - loss: 1.0004 - acc: 0.7096 - val_loss: 0.7710 - val_acc: 0.7937\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.9437 - acc: 0.6983 - val_loss: 0.7320 - val_acc: 0.7602\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 0.8303 - acc: 0.7557\n",
-      "Epoch 00007: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 109s 2ms/step - loss: 0.8305 - acc: 0.7556 - val_loss: 0.5952 - val_acc: 0.8298\n",
-      "Epoch 00007: early stopping\n",
-      "10000/10000 [==============================] - 4s 440us/step\n",
-      "0.8342\n",
-      "\n",
-      " \n",
-      " Starting round with 38 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 149s 3ms/step - loss: 1.6836 - acc: 0.3679 - val_loss: 1.0321 - val_acc: 0.5525\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2798 - acc: 0.5245 - val_loss: 1.2246 - val_acc: 0.5232\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2359 - acc: 0.5473 - val_loss: 1.0268 - val_acc: 0.6307\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.3096 - acc: 0.5240 - val_loss: 1.3133 - val_acc: 0.5585\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.3253 - acc: 0.5264 - val_loss: 1.1199 - val_acc: 0.6213\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.2908 - acc: 0.5359\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 120s 2ms/step - loss: 1.2908 - acc: 0.5359 - val_loss: 1.0758 - val_acc: 0.6092\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.1847 - acc: 0.5702 - val_loss: 1.0181 - val_acc: 0.6180\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.1602 - acc: 0.5795\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.1603 - acc: 0.5796 - val_loss: 0.9631 - val_acc: 0.6223\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 5s 475us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 151s 3ms/step - loss: 1.6601 - acc: 0.3901 - val_loss: 1.1790 - val_acc: 0.5700\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2898 - acc: 0.5134 - val_loss: 1.3619 - val_acc: 0.4463\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2958 - acc: 0.5339 - val_loss: 1.0186 - val_acc: 0.6143\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2962 - acc: 0.5406 - val_loss: 1.0778 - val_acc: 0.6033\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.3240 - acc: 0.5334 - val_loss: 1.1436 - val_acc: 0.5823\n",
-      "Epoch 6/15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.4624 - acc: 0.4843\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 1.4623 - acc: 0.4843 - val_loss: 1.2412 - val_acc: 0.5240\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3307 - acc: 0.5085 - val_loss: 1.0924 - val_acc: 0.5707\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.2730 - acc: 0.5207\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2730 - acc: 0.5207 - val_loss: 1.1310 - val_acc: 0.5563\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 5s 483us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 155s 3ms/step - loss: 1.6491 - acc: 0.3853 - val_loss: 1.0922 - val_acc: 0.5350\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.2936 - acc: 0.5155 - val_loss: 1.1264 - val_acc: 0.5693\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.2647 - acc: 0.5244 - val_loss: 1.2683 - val_acc: 0.4367\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.2983 - acc: 0.5229 - val_loss: 1.0166 - val_acc: 0.6105\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2830 - acc: 0.5345 - val_loss: 1.1234 - val_acc: 0.5955\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.4061 - acc: 0.5150 - val_loss: 1.1489 - val_acc: 0.5778\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3552 - acc: 0.5372\n",
-      "Epoch 00007: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 121s 2ms/step - loss: 1.3553 - acc: 0.5372 - val_loss: 1.1802 - val_acc: 0.5625\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.3149 - acc: 0.5201 - val_loss: 1.0453 - val_acc: 0.5953\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.2243 - acc: 0.5628 - val_loss: 0.9760 - val_acc: 0.6693\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.1691 - acc: 0.6044 - val_loss: 0.9804 - val_acc: 0.6820\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.1175 - acc: 0.6196 - val_loss: 0.9022 - val_acc: 0.7180\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 119s 2ms/step - loss: 1.2511 - acc: 0.5563 - val_loss: 1.0307 - val_acc: 0.6378\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2222 - acc: 0.5583 - val_loss: 0.9987 - val_acc: 0.5960\n",
-      "Epoch 14/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.2075 - acc: 0.5758\n",
-      "Epoch 00014: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.2075 - acc: 0.5758 - val_loss: 1.0013 - val_acc: 0.6497\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 118s 2ms/step - loss: 1.1483 - acc: 0.6078 - val_loss: 0.9116 - val_acc: 0.6747\n",
-      "10000/10000 [==============================] - 5s 484us/step\n",
-      "0.7813\n",
-      "\n",
-      " \n",
-      " Starting round with 41 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 166s 3ms/step - loss: 1.7162 - acc: 0.3313 - val_loss: 1.3392 - val_acc: 0.4265\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5130 - acc: 0.4142 - val_loss: 1.3409 - val_acc: 0.4567\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5507 - acc: 0.4148 - val_loss: 1.4844 - val_acc: 0.4730\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5896 - acc: 0.4080 - val_loss: 1.6586 - val_acc: 0.3682\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5646 - acc: 0.4067 - val_loss: 1.3938 - val_acc: 0.4395\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.5245 - acc: 0.3933\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.5245 - acc: 0.3932 - val_loss: 1.3765 - val_acc: 0.4067\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.4536 - acc: 0.4247 - val_loss: 1.3601 - val_acc: 0.4433\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.4542 - acc: 0.4113 - val_loss: 1.3286 - val_acc: 0.4810\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5170 - acc: 0.4105 - val_loss: 1.5308 - val_acc: 0.3923\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.5700 - acc: 0.3897 - val_loss: 1.3754 - val_acc: 0.4338\n",
-      "Epoch 11/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.4620 - acc: 0.4209 - val_loss: 1.2307 - val_acc: 0.4978\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.3998 - acc: 0.4419 - val_loss: 1.2595 - val_acc: 0.4745\n",
-      "Epoch 13/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.4055 - acc: 0.4387 - val_loss: 1.2098 - val_acc: 0.5053\n",
-      "Epoch 14/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.3603 - acc: 0.4565 - val_loss: 1.1731 - val_acc: 0.4768\n",
-      "Epoch 15/15\n",
-      "54000/54000 [==============================] - 126s 2ms/step - loss: 1.3883 - acc: 0.4560 - val_loss: 1.2640 - val_acc: 0.4840\n",
-      "10000/10000 [==============================] - 5s 526us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 169s 3ms/step - loss: 1.9265 - acc: 0.2779 - val_loss: 1.6844 - val_acc: 0.3985\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.5785 - acc: 0.3987 - val_loss: 1.3644 - val_acc: 0.4360\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.5925 - acc: 0.3627 - val_loss: 1.4525 - val_acc: 0.3750\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.7081 - acc: 0.3271 - val_loss: 1.5706 - val_acc: 0.3308\n",
-      "Epoch 5/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.6317 - acc: 0.3527\n",
-      "Epoch 00005: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 130s 2ms/step - loss: 1.6318 - acc: 0.3526 - val_loss: 1.5858 - val_acc: 0.3957\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.5363 - acc: 0.3693 - val_loss: 1.4134 - val_acc: 0.4028\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.5147 - acc: 0.3731\n",
-      "Epoch 00007: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.5148 - acc: 0.3731 - val_loss: 1.4634 - val_acc: 0.4002\n",
-      "Epoch 00007: early stopping\n",
-      "10000/10000 [==============================] - 5s 535us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 174s 3ms/step - loss: 1.7023 - acc: 0.3497 - val_loss: 1.1769 - val_acc: 0.5008\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.3519 - acc: 0.5148 - val_loss: 1.0354 - val_acc: 0.5532\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.4692 - acc: 0.4766 - val_loss: 1.4430 - val_acc: 0.4168\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.5971 - acc: 0.4095 - val_loss: 1.4734 - val_acc: 0.4135\n",
-      "Epoch 5/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.5374 - acc: 0.4192\n",
-      "Epoch 00005: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 131s 2ms/step - loss: 1.5374 - acc: 0.4192 - val_loss: 1.3311 - val_acc: 0.4423\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.4046 - acc: 0.4509 - val_loss: 1.2628 - val_acc: 0.4838\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.3967 - acc: 0.4618\n",
-      "Epoch 00007: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 127s 2ms/step - loss: 1.3966 - acc: 0.4618 - val_loss: 1.3484 - val_acc: 0.4568\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 00007: early stopping\n",
-      "10000/10000 [==============================] - 6s 553us/step\n",
-      "0.6553\n",
-      "\n",
-      " \n",
-      " Starting round with 44 layers\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 189s 3ms/step - loss: 1.7660 - acc: 0.3470 - val_loss: 1.1964 - val_acc: 0.4793\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.4717 - acc: 0.4286 - val_loss: 1.4418 - val_acc: 0.3628\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.5745 - acc: 0.3834 - val_loss: 1.3689 - val_acc: 0.4892\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.4541 - acc: 0.4387 - val_loss: 1.3224 - val_acc: 0.4720\n",
-      "Epoch 5/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.6842 - acc: 0.3848 - val_loss: 1.7230 - val_acc: 0.3873\n",
-      "Epoch 6/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.6507 - acc: 0.3629\n",
-      "Epoch 00006: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 1.6508 - acc: 0.3629 - val_loss: 1.3781 - val_acc: 0.4370\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.5033 - acc: 0.3959 - val_loss: 1.3533 - val_acc: 0.4427\n",
-      "Epoch 8/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.4819 - acc: 0.4020\n",
-      "Epoch 00008: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.4818 - acc: 0.4019 - val_loss: 1.3030 - val_acc: 0.4490\n",
-      "Epoch 00008: early stopping\n",
-      "10000/10000 [==============================] - 6s 611us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 192s 4ms/step - loss: 1.8588 - acc: 0.3186 - val_loss: 1.5942 - val_acc: 0.4332\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.6491 - acc: 0.3971 - val_loss: 1.3702 - val_acc: 0.4440\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.5632 - acc: 0.3989 - val_loss: 1.4778 - val_acc: 0.4072\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.6650 - acc: 0.3712 - val_loss: 1.7220 - val_acc: 0.3457\n",
-      "Epoch 5/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.8096 - acc: 0.3554\n",
-      "Epoch 00005: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 143s 3ms/step - loss: 1.8097 - acc: 0.3554 - val_loss: 1.7808 - val_acc: 0.3617\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.6158 - acc: 0.4158 - val_loss: 1.3793 - val_acc: 0.4612\n",
-      "Epoch 7/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.5035 - acc: 0.4369 - val_loss: 1.3492 - val_acc: 0.4722\n",
-      "Epoch 8/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.4500 - acc: 0.4412 - val_loss: 1.3339 - val_acc: 0.4812\n",
-      "Epoch 9/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.4565 - acc: 0.4446 - val_loss: 1.3018 - val_acc: 0.4552\n",
-      "Epoch 10/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.4262 - acc: 0.4391 - val_loss: 1.3035 - val_acc: 0.4622\n",
-      "Epoch 11/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.4460 - acc: 0.4227\n",
-      "Epoch 00011: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.4461 - acc: 0.4227 - val_loss: 1.3582 - val_acc: 0.4480\n",
-      "Epoch 12/15\n",
-      "54000/54000 [==============================] - 138s 3ms/step - loss: 1.4534 - acc: 0.4221 - val_loss: 1.3006 - val_acc: 0.4597\n",
-      "Epoch 13/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.4599 - acc: 0.4159\n",
-      "Epoch 00013: reducing learning rate to 0.0004287500050850212.\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 1.4601 - acc: 0.4158 - val_loss: 1.3578 - val_acc: 0.4443\n",
-      "Epoch 00013: early stopping\n",
-      "10000/10000 [==============================] - 6s 613us/step\n",
-      "Train on 54000 samples, validate on 6000 samples\n",
-      "Epoch 1/15\n",
-      "54000/54000 [==============================] - 198s 4ms/step - loss: 1.8603 - acc: 0.3091 - val_loss: 1.3816 - val_acc: 0.4453\n",
-      "Epoch 2/15\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 1.4916 - acc: 0.4156 - val_loss: 1.1812 - val_acc: 0.5210\n",
-      "Epoch 3/15\n",
-      "54000/54000 [==============================] - 142s 3ms/step - loss: 1.4895 - acc: 0.4398 - val_loss: 1.7455 - val_acc: 0.4738\n",
-      "Epoch 4/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.6391 - acc: 0.3839 - val_loss: 1.5984 - val_acc: 0.3573\n",
-      "Epoch 5/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.6155 - acc: 0.3716\n",
-      "Epoch 00005: reducing learning rate to 0.0034999999217689036.\n",
-      "54000/54000 [==============================] - 144s 3ms/step - loss: 1.6154 - acc: 0.3716 - val_loss: 1.5327 - val_acc: 0.4082\n",
-      "Epoch 6/15\n",
-      "54000/54000 [==============================] - 139s 3ms/step - loss: 1.5438 - acc: 0.3925 - val_loss: 1.3389 - val_acc: 0.4248\n",
-      "Epoch 7/15\n",
-      "53984/54000 [============================>.] - ETA: 0s - loss: 1.5070 - acc: 0.3935\n",
