Traffic_Sign_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Step 0: Load The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(34799, 32, 32, 3)\n",
      "(34799,)\n"
     ]
    }
   ],
   "source": [
    "# Load pickled data\n",
    "import pickle\n",
    "\n",
    "# TODO: Fill this in based on where you saved the training and testing data\n",
    "\n",
    "training_file = \"dataset/train.p\"\n",
    "validation_file= \"dataset/valid.p\"\n",
    "testing_file = \"dataset/test.p\"\n",
    "\n",
    "with open(training_file, mode='rb') as f:\n",
    "    train = pickle.load(f)\n",
    "with open(validation_file, mode='rb') as f:\n",
    "    valid = pickle.load(f)\n",
    "with open(testing_file, mode='rb') as f:\n",
    "    test = pickle.load(f)\n",
    "    \n",
    "X_train, y_train = train['features'], train['labels']\n",
    "X_valid, y_valid = valid['features'], valid['labels']\n",
    "X_test, y_test = test['features'], test['labels']\n",
    "\n",
    "# Print the shape of variables\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 1: Dataset Summary & Exploration\n",
    "\n",
    "The pickled data is a dictionary with 4 key/value pairs:\n",
    "\n",
    "- `'features'` is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).\n",
    "- `'labels'` is a 1D array containing the label/class id of the traffic sign. The file `signnames.csv` contains id -> name mappings for each id.\n",
    "- `'sizes'` is a list containing tuples, (width, height) representing the original width and height the image.\n",
    "- `'coords'` is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. **THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES**\n",
    "\n",
    "Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the [pandas shape method](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html) might be useful for calculating some of the summary results. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples = 34799\n",
      "Number of testing examples = 12630\n",
      "Image data shape = (32, 32, 3)\n",
      "Number of classes = 43\n"
     ]
    }
   ],
   "source": [
    "### Replace each question mark with the appropriate value. \n",
    "### Use python, pandas or numpy methods rather than hard coding the results\n",
    "\n",
    "# TODO: Number of training example\n",
    "n_train = X_train.shape[0]\n",
    "\n",
    "# TODO: Number of validation example\n",
    "n_validation = X_valid.shape[0]\n",
    "\n",
    "# TODO: Number of testing example.\n",
    "n_test = X_test.shape[0]\n",
    "\n",
    "# TODO: What's the shape of an traffic sign image?\n",
    "image_shape = X_train.shape[1:]\n",
    "\n",
    "# TODO: How many unique classes/labels there are in the dataset.\n",
    "n_classes = len(set(y_train))\n",
    "\n",
    "print(\"Number of training examples =\", n_train)\n",
    "print(\"Number of testing examples =\", n_test)\n",
    "print(\"Image data shape =\", image_shape)\n",
    "print(\"Number of classes =\", n_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Include an exploratory visualization of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc. \n",
    "\n",
    "The [Matplotlib](http://matplotlib.org/) [examples](http://matplotlib.org/examples/index.html) and [gallery](http://matplotlib.org/gallery.html) pages are a great resource for doing visualizations in Python.\n",
    "\n",
    "**NOTE:** It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Ppse3b9mZUtpQ9ziOhl5sis9nX3CkDBB8sj2jGJKNl/7SzY3R+fayjLOJp2h7DHDC5Z5u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\n1rz/EwCECmnUBCAh53ISaWIWSCe6am3h+9diRzTJTSKpRl5TrO8aO43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bBIyUeIn1ow89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PntB0pj/Aht6/fLz6c1Pk4A3W4k23cVzUxXr3NjnTuvo7mVrH+dmDj9FM0df+RBN3aujx9X13T7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\niCMRFpb9+tQLjoUkKXi8Q5zp4XFcj+zZl1L6lPy1v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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec8bd53898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common index\n",
      "index: 2 => Speed limit (50km/h) = 0.0591355252409\n",
      "index: 1 => Speed limit (30km/h) = 0.0582529054612\n",
      "index: 13 => Yield = 0.0564876659017\n",
      "index: 12 => Priority road = 0.055605046122\n",
      "index: 38 => Keep right = 0.0547224263423\n",
      "index: 10 => No passing for vehicles over 3.5 metric tons = 0.0529571867829\n",
      "index: 4 => Speed limit (70km/h) = 0.0520745670032\n",
      "index: 5 => Speed limit (80km/h) = 0.0485440878843\n",
      "index: 25 => Road work = 0.0397178900872\n",
      "index: 9 => No passing = 0.0388352703074\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec6d15eda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Data exploration visualization code goes here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import random\n",
    "from PIL import Image, ImageEnhance\n",
    "# Visualizations will be shown in the notebook.\n",
    "%matplotlib inline\n",
    "\n",
    "# Load name of id\n",
    "with open(\"signnames.csv\", \"r\") as f:\n",
    "    signnames = f.read()\n",
    "id_to_name = { int(line.split(\",\")[0]):line.split(\",\")[1] for line in signnames.split(\"\\n\")[1:] if len(line) > 0}\n",
    "\n",
    "\n",
    "graph_size = 3\n",
    "random_index_list = [random.randint(0, X_train.shape[0]) for _ in range(graph_size * graph_size)]\n",
    "fig = plt.figure(figsize=(15, 15))\n",
    "for i, index in enumerate(random_index_list):\n",
    "    a=fig.add_subplot(graph_size, graph_size, i+1)\n",
    "    #im = Image.fromarray(np.rollaxis(X_train[index] * 255, 0,3))\n",
    "    imgplot = plt.imshow(X_train[index])\n",
    "    # Plot some images\n",
    "    a.set_title('%s' % id_to_name[y_train[index]])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "# the histogram of the data\n",
    "values, bins, patches = ax.hist(y_train, n_classes, normed=10)\n",
    "\n",
    "# add a 'best fit' line\n",
    "ax.set_xlabel('Smarts')\n",
    "ax.set_title(r'Histogram of classess')\n",
    "\n",
    "# Tweak spacing to prevent clipping of ylabel\n",
    "fig.tight_layout()\n",
    "\n",
    "print (\"Most common index\")\n",
    "most_common_index = sorted(range(len(values)), key=lambda k: values[k], reverse=True)\n",
    "for index in most_common_index[:10]:\n",
    "    print(\"index: %s => %s = %s\" % (index, id_to_name[index], values[index]))\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "\n",
    "## Step 2: Design and Test a Model Architecture\n",
    "\n",
    "Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the [German Traffic Sign Dataset](http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset).\n",
    "\n",
    "The LeNet-5 implementation shown in the [classroom](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play! \n",
    "\n",
    "With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission. \n",
    "\n",
    "There are various aspects to consider when thinking about this problem:\n",
    "\n",
    "- Neural network architecture (is the network over or underfitting?)\n",
    "- Play around preprocessing techniques (normalization, rgb to grayscale, etc)\n",
    "- Number of examples per label (some have more than others).\n",
    "- Generate fake data.\n",
    "\n",
    "Here is an example of a [published baseline model on this problem](http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf). It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-process the Data Set (normalization, grayscale, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, `(pixel - 128)/ 128` is a quick way to approximately normalize the data and can be used in this project. \n",
    "\n",
    "Other pre-processing steps are optional. You can try different techniques to see if it improves performance. \n",
    "\n",
    "Use the code cell (or multiple code cells, if necessary) to implement the first step of your project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include \n",
    "### converting to grayscale, etc.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "#  I used keras only for the ImageDataGenerator\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "\n",
    "X_train = X_train / 255\n",
    "X_valid = X_valid / 255\n",
    "X_test = X_test / 255\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def preprocessing_function(img):\n",
    "    \"\"\"\n",
    "        Custom preprocessing_function\n",
    "    \"\"\"\n",
    "    img = img * 255\n",
    "    img = Image.fromarray(img.astype('uint8'), 'RGB')\n",
    "    img = ImageEnhance.Brightness(img).enhance(random.uniform(0.6, 1.5))\n",
    "    img = ImageEnhance.Contrast(img).enhance(random.uniform(0.6, 1.5))\n",
    "\n",
    "    return np.array(img) / 255\n",
    "\n",
    "train_datagen = ImageDataGenerator()\n",
    "train_datagen_augmented = ImageDataGenerator(\n",
    "    rotation_range=20,\n",
    "    shear_range=0.2,\n",
    "    width_shift_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    preprocessing_function=preprocessing_function)\n",
    "inference_datagen = ImageDataGenerator()\n",
    "train_datagen.fit(X_train)\n",
    "train_datagen_augmented.fit(X_train)\n",
    "inference_datagen.fit(X_valid)\n",
    "inference_datagen.fit(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exemple of augmented images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aSk+wptKFj1mNpaIyhvLOZYssuts9ycvWvSC6a6ZT6zFA7doaszxlypxvi7Urdp77m5QL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\nbeYyWK9M533qMauBdWuVfph3jVG+/z2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GehLyDHM5KJQFao+zODm70h8DeN17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\n10RYRiUw/iIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febbb1a5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "n = 0\n",
    "\n",
    "graph_size = 3\n",
    "\n",
    "for x_batch, y_batch in train_datagen_augmented.flow(X_train, y_train, batch_size=1):\n",
    "    a=fig.add_subplot(graph_size, graph_size, n+1)\n",
    "    imgplot = plt.imshow(x_batch[0])\n",
    "    n = n + 1\n",
    "    if n > 8:\n",
    "        break\n",
    "\n",
    "    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CapsNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def conv_caps_layer(input_layer, capsules_size, nb_filters, kernel, stride=2):\n",
    "    \"\"\"\n",
    "        Capsule layer for the convolutional inputs\n",
    "        **input:\n",
    "            *input_layer: (Tensor)\n",
    "            *capsule_numbers: (Integer) the number of capsule in this layer.\n",
    "            *kernel_size: (Integer) Size of the kernel for each filter.\n",
    "            *stride: (Integer) 2 by default\n",
    "    \"\"\"\n",
    "    # \"In convolutional capsule layers each unit in a capsule is a convolutional unit.\n",
    "    # Therefore, each capsule will output a grid of vectors rather than a single vector output.\"\n",
    "    capsules = tf.contrib.layers.conv2d(\n",
    "        input_layer, nb_filters * capsules_size, kernel, stride, padding=\"VALID\")\n",
    "    # conv shape: [?, kernel, kernel, nb_filters]\n",
    "    shape = capsules.get_shape().as_list()\n",
    "    capsules = tf.reshape(capsules, shape=(-1, np.prod(shape[1:3]) * nb_filters, capsules_size, 1))\n",
    "    # capsules shape: [?, nb_capsules, capsule_size, 1]\n",
    "    return squash(capsules)\n",
    "\n",
    "def routing(u_hat, b_ij, nb_capsules, nb_capsules_p, iterations=4):\n",
    "    \"\"\"\n",
    "        Routing algorithm\n",
    "\n",
    "        **input:\n",
    "            *u_hat: Dot product (weights between previous capsule and current capsule)\n",
    "            *b_ij: the log prior probabilities that capsule i should be coupled to capsule j\n",
    "            *nb_capsules_p: Number of capsule in the previous layer\n",
    "            *nb_capsules: Number of capsule in this layer\n",
    "    \"\"\"\n",
    "    # Start the routing algorithm\n",
    "    for it in range(iterations):\n",
    "        with tf.variable_scope('routing_' + str(it)):\n",
    "            # Line 4 of algo\n",
    "            # probabilities that capsule i should be coupled to capsule j.\n",
    "            # c_ij:  [nb_capsules_p, nb_capsules, 1, 1]\n",
    "            c_ij = tf.nn.softmax(b_ij, dim=2)\n",
    "\n",
    "            # Line 5 of algo\n",
    "            # c_ij:  [      nb_capsules_p, nb_capsules, 1,         1]\n",
    "            # u_hat: [?,    nb_capsules_p, nb_capsules, len_v_j,   1]\n",
    "            s_j = tf.multiply(c_ij, u_hat)\n",
    "            # s_j: [?, nb_capsules_p, nb_capsules, len_v_j, 1]\n",
    "            s_j = tf.reduce_sum(s_j, axis=1, keep_dims=True)\n",
    "            # s_j: [?, 1, nb_capsules, len_v_j, 1)\n",
    "\n",
    "            # line 6:\n",
    "            # squash using Eq.1,\n",
    "            v_j = squash(s_j)\n",
    "            # v_j: [1, 1, nb_capsules, len_v_j, 1)\n",
    "\n",
    "            # line 7:\n",
    "            # Frist reshape & tile v_j\n",
    "            # [? ,  1,              nb_capsules,    len_v_j, 1] ->\n",
    "            # [?,   nb_capsules_p,  nb_capsules,    len_v_j, 1]\n",
    "            v_j_tiled = tf.tile(v_j, [1, nb_capsules_p, 1, 1, 1])\n",
    "            # u_hat:    [?,             nb_capsules_p, nb_capsules, len_v_j, 1]\n",
    "            # v_j_tiled [1,             nb_capsules_p, nb_capsules, len_v_j, 1]\n",
    "            u_dot_v = tf.matmul(u_hat, v_j_tiled, transpose_a=True)\n",
    "            # u_produce_v: [?, nb_capsules_p, nb_capsules, 1, 1]\n",
    "            b_ij += tf.reduce_sum(u_dot_v, axis=0, keep_dims=True)\n",
    "            #b_ih: [1, nb_capsules_p, nb_capsules, 1, 1]\n",
    "\n",
    "    return tf.squeeze(v_j, axis=1)\n",
    "\n",
    "def fully_connected_caps_layer(input_layer, capsules_size, nb_capsules, iterations=4):\n",
    "    \"\"\"\n",
    "        Second layer receiving inputs from all capsules of the layer below\n",
    "            **input:\n",
    "                *input_layer: (Tensor)\n",
    "                *capsules_size: (Integer) Size of each capsule\n",
    "                *nb_capsules: (Integer) Number of capsule\n",
    "                *iterations: (Integer) Number of iteration for the routing algorithm\n",
    "\n",
    "            i refer to the layer below.\n",
    "            j refer to the layer above (the current layer).\n",
    "    \"\"\"\n",
    "    shape = input_layer.get_shape().as_list()\n",
    "    # Get the size of each capsule in the previous layer and the current layer.\n",
    "    len_u_i = np.prod(shape[2])\n",
    "    len_v_j = capsules_size\n",