-      "Epoch 00007: reducing learning rate to 0.0012249999563209713.\n",
-      "54000/54000 [==============================] - 140s 3ms/step - loss: 1.5070 - acc: 0.3935 - val_loss: 1.3567 - val_acc: 0.4632\n",
-      "Epoch 00007: early stopping\n",
-      "10000/10000 [==============================] - 6s 612us/step\n",
-      "0.5968\n",
-      "\n",
-      " \n",
-      " \n",
-      "\n",
-      "Logs swish:  [[23, 0.97789999999999999, 0.98109999999999997, 0.98380000000000001], [26, 0.9748, 0.9667, 0.9718], [29, 0.95650000000000002, 0.95889999999999997, 0.95240000000000002], [32, 0.9506, 0.65339999999999998, 0.65449999999999997], [35, 0.57069999999999999, 0.55630000000000002, 0.8357], [38, 0.62029999999999996, 0.55879999999999996, 0.68069999999999997], [41, 0.4733, 0.39119999999999999, 0.45540000000000003], [44, 0.4461, 0.44290000000000002, 0.45960000000000001]]\n",
-      "Record swish:  [[23, 0.9855], [26, 0.9771], [29, 0.9682], [32, 0.9467], [35, 0.8342], [38, 0.7813], [41, 0.6553], [44, 0.5968]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "act = swish\n",
-    "logs.info(\"\\n \\n \\n\"+\"\\n \\n \\n\")\n",
-    "logs.info(\"SWISH 23-42\")\n",
-    "\n",
-    "logs_swish = []\n",
-    "record_swish = []\n",
-    "for n in range(23,45,3):\n",
-    "    ensembler = 0\n",
-    "    logger = [n]\n",
-    "    logs.info(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    print(\"\\n \\n Starting round with {0} layers\".format(n))\n",
-    "    for i in range(3):\n",
-    "        # Garbage collector\n",
-    "        gc.collect()\n",
-    "        # Set optimizer\n",
-    "        opt = SGD(lr=0.01, momentum=0.9)\n",
-    "        # Set callbacks (learning rate reducer and early stopping)\n",
-    "        learning_rate_reduction = ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.35, min_lr=0.00001)\n",
-    "        early_stop = EarlyStopping(monitor='val_acc', patience=5, verbose = 1)\n",
-    "        # Common params \n",
-    "        epochs = 15\n",
-    "        batch_size = 128\n",
-    "        # Create and compile the model\n",
-    "        model = create(act, n)\n",
-    "        # Compile the model\n",
-    "        model.compile(optimizer = opt , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
-    "        # Train the model\n",
-    "        history = model.fit(X_train,Y_train, epochs = epochs, validation_data = (X_val,Y_val),\n",
-    "                            verbose = 1 , callbacks=[learning_rate_reduction, early_stop])\n",
-    "        \n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        logs.info(\"Accuracy \"+str(i)+\": \"+str(logger[-1]))\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, history, learning_rate_reduction, early_stop, opt\n",
-    "     \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    # Save the ensembled accuracy and the three models accuracy\n",
-    "    record_swish.append([n, ensembled])\n",
-    "    logs_swish.append(logger)\n",
-    "    del ensembler, ensembled\n",
-    "    logs.info(\"Ensembled accuracy: \"+str(record_swish[-1]))\n",
-    "    logs.info(\"Logs: +\"+str(logs_swish[-1]))\n",
-    "    \n",
-    "logs.info(\"\\n \\n \\n\")\n",
-    "logs.info(\"Logs swish: \"+str(logs_swish))\n",
-    "logs.info(\"Record swish: \"+str(record_swish))\n",
-    "    \n",
-    "print(\"\\n \\n \\n\")\n",
-    "print(\"Logs swish: \", logs_swish)\n",
-    "print(\"Record swish: \", record_swish)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/mnist/deep_nets/plotter.py b/mnist/deep_nets/plotter.py
deleted file mode 100644
index 73f2860..0000000
--- a/mnist/deep_nets/plotter.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt 
-
-n_layers = major_ticks = np.arange(23, 45, 3) 
-
-arr = np.array([
-	[0.9717, 0.9253, 0.8639, 0.6807, 0.5834, 0.1135, 0.1572, 0.1152], 			# relu
-	[0.9753, 0.9682, 0.9487, 0.9353, 0.9047, 0.7936, 0.6790, 0.6189], 	# e-swish
-	[0.9855, 0.9771, 0.9682, 0.9467, 0.8342, 0.7813, 0.6553, 0.5968]  # swish
-	])
-
-fig, ax = plt.subplots()
-for item in arr:
-    ax.plot([x for x in range(23,45,3)], [y for y in item], '-o')
-
-plt.grid()
-plt.legend(["relu", "e-swish", "swish"], loc='upper right')
-ax.set_ylim(0,1)
-plt.title("Training deep nets on MNIST")
-plt.ylabel("Test accuracy (median of 3 runs)")
-plt.xlabel("Number of layers")
-plt.xticks(range(23,45,3))
-plt.show()
diff --git a/mnist/mnist_median_3_runs.ipynb b/mnist/mnist_median_3_runs.ipynb
deleted file mode 100644
index 59a8a6e..0000000
--- a/mnist/mnist_median_3_runs.ipynb
+++ /dev/null
@@ -1,747 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n",
-      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
-      "  return f(*args, **kwds)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Using Keras to create a 10 neurons and single layer NN\n",
-    "# Against the MNIST databesa\n",
-    "\n",
-    "import gc\n",
-    "from keras.datasets import mnist\n",
-    "from keras.models import Sequential\n",
-    "from keras.optimizers import SGD\n",
-    "from keras.layers.core import Dense, Dropout, Activation\n",
-    "from keras.utils import np_utils\n",
-    "import keras.backend as K\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.rcParams['figure.figsize'] = (7,7) # Make the figures a bit bigger\n",
-    "nb_classes = 10\n",
-    "\n",
-    "np.random.seed(42)\n",
-    "%matplotlib notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X_train original shape (60000, 28, 28)\n",
-      "y_train original shape (60000,)\n",
-      "Training matrix shape (60000, 784)\n",
-      "Testing matrix shape (10000, 784)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# the data, shuffled and split between train and test sets\n",
-    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
-    "print(\"X_train original shape\", X_train.shape)\n",
-    "print(\"y_train original shape\", y_train.shape)\n",
-    "\n",
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def leaky(x):\n",
-    "    return K.maximum(0.3*x, x)\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish_2(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# show(X_train, y_train)\n",
-    "\n",
-    "# Reshape arrays\n",
-    "X_train = X_train.reshape(60000, 784)\n",
-    "X_test = X_test.reshape(10000, 784)\n",
-    "X_train = X_train.astype('float32')\n",
-    "X_test = X_test.astype('float32')\n",
-    "X_train /= 255\n",
-    "X_test /= 255\n",
-    "print(\"Training matrix shape\", X_train.shape)\n",
-    "print(\"Testing matrix shape\", X_test.shape)\n",
-    "\n",
-    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
-    "Y_test = np_utils.to_categorical(y_test, nb_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['e_swish_2', 'swish', 'relu', 'elu', 'leaky']\n"
-     ]
-    }
-   ],
-   "source": [
-    "activations = [e_swish_2, swish, \"relu\", \"elu\", leaky] # e_swish_1, \"sigmoid\", \"tanh\"\n",
-    "names = activations[:]\n",
-    "for i,a in enumerate(names):\n",
-    "    if not isinstance(a, str):\n",
-    "        names[i] = a.__name__\n",
-    "print(names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def accuracy(y_pred, y_test):\n",
-    "    y_hat = np.argmax(y_pred, axis=1)\n",
-    "    y = np.argmax(y_test, axis=1)\n",
-    "\n",
-    "    good = np.sum(np.equal(y, y_hat))\n",
-    "    return float(good/len(y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def create(act):\n",
-    "    models = []\n",
-    "\n",
-    "    sgd = SGD(lr=0.1)\n",
-    "    # create model\n",
-    "    \n",
-    "    model1 = Sequential()\n",
-    "    model1.add(Dense(200, input_dim=784, activation=act))\n",
-    "    model1.add(Dropout(0.2))\n",
-    "    model1.add(Dense(100, activation=act))\n",
-    "    model1.add(Dropout(0.2))\n",
-    "    model1.add(Dense(60, activation=act))\n",
-    "    model1.add(Dropout(0.2))\n",
-    "    model1.add(Dense(30, activation=act))\n",
-    "    model1.add(Dropout(0.2))\n",
-    "    model1.add(Dense(10, activation='softmax'))\n",
-    "\n",
-    "    # Compile model\n",
-    "    model1.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
-    "    \n",
-    "    return model1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# Configure logs\n",
-    "import logging\n",
-    "\n",
-    "# Record settings\n",
-    "LOG_FORMAT = \"%(levelname)s %(asctime)s - %(message)s\"\n",
-    "logging.basicConfig(filename=\"mnist_median_3_runs_log.txt\",format = LOG_FORMAT, level = logging.DEBUG, filemode = \"a\")\n",
-    "logs = logging.getLogger()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 23s 389us/step - loss: 0.4613 - acc: 0.8628 - val_loss: 0.1858 - val_acc: 0.9441\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 14s 239us/step - loss: 0.2169 - acc: 0.9396 - val_loss: 0.1310 - val_acc: 0.9609\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 14s 237us/step - loss: 0.1706 - acc: 0.9523 - val_loss: 0.1106 - val_acc: 0.9675\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 14s 238us/step - loss: 0.1393 - acc: 0.9616 - val_loss: 0.0953 - val_acc: 0.9721\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 14s 238us/step - loss: 0.1221 - acc: 0.9662 - val_loss: 0.0948 - val_acc: 0.9720\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 14s 241us/step - loss: 0.1080 - acc: 0.9704 - val_loss: 0.0887 - val_acc: 0.9733\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 14s 237us/step - loss: 0.1022 - acc: 0.9723 - val_loss: 0.0890 - val_acc: 0.9742\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 14s 233us/step - loss: 0.0911 - acc: 0.9746 - val_loss: 0.0797 - val_acc: 0.9765\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 14s 235us/step - loss: 0.0822 - acc: 0.9767 - val_loss: 0.0785 - val_acc: 0.9780\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 14s 239us/step - loss: 0.0790 - acc: 0.9782 - val_loss: 0.0815 - val_acc: 0.9770\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 14s 238us/step - loss: 0.0742 - acc: 0.9788 - val_loss: 0.0752 - val_acc: 0.9790\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 14s 238us/step - loss: 0.0682 - acc: 0.9800 - val_loss: 0.0729 - val_acc: 0.9804\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 14s 237us/step - loss: 0.0628 - acc: 0.9828 - val_loss: 0.0747 - val_acc: 0.9801\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 14s 238us/step - loss: 0.0597 - acc: 0.9832 - val_loss: 0.0783 - val_acc: 0.9784\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 14s 240us/step - loss: 0.0547 - acc: 0.9849 - val_loss: 0.0753 - val_acc: 0.9809\n",
-      "10000/10000 [==============================] - 1s 122us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 18s 295us/step - loss: 0.4525 - acc: 0.8636 - val_loss: 0.1529 - val_acc: 0.9534\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.2191 - acc: 0.9391 - val_loss: 0.1399 - val_acc: 0.9579\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 15s 245us/step - loss: 0.1704 - acc: 0.9528 - val_loss: 0.1087 - val_acc: 0.9657\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.1423 - acc: 0.9609 - val_loss: 0.0951 - val_acc: 0.9719\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 14s 233us/step - loss: 0.1241 - acc: 0.9657 - val_loss: 0.0849 - val_acc: 0.9755\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 15s 244us/step - loss: 0.1107 - acc: 0.9691 - val_loss: 0.0828 - val_acc: 0.9745\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 15s 255us/step - loss: 0.0984 - acc: 0.9721 - val_loss: 0.0828 - val_acc: 0.9758\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.0928 - acc: 0.9740 - val_loss: 0.0803 - val_acc: 0.9769\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 15s 246us/step - loss: 0.0852 - acc: 0.9758 - val_loss: 0.0803 - val_acc: 0.9791\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 15s 242us/step - loss: 0.0787 - acc: 0.9777 - val_loss: 0.0721 - val_acc: 0.9803\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.0742 - acc: 0.9791 - val_loss: 0.0784 - val_acc: 0.9788\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 15s 248us/step - loss: 0.0672 - acc: 0.9809 - val_loss: 0.0745 - val_acc: 0.9800\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.0662 - acc: 0.9812 - val_loss: 0.0727 - val_acc: 0.9816\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 15s 244us/step - loss: 0.0589 - acc: 0.9829 - val_loss: 0.0738 - val_acc: 0.9823\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 15s 257us/step - loss: 0.0596 - acc: 0.9832 - val_loss: 0.0733 - val_acc: 0.9811\n",
-      "10000/10000 [==============================] - 1s 139us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 18s 308us/step - loss: 0.4525 - acc: 0.8648 - val_loss: 0.1772 - val_acc: 0.9474\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 15s 242us/step - loss: 0.2154 - acc: 0.9403 - val_loss: 0.1137 - val_acc: 0.9664\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 15s 247us/step - loss: 0.1686 - acc: 0.9532 - val_loss: 0.1132 - val_acc: 0.9668\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 15s 247us/step - loss: 0.1414 - acc: 0.9600 - val_loss: 0.0991 - val_acc: 0.9707\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 15s 246us/step - loss: 0.1227 - acc: 0.9662 - val_loss: 0.0873 - val_acc: 0.9752\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 15s 246us/step - loss: 0.1088 - acc: 0.9699 - val_loss: 0.0843 - val_acc: 0.9772\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 15s 245us/step - loss: 0.0977 - acc: 0.9734 - val_loss: 0.0816 - val_acc: 0.9781\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 15s 249us/step - loss: 0.0882 - acc: 0.9750 - val_loss: 0.0816 - val_acc: 0.9775\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 15s 249us/step - loss: 0.0861 - acc: 0.9765 - val_loss: 0.0745 - val_acc: 0.9785\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 15s 246us/step - loss: 0.0779 - acc: 0.9780 - val_loss: 0.0838 - val_acc: 0.9775\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 15s 245us/step - loss: 0.0730 - acc: 0.9790 - val_loss: 0.0844 - val_acc: 0.9765\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 15s 247us/step - loss: 0.0681 - acc: 0.9805 - val_loss: 0.0737 - val_acc: 0.9802\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 14s 241us/step - loss: 0.0629 - acc: 0.9816 - val_loss: 0.0691 - val_acc: 0.9816\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 14s 241us/step - loss: 0.0602 - acc: 0.9828 - val_loss: 0.0692 - val_acc: 0.9807\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 15s 247us/step - loss: 0.0578 - acc: 0.9839 - val_loss: 0.0682 - val_acc: 0.9823\n",
-      "10000/10000 [==============================] - 1s 127us/step\n",
-      "0.9846\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 19s 317us/step - loss: 0.6586 - acc: 0.7980 - val_loss: 0.2213 - val_acc: 0.9333\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 14s 235us/step - loss: 0.2778 - acc: 0.9183 - val_loss: 0.1524 - val_acc: 0.9500\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 14s 233us/step - loss: 0.2073 - acc: 0.9406 - val_loss: 0.1219 - val_acc: 0.9627\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 14s 233us/step - loss: 0.1733 - acc: 0.9514 - val_loss: 0.1115 - val_acc: 0.9651\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.1452 - acc: 0.9583 - val_loss: 0.0938 - val_acc: 0.9718\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.1306 - acc: 0.9626 - val_loss: 0.0971 - val_acc: 0.9695\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 14s 238us/step - loss: 0.1168 - acc: 0.9663 - val_loss: 0.0843 - val_acc: 0.9745\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.1067 - acc: 0.9694 - val_loss: 0.0855 - val_acc: 0.9748\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 14s 240us/step - loss: 0.0977 - acc: 0.9720 - val_loss: 0.0782 - val_acc: 0.9769\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 14s 226us/step - loss: 0.0922 - acc: 0.9731 - val_loss: 0.0812 - val_acc: 0.9755\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.0859 - acc: 0.9747 - val_loss: 0.0820 - val_acc: 0.9750\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 14s 231us/step - loss: 0.0796 - acc: 0.9769 - val_loss: 0.0750 - val_acc: 0.9772\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.0765 - acc: 0.9781 - val_loss: 0.0698 - val_acc: 0.9789\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.0701 - acc: 0.9788 - val_loss: 0.0682 - val_acc: 0.9802\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 14s 233us/step - loss: 0.0666 - acc: 0.9805 - val_loss: 0.0690 - val_acc: 0.9795\n",
-      "10000/10000 [==============================] - 1s 129us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 20s 339us/step - loss: 0.6609 - acc: 0.7959 - val_loss: 0.2227 - val_acc: 0.9319\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 14s 235us/step - loss: 0.2775 - acc: 0.9185 - val_loss: 0.1494 - val_acc: 0.9551\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 14s 236us/step - loss: 0.2064 - acc: 0.9397 - val_loss: 0.1330 - val_acc: 0.9577\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 17s 275us/step - loss: 0.1739 - acc: 0.9498 - val_loss: 0.1092 - val_acc: 0.9651\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 16s 261us/step - loss: 0.1490 - acc: 0.9573 - val_loss: 0.1002 - val_acc: 0.9713\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 15s 242us/step - loss: 0.1332 - acc: 0.9610 - val_loss: 0.0939 - val_acc: 0.9696\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 17s 277us/step - loss: 0.1207 - acc: 0.9651 - val_loss: 0.0873 - val_acc: 0.9723\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 20s 333us/step - loss: 0.1103 - acc: 0.9681 - val_loss: 0.0831 - val_acc: 0.9749\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 20s 338us/step - loss: 0.1021 - acc: 0.9707 - val_loss: 0.0789 - val_acc: 0.9764\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 16s 265us/step - loss: 0.0921 - acc: 0.9730 - val_loss: 0.0764 - val_acc: 0.9771\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 18s 300us/step - loss: 0.0866 - acc: 0.9740 - val_loss: 0.0721 - val_acc: 0.9781\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 17s 283us/step - loss: 0.0815 - acc: 0.9759 - val_loss: 0.0729 - val_acc: 0.9782\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 17s 276us/step - loss: 0.0762 - acc: 0.9775 - val_loss: 0.0742 - val_acc: 0.9776\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 16s 262us/step - loss: 0.0725 - acc: 0.9784 - val_loss: 0.0688 - val_acc: 0.9791\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 15s 257us/step - loss: 0.0700 - acc: 0.9793 - val_loss: 0.0703 - val_acc: 0.9795\n",
-      "10000/10000 [==============================] - 1s 142us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 26s 432us/step - loss: 0.6620 - acc: 0.7924 - val_loss: 0.2214 - val_acc: 0.9321\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 18s 300us/step - loss: 0.2730 - acc: 0.9213 - val_loss: 0.1534 - val_acc: 0.9497\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 15s 246us/step - loss: 0.2064 - acc: 0.9397 - val_loss: 0.1253 - val_acc: 0.9619\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 21s 347us/step - loss: 0.1673 - acc: 0.9509 - val_loss: 0.1121 - val_acc: 0.9662\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 20s 335us/step - loss: 0.1433 - acc: 0.9590 - val_loss: 0.1024 - val_acc: 0.9710\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 22s 359us/step - loss: 0.1312 - acc: 0.9620 - val_loss: 0.0937 - val_acc: 0.9727\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 20s 330us/step - loss: 0.1200 - acc: 0.9648 - val_loss: 0.0879 - val_acc: 0.9730\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 20s 329us/step - loss: 0.1088 - acc: 0.9680 - val_loss: 0.0804 - val_acc: 0.9754\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 21s 350us/step - loss: 0.0992 - acc: 0.9704 - val_loss: 0.0786 - val_acc: 0.9760\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 21s 356us/step - loss: 0.0905 - acc: 0.9731 - val_loss: 0.0739 - val_acc: 0.9772\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 17s 282us/step - loss: 0.0863 - acc: 0.9743 - val_loss: 0.0770 - val_acc: 0.9761\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 14s 235us/step - loss: 0.0814 - acc: 0.9759 - val_loss: 0.0753 - val_acc: 0.9791\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 20s 336us/step - loss: 0.0753 - acc: 0.9780 - val_loss: 0.0725 - val_acc: 0.9787\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 21s 342us/step - loss: 0.0688 - acc: 0.9797 - val_loss: 0.0707 - val_acc: 0.9811\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 21s 354us/step - loss: 0.0662 - acc: 0.9797 - val_loss: 0.0724 - val_acc: 0.9808\n",
-      "10000/10000 [==============================] - 1s 149us/step\n",
-      "0.9831\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 28s 458us/step - loss: 0.6033 - acc: 0.8130 - val_loss: 0.1900 - val_acc: 0.9442\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 15s 253us/step - loss: 0.2562 - acc: 0.9304 - val_loss: 0.1354 - val_acc: 0.9628\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 17s 277us/step - loss: 0.1913 - acc: 0.9485 - val_loss: 0.1160 - val_acc: 0.9664\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 18s 306us/step - loss: 0.1605 - acc: 0.9571 - val_loss: 0.1059 - val_acc: 0.9709\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 18s 298us/step - loss: 0.1414 - acc: 0.9625 - val_loss: 0.1011 - val_acc: 0.9727\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 17s 290us/step - loss: 0.1293 - acc: 0.9660 - val_loss: 0.0872 - val_acc: 0.9765\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 18s 307us/step - loss: 0.1124 - acc: 0.9702 - val_loss: 0.0989 - val_acc: 0.9744\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 17s 282us/step - loss: 0.1026 - acc: 0.9728 - val_loss: 0.0891 - val_acc: 0.9768\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 20s 336us/step - loss: 0.0953 - acc: 0.9743 - val_loss: 0.0891 - val_acc: 0.9772\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 18s 301us/step - loss: 0.0909 - acc: 0.9761 - val_loss: 0.0786 - val_acc: 0.9798\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.0862 - acc: 0.9772 - val_loss: 0.0774 - val_acc: 0.9805\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.0832 - acc: 0.9781 - val_loss: 0.0871 - val_acc: 0.9789\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 21s 349us/step - loss: 0.0736 - acc: 0.9804 - val_loss: 0.0789 - val_acc: 0.9796\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 18s 292us/step - loss: 0.0727 - acc: 0.9804 - val_loss: 0.0756 - val_acc: 0.9807\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 20s 338us/step - loss: 0.0693 - acc: 0.9813 - val_loss: 0.0750 - val_acc: 0.9819\n",
-      "10000/10000 [==============================] - 2s 208us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 30s 496us/step - loss: 0.6291 - acc: 0.8042 - val_loss: 0.1996 - val_acc: 0.9412\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 17s 278us/step - loss: 0.2683 - acc: 0.9288 - val_loss: 0.1327 - val_acc: 0.9631\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 13s 209us/step - loss: 0.2014 - acc: 0.9469 - val_loss: 0.1228 - val_acc: 0.9652\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 13s 223us/step - loss: 0.1662 - acc: 0.9557 - val_loss: 0.1081 - val_acc: 0.9690\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 13s 220us/step - loss: 0.1454 - acc: 0.9618 - val_loss: 0.1046 - val_acc: 0.9713\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 13s 224us/step - loss: 0.1294 - acc: 0.9666 - val_loss: 0.1009 - val_acc: 0.9717\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 13s 219us/step - loss: 0.1189 - acc: 0.9690 - val_loss: 0.1070 - val_acc: 0.9714\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 13s 220us/step - loss: 0.1065 - acc: 0.9720 - val_loss: 0.0836 - val_acc: 0.9758\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 13s 220us/step - loss: 0.1005 - acc: 0.9732 - val_loss: 0.1007 - val_acc: 0.9720\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 13s 220us/step - loss: 0.0926 - acc: 0.9756 - val_loss: 0.0889 - val_acc: 0.9766\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 13s 221us/step - loss: 0.0892 - acc: 0.9763 - val_loss: 0.0815 - val_acc: 0.9785\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 13s 221us/step - loss: 0.0844 - acc: 0.9777 - val_loss: 0.0810 - val_acc: 0.9785\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 13s 219us/step - loss: 0.0800 - acc: 0.9788 - val_loss: 0.0851 - val_acc: 0.9761\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 13s 221us/step - loss: 0.0721 - acc: 0.9807 - val_loss: 0.0851 - val_acc: 0.9774\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 13s 219us/step - loss: 0.0712 - acc: 0.9812 - val_loss: 0.0795 - val_acc: 0.9806\n",
-      "10000/10000 [==============================] - 1s 131us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 25s 423us/step - loss: 0.5997 - acc: 0.8160 - val_loss: 0.2034 - val_acc: 0.9423\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 13s 224us/step - loss: 0.2570 - acc: 0.9299 - val_loss: 0.1405 - val_acc: 0.9590\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 14s 232us/step - loss: 0.1960 - acc: 0.9481 - val_loss: 0.1107 - val_acc: 0.9681\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 14s 230us/step - loss: 0.1647 - acc: 0.9570 - val_loss: 0.1149 - val_acc: 0.9681\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 14s 229us/step - loss: 0.1420 - acc: 0.9623 - val_loss: 0.1048 - val_acc: 0.9704\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 14s 228us/step - loss: 0.1278 - acc: 0.9657 - val_loss: 0.0941 - val_acc: 0.9731\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 14s 229us/step - loss: 0.1179 - acc: 0.9686 - val_loss: 0.1018 - val_acc: 0.9723\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 14s 227us/step - loss: 0.1063 - acc: 0.9721 - val_loss: 0.0969 - val_acc: 0.9729\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 14s 229us/step - loss: 0.0947 - acc: 0.9750 - val_loss: 0.0864 - val_acc: 0.9766\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 14s 229us/step - loss: 0.0915 - acc: 0.9753 - val_loss: 0.0882 - val_acc: 0.9769\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 14s 230us/step - loss: 0.0882 - acc: 0.9765 - val_loss: 0.0859 - val_acc: 0.9771\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 14s 229us/step - loss: 0.0802 - acc: 0.9783 - val_loss: 0.0807 - val_acc: 0.9793\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 13s 219us/step - loss: 0.0764 - acc: 0.9794 - val_loss: 0.0951 - val_acc: 0.9754\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 14s 231us/step - loss: 0.0732 - acc: 0.9800 - val_loss: 0.0790 - val_acc: 0.9803\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 14s 228us/step - loss: 0.0665 - acc: 0.9815 - val_loss: 0.0899 - val_acc: 0.9775\n",
-      "10000/10000 [==============================] - 1s 131us/step\n",