    "    # Get the number of capsule in the layer bellow.\n",
    "    nb_capsules_p = np.prod(shape[1])\n",
    "\n",
    "    # w_ij: Used to compute u_hat by multiplying the output ui of a capsule in the layer below\n",
    "    # with this matrix\n",
    "    # [nb_capsules_p, nb_capsules, len_v_j, len_u_i]\n",
    "    _init = tf.random_normal_initializer(stddev=0.01, seed=0)\n",
    "    _shape = (nb_capsules_p, nb_capsules, len_v_j, len_u_i)\n",
    "    w_ij = tf.get_variable('weight', shape=_shape, dtype=tf.float32, initializer=_init)\n",
    "\n",
    "    # Adding one dimension to the input [batch_size, nb_capsules_p,    length(u_i), 1] ->\n",
    "    #                                   [batch_size, nb_capsules_p, 1, length(u_i), 1]\n",
    "    # To allow the next dot product\n",
    "    input_layer = tf.reshape(input_layer, shape=(-1, nb_capsules_p, 1, len_u_i, 1))\n",
    "    input_layer = tf.tile(input_layer, [1, 1, nb_capsules, 1, 1])\n",
    "\n",
    "    # Eq.2, calc u_hat\n",
    "    # Prediction uj|i made by capsule i\n",
    "    # w_ij:  [              nb_capsules_p, nb_capsules, len_v_j,  len_u_i, ]\n",
    "    # input: [batch_size,   nb_capsules_p, nb_capsules, len_ui,   1]\n",
    "    # u_hat: [batch_size,   nb_capsules_p, nb_capsules, len_v_j, 1]\n",
    "    # Each capsule of the previous layer capsule layer is associated to a capsule of this layer\n",
    "    u_hat = tf.einsum('abdc,iabcf->iabdf', w_ij, input_layer)\n",
    "\n",
    "    # bij are the log prior probabilities that capsule i should be coupled to capsule j\n",
    "    # [nb_capsules_p, nb_capsules, 1, 1]\n",
    "    b_ij = tf.zeros(shape=[nb_capsules_p, nb_capsules, 1, 1], dtype=np.float32)\n",
    "\n",
    "    return routing(u_hat, b_ij, nb_capsules, nb_capsules_p, iterations=iterations)\n",
    "\n",
    "def squash(vector):\n",
    "    \"\"\"\n",
    "        Squashing function corresponding to Eq. 1\n",
    "        **input: **\n",
    "            *vector\n",
    "    \"\"\"\n",
    "    vector += 0.00001 # Workaround for the squashing function ...\n",
    "    vec_squared_norm = tf.reduce_sum(tf.square(vector), -2, keep_dims=True)\n",
    "    scalar_factor = vec_squared_norm / (1 + vec_squared_norm) / tf.sqrt(vec_squared_norm)\n",
    "    vec_squashed = scalar_factor * vector  # element-wise\n",
    "    return(vec_squashed)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/python3\n",
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import numpy as np\n",
    "from model_base import ModelBase\n",
    "import tensorflow as tf\n",
    "\n",
    "class ModelTrafficSign(ModelBase):\n",
    "    \"\"\"\n",
    "        ModelTrafficSign.\n",
    "        This class is used to create the conv graph using:\n",
    "            Dynamic Routing Between Capsules\n",
    "    \"\"\"\n",
    "\n",
    "    # Numbers of label to predict\n",
    "    NB_LABELS = 43\n",
    "\n",
    "    def __init__(self, model_name, output_folder):\n",
    "        \"\"\"\n",
    "            **input:\n",
    "                *model_name: (Integer) Name of this model\n",
    "                *output_folder: Output folder to saved data (tensorboard, checkpoints)\n",
    "        \"\"\"\n",
    "        ModelBase.__init__(self, model_name, output_folder=output_folder)\n",
    "\n",
    "    def _build_inputs(self):\n",
    "        \"\"\"\n",
    "            Build tensorflow inputs\n",
    "            (Placeholder)\n",
    "            **return: **\n",
    "                *tf_images: Images Placeholder\n",
    "                *tf_labels: Labels Placeholder\n",
    "        \"\"\"\n",
    "        # Images 32*32*3\n",
    "        tf_images = tf.placeholder(tf.float32, [None, 32, 32, 3], name='images')\n",
    "        # Labels: [0, 1, 6, 20, ...]\n",
    "        tf_labels = tf.placeholder(tf.int64, [None], name='labels')\n",
    "        return tf_images, tf_labels\n",
    "\n",
    "    def _build_main_network(self, images, conv_2_dropout):\n",
    "        \"\"\"\n",
    "            This method is used to create the two convolutions and the CapsNet on the top\n",
    "            **input:\n",
    "                *images: Image PLaceholder\n",
    "                *conv_2_dropout: Dropout value placeholder\n",
    "            **return: **\n",
    "                *Caps1: Output of first Capsule layer\n",
    "                *Caps2: Output of second Capsule layer\n",
    "        \"\"\"\n",
    "        # First BLock:\n",
    "        # Layer 1: Convolution.\n",
    "        shape = (self.h.conv_1_size, self.h.conv_1_size, 3, self.h.conv_1_nb)\n",
    "        conv1 = self._create_conv(self.tf_images, shape, relu=True, max_pooling=False, padding='VALID')\n",
    "        # Layer 2: Convolution.\n",
    "        #shape = (self.h.conv_2_size, self.h.conv_2_size, self.h.conv_1_nb, self.h.conv_2_nb)\n",
    "        #conv2 = self._create_conv(conv1, shape, relu=True, max_pooling=False, padding='VALID')\n",
    "        conv1 = tf.nn.dropout(conv1, keep_prob=conv_2_dropout)\n",
    "\n",
    "        # Create the first capsules layer\n",
    "        caps1 = conv_caps_layer(\n",
    "            input_layer=conv1,\n",
    "            capsules_size=self.h.caps_1_vec_len,\n",
    "            nb_filters=self.h.caps_1_nb_filter,\n",
    "            kernel=self.h.caps_1_size)\n",
    "        # Create the second capsules layer used to predict the output\n",
    "        caps2 = fully_connected_caps_layer(\n",
    "            input_layer=caps1,\n",
    "            capsules_size=self.h.caps_2_vec_len,\n",
    "            nb_capsules=self.NB_LABELS,\n",
    "            iterations=self.h.routing_steps)\n",
    "\n",
    "        return caps1, caps2\n",
    "\n",
    "    def _build_decoder(self, caps2, one_hot_labels, batch_size):\n",
    "        \"\"\"\n",
    "            Build the decoder part from the last capsule layer\n",
    "            **input:\n",
    "                *Caps2:  Output of second Capsule layer\n",
    "                *one_hot_labels\n",
    "                *batch_size\n",
    "        \"\"\"\n",
    "        labels = tf.reshape(one_hot_labels, (-1, self.NB_LABELS, 1))\n",
    "        # squeeze(caps2):   [?, len_v_j,    capsules_nb]\n",
    "        # labels:           [?, NB_LABELS,  1] with capsules_nb == NB_LABELS\n",
    "        mask = tf.matmul(tf.squeeze(caps2), labels, transpose_a=True)\n",
    "        # Select the good capsule vector\n",
    "        capsule_vector = tf.reshape(mask, shape=(batch_size, self.h.caps_2_vec_len))\n",
    "        # capsule_vector: [?, len_v_j]\n",
    "\n",
    "        # Reconstruct image\n",
    "        fc1 = tf.contrib.layers.fully_connected(capsule_vector, num_outputs=400)\n",
    "        fc1 = tf.reshape(fc1, shape=(batch_size, 5, 5, 16))\n",
    "        upsample1 = tf.image.resize_nearest_neighbor(fc1, (8, 8))\n",
    "        conv1 = tf.layers.conv2d(upsample1, 4, (3,3), padding='same', activation=tf.nn.relu)\n",
    "\n",
    "        upsample2 = tf.image.resize_nearest_neighbor(conv1, (16, 16))\n",
    "        conv2 = tf.layers.conv2d(upsample2, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
    "\n",
    "        upsample3 = tf.image.resize_nearest_neighbor(conv2, (32, 32))\n",
    "        conv6 = tf.layers.conv2d(upsample3, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
    "\n",
    "        # 3 channel for RGG\n",
    "        logits = tf.layers.conv2d(conv6, 3, (3,3), padding='same', activation=None)\n",
    "        decoded = tf.nn.sigmoid(logits, name='decoded')\n",
    "        tf.summary.image('reconstruction_img', decoded)\n",
    "\n",
    "        return decoded\n",
    "\n",
    "    def init(self):\n",
    "        \"\"\"\n",
    "            Init the graph\n",
    "        \"\"\"\n",
    "        # Get graph inputs\n",
    "        self.tf_images, self.tf_labels = self._build_inputs()\n",
    "        # Dropout inputs\n",
    "        self.tf_conv_2_dropout = tf.placeholder(tf.float32, shape=(), name='conv_2_dropout')\n",
    "        # Dynamic batch size\n",
    "        batch_size = tf.shape(self.tf_images)[0]\n",
    "        # Translate labels to one hot array\n",
    "        one_hot_labels = tf.one_hot(self.tf_labels, depth=self.NB_LABELS)\n",
    "        # Create the first convolution and the CapsNet\n",
    "        self.tf_caps1, self.tf_caps2 = self._build_main_network(self.tf_images, self.tf_conv_2_dropout)\n",
    "\n",
    "        # Build the images reconstruction\n",
    "        self.tf_decoded = self._build_decoder(self.tf_caps2, one_hot_labels, batch_size)\n",
    "\n",
    "        # Build the loss\n",
    "        _loss = self._build_loss(\n",
    "            self.tf_caps2, one_hot_labels, self.tf_labels, self.tf_decoded, self.tf_images)\n",
    "        (self.tf_loss_squared_rec, self.tf_margin_loss_sum, self.tf_predicted_class,\n",
    "         self.tf_correct_prediction, self.tf_accuracy, self.tf_loss, self.tf_margin_loss,\n",
    "         self.tf_reconstruction_loss) = _loss\n",
    "\n",
    "        # Build optimizer\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=self.h.learning_rate)\n",
    "        self.tf_optimizer = optimizer.minimize(self.tf_loss, global_step=tf.Variable(0, trainable=False))\n",
    "\n",
    "        # Log value into tensorboard\n",
    "        tf.summary.scalar('margin_loss', self.tf_margin_loss)\n",
    "        tf.summary.scalar('accuracy', self.tf_accuracy)\n",
    "        tf.summary.scalar('total_loss', self.tf_loss)\n",
    "        tf.summary.scalar('reconstruction_loss', self.tf_reconstruction_loss)\n",
    "\n",
    "        self.tf_test = tf.random_uniform([2], minval=0, maxval=None, dtype=tf.float32, seed=None, name=\"tf_test\")\n",
    "\n",
    "        self.init_session()\n",
    "\n",
    "\n",
    "    def _build_loss(self, caps2, one_hot_labels, labels, decoded, images):\n",
    "        \"\"\"\n",
    "            Build the loss of the graph\n",
    "        \"\"\"\n",
    "        # Get the length of each capsule\n",
    "        capsules_length = tf.sqrt(tf.reduce_sum(tf.square(caps2), axis=2, keep_dims=True))\n",
    "\n",
    "        max_l = tf.square(tf.maximum(0., 0.9 - capsules_length))\n",
    "        max_l = tf.reshape(max_l, shape=(-1, self.NB_LABELS))\n",
    "        max_r = tf.square(tf.maximum(0., capsules_length - 0.1))\n",
    "        max_r = tf.reshape(max_r, shape=(-1, self.NB_LABELS))\n",
    "        t_c = one_hot_labels\n",
    "        m_loss = t_c * max_l + 0.5 * (1 - t_c) * max_r\n",
    "        margin_loss_sum = tf.reduce_sum(m_loss, axis=1)\n",
    "        margin_loss = tf.reduce_mean(margin_loss_sum)\n",
    "\n",
    "        # Reconstruction loss\n",
    "        loss_squared_rec = tf.square(decoded - images)\n",
    "        reconstruction_loss = tf.reduce_mean(loss_squared_rec)\n",
    "\n",
    "        # 3. Total loss\n",
    "        loss = margin_loss + (0.0005 * reconstruction_loss)\n",
    "\n",
    "        # Accuracy\n",
    "        predicted_class = tf.argmax(capsules_length, axis=1)\n",
    "        predicted_class = tf.reshape(predicted_class, [tf.shape(capsules_length)[0]])\n",
    "        correct_prediction = tf.equal(predicted_class, labels)\n",
    "        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "        return (loss_squared_rec, margin_loss_sum, predicted_class, correct_prediction, accuracy,\n",
    "                loss, margin_loss, reconstruction_loss)\n",
    "\n",
    "    def optimize(self, images, labels, tb_save=True):\n",
    "        \"\"\"\n",
    "            Train the model\n",
    "            **input: **\n",
    "                *images: Image to train the model on\n",
    "                *labels: True classes\n",
    "                *tb_save: (Boolean) Log this optimization in tensorboard\n",
    "            **return: **\n",
    "                Loss: The loss of the model on this batch\n",
    "                Acc: Accuracy of the model on this batch\n",
    "        \"\"\"\n",
    "        tensors = [self.tf_optimizer, self.tf_margin_loss, self.tf_accuracy, self.tf_tensorboard]\n",
    "        _, loss, acc, summary = self.sess.run(tensors,\n",
    "            feed_dict={\n",
    "            self.tf_images: images,\n",
    "            self.tf_labels: labels,\n",
    "            self.tf_conv_2_dropout: self.h.conv_2_dropout\n",
    "        })\n",
    "\n",
    "        if tb_save:\n",
    "            # Write data to tensorboard\n",
    "            self.train_writer.add_summary(summary, self.train_writer_it)\n",
    "            self.train_writer_it += 1\n",
    "\n",
    "        return loss, acc\n",
    "\n",
    "    def evaluate(self, images, labels, tb_train_save=False, tb_test_save=False):\n",
    "        \"\"\"\n",
    "            Evaluate dataset\n",
    "            **input: **\n",
    "                *images: Image to train the model on\n",
    "                *labels: True classes\n",
    "                *tb_train_save: (Boolean) Log this optimization in tensorboard under the train part\n",
    "                *tb_test_save: (Boolean) Log this optimization in tensorboard under the test part\n",
    "            **return: **\n",
    "                Loss: The loss of the model on this batch\n",
    "                Acc: Accuracy of the model on this batch\n",
    "        \"\"\"\n",
    "        tensors = [self.tf_margin_loss, self.tf_accuracy, self.tf_tensorboard]\n",
    "        loss, acc, summary = self.sess.run(tensors,\n",
    "                feed_dict={\n",
    "                self.tf_images: images,\n",
    "                self.tf_labels: labels,\n",
    "                self.tf_conv_2_dropout: 1.\n",
    "            })\n",
    "\n",
    "        if tb_test_save:\n",
    "            # Write data to tensorboard\n",
    "            self.test_writer.add_summary(summary, self.test_writer_it)\n",
    "            self.test_writer_it += 1\n",
    "\n",
    "        if tb_train_save:\n",
    "            # Write data to tensorboard\n",
    "            self.train_writer.add_summary(summary, self.train_writer_it)\n",
    "            self.train_writer_it += 1\n",
    "\n",
    "        return loss, acc\n",
    "\n",
    "    def predict(self, images):\n",
    "        \"\"\"\n",
    "            Method used to predict a class\n",
    "            Return a softmax\n",
    "            **input: **\n",
    "                *images: Image to train the model on\n",
    "            **return:\n",
    "                *softmax: Softmax between all capsules\n",
    "        \"\"\"\n",
    "        tensors = [self.tf_caps2]\n",
    "\n",
    "        caps2 = self.sess.run(tensors,\n",
    "            feed_dict={\n",
    "            self.tf_images: images,\n",