-      "0.983\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 24s 401us/step - loss: 0.4731 - acc: 0.8541 - val_loss: 0.2080 - val_acc: 0.9360\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.2845 - acc: 0.9167 - val_loss: 0.1682 - val_acc: 0.9500\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.2332 - acc: 0.9316 - val_loss: 0.1310 - val_acc: 0.9591\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 14s 239us/step - loss: 0.2043 - acc: 0.9395 - val_loss: 0.1326 - val_acc: 0.9569\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 14s 235us/step - loss: 0.1837 - acc: 0.9446 - val_loss: 0.1127 - val_acc: 0.9656\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 14s 237us/step - loss: 0.1655 - acc: 0.9504 - val_loss: 0.1108 - val_acc: 0.9668\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 14s 239us/step - loss: 0.1562 - acc: 0.9541 - val_loss: 0.1038 - val_acc: 0.9671\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 14s 236us/step - loss: 0.1456 - acc: 0.9570 - val_loss: 0.0917 - val_acc: 0.9710\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 17s 282us/step - loss: 0.1362 - acc: 0.9604 - val_loss: 0.0945 - val_acc: 0.9718\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 14s 238us/step - loss: 0.1282 - acc: 0.9622 - val_loss: 0.0955 - val_acc: 0.9707\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 14s 239us/step - loss: 0.1204 - acc: 0.9635 - val_loss: 0.0914 - val_acc: 0.9714\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 15s 252us/step - loss: 0.1162 - acc: 0.9655 - val_loss: 0.0856 - val_acc: 0.9756\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 14s 239us/step - loss: 0.1139 - acc: 0.9660 - val_loss: 0.0802 - val_acc: 0.9760\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 14s 239us/step - loss: 0.1064 - acc: 0.9673 - val_loss: 0.0814 - val_acc: 0.9745\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 14s 238us/step - loss: 0.1040 - acc: 0.9689 - val_loss: 0.0790 - val_acc: 0.9775\n",
-      "10000/10000 [==============================] - 1s 139us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 25s 422us/step - loss: 0.4756 - acc: 0.8550 - val_loss: 0.2068 - val_acc: 0.9349\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.2828 - acc: 0.9156 - val_loss: 0.1661 - val_acc: 0.9472\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 14s 237us/step - loss: 0.2314 - acc: 0.9306 - val_loss: 0.1484 - val_acc: 0.9543\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 14s 239us/step - loss: 0.2014 - acc: 0.9399 - val_loss: 0.1256 - val_acc: 0.9620\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 14s 232us/step - loss: 0.1816 - acc: 0.9452 - val_loss: 0.1135 - val_acc: 0.9647\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 14s 226us/step - loss: 0.1652 - acc: 0.9506 - val_loss: 0.1080 - val_acc: 0.9667\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 14s 236us/step - loss: 0.1546 - acc: 0.9541 - val_loss: 0.1053 - val_acc: 0.9673\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 14s 230us/step - loss: 0.1452 - acc: 0.9570 - val_loss: 0.0936 - val_acc: 0.9712\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 14s 233us/step - loss: 0.1364 - acc: 0.9594 - val_loss: 0.0898 - val_acc: 0.9715\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 14s 235us/step - loss: 0.1286 - acc: 0.9611 - val_loss: 0.0933 - val_acc: 0.9698\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 14s 238us/step - loss: 0.1243 - acc: 0.9625 - val_loss: 0.0863 - val_acc: 0.9723\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 14s 234us/step - loss: 0.1168 - acc: 0.9654 - val_loss: 0.0850 - val_acc: 0.9765\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 14s 238us/step - loss: 0.1117 - acc: 0.9673 - val_loss: 0.0878 - val_acc: 0.9745\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 14s 235us/step - loss: 0.1096 - acc: 0.9669 - val_loss: 0.0867 - val_acc: 0.9734\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 14s 236us/step - loss: 0.1023 - acc: 0.9693 - val_loss: 0.0833 - val_acc: 0.9756\n",
-      "10000/10000 [==============================] - 1s 141us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 26s 435us/step - loss: 0.4791 - acc: 0.8541 - val_loss: 0.2237 - val_acc: 0.9306\n",
-      "Epoch 2/15\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "60000/60000 [==============================] - 13s 220us/step - loss: 0.2818 - acc: 0.9158 - val_loss: 0.1658 - val_acc: 0.9475\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 14s 239us/step - loss: 0.2298 - acc: 0.9305 - val_loss: 0.1345 - val_acc: 0.9565\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 14s 237us/step - loss: 0.2023 - acc: 0.9396 - val_loss: 0.1266 - val_acc: 0.9613\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 21s 355us/step - loss: 0.1815 - acc: 0.9460 - val_loss: 0.1106 - val_acc: 0.9632\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 29s 475us/step - loss: 0.1654 - acc: 0.9507 - val_loss: 0.1158 - val_acc: 0.9621\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 20s 330us/step - loss: 0.1546 - acc: 0.9538 - val_loss: 0.1014 - val_acc: 0.9664\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 21s 354us/step - loss: 0.1402 - acc: 0.9581 - val_loss: 0.0904 - val_acc: 0.9723\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 24s 407us/step - loss: 0.1361 - acc: 0.9594 - val_loss: 0.0896 - val_acc: 0.9730\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 21s 344us/step - loss: 0.1280 - acc: 0.9614 - val_loss: 0.0781 - val_acc: 0.9743\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 19s 320us/step - loss: 0.1225 - acc: 0.9639 - val_loss: 0.0901 - val_acc: 0.9727\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 21s 348us/step - loss: 0.1193 - acc: 0.9649 - val_loss: 0.0769 - val_acc: 0.9763\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 18s 304us/step - loss: 0.1103 - acc: 0.9672 - val_loss: 0.0809 - val_acc: 0.9758\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 19s 311us/step - loss: 0.1073 - acc: 0.9678 - val_loss: 0.0710 - val_acc: 0.9782loss:  - ETA: 1s \n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 21s 354us/step - loss: 0.1044 - acc: 0.9689 - val_loss: 0.0730 - val_acc: 0.9779\n",
-      "10000/10000 [==============================] - 2s 224us/step\n",
-      "0.9792\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 39s 649us/step - loss: 0.5385 - acc: 0.8340 - val_loss: 0.2084 - val_acc: 0.9356\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 19s 311us/step - loss: 0.2773 - acc: 0.9193 - val_loss: 0.1733 - val_acc: 0.9455\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 15s 251us/step - loss: 0.2254 - acc: 0.9344 - val_loss: 0.1445 - val_acc: 0.9535\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 14s 240us/step - loss: 0.2006 - acc: 0.9425 - val_loss: 0.1272 - val_acc: 0.9602\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 18s 298us/step - loss: 0.1839 - acc: 0.9464 - val_loss: 0.1170 - val_acc: 0.9639\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 20s 340us/step - loss: 0.1693 - acc: 0.9506 - val_loss: 0.1090 - val_acc: 0.9658\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 18s 308us/step - loss: 0.1553 - acc: 0.9549 - val_loss: 0.1064 - val_acc: 0.9677\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 15s 254us/step - loss: 0.1481 - acc: 0.9560 - val_loss: 0.1170 - val_acc: 0.9635\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 15s 247us/step - loss: 0.1422 - acc: 0.9577 - val_loss: 0.0970 - val_acc: 0.9697\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 15s 244us/step - loss: 0.1350 - acc: 0.9604 - val_loss: 0.0940 - val_acc: 0.9714\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.1294 - acc: 0.9614 - val_loss: 0.0947 - val_acc: 0.9709\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.1245 - acc: 0.9625 - val_loss: 0.0817 - val_acc: 0.9748\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 15s 252us/step - loss: 0.1203 - acc: 0.9635 - val_loss: 0.0949 - val_acc: 0.9700\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 15s 246us/step - loss: 0.1143 - acc: 0.9652 - val_loss: 0.0847 - val_acc: 0.9753\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.1120 - acc: 0.9660 - val_loss: 0.0905 - val_acc: 0.9732\n",
-      "10000/10000 [==============================] - 1s 130us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 29s 481us/step - loss: 0.5316 - acc: 0.8369 - val_loss: 0.2095 - val_acc: 0.9357\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 15s 245us/step - loss: 0.2675 - acc: 0.9213 - val_loss: 0.1749 - val_acc: 0.9442\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 14s 236us/step - loss: 0.2221 - acc: 0.9359 - val_loss: 0.1428 - val_acc: 0.9547\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 15s 244us/step - loss: 0.1938 - acc: 0.9435 - val_loss: 0.1313 - val_acc: 0.9605\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 15s 258us/step - loss: 0.1755 - acc: 0.9495 - val_loss: 0.1090 - val_acc: 0.9668\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 15s 250us/step - loss: 0.1629 - acc: 0.9532 - val_loss: 0.1056 - val_acc: 0.9682\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 23s 377us/step - loss: 0.1547 - acc: 0.9544 - val_loss: 0.0985 - val_acc: 0.9683\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 17s 288us/step - loss: 0.1460 - acc: 0.9574 - val_loss: 0.1042 - val_acc: 0.9680\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 15s 249us/step - loss: 0.1379 - acc: 0.9590 - val_loss: 0.0963 - val_acc: 0.9708\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 15s 244us/step - loss: 0.1322 - acc: 0.9602 - val_loss: 0.0907 - val_acc: 0.9724\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 15s 258us/step - loss: 0.1278 - acc: 0.9629 - val_loss: 0.1382 - val_acc: 0.9552\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 15s 248us/step - loss: 0.1230 - acc: 0.9630 - val_loss: 0.0865 - val_acc: 0.9745\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 15s 246us/step - loss: 0.1171 - acc: 0.9648 - val_loss: 0.0887 - val_acc: 0.9721\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 15s 242us/step - loss: 0.1182 - acc: 0.9642 - val_loss: 0.1078 - val_acc: 0.9661\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 15s 242us/step - loss: 0.1124 - acc: 0.9661 - val_loss: 0.0842 - val_acc: 0.9746\n",
-      "10000/10000 [==============================] - 1s 146us/step\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/15\n",
-      "60000/60000 [==============================] - 30s 493us/step - loss: 0.5177 - acc: 0.8435 - val_loss: 0.2368 - val_acc: 0.9276\n",
-      "Epoch 2/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.2766 - acc: 0.9191 - val_loss: 0.1591 - val_acc: 0.9528\n",
-      "Epoch 3/15\n",
-      "60000/60000 [==============================] - 15s 244us/step - loss: 0.2234 - acc: 0.9357 - val_loss: 0.1310 - val_acc: 0.9595\n",
-      "Epoch 4/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.1992 - acc: 0.9419 - val_loss: 0.1275 - val_acc: 0.9607\n",
-      "Epoch 5/15\n",
-      "60000/60000 [==============================] - 15s 246us/step - loss: 0.1804 - acc: 0.9467 - val_loss: 0.1255 - val_acc: 0.9598\n",
-      "Epoch 6/15\n",
-      "60000/60000 [==============================] - 15s 242us/step - loss: 0.1672 - acc: 0.9510 - val_loss: 0.1181 - val_acc: 0.9622\n",
-      "Epoch 7/15\n",
-      "60000/60000 [==============================] - 15s 245us/step - loss: 0.1559 - acc: 0.9541 - val_loss: 0.0980 - val_acc: 0.9680\n",
-      "Epoch 8/15\n",
-      "60000/60000 [==============================] - 15s 244us/step - loss: 0.1448 - acc: 0.9564 - val_loss: 0.1009 - val_acc: 0.9703\n",
-      "Epoch 9/15\n",
-      "60000/60000 [==============================] - 15s 242us/step - loss: 0.1386 - acc: 0.9591 - val_loss: 0.0958 - val_acc: 0.9706\n",
-      "Epoch 10/15\n",
-      "60000/60000 [==============================] - 14s 229us/step - loss: 0.1346 - acc: 0.9590 - val_loss: 0.0882 - val_acc: 0.9714\n",
-      "Epoch 11/15\n",
-      "60000/60000 [==============================] - 15s 246us/step - loss: 0.1270 - acc: 0.9624 - val_loss: 0.0876 - val_acc: 0.9733\n",
-      "Epoch 12/15\n",
-      "60000/60000 [==============================] - 15s 245us/step - loss: 0.1229 - acc: 0.9639 - val_loss: 0.1158 - val_acc: 0.9638\n",
-      "Epoch 13/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.1175 - acc: 0.9650 - val_loss: 0.0850 - val_acc: 0.9740\n",
-      "Epoch 14/15\n",
-      "60000/60000 [==============================] - 15s 246us/step - loss: 0.1176 - acc: 0.9640 - val_loss: 0.0825 - val_acc: 0.9754\n",
-      "Epoch 15/15\n",
-      "60000/60000 [==============================] - 15s 243us/step - loss: 0.1132 - acc: 0.9662 - val_loss: 0.0825 - val_acc: 0.9750\n",
-      "10000/10000 [==============================] - 1s 143us/step\n",
-      "0.9771\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gc\n",
-    "for i,act in enumerate(activations):\n",
-    "    logs.info(\"\\n \\n Starting traiing for \"+names[i])\n",
-    "    ensembler = 0\n",
-    "    logger = [names[i]]\n",
-    "    for i in range(3):\n",
-    "        gc.collect()\n",
-    "        model = create(act)\n",
-    "        his = model.fit(X_train, Y_train, epochs=15, batch_size=64,  verbose=1, validation_data=(X_test, Y_test))\n",
-    "        # Record accuracy of each model and save it\n",
-    "        logger.append(model.evaluate(X_test, Y_test)[1])\n",
-    "        # Calculate probabilities of test data and sum them toghether\n",
-    "        ensembler += model.predict_proba(X_test)\n",
-    "        # Clear session (GPU MEMORY)\n",
-    "        K.get_session().close()\n",
-    "        K.set_session(K.tf.Session())\n",
-    "        del model, his\n",
-    "        \n",
-    "    # Calculate the median accuracy\n",
-    "    ensembled = accuracy(ensembler, Y_test)\n",
-    "    print(ensembled)\n",
-    "    logs.info(str(logger))\n",
-    "    logs.info(str(ensembled))\n",
-    "    del ensembler, ensembled"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/mnist/mnist_median_3_runs_log.txt b/mnist/mnist_median_3_runs_log.txt
deleted file mode 100644
index 53f3e26..0000000
--- a/mnist/mnist_median_3_runs_log.txt
+++ /dev/null
@@ -1,23 +0,0 @@
- Starting training for e_swish_2
-INFO 2017-12-16 18:01:19,468 - ['e_swish_2', 0.98089999999999999, 0.98109999999999997, 0.98229999999999995]
-INFO 2017-12-16 18:01:19,468 - 0.9846
-INFO 2017-12-16 18:01:19,469 - 
- 
- Starting training for swish
-INFO 2017-12-16 18:14:18,138 - ['swish', 0.97950000000000004, 0.97950000000000004, 0.98080000000000001]
-INFO 2017-12-16 18:14:18,139 - 0.9831
-INFO 2017-12-16 18:14:18,140 - 
- 
- Starting training for relu