    "            self.tf_conv_2_dropout: 1.\n",
    "        })[0]\n",
    "\n",
    "        # tf.sqrt(tf.reduce_sum(tf.square(caps2), axis=2, keep_dims=True))\n",
    "        caps2 = np.sqrt(np.sum(np.square(caps2), axis=2, keepdims=True))\n",
    "        caps2 = np.reshape(caps2, (len(images), self.NB_LABELS))\n",
    "        # softmax\n",
    "        softmax = np.exp(caps2) / np.sum(np.exp(caps2), axis=1, keepdims=True)\n",
    "\n",
    "        return softmax\n",
    "\n",
    "    def reconstruction(self, images, labels):\n",
    "        \"\"\"\n",
    "            Method used to get the reconstructions given a batch\n",
    "            Return the result as a softmax\n",
    "            **input: **\n",
    "                *images: Image to train the model on\n",
    "                *labels: True classes\n",
    "        \"\"\"\n",
    "        tensors = [self.tf_decoded]\n",
    "\n",
    "        decoded = self.sess.run(tensors,\n",
    "            feed_dict={\n",
    "            self.tf_images: images,\n",
    "            self.tf_labels: labels,\n",
    "            self.tf_conv_2_dropout: 1.\n",
    "        })[0]\n",
    "\n",
    "        return decoded\n",
    "\n",
    "    def evaluate_dataset(self, images, labels, batch_size=10):\n",
    "        \"\"\"\n",
    "            Evaluate a full dataset\n",
    "            This method is used to fully evaluate the dataset batch per batch. Useful when\n",
    "            the dataset can't be fit inside to the GPU.\n",
    "            *input: **\n",
    "                *images: Image to train the model on\n",
    "                *labels: True classes\n",
    "            *return: **\n",
    "                *loss: Loss overall your dataset\n",
    "                *accuracy: Accuracy overall your dataset\n",
    "                *predicted_class: Predicted class\n",
    "        \"\"\"\n",
    "        tensors = [self.tf_loss_squared_rec, self.tf_margin_loss_sum, self.tf_correct_prediction,\n",
    "                   self.tf_predicted_class]\n",
    "\n",
    "        loss_squared_rec_list = None\n",
    "        margin_loss_sum_list = None\n",
    "        correct_prediction_list = None\n",
    "        predicted_class = None\n",
    "\n",
    "        b = 0\n",
    "        for batch in self.get_batches([images, labels], batch_size, shuffle=False):\n",
    "            images_batch, labels_batch = batch\n",
    "            loss_squared_rec, margin_loss_sum, correct_prediction, classes = self.sess.run(tensors,\n",
    "                feed_dict={\n",
    "                self.tf_images: images_batch,\n",
    "                self.tf_labels: labels_batch,\n",
    "                self.tf_conv_2_dropout: 1.\n",
    "            })\n",
    "            if loss_squared_rec_list is not None:\n",
    "                predicted_class = np.concatenate((predicted_class, classes))\n",
    "                loss_squared_rec_list = np.concatenate((loss_squared_rec_list, loss_squared_rec))\n",
    "                margin_loss_sum_list = np.concatenate((margin_loss_sum_list, margin_loss_sum))\n",
    "                correct_prediction_list = np.concatenate((correct_prediction_list, correct_prediction))\n",
    "            else:\n",
    "                predicted_class = classes\n",
    "                loss_squared_rec_list = loss_squared_rec\n",
    "                margin_loss_sum_list = margin_loss_sum\n",
    "                correct_prediction_list = correct_prediction\n",
    "            b += batch_size\n",
    "\n",
    "        margin_loss = np.mean(margin_loss_sum_list)\n",
    "        reconstruction_loss = np.mean(loss_squared_rec_list)\n",
    "        accuracy = np.mean(correct_prediction_list)\n",
    "\n",
    "        loss = margin_loss\n",
    "\n",
    "        return loss, accuracy, predicted_class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train, Validate and Test the Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation\n",
    "sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Init model\n",
    "model = ModelTrafficSign(\"TrafficSign\", output_folder=\"outputs\")\n",
    "model.init()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 0, loss = 2.42539, acc = 0.02\n",
      "[Validation] Batch ID = 0, loss = 0.800529, acc = 0.04\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.781535 Best loss: None\n",
      "[TOTAL Validation] Batch ID = 0, loss = 0.781535, acc = 0.0185941043084\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.891\n",
      "[Train] Batch ID = 10, loss = 0.754666, acc = 0.04\n",
      "[Validation] Batch ID = 10, loss = 0.753758, acc = 0.04\n",
      "[Train] Batch ID = 20, loss = 0.690922, acc = 0.06\n",
      "[Validation] Batch ID = 20, loss = 0.702244, acc = 0.06\n",
      "[Train] Batch ID = 30, loss = 0.6284, acc = 0.1\n",
      "[Validation] Batch ID = 30, loss = 0.659595, acc = 0.06\n",
      "[Train] Batch ID = 40, loss = 0.63592, acc = 0.1\n",
      "[Validation] Batch ID = 40, loss = 0.659709, acc = 0.04\n",
      "[Train] Batch ID = 50, loss = 0.616082, acc = 0.16\n",
      "[Validation] Batch ID = 50, loss = 0.599504, acc = 0.06\n",
      "[Train] Batch ID = 60, loss = 0.621295, acc = 0.1\n",
      "[Validation] Batch ID = 60, loss = 0.604651, acc = 0.1\n",
      "[Train] Batch ID = 70, loss = 0.617128, acc = 0.06\n",
      "[Validation] Batch ID = 70, loss = 0.597139, acc = 0.1\n",
      "[Train] Batch ID = 80, loss = 0.590053, acc = 0.14\n",
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      "[Validation] Batch ID = 760, loss = 0.427461, acc = 0.36\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 770, loss = 0.480728, acc = 0.32\n",
      "[Validation] Batch ID = 770, loss = 0.491949, acc = 0.34\n",
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      "[Validation] Batch ID = 930, loss = 0.376776, acc = 0.54\n",
      "[Train] Batch ID = 940, loss = 0.434811, acc = 0.44\n",
      "[Validation] Batch ID = 940, loss = 0.467765, acc = 0.38\n",
      "[Train] Batch ID = 950, loss = 0.483722, acc = 0.3\n",
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      "[Validation] Batch ID = 960, loss = 0.452141, acc = 0.4\n",
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      "[Validation] Batch ID = 970, loss = 0.407389, acc = 0.4\n",
      "[Train] Batch ID = 980, loss = 0.442751, acc = 0.34\n",
      "[Validation] Batch ID = 980, loss = 0.406002, acc = 0.48\n",
      "[Train] Batch ID = 990, loss = 0.506468, acc = 0.26\n",
      "[Validation] Batch ID = 990, loss = 0.434915, acc = 0.42\n",
      "[Train] Batch ID = 1000, loss = 0.497463, acc = 0.26\n",
      "[Validation] Batch ID = 1000, loss = 0.4204, acc = 0.4\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.420879 Best loss: 0.781535\n",
      "[TOTAL Validation] Batch ID = 1000, loss = 0.420879, acc = 0.470975056689\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.8019000000000001\n",
      "[Train] Batch ID = 1010, loss = 0.496853, acc = 0.24\n",
      "[Validation] Batch ID = 1010, loss = 0.409362, acc = 0.62\n",
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      "[Validation] Batch ID = 1450, loss = 0.27971, acc = 0.72\n",
      "[Train] Batch ID = 1460, loss = 0.25103, acc = 0.8\n",
      "[Validation] Batch ID = 1460, loss = 0.319824, acc = 0.74\n",
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      "[Validation] Batch ID = 1740, loss = 0.285149, acc = 0.66\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 1750, loss = 0.283401, acc = 0.7\n",
      "[Validation] Batch ID = 1750, loss = 0.29662, acc = 0.74\n",
      "[Train] Batch ID = 1760, loss = 0.410071, acc = 0.52\n",
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      "[Validation] Batch ID = 1790, loss = 0.325985, acc = 0.5\n",
      "[Train] Batch ID = 1800, loss = 0.377839, acc = 0.52\n",
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      "[Validation] Batch ID = 1820, loss = 0.30385, acc = 0.7\n",
      "[Train] Batch ID = 1830, loss = 0.441479, acc = 0.34\n",
      "[Validation] Batch ID = 1830, loss = 0.276286, acc = 0.74\n",
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      "[Validation] Batch ID = 1840, loss = 0.355448, acc = 0.54\n",
      "[Train] Batch ID = 1850, loss = 0.433524, acc = 0.44\n",
      "[Validation] Batch ID = 1850, loss = 0.276556, acc = 0.76\n",
      "[Train] Batch ID = 1860, loss = 0.262997, acc = 0.8\n",
      "[Validation] Batch ID = 1860, loss = 0.280793, acc = 0.7\n",
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      "[Validation] Batch ID = 1870, loss = 0.262786, acc = 0.74\n",
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      "[Validation] Batch ID = 1880, loss = 0.305498, acc = 0.68\n",
      "[Train] Batch ID = 1890, loss = 0.416785, acc = 0.48\n",
      "[Validation] Batch ID = 1890, loss = 0.305132, acc = 0.56\n",
      "[Train] Batch ID = 1900, loss = 0.431304, acc = 0.5\n",
      "[Validation] Batch ID = 1900, loss = 0.290395, acc = 0.72\n",
      "[Train] Batch ID = 1910, loss = 0.395793, acc = 0.54\n",
      "[Validation] Batch ID = 1910, loss = 0.285612, acc = 0.84\n",
      "[Train] Batch ID = 1920, loss = 0.466263, acc = 0.32\n",
      "[Validation] Batch ID = 1920, loss = 0.313731, acc = 0.62\n",
      "[Train] Batch ID = 1930, loss = 0.392385, acc = 0.48\n",
      "[Validation] Batch ID = 1930, loss = 0.301737, acc = 0.68\n",
      "[Train] Batch ID = 1940, loss = 0.424688, acc = 0.36\n",
      "[Validation] Batch ID = 1940, loss = 0.281302, acc = 0.72\n",
      "[Train] Batch ID = 1950, loss = 0.400775, acc = 0.52\n",
      "[Validation] Batch ID = 1950, loss = 0.281192, acc = 0.7\n",
      "[Train] Batch ID = 1960, loss = 0.226825, acc = 0.9\n",
      "[Validation] Batch ID = 1960, loss = 0.262478, acc = 0.8\n",
      "[Train] Batch ID = 1970, loss = 0.403778, acc = 0.52\n",
      "[Validation] Batch ID = 1970, loss = 0.289501, acc = 0.74\n",
      "[Train] Batch ID = 1980, loss = 0.407642, acc = 0.5\n",
      "[Validation] Batch ID = 1980, loss = 0.288932, acc = 0.66\n",
      "[Train] Batch ID = 1990, loss = 0.236559, acc = 0.78\n",
      "[Validation] Batch ID = 1990, loss = 0.289652, acc = 0.66\n",
      "[Train] Batch ID = 2000, loss = 0.402175, acc = 0.44\n",
      "[Validation] Batch ID = 2000, loss = 0.292529, acc = 0.68\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.287766 Best loss: 0.420879\n",
      "[TOTAL Validation] Batch ID = 2000, loss = 0.287766, acc = 0.714285714286\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.7217100000000001\n",
      "[Train] Batch ID = 2010, loss = 0.424638, acc = 0.46\n",
      "[Validation] Batch ID = 2010, loss = 0.315895, acc = 0.64\n",
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      "[Validation] Batch ID = 2740, loss = 0.221936, acc = 0.78\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 2750, loss = 0.192231, acc = 0.9\n",
      "[Validation] Batch ID = 2750, loss = 0.218081, acc = 0.8\n",
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      "[Validation] Batch ID = 2810, loss = 0.185864, acc = 0.84\n",
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      "[Validation] Batch ID = 2930, loss = 0.207516, acc = 0.86\n",
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      "[Validation] Batch ID = 2940, loss = 0.238631, acc = 0.76\n",
      "[Train] Batch ID = 2950, loss = 0.351987, acc = 0.52\n",
      "[Validation] Batch ID = 2950, loss = 0.229763, acc = 0.78\n",
      "[Train] Batch ID = 2960, loss = 0.379678, acc = 0.5\n",
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      "[Validation] Batch ID = 2980, loss = 0.196173, acc = 0.84\n",
      "[Train] Batch ID = 2990, loss = 0.40528, acc = 0.5\n",
      "[Validation] Batch ID = 2990, loss = 0.214772, acc = 0.9\n",
      "[Train] Batch ID = 3000, loss = 0.384336, acc = 0.56\n",
      "[Validation] Batch ID = 3000, loss = 0.261725, acc = 0.66\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.21994 Best loss: 0.287766\n",
      "[TOTAL Validation] Batch ID = 3000, loss = 0.21994, acc = 0.790249433107\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.6495390000000001\n",
      "[Train] Batch ID = 3010, loss = 0.445161, acc = 0.34\n",
      "[Validation] Batch ID = 3010, loss = 0.21014, acc = 0.86\n",
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      "[Validation] Batch ID = 3070, loss = 0.197524, acc = 0.9\n",
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      "[Validation] Batch ID = 3740, loss = 0.180565, acc = 0.88\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 3750, loss = 0.152761, acc = 0.92\n",
      "[Validation] Batch ID = 3750, loss = 0.184382, acc = 0.84\n",
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      "[Validation] Batch ID = 3950, loss = 0.177488, acc = 0.9\n",
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      "[Validation] Batch ID = 3960, loss = 0.136452, acc = 0.96\n",
      "[Train] Batch ID = 3970, loss = 0.135189, acc = 0.94\n",
      "[Validation] Batch ID = 3970, loss = 0.139566, acc = 0.9\n",
      "[Train] Batch ID = 3980, loss = 0.0979463, acc = 0.96\n",
      "[Validation] Batch ID = 3980, loss = 0.150806, acc = 0.88\n",
      "[Train] Batch ID = 3990, loss = 0.376017, acc = 0.5\n",
      "[Validation] Batch ID = 3990, loss = 0.168038, acc = 0.86\n",
      "[Train] Batch ID = 4000, loss = 0.111328, acc = 0.98\n",
      "[Validation] Batch ID = 4000, loss = 0.144933, acc = 0.9\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.168757 Best loss: 0.21994\n",
      "[TOTAL Validation] Batch ID = 4000, loss = 0.168757, acc = 0.870975056689\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.5845851000000001\n",
      "[Train] Batch ID = 4010, loss = 0.12251, acc = 0.92\n",
      "[Validation] Batch ID = 4010, loss = 0.16558, acc = 0.84\n",
      "[Train] Batch ID = 4020, loss = 0.112086, acc = 0.92\n",
      "[Validation] Batch ID = 4020, loss = 0.15856, acc = 0.88\n",
      "[Train] Batch ID = 4030, loss = 0.313121, acc = 0.68\n",
      "[Validation] Batch ID = 4030, loss = 0.15399, acc = 0.9\n",
      "[Train] Batch ID = 4040, loss = 0.367862, acc = 0.54\n",
      "[Validation] Batch ID = 4040, loss = 0.145397, acc = 0.86\n",
      "[Train] Batch ID = 4050, loss = 0.271752, acc = 0.74\n",
      "[Validation] Batch ID = 4050, loss = 0.141684, acc = 0.9\n",
      "[Train] Batch ID = 4060, loss = 0.333086, acc = 0.58\n",
      "[Validation] Batch ID = 4060, loss = 0.174225, acc = 0.86\n",
      "[Train] Batch ID = 4070, loss = 0.107873, acc = 0.96\n",
      "[Validation] Batch ID = 4070, loss = 0.165873, acc = 0.82\n",
      "[Train] Batch ID = 4080, loss = 0.100479, acc = 0.98\n",
      "[Validation] Batch ID = 4080, loss = 0.148699, acc = 0.88\n",
      "[Train] Batch ID = 4090, loss = 0.123076, acc = 0.9\n",
      "[Validation] Batch ID = 4090, loss = 0.166938, acc = 0.88\n",
      "[Train] Batch ID = 4100, loss = 0.105591, acc = 0.98\n",
      "[Validation] Batch ID = 4100, loss = 0.15512, acc = 0.92\n",
      "[Train] Batch ID = 4110, loss = 0.109869, acc = 0.96\n",
      "[Validation] Batch ID = 4110, loss = 0.168539, acc = 0.88\n",
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      "[Validation] Batch ID = 4740, loss = 0.140595, acc = 0.82\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 4750, loss = 0.338575, acc = 0.6\n",
      "[Validation] Batch ID = 4750, loss = 0.129274, acc = 0.96\n",
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      "[Validation] Batch ID = 4920, loss = 0.160648, acc = 0.84\n",
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      "[Validation] Batch ID = 4940, loss = 0.172178, acc = 0.9\n",
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      "[Validation] Batch ID = 4950, loss = 0.113408, acc = 0.98\n",
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      "[Validation] Batch ID = 4970, loss = 0.121926, acc = 0.9\n",
      "[Train] Batch ID = 4980, loss = 0.088781, acc = 1.0\n",
      "[Validation] Batch ID = 4980, loss = 0.0940469, acc = 0.96\n",
      "[Train] Batch ID = 4990, loss = 0.296505, acc = 0.68\n",
      "[Validation] Batch ID = 4990, loss = 0.132791, acc = 0.9\n",
      "[Train] Batch ID = 5000, loss = 0.0781333, acc = 0.98\n",
      "[Validation] Batch ID = 5000, loss = 0.0992442, acc = 0.98\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.129008 Best loss: 0.168757\n",
      "[TOTAL Validation] Batch ID = 5000, loss = 0.129008, acc = 0.910657596372\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.5261265900000001\n",
      "[Train] Batch ID = 5010, loss = 0.111215, acc = 0.96\n",
      "[Validation] Batch ID = 5010, loss = 0.13073, acc = 0.9\n",
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      "[Validation] Batch ID = 5740, loss = 0.117947, acc = 0.9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 5750, loss = 0.30507, acc = 0.7\n",
      "[Validation] Batch ID = 5750, loss = 0.10795, acc = 0.92\n",
      "[Train] Batch ID = 5760, loss = 0.0643996, acc = 1.0\n",
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      "[Train] Batch ID = 5850, loss = 0.0869699, acc = 0.98\n",
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      "[Validation] Batch ID = 5870, loss = 0.0950392, acc = 0.96\n",
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      "[Validation] Batch ID = 5890, loss = 0.146261, acc = 0.88\n",
      "[Train] Batch ID = 5900, loss = 0.0832616, acc = 0.94\n",
      "[Validation] Batch ID = 5900, loss = 0.0723635, acc = 1.0\n",
      "[Train] Batch ID = 5910, loss = 0.295888, acc = 0.64\n",
      "[Validation] Batch ID = 5910, loss = 0.0838609, acc = 0.98\n",
      "[Train] Batch ID = 5920, loss = 0.0430435, acc = 1.0\n",
      "[Validation] Batch ID = 5920, loss = 0.113322, acc = 0.92\n",
      "[Train] Batch ID = 5930, loss = 0.0716543, acc = 0.96\n",
      "[Validation] Batch ID = 5930, loss = 0.141552, acc = 0.92\n",
      "[Train] Batch ID = 5940, loss = 0.0737806, acc = 0.96\n",
      "[Validation] Batch ID = 5940, loss = 0.118766, acc = 0.92\n",
      "[Train] Batch ID = 5950, loss = 0.292045, acc = 0.62\n",
      "[Validation] Batch ID = 5950, loss = 0.10995, acc = 0.96\n",
      "[Train] Batch ID = 5960, loss = 0.0665943, acc = 1.0\n",
      "[Validation] Batch ID = 5960, loss = 0.11419, acc = 0.94\n",
      "[Train] Batch ID = 5970, loss = 0.0669147, acc = 1.0\n",
      "[Validation] Batch ID = 5970, loss = 0.076357, acc = 0.96\n",
      "[Train] Batch ID = 5980, loss = 0.0652864, acc = 0.98\n",
      "[Validation] Batch ID = 5980, loss = 0.117701, acc = 0.88\n",
      "[Train] Batch ID = 5990, loss = 0.301814, acc = 0.6\n",
      "[Validation] Batch ID = 5990, loss = 0.0847288, acc = 0.96\n",
      "[Train] Batch ID = 6000, loss = 0.0683455, acc = 1.0\n",
      "[Validation] Batch ID = 6000, loss = 0.105057, acc = 0.92\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.109951 Best loss: 0.129008\n",
      "[TOTAL Validation] Batch ID = 6000, loss = 0.109951, acc = 0.933106575964\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.47351393100000005\n",
      "[Train] Batch ID = 6010, loss = 0.353551, acc = 0.58\n",
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      "[Validation] Batch ID = 6740, loss = 0.106205, acc = 0.92\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 6750, loss = 0.040191, acc = 1.0\n",
      "[Validation] Batch ID = 6750, loss = 0.0912596, acc = 0.96\n",
      "[Train] Batch ID = 6760, loss = 0.0477022, acc = 1.0\n",
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      "[Train] Batch ID = 6800, loss = 0.274583, acc = 0.72\n",
      "[Validation] Batch ID = 6800, loss = 0.0661882, acc = 0.98\n",
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      "[Validation] Batch ID = 6810, loss = 0.0902677, acc = 0.96\n",
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      "[Train] Batch ID = 6830, loss = 0.272607, acc = 0.74\n",
      "[Validation] Batch ID = 6830, loss = 0.0810471, acc = 0.96\n",
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      "[Validation] Batch ID = 6860, loss = 0.0814948, acc = 0.94\n",
      "[Train] Batch ID = 6870, loss = 0.299192, acc = 0.72\n",
      "[Validation] Batch ID = 6870, loss = 0.121373, acc = 0.92\n",
      "[Train] Batch ID = 6880, loss = 0.345319, acc = 0.58\n",
      "[Validation] Batch ID = 6880, loss = 0.0548027, acc = 1.0\n",
      "[Train] Batch ID = 6890, loss = 0.0537293, acc = 1.0\n",
      "[Validation] Batch ID = 6890, loss = 0.0882011, acc = 0.96\n",
      "[Train] Batch ID = 6900, loss = 0.283388, acc = 0.62\n",
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      "[Train] Batch ID = 6910, loss = 0.0401841, acc = 1.0\n",
      "[Validation] Batch ID = 6910, loss = 0.0863653, acc = 0.94\n",
      "[Train] Batch ID = 6920, loss = 0.338397, acc = 0.44\n",
      "[Validation] Batch ID = 6920, loss = 0.0949168, acc = 0.94\n",
      "[Train] Batch ID = 6930, loss = 0.299574, acc = 0.7\n",
      "[Validation] Batch ID = 6930, loss = 0.0776279, acc = 0.96\n",
      "[Train] Batch ID = 6940, loss = 0.263118, acc = 0.7\n",
      "[Validation] Batch ID = 6940, loss = 0.103064, acc = 0.92\n",
      "[Train] Batch ID = 6950, loss = 0.061091, acc = 1.0\n",
      "[Validation] Batch ID = 6950, loss = 0.118397, acc = 0.94\n",
      "[Train] Batch ID = 6960, loss = 0.257486, acc = 0.76\n",
      "[Validation] Batch ID = 6960, loss = 0.111664, acc = 0.86\n",
      "[Train] Batch ID = 6970, loss = 0.054967, acc = 0.98\n",
      "[Validation] Batch ID = 6970, loss = 0.0852872, acc = 0.98\n",
      "[Train] Batch ID = 6980, loss = 0.0523128, acc = 0.98\n",
      "[Validation] Batch ID = 6980, loss = 0.0755873, acc = 0.96\n",
      "[Train] Batch ID = 6990, loss = 0.254943, acc = 0.76\n",
      "[Validation] Batch ID = 6990, loss = 0.0773402, acc = 0.92\n",
      "[Train] Batch ID = 7000, loss = 0.271886, acc = 0.74\n",
      "[Validation] Batch ID = 7000, loss = 0.0968943, acc = 0.92\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0913591 Best loss: 0.109951\n",
      "[TOTAL Validation] Batch ID = 7000, loss = 0.0913591, acc = 0.949433106576\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.4261625379000001\n",
      "[Train] Batch ID = 7010, loss = 0.0487198, acc = 1.0\n",
      "[Validation] Batch ID = 7010, loss = 0.0986421, acc = 0.9\n",
      "[Train] Batch ID = 7020, loss = 0.0339564, acc = 1.0\n",
      "[Validation] Batch ID = 7020, loss = 0.106646, acc = 0.96\n",
      "[Train] Batch ID = 7030, loss = 0.245646, acc = 0.76\n",
      "[Validation] Batch ID = 7030, loss = 0.0894943, acc = 1.0\n",
      "[Train] Batch ID = 7040, loss = 0.0515622, acc = 0.98\n",
      "[Validation] Batch ID = 7040, loss = 0.101572, acc = 0.96\n",
      "[Train] Batch ID = 7050, loss = 0.0724402, acc = 0.96\n",
      "[Validation] Batch ID = 7050, loss = 0.0889175, acc = 0.96\n",
      "[Train] Batch ID = 7060, loss = 0.259647, acc = 0.66\n",
      "[Validation] Batch ID = 7060, loss = 0.0868668, acc = 0.98\n",
      "[Train] Batch ID = 7070, loss = 0.0438396, acc = 1.0\n",
      "[Validation] Batch ID = 7070, loss = 0.103552, acc = 0.96\n",
      "[Train] Batch ID = 7080, loss = 0.0423951, acc = 1.0\n",
      "[Validation] Batch ID = 7080, loss = 0.0937201, acc = 0.94\n",
      "[Train] Batch ID = 7090, loss = 0.0470159, acc = 1.0\n",
      "[Validation] Batch ID = 7090, loss = 0.0955545, acc = 0.96\n",
      "[Train] Batch ID = 7100, loss = 0.0515601, acc = 0.98\n",
      "[Validation] Batch ID = 7100, loss = 0.0995023, acc = 0.92\n",
      "[Train] Batch ID = 7110, loss = 0.0541927, acc = 1.0\n",
      "[Validation] Batch ID = 7110, loss = 0.097404, acc = 0.9\n",
      "[Train] Batch ID = 7120, loss = 0.31425, acc = 0.68\n",
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      "[Train] Batch ID = 7130, loss = 0.295422, acc = 0.66\n",
      "[Validation] Batch ID = 7130, loss = 0.0838397, acc = 0.96\n",
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      "[Validation] Batch ID = 7740, loss = 0.0981987, acc = 0.94\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 7750, loss = 0.0403732, acc = 1.0\n",
      "[Validation] Batch ID = 7750, loss = 0.0712164, acc = 1.0\n",
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      "[Validation] Batch ID = 7800, loss = 0.0890185, acc = 0.96\n",
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      "[Validation] Batch ID = 7810, loss = 0.0767867, acc = 0.98\n",
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      "[Validation] Batch ID = 7820, loss = 0.0952505, acc = 0.94\n",
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      "[Validation] Batch ID = 7830, loss = 0.0820147, acc = 0.96\n",
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      "[Validation] Batch ID = 7840, loss = 0.0980256, acc = 0.94\n",
      "[Train] Batch ID = 7850, loss = 0.0398391, acc = 1.0\n",
      "[Validation] Batch ID = 7850, loss = 0.124014, acc = 0.88\n",
      "[Train] Batch ID = 7860, loss = 0.329532, acc = 0.62\n",
      "[Validation] Batch ID = 7860, loss = 0.0787504, acc = 0.96\n",
      "[Train] Batch ID = 7870, loss = 0.0362406, acc = 1.0\n",
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      "[Validation] Batch ID = 7880, loss = 0.0923428, acc = 0.98\n",
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      "[Validation] Batch ID = 7890, loss = 0.0775265, acc = 0.96\n",
      "[Train] Batch ID = 7900, loss = 0.0457938, acc = 0.98\n",
      "[Validation] Batch ID = 7900, loss = 0.0856165, acc = 0.96\n",
      "[Train] Batch ID = 7910, loss = 0.264536, acc = 0.7\n",
      "[Validation] Batch ID = 7910, loss = 0.118862, acc = 0.96\n",
      "[Train] Batch ID = 7920, loss = 0.0331632, acc = 1.0\n",
      "[Validation] Batch ID = 7920, loss = 0.0454306, acc = 1.0\n",
      "[Train] Batch ID = 7930, loss = 0.0375573, acc = 1.0\n",
      "[Validation] Batch ID = 7930, loss = 0.105206, acc = 0.9\n",
      "[Train] Batch ID = 7940, loss = 0.22417, acc = 0.88\n",
      "[Validation] Batch ID = 7940, loss = 0.0847631, acc = 0.96\n",
      "[Train] Batch ID = 7950, loss = 0.27494, acc = 0.7\n",
      "[Validation] Batch ID = 7950, loss = 0.0998986, acc = 0.88\n",
      "[Train] Batch ID = 7960, loss = 0.0510396, acc = 0.98\n",
      "[Validation] Batch ID = 7960, loss = 0.0802052, acc = 0.98\n",
      "[Train] Batch ID = 7970, loss = 0.257686, acc = 0.72\n",
      "[Validation] Batch ID = 7970, loss = 0.0668219, acc = 0.98\n",
      "[Train] Batch ID = 7980, loss = 0.278713, acc = 0.68\n",
      "[Validation] Batch ID = 7980, loss = 0.0821996, acc = 0.94\n",
      "[Train] Batch ID = 7990, loss = 0.24132, acc = 0.76\n",
      "[Validation] Batch ID = 7990, loss = 0.0855121, acc = 0.96\n",
      "[Train] Batch ID = 8000, loss = 0.0290238, acc = 1.0\n",
      "[Validation] Batch ID = 8000, loss = 0.0743022, acc = 0.96\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0818688 Best loss: 0.0913591\n",
      "[TOTAL Validation] Batch ID = 8000, loss = 0.0818688, acc = 0.956235827664\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.3835462841100001\n",
      "[Train] Batch ID = 8010, loss = 0.27528, acc = 0.62\n",
      "[Validation] Batch ID = 8010, loss = 0.0748837, acc = 0.92\n",
      "[Train] Batch ID = 8020, loss = 0.292786, acc = 0.6\n",
      "[Validation] Batch ID = 8020, loss = 0.0763902, acc = 0.96\n",
      "[Train] Batch ID = 8030, loss = 0.282813, acc = 0.74\n",
      "[Validation] Batch ID = 8030, loss = 0.0640242, acc = 0.98\n",
      "[Train] Batch ID = 8040, loss = 0.0348446, acc = 1.0\n",
      "[Validation] Batch ID = 8040, loss = 0.108665, acc = 0.92\n",
      "[Train] Batch ID = 8050, loss = 0.0668036, acc = 0.98\n",
      "[Validation] Batch ID = 8050, loss = 0.0669015, acc = 0.98\n",
      "[Train] Batch ID = 8060, loss = 0.0382698, acc = 1.0\n",
      "[Validation] Batch ID = 8060, loss = 0.0759994, acc = 0.94\n",
      "[Train] Batch ID = 8070, loss = 0.0416656, acc = 1.0\n",
      "[Validation] Batch ID = 8070, loss = 0.100446, acc = 0.94\n",
      "[Train] Batch ID = 8080, loss = 0.256744, acc = 0.74\n",
      "[Validation] Batch ID = 8080, loss = 0.0728336, acc = 0.96\n",
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      "[Validation] Batch ID = 8740, loss = 0.0716525, acc = 0.94\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 8750, loss = 0.200257, acc = 0.82\n",
      "[Validation] Batch ID = 8750, loss = 0.0666566, acc = 0.94\n",
      "[Train] Batch ID = 8760, loss = 0.253806, acc = 0.76\n",
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      "[Train] Batch ID = 8770, loss = 0.221337, acc = 0.88\n",
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      "[Validation] Batch ID = 8790, loss = 0.0509318, acc = 1.0\n",
      "[Train] Batch ID = 8800, loss = 0.0462745, acc = 1.0\n",
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      "[Validation] Batch ID = 8820, loss = 0.111003, acc = 0.92\n",
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      "[Validation] Batch ID = 8830, loss = 0.0815632, acc = 0.94\n",
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      "[Validation] Batch ID = 8840, loss = 0.0525029, acc = 0.96\n",
      "[Train] Batch ID = 8850, loss = 0.0329526, acc = 1.0\n",
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      "[Train] Batch ID = 8860, loss = 0.0312124, acc = 1.0\n",
      "[Validation] Batch ID = 8860, loss = 0.0950556, acc = 0.9\n",
      "[Train] Batch ID = 8870, loss = 0.0351712, acc = 1.0\n",
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      "[Train] Batch ID = 8880, loss = 0.019568, acc = 1.0\n",