-INFO 2017-12-16 18:26:25,904 - ['relu', 0.9819, 0.98060000000000003, 0.97750000000000004]
-INFO 2017-12-16 18:26:25,905 - 0.983
-INFO 2017-12-16 18:26:25,906 - 
- 
- Starting training for elu
-INFO 2017-12-16 18:39:15,376 - ['elu', 0.97750000000000004, 0.97560000000000002, 0.97789999999999999]
-INFO 2017-12-16 18:39:15,377 - 0.9792
-INFO 2017-12-16 18:39:15,377 - 
- 
- Starting training for leaky
-INFO 2017-12-16 18:51:58,089 - ['leaky', 0.97319999999999995, 0.97460000000000002, 0.97499999999999998]
-INFO 2017-12-16 18:51:58,089 - 0.9771
diff --git a/mnist/mnist_single_model.ipynb b/mnist/mnist_single_model.ipynb
deleted file mode 100644
index 8fa5645..0000000
--- a/mnist/mnist_single_model.ipynb
+++ /dev/null
@@ -1,1751 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Using TensorFlow backend.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Using Keras to create a 10 neurons and single layer NN\n",
-    "# Against the MNIST databesa\n",
-    "\n",
-    "import tensorflow as tf\n",
-    "from keras.datasets import mnist\n",
-    "from keras.models import Sequential\n",
-    "from keras.optimizers import SGD\n",
-    "from keras.layers.core import Dense, Dropout, Activation\n",
-    "from keras.utils import np_utils\n",
-    "import keras.backend as K\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.rcParams['figure.figsize'] = (7,7) # Make the figures a bit bigger\n",
-    "nb_classes = 10\n",
-    "\n",
-    "np.random.seed(42)\n",
-    "%matplotlib notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X_train original shape (60000, 28, 28)\n",
-      "y_train original shape (60000,)\n",
-      "Training matrix shape (60000, 784)\n",
-      "Testing matrix shape (10000, 784)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# the data, shuffled and split between train and test sets\n",
-    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
-    "print(\"X_train original shape\", X_train.shape)\n",
-    "print(\"y_train original shape\", y_train.shape)\n",
-    "\n",
-    "# Show some images\n",
-    "def show(X_train, y_train):\n",
-    "\tfor i in range(9):\n",
-    "\t    plt.subplot(3,3,i+1)\n",
-    "\t    plt.imshow(X_train[i], cmap='gray', interpolation='none')\n",
-    "\t    plt.title(\"Class {}\".format(y_train[i]))\n",
-    "\tplt.show()\n",
-    "\n",
-    "# Swish activation function\n",
-    "# x*sigmoid(x)\n",
-    "def swish(x):\n",
-    "    return x*K.sigmoid(x)\n",
-    "\n",
-    "# Custom activation function 1\n",
-    "# mix between relu and positive part of swish mirrored across x=1\n",
-    "def e_swish_1(x):\n",
-    "    return K.maximum(0.0, x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Custom activation function 2\n",
-    "# positive part of swish mirrored across x=1\n",
-    "def e_swish(x):\n",
-    "    return K.maximum(x*K.sigmoid(x), x*(2-K.sigmoid(x)))\n",
-    "\n",
-    "# Leaky ReLU\n",
-    "def leaky(x, alpha=0.3):\n",
-    "    return K.maximum(x*alpha, x)\n",
-    "\n",
-    "show(X_train, y_train)\n",
-    "\n",
-    "# Reshape arrays\n",
-    "X_train = X_train.reshape(60000, 784)\n",
-    "X_test = X_test.reshape(10000, 784)\n",
-    "X_train = X_train.astype('float32')\n",
-    "X_test = X_test.astype('float32')\n",
-    "X_train /= 255\n",
-    "X_test /= 255\n",
-    "print(\"Training matrix shape\", X_train.shape)\n",
-    "print(\"Testing matrix shape\", X_test.shape)\n",
-    "\n",
-    "Y_train = np_utils.to_categorical(y_train, nb_classes)\n",
-    "Y_test = np_utils.to_categorical(y_test, nb_classes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['e_swish', 'swish', 'relu', 'elu', 'leaky']\n"
-     ]
-    }
-   ],
-   "source": [
-    "activations = [e_swish, swish, \"relu\", \"elu\", leaky] # e_swish_1, \n",
-    "names = activations[:]\n",
-    "for i,a in enumerate(names):\n",
-    "    if not isinstance(a, str):\n",
-    "        names[i] = a.__name__\n",
-    "print(names)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def create_models():\n",
-    "    models = []\n",
-    "\n",
-    "    sgd = SGD(lr=0.1)\n",
-    "    # create models - ALCAIDES, SWISH, RELU, DIGMOID, TANH\n",
-    "    for act in activations:\n",
-    "        model1 = Sequential()\n",
-    "        model1.add(Dense(200, input_dim=784, activation=act))\n",
-    "        model1.add(Dropout(0.2))\n",
-    "        model1.add(Dense(100, activation=act))\n",
-    "        model1.add(Dropout(0.2))\n",
-    "        model1.add(Dense(60, activation=act))\n",
-    "        model1.add(Dropout(0.2))\n",
-    "        model1.add(Dense(30, activation=act))\n",
-    "        model1.add(Dropout(0.2))\n",
-    "        model1.add(Dense(10, activation='softmax'))\n",
-    "        models.append(model1)\n",
-    "\n",
-    "    # Compile models\n",
-    "    for model in models:\n",
-    "        with tf.device('/cpu:0'):\n",
-    "            model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
-    "    \n",
-    "    # Create Accuracy history\n",
-    "    acc = []\n",
-    "    for model in models: acc.append([0])\n",
-    "        \n",
-    "    return models, acc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def test_against(models, epochs, acc):\n",
-    "   \n",
-    "        \n",
-    "    for j in range(epochs):\n",
-    "        for i,model in enumerate(models):\n",
-    "            print(\"Training model {0}\".format(i))\n",
-    "            with tf.device('/cpu:0'):\n",
-    "                model.fit(X_train, Y_train, epochs=1, batch_size=64,  verbose=1, validation_data=(X_test, Y_test))\n",
-    "                scores = model.evaluate(X_test, Y_test)\n",
-    "            acc[i].append(scores[1]*100)\n",
-    "        print(\"///////////////////////////////////////////////\")\n",
-    "        print(\"/////////////////////{0}//////////////////////////\".format(j))\n",
-    "        print(\"///////////////////////////////////////////////\")\n",
-    "\n",
-    "    return models, acc\n",
-    "\n",
-    "# Plot the evolution of the accuracies\n",
-    "def plot_results(accs):\n",
-    "    plt.figure()\n",
-    "    plt.title(\"Test error for different activation functions on MNIST\")\n",
-    "    plt.xlabel(\"Number of epochs\")\n",
-    "    plt.ylabel(\"Test error (%)\")\n",
-    "    plt.xticks(range(21))\n",
-    "    for i,acc in enumerate(accs):\n",
-    "        if i>0:plt.plot(100-np.array(acc))\n",
-    "    plt.grid()\n",
-    "    plt.legend(names[0:], loc='upper right')\n",
-    "    plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "kernels, acc = create_models()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 7s 120us/step - loss: 0.5475 - acc: 0.8338 - val_loss: 0.1935 - val_acc: 0.9434\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 104us/step - loss: 0.4499 - acc: 0.8665 - val_loss: 0.1629 - val_acc: 0.9498\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 100us/step - loss: 0.6578 - acc: 0.7983 - val_loss: 0.2293 - val_acc: 0.9338\n",
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.6364 - acc: 0.7972 - val_loss: 0.1862 - val_acc: 0.9465\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 97us/step - loss: 0.4814 - acc: 0.8525 - val_loss: 0.2242 - val_acc: 0.9274\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 100us/step - loss: 0.5133 - acc: 0.8412 - val_loss: 0.2269 - val_acc: 0.9267\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////0//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.2563 - acc: 0.9315 - val_loss: 0.1510 - val_acc: 0.9563\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 99us/step - loss: 0.2151 - acc: 0.9405 - val_loss: 0.1205 - val_acc: 0.9639\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.2740 - acc: 0.9210 - val_loss: 0.1586 - val_acc: 0.9517\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 92us/step - loss: 0.2644 - acc: 0.9278 - val_loss: 0.2038 - val_acc: 0.9403\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.2879 - acc: 0.9139 - val_loss: 0.1932 - val_acc: 0.9367\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.2730 - acc: 0.9218 - val_loss: 0.1541 - val_acc: 0.9516\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////1//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 98us/step - loss: 0.2033 - acc: 0.9470 - val_loss: 0.1228 - val_acc: 0.9657\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 99us/step - loss: 0.1634 - acc: 0.9552 - val_loss: 0.1023 - val_acc: 0.9680\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.2043 - acc: 0.9412 - val_loss: 0.1287 - val_acc: 0.9596\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.2041 - acc: 0.9459 - val_loss: 0.1129 - val_acc: 0.9680\n",
-      "10000/10000 [==============================] - 0s 35us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.2331 - acc: 0.9298 - val_loss: 0.1443 - val_acc: 0.9567\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.2243 - acc: 0.9351 - val_loss: 0.1375 - val_acc: 0.9573\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////2//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 97us/step - loss: 0.1695 - acc: 0.9557 - val_loss: 0.1096 - val_acc: 0.9712\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 97us/step - loss: 0.1406 - acc: 0.9603 - val_loss: 0.0989 - val_acc: 0.9692\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.1718 - acc: 0.9504 - val_loss: 0.1216 - val_acc: 0.9629\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 92us/step - loss: 0.1659 - acc: 0.9558 - val_loss: 0.1039 - val_acc: 0.9705\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 89us/step - loss: 0.2050 - acc: 0.9390 - val_loss: 0.1492 - val_acc: 0.9507\n",
-      "10000/10000 [==============================] - 0s 34us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.1976 - acc: 0.9424 - val_loss: 0.1263 - val_acc: 0.9588\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////3//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.1532 - acc: 0.9607 - val_loss: 0.1033 - val_acc: 0.9711\n",
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 97us/step - loss: 0.1238 - acc: 0.9653 - val_loss: 0.0923 - val_acc: 0.9734\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.1470 - acc: 0.9582 - val_loss: 0.1023 - val_acc: 0.9695\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.1451 - acc: 0.9622 - val_loss: 0.0950 - val_acc: 0.9726\n",
-      "10000/10000 [==============================] - 0s 35us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.1832 - acc: 0.9449 - val_loss: 0.1185 - val_acc: 0.9628\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.1804 - acc: 0.9477 - val_loss: 0.1490 - val_acc: 0.9523\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////4//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.1392 - acc: 0.9638 - val_loss: 0.1065 - val_acc: 0.9704\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 100us/step - loss: 0.1071 - acc: 0.9696 - val_loss: 0.0836 - val_acc: 0.9749\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.1319 - acc: 0.9621 - val_loss: 0.1713 - val_acc: 0.9455\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.1316 - acc: 0.9654 - val_loss: 0.0900 - val_acc: 0.9753\n",
-      "10000/10000 [==============================] - 0s 35us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.1685 - acc: 0.9491 - val_loss: 0.1141 - val_acc: 0.9656\n",
-      "10000/10000 [==============================] - 0s 35us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.1696 - acc: 0.9500 - val_loss: 0.1322 - val_acc: 0.9583\n",
-      "10000/10000 [==============================] - 0s 41us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////5//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 98us/step - loss: 0.1236 - acc: 0.9680 - val_loss: 0.0936 - val_acc: 0.9747\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 100us/step - loss: 0.0998 - acc: 0.9722 - val_loss: 0.0818 - val_acc: 0.9755\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.1206 - acc: 0.9654 - val_loss: 0.0871 - val_acc: 0.9726\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.1155 - acc: 0.9692 - val_loss: 0.0862 - val_acc: 0.9745\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.1567 - acc: 0.9527 - val_loss: 0.1017 - val_acc: 0.9675\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.1555 - acc: 0.9536 - val_loss: 0.1003 - val_acc: 0.9670\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////6//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.1146 - acc: 0.9701 - val_loss: 0.1045 - val_acc: 0.9720\n",
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 99us/step - loss: 0.0900 - acc: 0.9750 - val_loss: 0.0826 - val_acc: 0.9771\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.1087 - acc: 0.9687 - val_loss: 0.0777 - val_acc: 0.9757\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.1103 - acc: 0.9708 - val_loss: 0.0842 - val_acc: 0.9760\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 92us/step - loss: 0.1434 - acc: 0.9575 - val_loss: 0.0958 - val_acc: 0.9716\n",
-      "10000/10000 [==============================] - 0s 35us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.1485 - acc: 0.9556 - val_loss: 0.1001 - val_acc: 0.9698\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////7//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.1073 - acc: 0.9723 - val_loss: 0.0945 - val_acc: 0.9750\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 99us/step - loss: 0.0841 - acc: 0.9760 - val_loss: 0.0784 - val_acc: 0.9792\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.1006 - acc: 0.9708 - val_loss: 0.0787 - val_acc: 0.9757\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 92us/step - loss: 0.1008 - acc: 0.9735 - val_loss: 0.0782 - val_acc: 0.9776\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.1335 - acc: 0.9600 - val_loss: 0.0950 - val_acc: 0.9719\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.1433 - acc: 0.9580 - val_loss: 0.1030 - val_acc: 0.9680\n",