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      "[Validation] Batch ID = 8890, loss = 0.0472877, acc = 1.0\n",
      "[Train] Batch ID = 8900, loss = 0.0441991, acc = 0.96\n",
      "[Validation] Batch ID = 8900, loss = 0.0555078, acc = 0.96\n",
      "[Train] Batch ID = 8910, loss = 0.261854, acc = 0.72\n",
      "[Validation] Batch ID = 8910, loss = 0.0588934, acc = 0.98\n",
      "[Train] Batch ID = 8920, loss = 0.0327642, acc = 0.98\n",
      "[Validation] Batch ID = 8920, loss = 0.0632027, acc = 0.98\n",
      "[Train] Batch ID = 8930, loss = 0.0298702, acc = 1.0\n",
      "[Validation] Batch ID = 8930, loss = 0.0688324, acc = 0.96\n",
      "[Train] Batch ID = 8940, loss = 0.037612, acc = 1.0\n",
      "[Validation] Batch ID = 8940, loss = 0.0776624, acc = 0.96\n",
      "[Train] Batch ID = 8950, loss = 0.0400275, acc = 1.0\n",
      "[Validation] Batch ID = 8950, loss = 0.0566437, acc = 0.98\n",
      "[Train] Batch ID = 8960, loss = 0.261337, acc = 0.72\n",
      "[Validation] Batch ID = 8960, loss = 0.0795078, acc = 0.94\n",
      "[Train] Batch ID = 8970, loss = 0.274757, acc = 0.78\n",
      "[Validation] Batch ID = 8970, loss = 0.0614441, acc = 0.96\n",
      "[Train] Batch ID = 8980, loss = 0.0376411, acc = 1.0\n",
      "[Validation] Batch ID = 8980, loss = 0.0612421, acc = 0.98\n",
      "[Train] Batch ID = 8990, loss = 0.033753, acc = 1.0\n",
      "[Validation] Batch ID = 8990, loss = 0.0590532, acc = 0.98\n",
      "[Train] Batch ID = 9000, loss = 0.270359, acc = 0.68\n",
      "[Validation] Batch ID = 9000, loss = 0.0639195, acc = 0.96\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0730149 Best loss: 0.0818688\n",
      "[TOTAL Validation] Batch ID = 9000, loss = 0.0730149, acc = 0.956916099773\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.34519165569900007\n",
      "[Train] Batch ID = 9010, loss = 0.0371402, acc = 0.98\n",
      "[Validation] Batch ID = 9010, loss = 0.0990682, acc = 0.92\n",
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      "[Validation] Batch ID = 9020, loss = 0.0506787, acc = 1.0\n",
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      "[Validation] Batch ID = 9030, loss = 0.0584152, acc = 0.96\n",
      "[Train] Batch ID = 9040, loss = 0.0230043, acc = 1.0\n",
      "[Validation] Batch ID = 9040, loss = 0.0782268, acc = 0.96\n",
      "[Train] Batch ID = 9050, loss = 0.02809, acc = 1.0\n",
      "[Validation] Batch ID = 9050, loss = 0.0911876, acc = 0.94\n",
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      "[Train] Batch ID = 9080, loss = 0.0182243, acc = 1.0\n",
      "[Validation] Batch ID = 9080, loss = 0.0564094, acc = 0.98\n",
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      "[Validation] Batch ID = 9110, loss = 0.0645217, acc = 0.96\n",
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      "[Validation] Batch ID = 9160, loss = 0.059866, acc = 0.96\n",
      "[Train] Batch ID = 9170, loss = 0.0285539, acc = 1.0\n",
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      "[Train] Batch ID = 9190, loss = 0.0494826, acc = 1.0\n",
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      "[Validation] Batch ID = 9230, loss = 0.105097, acc = 0.88\n",
      "[Train] Batch ID = 9240, loss = 0.248899, acc = 0.88\n",
      "[Validation] Batch ID = 9240, loss = 0.0569448, acc = 0.96\n",
      "[Train] Batch ID = 9250, loss = 0.0319467, acc = 1.0\n",
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      "[Validation] Batch ID = 9330, loss = 0.0674733, acc = 0.94\n",
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      "[Validation] Batch ID = 9340, loss = 0.0390324, acc = 1.0\n",
      "[Train] Batch ID = 9350, loss = 0.0282579, acc = 1.0\n",
      "[Validation] Batch ID = 9350, loss = 0.0569153, acc = 0.98\n",
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      "[Validation] Batch ID = 9360, loss = 0.0835898, acc = 0.96\n",
      "[Train] Batch ID = 9370, loss = 0.250693, acc = 0.78\n",
      "[Validation] Batch ID = 9370, loss = 0.0923268, acc = 0.94\n",
      "[Train] Batch ID = 9380, loss = 0.0383909, acc = 1.0\n",
      "[Validation] Batch ID = 9380, loss = 0.0753173, acc = 0.98\n",
      "[Train] Batch ID = 9390, loss = 0.28642, acc = 0.68\n",
      "[Validation] Batch ID = 9390, loss = 0.0844799, acc = 0.96\n",
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      "[Validation] Batch ID = 9400, loss = 0.0748619, acc = 0.94\n",
      "[Train] Batch ID = 9410, loss = 0.299166, acc = 0.62\n",
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      "[Train] Batch ID = 9420, loss = 0.230754, acc = 0.84\n",
      "[Validation] Batch ID = 9420, loss = 0.0900916, acc = 0.92\n",
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      "[Validation] Batch ID = 9730, loss = 0.0530235, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 9740, loss = 0.0250429, acc = 1.0\n",
      "[Validation] Batch ID = 9740, loss = 0.0458096, acc = 1.0\n",
      "[Train] Batch ID = 9750, loss = 0.0339193, acc = 1.0\n",
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      "[Train] Batch ID = 9850, loss = 0.0285242, acc = 1.0\n",
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      "[Validation] Batch ID = 9890, loss = 0.108747, acc = 0.9\n",
      "[Train] Batch ID = 9900, loss = 0.035039, acc = 1.0\n",
      "[Validation] Batch ID = 9900, loss = 0.0791783, acc = 0.94\n",
      "[Train] Batch ID = 9910, loss = 0.242656, acc = 0.72\n",
      "[Validation] Batch ID = 9910, loss = 0.0469035, acc = 1.0\n",
      "[Train] Batch ID = 9920, loss = 0.278609, acc = 0.66\n",
      "[Validation] Batch ID = 9920, loss = 0.0769367, acc = 0.94\n",
      "[Train] Batch ID = 9930, loss = 0.269497, acc = 0.68\n",
      "[Validation] Batch ID = 9930, loss = 0.0846269, acc = 0.96\n",
      "[Train] Batch ID = 9940, loss = 0.023544, acc = 1.0\n",
      "[Validation] Batch ID = 9940, loss = 0.0624251, acc = 1.0\n",
      "[Train] Batch ID = 9950, loss = 0.0249742, acc = 1.0\n",
      "[Validation] Batch ID = 9950, loss = 0.074779, acc = 0.94\n",
      "[Train] Batch ID = 9960, loss = 0.168816, acc = 0.9\n",
      "[Validation] Batch ID = 9960, loss = 0.0835778, acc = 0.92\n",
      "[Train] Batch ID = 9970, loss = 0.0314519, acc = 1.0\n",
      "[Validation] Batch ID = 9970, loss = 0.0639506, acc = 1.0\n",
      "[Train] Batch ID = 9980, loss = 0.26008, acc = 0.74\n",
      "[Validation] Batch ID = 9980, loss = 0.0971057, acc = 0.94\n",
      "[Train] Batch ID = 9990, loss = 0.214483, acc = 0.82\n",
      "[Validation] Batch ID = 9990, loss = 0.0537682, acc = 0.98\n",
      "[Train] Batch ID = 10000, loss = 0.274471, acc = 0.6\n",
      "[Validation] Batch ID = 10000, loss = 0.0670121, acc = 0.92\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0658573 Best loss: 0.0730149\n",
      "[TOTAL Validation] Batch ID = 10000, loss = 0.0658573, acc = 0.962585034014\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.3106724901291001\n",
      "[Train] Batch ID = 10010, loss = 0.0320103, acc = 1.0\n",
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      "[Validation] Batch ID = 10720, loss = 0.0553731, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 10730, loss = 0.028526, acc = 1.0\n",
      "[Validation] Batch ID = 10730, loss = 0.0439219, acc = 0.98\n",
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      "[Train] Batch ID = 10750, loss = 0.0290804, acc = 1.0\n",
      "[Validation] Batch ID = 10750, loss = 0.0532277, acc = 0.96\n",
      "[Train] Batch ID = 10760, loss = 0.0244261, acc = 1.0\n",
      "[Validation] Batch ID = 10760, loss = 0.0606453, acc = 0.98\n",
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      "[Train] Batch ID = 10900, loss = 0.26162, acc = 0.8\n",
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      "[Train] Batch ID = 10910, loss = 0.0307617, acc = 1.0\n",
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      "[Train] Batch ID = 10920, loss = 0.247803, acc = 0.82\n",
      "[Validation] Batch ID = 10920, loss = 0.0725425, acc = 0.94\n",
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      "[Validation] Batch ID = 10930, loss = 0.0892456, acc = 0.9\n",
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      "[Validation] Batch ID = 10940, loss = 0.0423664, acc = 0.98\n",
      "[Train] Batch ID = 10950, loss = 0.026734, acc = 1.0\n",
      "[Validation] Batch ID = 10950, loss = 0.0483184, acc = 0.98\n",
      "[Train] Batch ID = 10960, loss = 0.0158559, acc = 1.0\n",
      "[Validation] Batch ID = 10960, loss = 0.0472608, acc = 1.0\n",
      "[Train] Batch ID = 10970, loss = 0.205094, acc = 0.82\n",
      "[Validation] Batch ID = 10970, loss = 0.0575934, acc = 0.98\n",
      "[Train] Batch ID = 10980, loss = 0.264071, acc = 0.76\n",
      "[Validation] Batch ID = 10980, loss = 0.043741, acc = 0.98\n",
      "[Train] Batch ID = 10990, loss = 0.231684, acc = 0.8\n",
      "[Validation] Batch ID = 10990, loss = 0.0772302, acc = 0.96\n",
      "[Train] Batch ID = 11000, loss = 0.0215243, acc = 1.0\n",
      "[Validation] Batch ID = 11000, loss = 0.062636, acc = 0.98\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0642379 Best loss: 0.0658573\n",
      "[TOTAL Validation] Batch ID = 11000, loss = 0.0642379, acc = 0.962358276644\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.27960524111619006\n",
      "[Train] Batch ID = 11010, loss = 0.0267694, acc = 1.0\n",
      "[Validation] Batch ID = 11010, loss = 0.0556503, acc = 1.0\n",
      "[Train] Batch ID = 11020, loss = 0.0217711, acc = 1.0\n",
      "[Validation] Batch ID = 11020, loss = 0.0690887, acc = 0.96\n",
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      "[Validation] Batch ID = 11030, loss = 0.0602076, acc = 0.98\n",
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      "[Validation] Batch ID = 11040, loss = 0.0623529, acc = 0.94\n",
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      "[Validation] Batch ID = 11050, loss = 0.0533513, acc = 0.98\n",
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      "[Validation] Batch ID = 11060, loss = 0.103609, acc = 0.88\n",
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      "[Validation] Batch ID = 11070, loss = 0.0802765, acc = 0.94\n",
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      "[Validation] Batch ID = 11080, loss = 0.0401566, acc = 1.0\n",
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      "[Validation] Batch ID = 11720, loss = 0.040622, acc = 0.96\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 11730, loss = 0.0208527, acc = 1.0\n",
      "[Validation] Batch ID = 11730, loss = 0.0755471, acc = 0.96\n",
      "[Train] Batch ID = 11740, loss = 0.0155891, acc = 1.0\n",
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      "[Validation] Batch ID = 11760, loss = 0.0515345, acc = 0.96\n",
      "[Train] Batch ID = 11770, loss = 0.0151247, acc = 1.0\n",
      "[Validation] Batch ID = 11770, loss = 0.0697787, acc = 0.94\n",
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      "[Validation] Batch ID = 11800, loss = 0.0702373, acc = 0.94\n",
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      "[Validation] Batch ID = 11820, loss = 0.0395371, acc = 0.98\n",
      "[Train] Batch ID = 11830, loss = 0.0199083, acc = 1.0\n",
      "[Validation] Batch ID = 11830, loss = 0.0485425, acc = 0.98\n",
      "[Train] Batch ID = 11840, loss = 0.211462, acc = 0.8\n",
      "[Validation] Batch ID = 11840, loss = 0.0591463, acc = 0.96\n",
      "[Train] Batch ID = 11850, loss = 0.0233504, acc = 1.0\n",
      "[Validation] Batch ID = 11850, loss = 0.0383064, acc = 1.0\n",
      "[Train] Batch ID = 11860, loss = 0.0284902, acc = 1.0\n",
      "[Validation] Batch ID = 11860, loss = 0.0395091, acc = 0.98\n",
      "[Train] Batch ID = 11870, loss = 0.0270664, acc = 1.0\n",
      "[Validation] Batch ID = 11870, loss = 0.0797209, acc = 0.92\n",
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      "[Validation] Batch ID = 11880, loss = 0.057735, acc = 0.96\n",
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      "[Validation] Batch ID = 11890, loss = 0.0649975, acc = 0.96\n",
      "[Train] Batch ID = 11900, loss = 0.0239744, acc = 1.0\n",
      "[Validation] Batch ID = 11900, loss = 0.0714888, acc = 0.94\n",
      "[Train] Batch ID = 11910, loss = 0.208503, acc = 0.86\n",
      "[Validation] Batch ID = 11910, loss = 0.0766505, acc = 0.96\n",
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      "[Validation] Batch ID = 11920, loss = 0.0728248, acc = 0.94\n",
      "[Train] Batch ID = 11930, loss = 0.0152934, acc = 1.0\n",
      "[Validation] Batch ID = 11930, loss = 0.0674915, acc = 0.96\n",
      "[Train] Batch ID = 11940, loss = 0.021132, acc = 1.0\n",
      "[Validation] Batch ID = 11940, loss = 0.0491492, acc = 0.98\n",
      "[Train] Batch ID = 11950, loss = 0.0164855, acc = 1.0\n",
      "[Validation] Batch ID = 11950, loss = 0.0534265, acc = 0.98\n",
      "[Train] Batch ID = 11960, loss = 0.0180065, acc = 1.0\n",
      "[Validation] Batch ID = 11960, loss = 0.0387668, acc = 0.98\n",
      "[Train] Batch ID = 11970, loss = 0.0178865, acc = 1.0\n",
      "[Validation] Batch ID = 11970, loss = 0.0348324, acc = 1.0\n",
      "[Train] Batch ID = 11980, loss = 0.0172653, acc = 1.0\n",
      "[Validation] Batch ID = 11980, loss = 0.0583721, acc = 0.96\n",
      "[Train] Batch ID = 11990, loss = 0.215337, acc = 0.78\n",
      "[Validation] Batch ID = 11990, loss = 0.0599743, acc = 0.96\n",
      "[Train] Batch ID = 12000, loss = 0.0225344, acc = 1.0\n",
      "[Validation] Batch ID = 12000, loss = 0.0570171, acc = 0.94\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0569155 Best loss: 0.0642379\n",
      "[TOTAL Validation] Batch ID = 12000, loss = 0.0569155, acc = 0.965079365079\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.2516447170045711\n",
      "[Train] Batch ID = 12010, loss = 0.0175699, acc = 1.0\n",
      "[Validation] Batch ID = 12010, loss = 0.0805498, acc = 0.9\n",
      "[Train] Batch ID = 12020, loss = 0.0187703, acc = 1.0\n",
      "[Validation] Batch ID = 12020, loss = 0.0750015, acc = 0.94\n",
      "[Train] Batch ID = 12030, loss = 0.237082, acc = 0.74\n",
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      "[Validation] Batch ID = 12720, loss = 0.0546143, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 12730, loss = 0.0230284, acc = 1.0\n",
      "[Validation] Batch ID = 12730, loss = 0.0410447, acc = 0.98\n",
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      "[Validation] Batch ID = 12930, loss = 0.0572876, acc = 0.98\n",
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      "[Validation] Batch ID = 12940, loss = 0.0417758, acc = 1.0\n",
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      "[Validation] Batch ID = 12950, loss = 0.0516375, acc = 0.96\n",
      "[Train] Batch ID = 12960, loss = 0.0182955, acc = 1.0\n",
      "[Validation] Batch ID = 12960, loss = 0.0656333, acc = 0.92\n",
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      "[Validation] Batch ID = 12970, loss = 0.0401941, acc = 0.98\n",
      "[Train] Batch ID = 12980, loss = 0.220062, acc = 0.8\n",
      "[Validation] Batch ID = 12980, loss = 0.033187, acc = 1.0\n",
      "[Train] Batch ID = 12990, loss = 0.244467, acc = 0.78\n",
      "[Validation] Batch ID = 12990, loss = 0.0490043, acc = 0.98\n",
      "[Train] Batch ID = 13000, loss = 0.0107988, acc = 1.0\n",
      "[Validation] Batch ID = 13000, loss = 0.0635478, acc = 0.98\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0540795 Best loss: 0.0569155\n",
      "[TOTAL Validation] Batch ID = 13000, loss = 0.0540795, acc = 0.966213151927\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.22648024530411398\n",
      "[Train] Batch ID = 13010, loss = 0.0217956, acc = 1.0\n",
      "[Validation] Batch ID = 13010, loss = 0.0555536, acc = 0.96\n",
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      "[Validation] Batch ID = 13080, loss = 0.0463636, acc = 1.0\n",
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      "[Train] Batch ID = 13110, loss = 0.244184, acc = 0.78\n",
      "[Validation] Batch ID = 13110, loss = 0.0339549, acc = 1.0\n",
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      "[Train] Batch ID = 13310, loss = 0.221866, acc = 0.8\n",
      "[Validation] Batch ID = 13310, loss = 0.0789589, acc = 0.94\n",
      "[Train] Batch ID = 13320, loss = 0.255754, acc = 0.78\n",
      "[Validation] Batch ID = 13320, loss = 0.107457, acc = 0.9\n",
      "[Train] Batch ID = 13330, loss = 0.0242144, acc = 1.0\n",
      "[Validation] Batch ID = 13330, loss = 0.0270562, acc = 0.98\n",
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      "[Validation] Batch ID = 13720, loss = 0.0610131, acc = 0.96\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 13730, loss = 0.0112979, acc = 1.0\n",
      "[Validation] Batch ID = 13730, loss = 0.0420173, acc = 0.98\n",
      "[Train] Batch ID = 13740, loss = 0.220664, acc = 0.82\n",
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      "[Train] Batch ID = 13900, loss = 0.0134029, acc = 1.0\n",
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      "[Validation] Batch ID = 13930, loss = 0.0714316, acc = 0.94\n",
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      "[Validation] Batch ID = 13940, loss = 0.0739671, acc = 0.92\n",
      "[Train] Batch ID = 13950, loss = 0.247025, acc = 0.76\n",
      "[Validation] Batch ID = 13950, loss = 0.0429501, acc = 0.98\n",
      "[Train] Batch ID = 13960, loss = 0.0195282, acc = 1.0\n",
      "[Validation] Batch ID = 13960, loss = 0.0639656, acc = 0.96\n",
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      "[Validation] Batch ID = 13970, loss = 0.061947, acc = 0.94\n",
      "[Train] Batch ID = 13980, loss = 0.0175333, acc = 1.0\n",
      "[Validation] Batch ID = 13980, loss = 0.0363198, acc = 1.0\n",
      "[Train] Batch ID = 13990, loss = 0.0146424, acc = 1.0\n",
      "[Validation] Batch ID = 13990, loss = 0.043886, acc = 0.98\n",
      "[Train] Batch ID = 14000, loss = 0.0214707, acc = 1.0\n",
      "[Validation] Batch ID = 14000, loss = 0.0592368, acc = 0.94\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0517805 Best loss: 0.0540795\n",
      "[TOTAL Validation] Batch ID = 14000, loss = 0.0517805, acc = 0.966213151927\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.20383222077370258\n",
      "[Train] Batch ID = 14010, loss = 0.203466, acc = 0.84\n",
      "[Validation] Batch ID = 14010, loss = 0.0562444, acc = 0.96\n",
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      "[Validation] Batch ID = 14690, loss = 0.0451521, acc = 1.0\n",
      "[Train] Batch ID = 14700, loss = 0.0221791, acc = 1.0\n",
      "[Validation] Batch ID = 14700, loss = 0.0446644, acc = 0.96\n",
      "[Train] Batch ID = 14710, loss = 0.00831289, acc = 1.0\n",
      "[Validation] Batch ID = 14710, loss = 0.0512175, acc = 0.92\n",
      "[Train] Batch ID = 14720, loss = 0.0184709, acc = 1.0\n",
      "[Validation] Batch ID = 14720, loss = 0.034469, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 14730, loss = 0.228537, acc = 0.84\n",
      "[Validation] Batch ID = 14730, loss = 0.0539398, acc = 0.96\n",
      "[Train] Batch ID = 14740, loss = 0.0124278, acc = 1.0\n",
      "[Validation] Batch ID = 14740, loss = 0.0494461, acc = 0.96\n",
      "[Train] Batch ID = 14750, loss = 0.266271, acc = 0.72\n",
      "[Validation] Batch ID = 14750, loss = 0.0475507, acc = 0.96\n",
      "[Train] Batch ID = 14760, loss = 0.0165065, acc = 1.0\n",
      "[Validation] Batch ID = 14760, loss = 0.0451781, acc = 0.96\n",
      "[Train] Batch ID = 14770, loss = 0.00855368, acc = 1.0\n",
      "[Validation] Batch ID = 14770, loss = 0.043562, acc = 0.98\n",
      "[Train] Batch ID = 14780, loss = 0.011566, acc = 1.0\n",
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      "[Train] Batch ID = 14790, loss = 0.011488, acc = 1.0\n",
      "[Validation] Batch ID = 14790, loss = 0.0405886, acc = 0.98\n",
      "[Train] Batch ID = 14800, loss = 0.234057, acc = 0.74\n",
      "[Validation] Batch ID = 14800, loss = 0.0325993, acc = 1.0\n",
      "[Train] Batch ID = 14810, loss = 0.0104344, acc = 1.0\n",
      "[Validation] Batch ID = 14810, loss = 0.0442596, acc = 1.0\n",
      "[Train] Batch ID = 14820, loss = 0.0171571, acc = 1.0\n",
      "[Validation] Batch ID = 14820, loss = 0.0417559, acc = 0.96\n",
      "[Train] Batch ID = 14830, loss = 0.00779435, acc = 1.0\n",
      "[Validation] Batch ID = 14830, loss = 0.0331379, acc = 0.96\n",
      "[Train] Batch ID = 14840, loss = 0.00860059, acc = 1.0\n",
      "[Validation] Batch ID = 14840, loss = 0.0706909, acc = 0.88\n",
      "[Train] Batch ID = 14850, loss = 0.0104501, acc = 1.0\n",
      "[Validation] Batch ID = 14850, loss = 0.0296497, acc = 0.98\n",
      "[Train] Batch ID = 14860, loss = 0.260989, acc = 0.7\n",
      "[Validation] Batch ID = 14860, loss = 0.0541074, acc = 0.96\n",
      "[Train] Batch ID = 14870, loss = 0.0100119, acc = 1.0\n",
      "[Validation] Batch ID = 14870, loss = 0.0282313, acc = 1.0\n",
      "[Train] Batch ID = 14880, loss = 0.225192, acc = 0.76\n",
      "[Validation] Batch ID = 14880, loss = 0.0380161, acc = 0.98\n",
      "[Train] Batch ID = 14890, loss = 0.208854, acc = 0.8\n",
      "[Validation] Batch ID = 14890, loss = 0.0246938, acc = 1.0\n",
      "[Train] Batch ID = 14900, loss = 0.0226865, acc = 1.0\n",
      "[Validation] Batch ID = 14900, loss = 0.0450178, acc = 1.0\n",
      "[Train] Batch ID = 14910, loss = 0.0164565, acc = 1.0\n",
      "[Validation] Batch ID = 14910, loss = 0.0307414, acc = 1.0\n",
      "[Train] Batch ID = 14920, loss = 0.0121235, acc = 1.0\n",
      "[Validation] Batch ID = 14920, loss = 0.0331958, acc = 0.98\n",
      "[Train] Batch ID = 14930, loss = 0.0153693, acc = 1.0\n",
      "[Validation] Batch ID = 14930, loss = 0.0278498, acc = 0.98\n",
      "[Train] Batch ID = 14940, loss = 0.012833, acc = 1.0\n",
      "[Validation] Batch ID = 14940, loss = 0.0336789, acc = 1.0\n",
      "[Train] Batch ID = 14950, loss = 0.009922, acc = 1.0\n",
      "[Validation] Batch ID = 14950, loss = 0.0497969, acc = 0.98\n",
      "[Train] Batch ID = 14960, loss = 0.0106934, acc = 1.0\n",
      "[Validation] Batch ID = 14960, loss = 0.0361566, acc = 0.98\n",
      "[Train] Batch ID = 14970, loss = 0.00720681, acc = 1.0\n",
      "[Validation] Batch ID = 14970, loss = 0.0216081, acc = 1.0\n",
      "[Train] Batch ID = 14980, loss = 0.00770243, acc = 1.0\n",
      "[Validation] Batch ID = 14980, loss = 0.0746249, acc = 0.92\n",
      "[Train] Batch ID = 14990, loss = 0.00857291, acc = 1.0\n",
      "[Validation] Batch ID = 14990, loss = 0.0442599, acc = 0.98\n",
      "[Train] Batch ID = 15000, loss = 0.00988107, acc = 1.0\n",
      "[Validation] Batch ID = 15000, loss = 0.0346062, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0475598 Best loss: 0.0517805\n",
      "[TOTAL Validation] Batch ID = 15000, loss = 0.0475598, acc = 0.968480725624\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.18344899869633233\n",
      "[Train] Batch ID = 15010, loss = 0.0195009, acc = 1.0\n",
      "[Validation] Batch ID = 15010, loss = 0.0537054, acc = 0.94\n",
      "[Train] Batch ID = 15020, loss = 0.0139916, acc = 1.0\n",
      "[Validation] Batch ID = 15020, loss = 0.0433528, acc = 0.96\n",
      "[Train] Batch ID = 15030, loss = 0.244256, acc = 0.74\n",
      "[Validation] Batch ID = 15030, loss = 0.033646, acc = 0.98\n",
      "[Train] Batch ID = 15040, loss = 0.011397, acc = 1.0\n",
      "[Validation] Batch ID = 15040, loss = 0.0215763, acc = 1.0\n",
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      "[Validation] Batch ID = 15050, loss = 0.0387565, acc = 1.0\n",
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      "[Train] Batch ID = 15110, loss = 0.0187059, acc = 1.0\n",
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      "[Validation] Batch ID = 15720, loss = 0.0470986, acc = 0.96\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 15730, loss = 0.0155977, acc = 1.0\n",
      "[Validation] Batch ID = 15730, loss = 0.04912, acc = 0.96\n",
      "[Train] Batch ID = 15740, loss = 0.0194605, acc = 1.0\n",
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      "[Validation] Batch ID = 15760, loss = 0.0460848, acc = 0.94\n",
      "[Train] Batch ID = 15770, loss = 0.0093744, acc = 1.0\n",
      "[Validation] Batch ID = 15770, loss = 0.047696, acc = 0.98\n",
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      "[Validation] Batch ID = 15780, loss = 0.0429961, acc = 0.96\n",
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      "[Validation] Batch ID = 15790, loss = 0.053271, acc = 0.94\n",
      "[Train] Batch ID = 15800, loss = 0.00886228, acc = 1.0\n",
      "[Validation] Batch ID = 15800, loss = 0.0485335, acc = 0.98\n",
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      "[Validation] Batch ID = 15820, loss = 0.0360679, acc = 0.98\n",
      "[Train] Batch ID = 15830, loss = 0.0148384, acc = 1.0\n",
      "[Validation] Batch ID = 15830, loss = 0.0222546, acc = 0.98\n",
      "[Train] Batch ID = 15840, loss = 0.243678, acc = 0.84\n",
      "[Validation] Batch ID = 15840, loss = 0.0439197, acc = 0.98\n",
      "[Train] Batch ID = 15850, loss = 0.00912225, acc = 1.0\n",
      "[Validation] Batch ID = 15850, loss = 0.0579148, acc = 0.96\n",
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      "[Validation] Batch ID = 15860, loss = 0.0420261, acc = 1.0\n",
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      "[Validation] Batch ID = 15870, loss = 0.0498796, acc = 0.96\n",
      "[Train] Batch ID = 15880, loss = 0.182638, acc = 0.92\n",
      "[Validation] Batch ID = 15880, loss = 0.0492946, acc = 0.96\n",
      "[Train] Batch ID = 15890, loss = 0.0109115, acc = 1.0\n",
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      "[Train] Batch ID = 15900, loss = 0.0132191, acc = 1.0\n",
      "[Validation] Batch ID = 15900, loss = 0.0412078, acc = 0.98\n",
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      "[Validation] Batch ID = 15920, loss = 0.0680322, acc = 0.94\n",
      "[Train] Batch ID = 15930, loss = 0.0174861, acc = 1.0\n",
      "[Validation] Batch ID = 15930, loss = 0.0315664, acc = 1.0\n",
      "[Train] Batch ID = 15940, loss = 0.0103326, acc = 1.0\n",
      "[Validation] Batch ID = 15940, loss = 0.0535319, acc = 0.96\n",
      "[Train] Batch ID = 15950, loss = 0.00871107, acc = 1.0\n",
      "[Validation] Batch ID = 15950, loss = 0.0493256, acc = 0.98\n",
      "[Train] Batch ID = 15960, loss = 0.0238107, acc = 1.0\n",
      "[Validation] Batch ID = 15960, loss = 0.0428512, acc = 1.0\n",
      "[Train] Batch ID = 15970, loss = 0.0140351, acc = 1.0\n",
      "[Validation] Batch ID = 15970, loss = 0.0627117, acc = 0.92\n",
      "[Train] Batch ID = 15980, loss = 0.0156468, acc = 1.0\n",
      "[Validation] Batch ID = 15980, loss = 0.0521207, acc = 0.98\n",
      "[Train] Batch ID = 15990, loss = 0.011368, acc = 1.0\n",
      "[Validation] Batch ID = 15990, loss = 0.0514348, acc = 0.96\n",
      "[Train] Batch ID = 16000, loss = 0.213225, acc = 0.78\n",
      "[Validation] Batch ID = 16000, loss = 0.0350242, acc = 0.98\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0446877 Best loss: 0.0475598\n",
      "[TOTAL Validation] Batch ID = 16000, loss = 0.0446877, acc = 0.968480725624\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.1651040988266991\n",
      "[Train] Batch ID = 16010, loss = 0.0111596, acc = 1.0\n",
      "[Validation] Batch ID = 16010, loss = 0.062686, acc = 0.92\n",
      "[Train] Batch ID = 16020, loss = 0.00728413, acc = 1.0\n",
      "[Validation] Batch ID = 16020, loss = 0.0375616, acc = 0.98\n",
      "[Train] Batch ID = 16030, loss = 0.0219501, acc = 1.0\n",
      "[Validation] Batch ID = 16030, loss = 0.0471256, acc = 0.98\n",
      "[Train] Batch ID = 16040, loss = 0.00826156, acc = 1.0\n",
      "[Validation] Batch ID = 16040, loss = 0.041215, acc = 0.98\n",
      "[Train] Batch ID = 16050, loss = 0.0118491, acc = 1.0\n",
      "[Validation] Batch ID = 16050, loss = 0.0551145, acc = 0.94\n",
      "[Train] Batch ID = 16060, loss = 0.0173468, acc = 1.0\n",
      "[Validation] Batch ID = 16060, loss = 0.0197725, acc = 1.0\n",
      "[Train] Batch ID = 16070, loss = 0.00950886, acc = 1.0\n",
      "[Validation] Batch ID = 16070, loss = 0.0636382, acc = 0.94\n",
      "[Train] Batch ID = 16080, loss = 0.011442, acc = 1.0\n",
      "[Validation] Batch ID = 16080, loss = 0.0706439, acc = 0.92\n",
      "[Train] Batch ID = 16090, loss = 0.0182996, acc = 1.0\n",
      "[Validation] Batch ID = 16090, loss = 0.0451216, acc = 0.94\n",
      "[Train] Batch ID = 16100, loss = 0.00755775, acc = 1.0\n",
      "[Validation] Batch ID = 16100, loss = 0.068078, acc = 0.92\n",
      "[Train] Batch ID = 16110, loss = 0.0202015, acc = 1.0\n",
      "[Validation] Batch ID = 16110, loss = 0.0587874, acc = 0.94\n",
      "[Train] Batch ID = 16120, loss = 0.0161636, acc = 1.0\n",
      "[Validation] Batch ID = 16120, loss = 0.040745, acc = 0.98\n",
      "[Train] Batch ID = 16130, loss = 0.234428, acc = 0.82\n",
      "[Validation] Batch ID = 16130, loss = 0.0424431, acc = 1.0\n",
      "[Train] Batch ID = 16140, loss = 0.00756473, acc = 1.0\n",
      "[Validation] Batch ID = 16140, loss = 0.0482823, acc = 0.96\n",
      "[Train] Batch ID = 16150, loss = 0.0139153, acc = 1.0\n",
      "[Validation] Batch ID = 16150, loss = 0.0616782, acc = 0.96\n",
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      "[Validation] Batch ID = 16160, loss = 0.0416498, acc = 0.94\n",