-      "10000/10000 [==============================] - 0s 35us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////8//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.0996 - acc: 0.9737 - val_loss: 0.0908 - val_acc: 0.9769\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 97us/step - loss: 0.0773 - acc: 0.9774 - val_loss: 0.0759 - val_acc: 0.9794\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.0938 - acc: 0.9725 - val_loss: 0.0796 - val_acc: 0.9753\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "60000/60000 [==============================] - 5s 89us/step - loss: 0.0929 - acc: 0.9752 - val_loss: 0.0943 - val_acc: 0.9759\n",
-      "10000/10000 [==============================] - 0s 35us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 92us/step - loss: 0.1283 - acc: 0.9613 - val_loss: 0.0899 - val_acc: 0.9730\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.1372 - acc: 0.9598 - val_loss: 0.0932 - val_acc: 0.9695\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////9//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 98us/step - loss: 0.0962 - acc: 0.9747 - val_loss: 0.0904 - val_acc: 0.9759\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 99us/step - loss: 0.0736 - acc: 0.9798 - val_loss: 0.0797 - val_acc: 0.9797\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.0862 - acc: 0.9746 - val_loss: 0.0720 - val_acc: 0.9773\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.0888 - acc: 0.9766 - val_loss: 0.0784 - val_acc: 0.9780\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.1217 - acc: 0.9636 - val_loss: 0.0865 - val_acc: 0.9733\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.1299 - acc: 0.9610 - val_loss: 0.0918 - val_acc: 0.9710\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////10//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.0896 - acc: 0.9767 - val_loss: 0.0979 - val_acc: 0.9741\n",
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 99us/step - loss: 0.0667 - acc: 0.9807 - val_loss: 0.0850 - val_acc: 0.9753\n",
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.0821 - acc: 0.9762 - val_loss: 0.0673 - val_acc: 0.9789\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.0821 - acc: 0.9781 - val_loss: 0.0974 - val_acc: 0.9751\n",
-      "10000/10000 [==============================] - 0s 35us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 92us/step - loss: 0.1137 - acc: 0.9658 - val_loss: 0.0846 - val_acc: 0.9742\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 98us/step - loss: 0.1260 - acc: 0.9623 - val_loss: 0.0981 - val_acc: 0.9706\n",
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////11//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 97us/step - loss: 0.0882 - acc: 0.9765 - val_loss: 0.0914 - val_acc: 0.9771\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 100us/step - loss: 0.0616 - acc: 0.9825 - val_loss: 0.0811 - val_acc: 0.9797\n",
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.0769 - acc: 0.9771 - val_loss: 0.0722 - val_acc: 0.9768\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.0784 - acc: 0.9787 - val_loss: 0.0739 - val_acc: 0.9793\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.1112 - acc: 0.9665 - val_loss: 0.0815 - val_acc: 0.9741\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.1221 - acc: 0.9636 - val_loss: 0.0938 - val_acc: 0.9707\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////12//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.0781 - acc: 0.9789 - val_loss: 0.0976 - val_acc: 0.9773\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 99us/step - loss: 0.0609 - acc: 0.9824 - val_loss: 0.0706 - val_acc: 0.9819\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.0724 - acc: 0.9784 - val_loss: 0.0664 - val_acc: 0.9804\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 92us/step - loss: 0.0715 - acc: 0.9808 - val_loss: 0.0740 - val_acc: 0.9805\n",
-      "10000/10000 [==============================] - 0s 34us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 88us/step - loss: 0.1072 - acc: 0.9679 - val_loss: 0.0802 - val_acc: 0.9771\n",
-      "10000/10000 [==============================] - 0s 34us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.1190 - acc: 0.9642 - val_loss: 0.0930 - val_acc: 0.9724\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////13//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.0775 - acc: 0.9793 - val_loss: 0.0776 - val_acc: 0.9810\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 100us/step - loss: 0.0577 - acc: 0.9836 - val_loss: 0.0701 - val_acc: 0.9819\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.0686 - acc: 0.9794 - val_loss: 0.0676 - val_acc: 0.9795\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 92us/step - loss: 0.0716 - acc: 0.9808 - val_loss: 0.0778 - val_acc: 0.9800\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.1019 - acc: 0.9686 - val_loss: 0.0777 - val_acc: 0.9760\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.1104 - acc: 0.9664 - val_loss: 0.0951 - val_acc: 0.9713\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////14//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.0745 - acc: 0.9799 - val_loss: 0.0880 - val_acc: 0.9789\n",
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 98us/step - loss: 0.0544 - acc: 0.9843 - val_loss: 0.0713 - val_acc: 0.9812\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.0622 - acc: 0.9812 - val_loss: 0.0672 - val_acc: 0.9806\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 92us/step - loss: 0.0698 - acc: 0.9815 - val_loss: 0.0752 - val_acc: 0.9815\n",
-      "10000/10000 [==============================] - 0s 34us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.1028 - acc: 0.9691 - val_loss: 0.0804 - val_acc: 0.9747\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.1123 - acc: 0.9663 - val_loss: 0.0796 - val_acc: 0.9753\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////15//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.0710 - acc: 0.9816 - val_loss: 0.0798 - val_acc: 0.9797\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 99us/step - loss: 0.0515 - acc: 0.9851 - val_loss: 0.0705 - val_acc: 0.9814\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.0614 - acc: 0.9818 - val_loss: 0.0701 - val_acc: 0.9799\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 92us/step - loss: 0.0668 - acc: 0.9830 - val_loss: 0.0840 - val_acc: 0.9783\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.0971 - acc: 0.9708 - val_loss: 0.0738 - val_acc: 0.9783\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.1077 - acc: 0.9668 - val_loss: 0.0863 - val_acc: 0.9734\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////16//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 96us/step - loss: 0.0688 - acc: 0.9818 - val_loss: 0.0821 - val_acc: 0.9799\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 99us/step - loss: 0.0489 - acc: 0.9860 - val_loss: 0.0768 - val_acc: 0.9814\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 94us/step - loss: 0.0569 - acc: 0.9831 - val_loss: 0.0719 - val_acc: 0.9798\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 92us/step - loss: 0.0599 - acc: 0.9836 - val_loss: 0.0802 - val_acc: 0.9806\n",
-      "10000/10000 [==============================] - 0s 34us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 90us/step - loss: 0.0932 - acc: 0.9716 - val_loss: 0.0808 - val_acc: 0.9761\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.1036 - acc: 0.9688 - val_loss: 0.0824 - val_acc: 0.9740\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////17//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 97us/step - loss: 0.0675 - acc: 0.9824 - val_loss: 0.0721 - val_acc: 0.9822\n",
-      "10000/10000 [==============================] - 0s 41us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 100us/step - loss: 0.0451 - acc: 0.9867 - val_loss: 0.0728 - val_acc: 0.9826\n",
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.0547 - acc: 0.9833 - val_loss: 0.0687 - val_acc: 0.9812\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 92us/step - loss: 0.0613 - acc: 0.9832 - val_loss: 0.0779 - val_acc: 0.9801\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 93us/step - loss: 0.0931 - acc: 0.9726 - val_loss: 0.0726 - val_acc: 0.9780\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.1012 - acc: 0.9685 - val_loss: 0.0768 - val_acc: 0.9759\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////18//////////////////////////\n",
-      "///////////////////////////////////////////////\n",
-      "Training model 0\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "60000/60000 [==============================] - 6s 97us/step - loss: 0.0647 - acc: 0.9825 - val_loss: 0.0787 - val_acc: 0.9796\n",
-      "10000/10000 [==============================] - 0s 38us/step\n",
-      "Training model 1\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 98us/step - loss: 0.0461 - acc: 0.9869 - val_loss: 0.0705 - val_acc: 0.9823\n",
-      "10000/10000 [==============================] - 0s 40us/step\n",
-      "Training model 2\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.0514 - acc: 0.9838 - val_loss: 0.0706 - val_acc: 0.9805\n",
-      "10000/10000 [==============================] - 0s 37us/step\n",
-      "Training model 3\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.0573 - acc: 0.9848 - val_loss: 0.0766 - val_acc: 0.9806\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 4\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 5s 91us/step - loss: 0.0902 - acc: 0.9732 - val_loss: 0.0726 - val_acc: 0.9778\n",
-      "10000/10000 [==============================] - 0s 36us/step\n",
-      "Training model 5\n",
-      "Train on 60000 samples, validate on 10000 samples\n",
-      "Epoch 1/1\n",
-      "60000/60000 [==============================] - 6s 95us/step - loss: 0.0985 - acc: 0.9702 - val_loss: 0.0885 - val_acc: 0.9735\n",
-      "10000/10000 [==============================] - 0s 39us/step\n",
-      "///////////////////////////////////////////////\n",
-      "/////////////////////19//////////////////////////\n",
-      "///////////////////////////////////////////////\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "kernels, acc = test_against(kernels, 20, acc)\n",
-    "# kernels_, acc = test_against(kernels_, 20, acc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "/* Put everything inside the global mpl namespace */\n",
-       "window.mpl = {};\n",
-       "\n",
-       "\n",
-       "mpl.get_websocket_type = function() {\n",
-       "    if (typeof(WebSocket) !== 'undefined') {\n",
-       "        return WebSocket;\n",
-       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
-       "        return MozWebSocket;\n",
-       "    } else {\n",
-       "        alert('Your browser does not have WebSocket support.' +\n",
-       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
-       "              'Firefox 4 and 5 are also supported but you ' +\n",
-       "              'have to enable WebSockets in about:config.');\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
-       "    this.id = figure_id;\n",
-       "\n",
-       "    this.ws = websocket;\n",
-       "\n",
-       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
-       "\n",
-       "    if (!this.supports_binary) {\n",
-       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
-       "        if (warnings) {\n",
-       "            warnings.style.display = 'block';\n",
-       "            warnings.textContent = (\n",
-       "                \"This browser does not support binary websocket messages. \" +\n",
-       "                    \"Performance may be slow.\");\n",
-       "        }\n",
-       "    }\n",
-       "\n",
-       "    this.imageObj = new Image();\n",
-       "\n",
-       "    this.context = undefined;\n",
-       "    this.message = undefined;\n",
-       "    this.canvas = undefined;\n",
-       "    this.rubberband_canvas = undefined;\n",
-       "    this.rubberband_context = undefined;\n",
-       "    this.format_dropdown = undefined;\n",
-       "\n",
-       "    this.image_mode = 'full';\n",
-       "\n",
-       "    this.root = $('<div/>');\n",
-       "    this._root_extra_style(this.root)\n",
-       "    this.root.attr('style', 'display: inline-block');\n",
-       "\n",
-       "    $(parent_element).append(this.root);\n",
-       "\n",
-       "    this._init_header(this);\n",
-       "    this._init_canvas(this);\n",
-       "    this._init_toolbar(this);\n",
-       "\n",
-       "    var fig = this;\n",
-       "\n",
-       "    this.waiting = false;\n",
-       "\n",
-       "    this.ws.onopen =  function () {\n",
-       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
-       "            fig.send_message(\"send_image_mode\", {});\n",
-       "            if (mpl.ratio != 1) {\n",
-       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
-       "            }\n",
-       "            fig.send_message(\"refresh\", {});\n",
-       "        }\n",
-       "\n",
-       "    this.imageObj.onload = function() {\n",
-       "            if (fig.image_mode == 'full') {\n",
-       "                // Full images could contain transparency (where diff images\n",
-       "                // almost always do), so we need to clear the canvas so that\n",
-       "                // there is no ghosting.\n",
-       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "            }\n",
-       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
-       "        };\n",
-       "\n",
-       "    this.imageObj.onunload = function() {\n",
-       "        this.ws.close();\n",
-       "    }\n",
-       "\n",
-       "    this.ws.onmessage = this._make_on_message_function(this);\n",
-       "\n",
-       "    this.ondownload = ondownload;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_header = function() {\n",
-       "    var titlebar = $(\n",
-       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
-       "        'ui-helper-clearfix\"/>');\n",
-       "    var titletext = $(\n",
-       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
-       "        'text-align: center; padding: 3px;\"/>');\n",
-       "    titlebar.append(titletext)\n",
-       "    this.root.append(titlebar);\n",
-       "    this.header = titletext[0];\n",
-       "}\n",
-       "\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_canvas = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var canvas_div = $('<div/>');\n",
-       "\n",
-       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
-       "\n",
-       "    function canvas_keyboard_event(event) {\n",
-       "        return fig.key_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
-       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
-       "    this.canvas_div = canvas_div\n",
-       "    this._canvas_extra_style(canvas_div)\n",
-       "    this.root.append(canvas_div);\n",
-       "\n",
-       "    var canvas = $('<canvas/>');\n",
-       "    canvas.addClass('mpl-canvas');\n",
-       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
-       "\n",
-       "    this.canvas = canvas[0];\n",
-       "    this.context = canvas[0].getContext(\"2d\");\n",
-       "\n",
-       "    var backingStore = this.context.backingStorePixelRatio ||\n",
-       "\tthis.context.webkitBackingStorePixelRatio ||\n",
-       "\tthis.context.mozBackingStorePixelRatio ||\n",
-       "\tthis.context.msBackingStorePixelRatio ||\n",
-       "\tthis.context.oBackingStorePixelRatio ||\n",
-       "\tthis.context.backingStorePixelRatio || 1;\n",
-       "\n",
-       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
-       "\n",
-       "    var rubberband = $('<canvas/>');\n",
-       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
-       "\n",
-       "    var pass_mouse_events = true;\n",
-       "\n",
-       "    canvas_div.resizable({\n",
-       "        start: function(event, ui) {\n",
-       "            pass_mouse_events = false;\n",
-       "        },\n",
-       "        resize: function(event, ui) {\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "        stop: function(event, ui) {\n",
-       "            pass_mouse_events = true;\n",
-       "            fig.request_resize(ui.size.width, ui.size.height);\n",
-       "        },\n",
-       "    });\n",
-       "\n",
-       "    function mouse_event_fn(event) {\n",
-       "        if (pass_mouse_events)\n",
-       "            return fig.mouse_event(event, event['data']);\n",
-       "    }\n",
-       "\n",
-       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
-       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
-       "    // Throttle sequential mouse events to 1 every 20ms.\n",
-       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
-       "\n",
-       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
-       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
-       "\n",
-       "    canvas_div.on(\"wheel\", function (event) {\n",
-       "        event = event.originalEvent;\n",
-       "        event['data'] = 'scroll'\n",
-       "        if (event.deltaY < 0) {\n",
-       "            event.step = 1;\n",
-       "        } else {\n",
-       "            event.step = -1;\n",
-       "        }\n",
-       "        mouse_event_fn(event);\n",
-       "    });\n",
-       "\n",
-       "    canvas_div.append(canvas);\n",
-       "    canvas_div.append(rubberband);\n",
-       "\n",
-       "    this.rubberband = rubberband;\n",
-       "    this.rubberband_canvas = rubberband[0];\n",
-       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
-       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
-       "\n",
-       "    this._resize_canvas = function(width, height) {\n",
-       "        // Keep the size of the canvas, canvas container, and rubber band\n",
-       "        // canvas in synch.\n",
-       "        canvas_div.css('width', width)\n",
-       "        canvas_div.css('height', height)\n",
-       "\n",
-       "        canvas.attr('width', width * mpl.ratio);\n",
-       "        canvas.attr('height', height * mpl.ratio);\n",
-       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
-       "\n",
-       "        rubberband.attr('width', width);\n",
-       "        rubberband.attr('height', height);\n",
-       "    }\n",
-       "\n",
-       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
-       "    // upon first draw.\n",
-       "    this._resize_canvas(600, 600);\n",
-       "\n",
-       "    // Disable right mouse context menu.\n",
-       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
-       "        return false;\n",
-       "    });\n",
-       "\n",
-       "    function set_focus () {\n",
-       "        canvas.focus();\n",
-       "        canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    window.setTimeout(set_focus, 100);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) {\n",
-       "            // put a spacer in here.\n",
-       "            continue;\n",
-       "        }\n",
-       "        var button = $('<button/>');\n",
-       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
-       "                        'ui-button-icon-only');\n",
-       "        button.attr('role', 'button');\n",
-       "        button.attr('aria-disabled', 'false');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "\n",
-       "        var icon_img = $('<span/>');\n",
-       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
-       "        icon_img.addClass(image);\n",
-       "        icon_img.addClass('ui-corner-all');\n",
-       "\n",
-       "        var tooltip_span = $('<span/>');\n",
-       "        tooltip_span.addClass('ui-button-text');\n",
-       "        tooltip_span.html(tooltip);\n",
-       "\n",
-       "        button.append(icon_img);\n",
-       "        button.append(tooltip_span);\n",
-       "\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    var fmt_picker_span = $('<span/>');\n",
-       "\n",
-       "    var fmt_picker = $('<select/>');\n",
-       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
-       "    fmt_picker_span.append(fmt_picker);\n",
-       "    nav_element.append(fmt_picker_span);\n",
-       "    this.format_dropdown = fmt_picker[0];\n",
-       "\n",
-       "    for (var ind in mpl.extensions) {\n",
-       "        var fmt = mpl.extensions[ind];\n",
-       "        var option = $(\n",
-       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
-       "        fmt_picker.append(option)\n",
-       "    }\n",
-       "\n",
-       "    // Add hover states to the ui-buttons\n",
-       "    $( \".ui-button\" ).hover(\n",
-       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
-       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
-       "    );\n",
-       "\n",
-       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
-       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
-       "    // which will in turn request a refresh of the image.\n",
-       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_message = function(type, properties) {\n",
-       "    properties['type'] = type;\n",
-       "    properties['figure_id'] = this.id;\n",
-       "    this.ws.send(JSON.stringify(properties));\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.send_draw_message = function() {\n",
-       "    if (!this.waiting) {\n",
-       "        this.waiting = true;\n",
-       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    var format_dropdown = fig.format_dropdown;\n",
-       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
-       "    fig.ondownload(fig, format);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
-       "    var size = msg['size'];\n",
-       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
-       "        fig._resize_canvas(size[0], size[1]);\n",
-       "        fig.send_message(\"refresh\", {});\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
-       "    var x0 = msg['x0'] / mpl.ratio;\n",
-       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
-       "    var x1 = msg['x1'] / mpl.ratio;\n",
-       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
-       "    x0 = Math.floor(x0) + 0.5;\n",
-       "    y0 = Math.floor(y0) + 0.5;\n",
-       "    x1 = Math.floor(x1) + 0.5;\n",
-       "    y1 = Math.floor(y1) + 0.5;\n",
-       "    var min_x = Math.min(x0, x1);\n",
-       "    var min_y = Math.min(y0, y1);\n",
-       "    var width = Math.abs(x1 - x0);\n",
-       "    var height = Math.abs(y1 - y0);\n",
-       "\n",
-       "    fig.rubberband_context.clearRect(\n",
-       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
-       "\n",
-       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
-       "    // Updates the figure title.\n",
-       "    fig.header.textContent = msg['label'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
-       "    var cursor = msg['cursor'];\n",
-       "    switch(cursor)\n",
-       "    {\n",
-       "    case 0:\n",
-       "        cursor = 'pointer';\n",
-       "        break;\n",
-       "    case 1:\n",
-       "        cursor = 'default';\n",
-       "        break;\n",
-       "    case 2:\n",
-       "        cursor = 'crosshair';\n",
-       "        break;\n",
-       "    case 3:\n",
-       "        cursor = 'move';\n",
-       "        break;\n",
-       "    }\n",
-       "    fig.rubberband_canvas.style.cursor = cursor;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
-       "    fig.message.textContent = msg['message'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
-       "    // Request the server to send over a new figure.\n",
-       "    fig.send_draw_message();\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
-       "    fig.image_mode = msg['mode'];\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Called whenever the canvas gets updated.\n",
-       "    this.send_message(\"ack\", {});\n",
-       "}\n",
-       "\n",
-       "// A function to construct a web socket function for onmessage handling.\n",
-       "// Called in the figure constructor.\n",
-       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
-       "    return function socket_on_message(evt) {\n",
-       "        if (evt.data instanceof Blob) {\n",
-       "            /* FIXME: We get \"Resource interpreted as Image but\n",
-       "             * transferred with MIME type text/plain:\" errors on\n",
-       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
-       "             * to be part of the websocket stream */\n",
-       "            evt.data.type = \"image/png\";\n",
-       "\n",
-       "            /* Free the memory for the previous frames */\n",
-       "            if (fig.imageObj.src) {\n",
-       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
-       "                    fig.imageObj.src);\n",
-       "            }\n",
-       "\n",
-       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
-       "                evt.data);\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
-       "            fig.imageObj.src = evt.data;\n",
-       "            fig.updated_canvas_event();\n",
-       "            fig.waiting = false;\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        var msg = JSON.parse(evt.data);\n",
-       "        var msg_type = msg['type'];\n",
-       "\n",
-       "        // Call the  \"handle_{type}\" callback, which takes\n",
-       "        // the figure and JSON message as its only arguments.\n",
-       "        try {\n",
-       "            var callback = fig[\"handle_\" + msg_type];\n",
-       "        } catch (e) {\n",
-       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
-       "            return;\n",
-       "        }\n",
-       "\n",
-       "        if (callback) {\n",
-       "            try {\n",
-       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
-       "                callback(fig, msg);\n",
-       "            } catch (e) {\n",
-       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
-       "            }\n",
-       "        }\n",
-       "    };\n",
-       "}\n",
-       "\n",
-       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
-       "mpl.findpos = function(e) {\n",
-       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
-       "    var targ;\n",
-       "    if (!e)\n",
-       "        e = window.event;\n",
-       "    if (e.target)\n",
-       "        targ = e.target;\n",
-       "    else if (e.srcElement)\n",
-       "        targ = e.srcElement;\n",
-       "    if (targ.nodeType == 3) // defeat Safari bug\n",
-       "        targ = targ.parentNode;\n",
-       "\n",
-       "    // jQuery normalizes the pageX and pageY\n",
-       "    // pageX,Y are the mouse positions relative to the document\n",
-       "    // offset() returns the position of the element relative to the document\n",
-       "    var x = e.pageX - $(targ).offset().left;\n",
-       "    var y = e.pageY - $(targ).offset().top;\n",
-       "\n",
-       "    return {\"x\": x, \"y\": y};\n",
-       "};\n",
-       "\n",
-       "/*\n",
-       " * return a copy of an object with only non-object keys\n",
-       " * we need this to avoid circular references\n",
-       " * http://stackoverflow.com/a/24161582/3208463\n",
-       " */\n",
-       "function simpleKeys (original) {\n",
-       "  return Object.keys(original).reduce(function (obj, key) {\n",
-       "    if (typeof original[key] !== 'object')\n",
-       "        obj[key] = original[key]\n",
-       "    return obj;\n",
-       "  }, {});\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
-       "    var canvas_pos = mpl.findpos(event)\n",
-       "\n",
-       "    if (name === 'button_press')\n",
-       "    {\n",
-       "        this.canvas.focus();\n",
-       "        this.canvas_div.focus();\n",