      "[Train] Batch ID = 16170, loss = 0.0174369, acc = 1.0\n",
      "[Validation] Batch ID = 16170, loss = 0.0381829, acc = 0.98\n",
      "[Train] Batch ID = 16180, loss = 0.00543556, acc = 1.0\n",
      "[Validation] Batch ID = 16180, loss = 0.0597887, acc = 0.96\n",
      "[Train] Batch ID = 16190, loss = 0.22521, acc = 0.78\n",
      "[Validation] Batch ID = 16190, loss = 0.0809974, acc = 0.92\n",
      "[Train] Batch ID = 16200, loss = 0.0122592, acc = 1.0\n",
      "[Validation] Batch ID = 16200, loss = 0.0306249, acc = 1.0\n",
      "[Train] Batch ID = 16210, loss = 0.194817, acc = 0.8\n",
      "[Validation] Batch ID = 16210, loss = 0.0383178, acc = 0.98\n",
      "[Train] Batch ID = 16220, loss = 0.179779, acc = 0.8\n",
      "[Validation] Batch ID = 16220, loss = 0.0444652, acc = 0.98\n",
      "[Train] Batch ID = 16230, loss = 0.0114048, acc = 1.0\n",
      "[Validation] Batch ID = 16230, loss = 0.0371979, acc = 0.98\n",
      "[Train] Batch ID = 16240, loss = 0.00890548, acc = 1.0\n",
      "[Validation] Batch ID = 16240, loss = 0.0296927, acc = 0.98\n",
      "[Train] Batch ID = 16250, loss = 0.183314, acc = 0.88\n",
      "[Validation] Batch ID = 16250, loss = 0.0376843, acc = 0.98\n",
      "[Train] Batch ID = 16260, loss = 0.260523, acc = 0.72\n",
      "[Validation] Batch ID = 16260, loss = 0.0430788, acc = 0.98\n",
      "[Train] Batch ID = 16270, loss = 0.0131867, acc = 1.0\n",
      "[Validation] Batch ID = 16270, loss = 0.0637037, acc = 0.92\n",
      "[Train] Batch ID = 16280, loss = 0.0142716, acc = 1.0\n",
      "[Validation] Batch ID = 16280, loss = 0.0570839, acc = 0.94\n",
      "[Train] Batch ID = 16290, loss = 0.0122782, acc = 1.0\n",
      "[Validation] Batch ID = 16290, loss = 0.0537266, acc = 0.98\n",
      "[Train] Batch ID = 16300, loss = 0.0126009, acc = 1.0\n",
      "[Validation] Batch ID = 16300, loss = 0.0448658, acc = 0.98\n",
      "[Train] Batch ID = 16310, loss = 0.0157917, acc = 1.0\n",
      "[Validation] Batch ID = 16310, loss = 0.0605974, acc = 0.94\n",
      "[Train] Batch ID = 16320, loss = 0.0139092, acc = 1.0\n",
      "[Validation] Batch ID = 16320, loss = 0.0395077, acc = 0.96\n",
      "[Train] Batch ID = 16330, loss = 0.0116256, acc = 1.0\n",
      "[Validation] Batch ID = 16330, loss = 0.0686646, acc = 0.96\n",
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      "[Validation] Batch ID = 16340, loss = 0.0281766, acc = 1.0\n",
      "[Train] Batch ID = 16350, loss = 0.011349, acc = 1.0\n",
      "[Validation] Batch ID = 16350, loss = 0.0491005, acc = 0.98\n",
      "[Train] Batch ID = 16360, loss = 0.0121404, acc = 1.0\n",
      "[Validation] Batch ID = 16360, loss = 0.0712704, acc = 0.92\n",
      "[Train] Batch ID = 16370, loss = 0.0123089, acc = 1.0\n",
      "[Validation] Batch ID = 16370, loss = 0.0956472, acc = 0.88\n",
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      "[Validation] Batch ID = 16380, loss = 0.0643815, acc = 0.9\n",
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      "[Train] Batch ID = 16400, loss = 0.209254, acc = 0.84\n",
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      "[Validation] Batch ID = 16430, loss = 0.0460424, acc = 0.98\n",
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      "[Validation] Batch ID = 16450, loss = 0.0646354, acc = 0.96\n",
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      "[Validation] Batch ID = 16460, loss = 0.065985, acc = 0.94\n",
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      "[Validation] Batch ID = 16720, loss = 0.0425249, acc = 0.96\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 16730, loss = 0.0101594, acc = 1.0\n",
      "[Validation] Batch ID = 16730, loss = 0.0519483, acc = 0.94\n",
      "[Train] Batch ID = 16740, loss = 0.00984201, acc = 1.0\n",
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      "[Train] Batch ID = 16760, loss = 0.179914, acc = 0.86\n",
      "[Validation] Batch ID = 16760, loss = 0.036911, acc = 0.98\n",
      "[Train] Batch ID = 16770, loss = 0.0173769, acc = 1.0\n",
      "[Validation] Batch ID = 16770, loss = 0.0518868, acc = 0.94\n",
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      "[Validation] Batch ID = 16790, loss = 0.0577672, acc = 0.92\n",
      "[Train] Batch ID = 16800, loss = 0.183624, acc = 0.86\n",
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      "[Train] Batch ID = 16830, loss = 0.243592, acc = 0.76\n",
      "[Validation] Batch ID = 16830, loss = 0.0399112, acc = 1.0\n",
      "[Train] Batch ID = 16840, loss = 0.0157409, acc = 1.0\n",
      "[Validation] Batch ID = 16840, loss = 0.0852602, acc = 0.9\n",
      "[Train] Batch ID = 16850, loss = 0.234445, acc = 0.78\n",
      "[Validation] Batch ID = 16850, loss = 0.0337557, acc = 1.0\n",
      "[Train] Batch ID = 16860, loss = 0.0186027, acc = 0.98\n",
      "[Validation] Batch ID = 16860, loss = 0.0637741, acc = 0.92\n",
      "[Train] Batch ID = 16870, loss = 0.214686, acc = 0.82\n",
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      "[Train] Batch ID = 16890, loss = 0.0084759, acc = 1.0\n",
      "[Validation] Batch ID = 16890, loss = 0.045178, acc = 0.98\n",
      "[Train] Batch ID = 16900, loss = 0.0101897, acc = 1.0\n",
      "[Validation] Batch ID = 16900, loss = 0.0283651, acc = 1.0\n",
      "[Train] Batch ID = 16910, loss = 0.224305, acc = 0.68\n",
      "[Validation] Batch ID = 16910, loss = 0.0467173, acc = 0.98\n",
      "[Train] Batch ID = 16920, loss = 0.00930812, acc = 1.0\n",
      "[Validation] Batch ID = 16920, loss = 0.0478665, acc = 0.98\n",
      "[Train] Batch ID = 16930, loss = 0.017007, acc = 1.0\n",
      "[Validation] Batch ID = 16930, loss = 0.0472921, acc = 1.0\n",
      "[Train] Batch ID = 16940, loss = 0.0108301, acc = 1.0\n",
      "[Validation] Batch ID = 16940, loss = 0.0370016, acc = 0.96\n",
      "[Train] Batch ID = 16950, loss = 0.226896, acc = 0.82\n",
      "[Validation] Batch ID = 16950, loss = 0.038037, acc = 0.96\n",
      "[Train] Batch ID = 16960, loss = 0.198158, acc = 0.84\n",
      "[Validation] Batch ID = 16960, loss = 0.0307961, acc = 0.98\n",
      "[Train] Batch ID = 16970, loss = 0.0169276, acc = 1.0\n",
      "[Validation] Batch ID = 16970, loss = 0.0791598, acc = 0.94\n",
      "[Train] Batch ID = 16980, loss = 0.00820132, acc = 1.0\n",
      "[Validation] Batch ID = 16980, loss = 0.0363663, acc = 0.98\n",
      "[Train] Batch ID = 16990, loss = 0.00814005, acc = 1.0\n",
      "[Validation] Batch ID = 16990, loss = 0.049557, acc = 0.94\n",
      "[Train] Batch ID = 17000, loss = 0.0081375, acc = 1.0\n",
      "[Validation] Batch ID = 17000, loss = 0.0368923, acc = 0.98\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0435779 Best loss: 0.0446877\n",
      "[TOTAL Validation] Batch ID = 17000, loss = 0.0435779, acc = 0.968480725624\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.1485936889440292\n",
      "[Train] Batch ID = 17010, loss = 0.0107931, acc = 1.0\n",
      "[Validation] Batch ID = 17010, loss = 0.0318854, acc = 0.98\n",
      "[Train] Batch ID = 17020, loss = 0.0109536, acc = 1.0\n",
      "[Validation] Batch ID = 17020, loss = 0.0277872, acc = 1.0\n",
      "[Train] Batch ID = 17030, loss = 0.00786561, acc = 1.0\n",
      "[Validation] Batch ID = 17030, loss = 0.0407727, acc = 0.96\n",
      "[Train] Batch ID = 17040, loss = 0.0107863, acc = 1.0\n",
      "[Validation] Batch ID = 17040, loss = 0.0496761, acc = 0.96\n",
      "[Train] Batch ID = 17050, loss = 0.0155346, acc = 1.0\n",
      "[Validation] Batch ID = 17050, loss = 0.0519211, acc = 0.94\n",
      "[Train] Batch ID = 17060, loss = 0.00993681, acc = 1.0\n",
      "[Validation] Batch ID = 17060, loss = 0.0298174, acc = 1.0\n",
      "[Train] Batch ID = 17070, loss = 0.0140413, acc = 1.0\n",
      "[Validation] Batch ID = 17070, loss = 0.0674547, acc = 0.92\n",
      "[Train] Batch ID = 17080, loss = 0.217565, acc = 0.78\n",
      "[Validation] Batch ID = 17080, loss = 0.0296709, acc = 0.98\n",
      "[Train] Batch ID = 17090, loss = 0.00863693, acc = 1.0\n",
      "[Validation] Batch ID = 17090, loss = 0.0712167, acc = 0.96\n",
      "[Train] Batch ID = 17100, loss = 0.20093, acc = 0.82\n",
      "[Validation] Batch ID = 17100, loss = 0.041432, acc = 0.98\n",
      "[Train] Batch ID = 17110, loss = 0.0140526, acc = 1.0\n",
      "[Validation] Batch ID = 17110, loss = 0.0345174, acc = 1.0\n",
      "[Train] Batch ID = 17120, loss = 0.0120637, acc = 1.0\n",
      "[Validation] Batch ID = 17120, loss = 0.0408866, acc = 0.98\n",
      "[Train] Batch ID = 17130, loss = 0.0109781, acc = 1.0\n",
      "[Validation] Batch ID = 17130, loss = 0.0650348, acc = 0.94\n",
      "[Train] Batch ID = 17140, loss = 0.0122201, acc = 1.0\n",
      "[Validation] Batch ID = 17140, loss = 0.0626181, acc = 0.96\n",
      "[Train] Batch ID = 17150, loss = 0.00975302, acc = 1.0\n",
      "[Validation] Batch ID = 17150, loss = 0.0617188, acc = 0.94\n",
      "[Train] Batch ID = 17160, loss = 0.0132185, acc = 1.0\n",
      "[Validation] Batch ID = 17160, loss = 0.0202728, acc = 1.0\n",
      "[Train] Batch ID = 17170, loss = 0.00865579, acc = 1.0\n",
      "[Validation] Batch ID = 17170, loss = 0.0396759, acc = 0.98\n",
      "[Train] Batch ID = 17180, loss = 0.0110322, acc = 1.0\n",
      "[Validation] Batch ID = 17180, loss = 0.0308778, acc = 0.98\n",
      "[Train] Batch ID = 17190, loss = 0.0132692, acc = 1.0\n",
      "[Validation] Batch ID = 17190, loss = 0.0445969, acc = 0.98\n",
      "[Train] Batch ID = 17200, loss = 0.241525, acc = 0.72\n",
      "[Validation] Batch ID = 17200, loss = 0.065758, acc = 0.92\n",
      "[Train] Batch ID = 17210, loss = 0.0126124, acc = 1.0\n",
      "[Validation] Batch ID = 17210, loss = 0.0751484, acc = 0.94\n",
      "[Train] Batch ID = 17220, loss = 0.00790253, acc = 1.0\n",
      "[Validation] Batch ID = 17220, loss = 0.0541396, acc = 0.96\n",
      "[Train] Batch ID = 17230, loss = 0.00621506, acc = 1.0\n",
      "[Validation] Batch ID = 17230, loss = 0.0347628, acc = 0.96\n",
      "[Train] Batch ID = 17240, loss = 0.00727094, acc = 1.0\n",
      "[Validation] Batch ID = 17240, loss = 0.0255589, acc = 0.96\n",
      "[Train] Batch ID = 17250, loss = 0.0104037, acc = 1.0\n",
      "[Validation] Batch ID = 17250, loss = 0.0229335, acc = 1.0\n",
      "[Train] Batch ID = 17260, loss = 0.0103698, acc = 1.0\n",
      "[Validation] Batch ID = 17260, loss = 0.0467634, acc = 0.98\n",
      "[Train] Batch ID = 17270, loss = 0.00828369, acc = 1.0\n",
      "[Validation] Batch ID = 17270, loss = 0.0585737, acc = 0.94\n",
      "[Train] Batch ID = 17280, loss = 0.179058, acc = 0.9\n",
      "[Validation] Batch ID = 17280, loss = 0.0597991, acc = 0.98\n",
      "[Train] Batch ID = 17290, loss = 0.00742065, acc = 1.0\n",
      "[Validation] Batch ID = 17290, loss = 0.0498983, acc = 0.96\n",
      "[Train] Batch ID = 17300, loss = 0.245728, acc = 0.74\n",
      "[Validation] Batch ID = 17300, loss = 0.0277732, acc = 0.98\n",
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      "[Validation] Batch ID = 17310, loss = 0.0597185, acc = 0.94\n",
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      "[Train] Batch ID = 17370, loss = 0.191618, acc = 0.82\n",
      "[Validation] Batch ID = 17370, loss = 0.0622186, acc = 0.94\n",
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      "[Validation] Batch ID = 17540, loss = 0.0777235, acc = 0.94\n",
      "[Train] Batch ID = 17550, loss = 0.00911315, acc = 1.0\n",
      "[Validation] Batch ID = 17550, loss = 0.017616, acc = 1.0\n",
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      "[Validation] Batch ID = 17560, loss = 0.0178919, acc = 1.0\n",
      "[Train] Batch ID = 17570, loss = 0.0114378, acc = 1.0\n",
      "[Validation] Batch ID = 17570, loss = 0.0814161, acc = 0.92\n",
      "[Train] Batch ID = 17580, loss = 0.00718676, acc = 1.0\n",
      "[Validation] Batch ID = 17580, loss = 0.0632809, acc = 0.94\n",
      "[Train] Batch ID = 17590, loss = 0.00754958, acc = 1.0\n",
      "[Validation] Batch ID = 17590, loss = 0.0332366, acc = 0.96\n",
      "[Train] Batch ID = 17600, loss = 0.00954201, acc = 1.0\n",
      "[Validation] Batch ID = 17600, loss = 0.0600804, acc = 0.94\n",
      "[Train] Batch ID = 17610, loss = 0.0107443, acc = 1.0\n",
      "[Validation] Batch ID = 17610, loss = 0.0401182, acc = 0.96\n",
      "[Train] Batch ID = 17620, loss = 0.0090585, acc = 1.0\n",
      "[Validation] Batch ID = 17620, loss = 0.0504681, acc = 0.98\n",
      "[Train] Batch ID = 17630, loss = 0.00620363, acc = 1.0\n",
      "[Validation] Batch ID = 17630, loss = 0.0467663, acc = 0.96\n",
      "[Train] Batch ID = 17640, loss = 0.243305, acc = 0.78\n",
      "[Validation] Batch ID = 17640, loss = 0.0646638, acc = 0.94\n",
      "[Train] Batch ID = 17650, loss = 0.00837727, acc = 1.0\n",
      "[Validation] Batch ID = 17650, loss = 0.0542992, acc = 0.98\n",
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      "[Validation] Batch ID = 17660, loss = 0.0562306, acc = 0.94\n",
      "[Train] Batch ID = 17670, loss = 0.00969348, acc = 1.0\n",
      "[Validation] Batch ID = 17670, loss = 0.0492313, acc = 0.96\n",
      "[Train] Batch ID = 17680, loss = 0.235892, acc = 0.76\n",
      "[Validation] Batch ID = 17680, loss = 0.0447485, acc = 0.94\n",
      "[Train] Batch ID = 17690, loss = 0.169564, acc = 0.92\n",
      "[Validation] Batch ID = 17690, loss = 0.0392783, acc = 0.98\n",
      "[Train] Batch ID = 17700, loss = 0.0122359, acc = 1.0\n",
      "[Validation] Batch ID = 17700, loss = 0.0245898, acc = 1.0\n",
      "[Train] Batch ID = 17710, loss = 0.213004, acc = 0.86\n",
      "[Validation] Batch ID = 17710, loss = 0.0592501, acc = 0.9\n",
      "[Train] Batch ID = 17720, loss = 0.00919184, acc = 1.0\n",
      "[Validation] Batch ID = 17720, loss = 0.0399713, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 17730, loss = 0.012463, acc = 1.0\n",
      "[Validation] Batch ID = 17730, loss = 0.0385852, acc = 0.98\n",
      "[Train] Batch ID = 17740, loss = 0.00961185, acc = 1.0\n",
      "[Validation] Batch ID = 17740, loss = 0.0605806, acc = 0.94\n",
      "[Train] Batch ID = 17750, loss = 0.200566, acc = 0.84\n",
      "[Validation] Batch ID = 17750, loss = 0.0392167, acc = 0.98\n",
      "[Train] Batch ID = 17760, loss = 0.00691875, acc = 1.0\n",
      "[Validation] Batch ID = 17760, loss = 0.0328109, acc = 0.98\n",
      "[Train] Batch ID = 17770, loss = 0.0101364, acc = 1.0\n",
      "[Validation] Batch ID = 17770, loss = 0.0231473, acc = 1.0\n",
      "[Train] Batch ID = 17780, loss = 0.00475077, acc = 1.0\n",
      "[Validation] Batch ID = 17780, loss = 0.0316256, acc = 0.98\n",
      "[Train] Batch ID = 17790, loss = 0.006053, acc = 1.0\n",