-       "    }\n",
-       "\n",
-       "    var x = canvas_pos.x * mpl.ratio;\n",
-       "    var y = canvas_pos.y * mpl.ratio;\n",
-       "\n",
-       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
-       "                             step: event.step,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "\n",
-       "    /* This prevents the web browser from automatically changing to\n",
-       "     * the text insertion cursor when the button is pressed.  We want\n",
-       "     * to control all of the cursor setting manually through the\n",
-       "     * 'cursor' event from matplotlib */\n",
-       "    event.preventDefault();\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    // Handle any extra behaviour associated with a key event\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.key_event = function(event, name) {\n",
-       "\n",
-       "    // Prevent repeat events\n",
-       "    if (name == 'key_press')\n",
-       "    {\n",
-       "        if (event.which === this._key)\n",
-       "            return;\n",
-       "        else\n",
-       "            this._key = event.which;\n",
-       "    }\n",
-       "    if (name == 'key_release')\n",
-       "        this._key = null;\n",
-       "\n",
-       "    var value = '';\n",
-       "    if (event.ctrlKey && event.which != 17)\n",
-       "        value += \"ctrl+\";\n",
-       "    if (event.altKey && event.which != 18)\n",
-       "        value += \"alt+\";\n",
-       "    if (event.shiftKey && event.which != 16)\n",
-       "        value += \"shift+\";\n",
-       "\n",
-       "    value += 'k';\n",
-       "    value += event.which.toString();\n",
-       "\n",
-       "    this._key_event_extra(event, name);\n",
-       "\n",
-       "    this.send_message(name, {key: value,\n",
-       "                             guiEvent: simpleKeys(event)});\n",
-       "    return false;\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
-       "    if (name == 'download') {\n",
-       "        this.handle_save(this, null);\n",
-       "    } else {\n",
-       "        this.send_message(\"toolbar_button\", {name: name});\n",
-       "    }\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
-       "    this.message.textContent = tooltip;\n",
-       "};\n",
-       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
-       "\n",
-       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
-       "\n",
-       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
-       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
-       "    // object with the appropriate methods. Currently this is a non binary\n",
-       "    // socket, so there is still some room for performance tuning.\n",
-       "    var ws = {};\n",
-       "\n",
-       "    ws.close = function() {\n",
-       "        comm.close()\n",
-       "    };\n",
-       "    ws.send = function(m) {\n",
-       "        //console.log('sending', m);\n",
-       "        comm.send(m);\n",
-       "    };\n",
-       "    // Register the callback with on_msg.\n",
-       "    comm.on_msg(function(msg) {\n",
-       "        //console.log('receiving', msg['content']['data'], msg);\n",
-       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
-       "        ws.onmessage(msg['content']['data'])\n",
-       "    });\n",
-       "    return ws;\n",
-       "}\n",
-       "\n",
-       "mpl.mpl_figure_comm = function(comm, msg) {\n",
-       "    // This is the function which gets called when the mpl process\n",
-       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
-       "\n",
-       "    var id = msg.content.data.id;\n",
-       "    // Get hold of the div created by the display call when the Comm\n",
-       "    // socket was opened in Python.\n",
-       "    var element = $(\"#\" + id);\n",
-       "    var ws_proxy = comm_websocket_adapter(comm)\n",
-       "\n",
-       "    function ondownload(figure, format) {\n",
-       "        window.open(figure.imageObj.src);\n",
-       "    }\n",
-       "\n",
-       "    var fig = new mpl.figure(id, ws_proxy,\n",
-       "                           ondownload,\n",
-       "                           element.get(0));\n",
-       "\n",
-       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
-       "    // web socket which is closed, not our websocket->open comm proxy.\n",
-       "    ws_proxy.onopen();\n",
-       "\n",
-       "    fig.parent_element = element.get(0);\n",
-       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
-       "    if (!fig.cell_info) {\n",
-       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
-       "        return;\n",
-       "    }\n",
-       "\n",
-       "    var output_index = fig.cell_info[2]\n",
-       "    var cell = fig.cell_info[0];\n",
-       "\n",
-       "};\n",
-       "\n",
-       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
-       "    var width = fig.canvas.width/mpl.ratio\n",
-       "    fig.root.unbind('remove')\n",
-       "\n",
-       "    // Update the output cell to use the data from the current canvas.\n",
-       "    fig.push_to_output();\n",
-       "    var dataURL = fig.canvas.toDataURL();\n",
-       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
-       "    // the notebook keyboard shortcuts fail.\n",
-       "    IPython.keyboard_manager.enable()\n",
-       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
-       "    fig.close_ws(fig, msg);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
-       "    fig.send_message('closing', msg);\n",
-       "    // fig.ws.close()\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
-       "    // Turn the data on the canvas into data in the output cell.\n",
-       "    var width = this.canvas.width/mpl.ratio\n",
-       "    var dataURL = this.canvas.toDataURL();\n",
-       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.updated_canvas_event = function() {\n",
-       "    // Tell IPython that the notebook contents must change.\n",
-       "    IPython.notebook.set_dirty(true);\n",
-       "    this.send_message(\"ack\", {});\n",
-       "    var fig = this;\n",
-       "    // Wait a second, then push the new image to the DOM so\n",
-       "    // that it is saved nicely (might be nice to debounce this).\n",
-       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._init_toolbar = function() {\n",
-       "    var fig = this;\n",
-       "\n",
-       "    var nav_element = $('<div/>')\n",
-       "    nav_element.attr('style', 'width: 100%');\n",
-       "    this.root.append(nav_element);\n",
-       "\n",
-       "    // Define a callback function for later on.\n",
-       "    function toolbar_event(event) {\n",
-       "        return fig.toolbar_button_onclick(event['data']);\n",
-       "    }\n",
-       "    function toolbar_mouse_event(event) {\n",
-       "        return fig.toolbar_button_onmouseover(event['data']);\n",
-       "    }\n",
-       "\n",
-       "    for(var toolbar_ind in mpl.toolbar_items){\n",
-       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
-       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
-       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
-       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
-       "\n",
-       "        if (!name) { continue; };\n",
-       "\n",
-       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
-       "        button.click(method_name, toolbar_event);\n",
-       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
-       "        nav_element.append(button);\n",
-       "    }\n",
-       "\n",
-       "    // Add the status bar.\n",
-       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
-       "    nav_element.append(status_bar);\n",
-       "    this.message = status_bar[0];\n",
-       "\n",
-       "    // Add the close button to the window.\n",
-       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
-       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
-       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
-       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
-       "    buttongrp.append(button);\n",
-       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
-       "    titlebar.prepend(buttongrp);\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._root_extra_style = function(el){\n",
-       "    var fig = this\n",
-       "    el.on(\"remove\", function(){\n",
-       "\tfig.close_ws(fig, {});\n",
-       "    });\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
-       "    // this is important to make the div 'focusable\n",
-       "    el.attr('tabindex', 0)\n",
-       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
-       "    // off when our div gets focus\n",
-       "\n",
-       "    // location in version 3\n",
-       "    if (IPython.notebook.keyboard_manager) {\n",
-       "        IPython.notebook.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "    else {\n",
-       "        // location in version 2\n",
-       "        IPython.keyboard_manager.register_events(el);\n",
-       "    }\n",
-       "\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
-       "    var manager = IPython.notebook.keyboard_manager;\n",
-       "    if (!manager)\n",
-       "        manager = IPython.keyboard_manager;\n",
-       "\n",
-       "    // Check for shift+enter\n",
-       "    if (event.shiftKey && event.which == 13) {\n",
-       "        this.canvas_div.blur();\n",
-       "        // select the cell after this one\n",
-       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
-       "        IPython.notebook.select(index + 1);\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
-       "    fig.ondownload(fig, null);\n",
-       "}\n",
-       "\n",
-       "\n",
-       "mpl.find_output_cell = function(html_output) {\n",
-       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
-       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
-       "    // IPython event is triggered only after the cells have been serialised, which for\n",
-       "    // our purposes (turning an active figure into a static one), is too late.\n",
-       "    var cells = IPython.notebook.get_cells();\n",
-       "    var ncells = cells.length;\n",
-       "    for (var i=0; i<ncells; i++) {\n",
-       "        var cell = cells[i];\n",
-       "        if (cell.cell_type === 'code'){\n",
-       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
-       "                var data = cell.output_area.outputs[j];\n",
-       "                if (data.data) {\n",
-       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
-       "                    data = data.data;\n",
-       "                }\n",
-       "                if (data['text/html'] == html_output) {\n",
-       "                    return [cell, data, j];\n",
-       "                }\n",
-       "            }\n",
-       "        }\n",
-       "    }\n",
-       "}\n",
-       "\n",
-       "// Register the function which deals with the matplotlib target/channel.\n",
-       "// The kernel may be null if the page has been refreshed.\n",
-       "if (IPython.notebook.kernel != null) {\n",
-       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
-       "}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"975\">"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0, 94.340000000000003, 95.63000000000001, 96.569999999999993, 97.11999999999999, 97.109999999999999, 97.040000000000006, 97.469999999999999, 97.200000000000003, 97.5, 97.689999999999998, 97.590000000000003, 97.409999999999997, 97.709999999999994, 97.72999999999999, 98.099999999999994, 97.890000000000001, 97.969999999999999, 97.989999999999995, 98.219999999999999, 97.960000000000008], [0, 94.980000000000004, 96.390000000000001, 96.799999999999997, 96.920000000000002, 97.340000000000003, 97.489999999999995, 97.549999999999997, 97.709999999999994, 97.920000000000002, 97.939999999999998, 97.969999999999999, 97.530000000000001, 97.969999999999999, 98.189999999999998, 98.189999999999998, 98.11999999999999, 98.140000000000001, 98.140000000000001, 98.260000000000005, 98.22999999999999], [0, 93.379999999999995, 95.170000000000002, 95.960000000000008, 96.289999999999992, 96.950000000000003, 94.549999999999997, 97.260000000000005, 97.570000000000007, 97.570000000000007, 97.530000000000001, 97.72999999999999, 97.890000000000001, 97.680000000000007, 98.040000000000006, 97.950000000000003, 98.060000000000002, 97.989999999999995, 97.980000000000004, 98.11999999999999, 98.049999999999997], [0, 94.650000000000006, 94.030000000000001, 96.799999999999997, 97.049999999999997, 97.260000000000005, 97.530000000000001, 97.450000000000003, 97.599999999999994, 97.760000000000005, 97.590000000000003, 97.799999999999997, 97.509999999999991, 97.929999999999993, 98.049999999999997, 98.0, 98.150000000000006, 97.829999999999998, 98.060000000000002, 98.009999999999991, 98.060000000000002], [0, 92.739999999999995, 93.670000000000002, 95.670000000000002, 95.069999999999993, 96.280000000000001, 96.560000000000002, 96.75, 97.159999999999997, 97.189999999999998, 97.299999999999997, 97.330000000000013, 97.420000000000002, 97.409999999999997, 97.709999999999994, 97.599999999999994, 97.469999999999999, 97.829999999999998, 97.609999999999999, 97.799999999999997, 97.780000000000001], [0, 92.670000000000002, 95.159999999999997, 95.730000000000004, 95.879999999999995, 95.230000000000004, 95.829999999999998, 96.700000000000003, 96.980000000000004, 96.799999999999997, 96.950000000000003, 97.099999999999994, 97.060000000000002, 97.070000000000007, 97.240000000000009, 97.13000000000001, 97.530000000000001, 97.340000000000003, 97.399999999999991, 97.590000000000003, 97.350000000000009]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "plot_results(acc)\n",
-    "print(acc)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}