      "[Validation] Batch ID = 17790, loss = 0.0335807, acc = 1.0\n",
      "[Train] Batch ID = 17800, loss = 0.0114584, acc = 1.0\n",
      "[Validation] Batch ID = 17800, loss = 0.0660712, acc = 0.96\n",
      "[Train] Batch ID = 17810, loss = 0.00913265, acc = 1.0\n",
      "[Validation] Batch ID = 17810, loss = 0.0580074, acc = 0.94\n",
      "[Train] Batch ID = 17820, loss = 0.00934535, acc = 1.0\n",
      "[Validation] Batch ID = 17820, loss = 0.0443817, acc = 0.96\n",
      "[Train] Batch ID = 17830, loss = 0.0105927, acc = 1.0\n",
      "[Validation] Batch ID = 17830, loss = 0.037933, acc = 0.98\n",
      "[Train] Batch ID = 17840, loss = 0.0131286, acc = 1.0\n",
      "[Validation] Batch ID = 17840, loss = 0.0540507, acc = 0.94\n",
      "[Train] Batch ID = 17850, loss = 0.00584333, acc = 1.0\n",
      "[Validation] Batch ID = 17850, loss = 0.0281537, acc = 0.98\n",
      "[Train] Batch ID = 17860, loss = 0.00587972, acc = 1.0\n",
      "[Validation] Batch ID = 17860, loss = 0.0236906, acc = 1.0\n",
      "[Train] Batch ID = 17870, loss = 0.00535287, acc = 1.0\n",
      "[Validation] Batch ID = 17870, loss = 0.0585847, acc = 0.92\n",
      "[Train] Batch ID = 17880, loss = 0.0116786, acc = 1.0\n",
      "[Validation] Batch ID = 17880, loss = 0.0466213, acc = 0.96\n",
      "[Train] Batch ID = 17890, loss = 0.00497176, acc = 1.0\n",
      "[Validation] Batch ID = 17890, loss = 0.047242, acc = 0.96\n",
      "[Train] Batch ID = 17900, loss = 0.0107329, acc = 1.0\n",
      "[Validation] Batch ID = 17900, loss = 0.0431746, acc = 0.94\n",
      "[Train] Batch ID = 17910, loss = 0.0166263, acc = 1.0\n",
      "[Validation] Batch ID = 17910, loss = 0.0702424, acc = 0.94\n",
      "[Train] Batch ID = 17920, loss = 0.014938, acc = 0.98\n",
      "[Validation] Batch ID = 17920, loss = 0.0423169, acc = 0.96\n",
      "[Train] Batch ID = 17930, loss = 0.0104178, acc = 1.0\n",
      "[Validation] Batch ID = 17930, loss = 0.0258603, acc = 1.0\n",
      "[Train] Batch ID = 17940, loss = 0.012507, acc = 1.0\n",
      "[Validation] Batch ID = 17940, loss = 0.0350385, acc = 0.96\n",
      "[Train] Batch ID = 17950, loss = 0.00797606, acc = 1.0\n",
      "[Validation] Batch ID = 17950, loss = 0.03458, acc = 0.98\n",
      "[Train] Batch ID = 17960, loss = 0.00722717, acc = 1.0\n",
      "[Validation] Batch ID = 17960, loss = 0.0523331, acc = 1.0\n",
      "[Train] Batch ID = 17970, loss = 0.185005, acc = 0.88\n",
      "[Validation] Batch ID = 17970, loss = 0.0382238, acc = 0.96\n",
      "[Train] Batch ID = 17980, loss = 0.0193121, acc = 1.0\n",
      "[Validation] Batch ID = 17980, loss = 0.072578, acc = 0.92\n",
      "[Train] Batch ID = 17990, loss = 0.210694, acc = 0.8\n",
      "[Validation] Batch ID = 17990, loss = 0.0476215, acc = 0.98\n",
      "[Train] Batch ID = 18000, loss = 0.0102234, acc = 1.0\n",
      "[Validation] Batch ID = 18000, loss = 0.0604841, acc = 0.96\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0424544 Best loss: 0.0435779\n",
      "[TOTAL Validation] Batch ID = 18000, loss = 0.0424544, acc = 0.971655328798\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.13373432004962627\n",
      "[Train] Batch ID = 18010, loss = 0.228132, acc = 0.86\n",
      "[Validation] Batch ID = 18010, loss = 0.0244347, acc = 0.98\n",
      "[Train] Batch ID = 18020, loss = 0.0103401, acc = 1.0\n",
      "[Validation] Batch ID = 18020, loss = 0.0459979, acc = 0.96\n",
      "[Train] Batch ID = 18030, loss = 0.0149227, acc = 1.0\n",
      "[Validation] Batch ID = 18030, loss = 0.0525117, acc = 0.96\n",
      "[Train] Batch ID = 18040, loss = 0.0111569, acc = 1.0\n",
      "[Validation] Batch ID = 18040, loss = 0.0251993, acc = 1.0\n",
      "[Train] Batch ID = 18050, loss = 0.250529, acc = 0.74\n",
      "[Validation] Batch ID = 18050, loss = 0.0445635, acc = 0.96\n",
      "[Train] Batch ID = 18060, loss = 0.0100271, acc = 1.0\n",
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      "[Validation] Batch ID = 18090, loss = 0.0485644, acc = 1.0\n",
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      "[Validation] Batch ID = 18100, loss = 0.0756436, acc = 0.9\n",
      "[Train] Batch ID = 18110, loss = 0.00493511, acc = 1.0\n",
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      "[Train] Batch ID = 18320, loss = 0.00833453, acc = 1.0\n",
      "[Validation] Batch ID = 18320, loss = 0.0225264, acc = 1.0\n",
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      "[Validation] Batch ID = 18360, loss = 0.0335051, acc = 0.96\n",
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      "[Train] Batch ID = 18380, loss = 0.014835, acc = 0.98\n",
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      "[Validation] Batch ID = 18390, loss = 0.0205305, acc = 1.0\n",
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      "[Validation] Batch ID = 18400, loss = 0.046115, acc = 0.96\n",
      "[Train] Batch ID = 18410, loss = 0.00778262, acc = 1.0\n",
      "[Validation] Batch ID = 18410, loss = 0.0280853, acc = 0.98\n",
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      "[Validation] Batch ID = 18420, loss = 0.0263137, acc = 0.96\n",
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      "[Validation] Batch ID = 18430, loss = 0.0471367, acc = 0.96\n",
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      "[Validation] Batch ID = 18440, loss = 0.0473484, acc = 0.94\n",
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      "[Validation] Batch ID = 18450, loss = 0.0646073, acc = 0.94\n",
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      "[Validation] Batch ID = 18460, loss = 0.0558958, acc = 0.94\n",
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      "[Validation] Batch ID = 18470, loss = 0.0218326, acc = 1.0\n",
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      "[Validation] Batch ID = 18480, loss = 0.0293012, acc = 0.98\n",
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      "[Validation] Batch ID = 18490, loss = 0.0419711, acc = 0.98\n",
      "[Train] Batch ID = 18500, loss = 0.0123012, acc = 1.0\n",
      "[Validation] Batch ID = 18500, loss = 0.0285383, acc = 1.0\n",
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      "[Validation] Batch ID = 18510, loss = 0.0563268, acc = 0.94\n",
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      "[Train] Batch ID = 18530, loss = 0.00624003, acc = 1.0\n",
      "[Validation] Batch ID = 18530, loss = 0.0299986, acc = 0.98\n",
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      "[Validation] Batch ID = 18560, loss = 0.0197975, acc = 1.0\n",
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      "[Validation] Batch ID = 18590, loss = 0.032789, acc = 1.0\n",
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      "[Validation] Batch ID = 18610, loss = 0.0428224, acc = 0.96\n",
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      "[Validation] Batch ID = 18620, loss = 0.0436938, acc = 0.98\n",
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      "[Validation] Batch ID = 18630, loss = 0.0377336, acc = 0.98\n",
      "[Train] Batch ID = 18640, loss = 0.00769659, acc = 1.0\n",
      "[Validation] Batch ID = 18640, loss = 0.0259619, acc = 1.0\n",
      "[Train] Batch ID = 18650, loss = 0.00803511, acc = 1.0\n",
      "[Validation] Batch ID = 18650, loss = 0.0653051, acc = 0.92\n",
      "[Train] Batch ID = 18660, loss = 0.00975562, acc = 1.0\n",
      "[Validation] Batch ID = 18660, loss = 0.0335881, acc = 0.96\n",
      "[Train] Batch ID = 18670, loss = 0.199579, acc = 0.8\n",
      "[Validation] Batch ID = 18670, loss = 0.0315993, acc = 0.98\n",
      "[Train] Batch ID = 18680, loss = 0.01301, acc = 1.0\n",
      "[Validation] Batch ID = 18680, loss = 0.0461032, acc = 0.98\n",
      "[Train] Batch ID = 18690, loss = 0.0120116, acc = 1.0\n",
      "[Validation] Batch ID = 18690, loss = 0.0350087, acc = 0.96\n",
      "[Train] Batch ID = 18700, loss = 0.0096466, acc = 1.0\n",
      "[Validation] Batch ID = 18700, loss = 0.0317747, acc = 1.0\n",
      "[Train] Batch ID = 18710, loss = 0.00397952, acc = 1.0\n",
      "[Validation] Batch ID = 18710, loss = 0.0408255, acc = 0.98\n",
      "[Train] Batch ID = 18720, loss = 0.00825438, acc = 1.0\n",
      "[Validation] Batch ID = 18720, loss = 0.0144541, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 18730, loss = 0.00760175, acc = 1.0\n",
      "[Validation] Batch ID = 18730, loss = 0.0363982, acc = 0.96\n",
      "[Train] Batch ID = 18740, loss = 0.00904103, acc = 1.0\n",
      "[Validation] Batch ID = 18740, loss = 0.0513596, acc = 0.96\n",
      "[Train] Batch ID = 18750, loss = 0.0154375, acc = 1.0\n",
      "[Validation] Batch ID = 18750, loss = 0.0225518, acc = 0.98\n",
      "[Train] Batch ID = 18760, loss = 0.0088225, acc = 1.0\n",
      "[Validation] Batch ID = 18760, loss = 0.0300064, acc = 0.98\n",
      "[Train] Batch ID = 18770, loss = 0.0132343, acc = 1.0\n",
      "[Validation] Batch ID = 18770, loss = 0.0285188, acc = 1.0\n",
      "[Train] Batch ID = 18780, loss = 0.209026, acc = 0.86\n",
      "[Validation] Batch ID = 18780, loss = 0.0604442, acc = 0.96\n",
      "[Train] Batch ID = 18790, loss = 0.00696319, acc = 1.0\n",
      "[Validation] Batch ID = 18790, loss = 0.0331932, acc = 0.96\n",
      "[Train] Batch ID = 18800, loss = 0.00568265, acc = 1.0\n",
      "[Validation] Batch ID = 18800, loss = 0.021478, acc = 0.98\n",
      "[Train] Batch ID = 18810, loss = 0.009537, acc = 1.0\n",
      "[Validation] Batch ID = 18810, loss = 0.026033, acc = 1.0\n",
      "[Train] Batch ID = 18820, loss = 0.00796829, acc = 1.0\n",
      "[Validation] Batch ID = 18820, loss = 0.0219744, acc = 0.98\n",
      "[Train] Batch ID = 18830, loss = 0.188369, acc = 0.8\n",
      "[Validation] Batch ID = 18830, loss = 0.0316925, acc = 0.98\n",
      "[Train] Batch ID = 18840, loss = 0.00704996, acc = 1.0\n",
      "[Validation] Batch ID = 18840, loss = 0.0356097, acc = 0.98\n",
      "[Train] Batch ID = 18850, loss = 0.16931, acc = 0.9\n",
      "[Validation] Batch ID = 18850, loss = 0.0395927, acc = 0.94\n",
      "[Train] Batch ID = 18860, loss = 0.00776523, acc = 1.0\n",
      "[Validation] Batch ID = 18860, loss = 0.0465741, acc = 0.98\n",
      "[Train] Batch ID = 18870, loss = 0.00953969, acc = 1.0\n",
      "[Validation] Batch ID = 18870, loss = 0.0664633, acc = 0.96\n",
      "[Train] Batch ID = 18880, loss = 0.00804948, acc = 1.0\n",
      "[Validation] Batch ID = 18880, loss = 0.0512156, acc = 0.94\n",
      "[Train] Batch ID = 18890, loss = 0.0102249, acc = 1.0\n",
      "[Validation] Batch ID = 18890, loss = 0.057554, acc = 0.96\n",
      "[Train] Batch ID = 18900, loss = 0.00914846, acc = 1.0\n",
      "[Validation] Batch ID = 18900, loss = 0.0496042, acc = 0.94\n",
      "[Train] Batch ID = 18910, loss = 0.185639, acc = 0.86\n",
      "[Validation] Batch ID = 18910, loss = 0.0385823, acc = 0.94\n",
      "[Train] Batch ID = 18920, loss = 0.0103829, acc = 1.0\n",
      "[Validation] Batch ID = 18920, loss = 0.0283785, acc = 0.98\n",
      "[Train] Batch ID = 18930, loss = 0.0152379, acc = 1.0\n",
      "[Validation] Batch ID = 18930, loss = 0.0486571, acc = 0.96\n",
      "[Train] Batch ID = 18940, loss = 0.0120799, acc = 1.0\n",
      "[Validation] Batch ID = 18940, loss = 0.0264706, acc = 0.96\n",
      "[Train] Batch ID = 18950, loss = 0.0125708, acc = 1.0\n",
      "[Validation] Batch ID = 18950, loss = 0.0302521, acc = 1.0\n",
      "[Train] Batch ID = 18960, loss = 0.0109831, acc = 1.0\n",
      "[Validation] Batch ID = 18960, loss = 0.0496431, acc = 0.96\n",
      "[Train] Batch ID = 18970, loss = 0.00794598, acc = 1.0\n",
      "[Validation] Batch ID = 18970, loss = 0.0472635, acc = 0.98\n",
      "[Train] Batch ID = 18980, loss = 0.0168584, acc = 1.0\n",
      "[Validation] Batch ID = 18980, loss = 0.0358551, acc = 0.98\n",
      "[Train] Batch ID = 18990, loss = 0.00900583, acc = 1.0\n",
      "[Validation] Batch ID = 18990, loss = 0.0343539, acc = 0.98\n",
      "[Train] Batch ID = 19000, loss = 0.00812799, acc = 1.0\n",
      "[Validation] Batch ID = 19000, loss = 0.049676, acc = 0.98\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.04133 Best loss: 0.0424544\n",
      "[TOTAL Validation] Batch ID = 19000, loss = 0.04133, acc = 0.968027210884\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.12036088804466365\n",
      "[Train] Batch ID = 19010, loss = 0.00829884, acc = 1.0\n",
      "[Validation] Batch ID = 19010, loss = 0.0244568, acc = 0.98\n",
      "[Train] Batch ID = 19020, loss = 0.00281226, acc = 1.0\n",
      "[Validation] Batch ID = 19020, loss = 0.0304167, acc = 0.96\n",
      "[Train] Batch ID = 19030, loss = 0.00811216, acc = 1.0\n",
      "[Validation] Batch ID = 19030, loss = 0.0430402, acc = 0.94\n",
      "[Train] Batch ID = 19040, loss = 0.190728, acc = 0.88\n",
      "[Validation] Batch ID = 19040, loss = 0.0323349, acc = 0.98\n",
      "[Train] Batch ID = 19050, loss = 0.00935074, acc = 1.0\n",
      "[Validation] Batch ID = 19050, loss = 0.049192, acc = 0.96\n",
      "[Train] Batch ID = 19060, loss = 0.00853486, acc = 1.0\n",
      "[Validation] Batch ID = 19060, loss = 0.0433526, acc = 0.98\n",
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      "[Validation] Batch ID = 19070, loss = 0.0507182, acc = 0.98\n",
      "[Train] Batch ID = 19080, loss = 0.0114892, acc = 1.0\n",
      "[Validation] Batch ID = 19080, loss = 0.0321919, acc = 1.0\n",
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      "[Validation] Batch ID = 19090, loss = 0.0314531, acc = 1.0\n",
      "[Train] Batch ID = 19100, loss = 0.00625073, acc = 1.0\n",
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      "[Train] Batch ID = 19110, loss = 0.0149874, acc = 1.0\n",
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      "[Validation] Batch ID = 19120, loss = 0.0285963, acc = 1.0\n",
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      "[Validation] Batch ID = 19720, loss = 0.0767592, acc = 0.9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 19730, loss = 0.0116919, acc = 1.0\n",
      "[Validation] Batch ID = 19730, loss = 0.0181293, acc = 1.0\n",
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      "[Train] Batch ID = 19960, loss = 0.0120941, acc = 1.0\n",
      "[Validation] Batch ID = 19960, loss = 0.0560773, acc = 0.96\n",
      "[Train] Batch ID = 19970, loss = 0.00970779, acc = 1.0\n",
      "[Validation] Batch ID = 19970, loss = 0.0310208, acc = 1.0\n",
      "[Train] Batch ID = 19980, loss = 0.00542317, acc = 1.0\n",
      "[Validation] Batch ID = 19980, loss = 0.0466412, acc = 0.98\n",
      "[Train] Batch ID = 19990, loss = 0.00734296, acc = 1.0\n",
      "[Validation] Batch ID = 19990, loss = 0.0440015, acc = 0.96\n",
      "[Train] Batch ID = 20000, loss = 0.225339, acc = 0.76\n",
      "[Validation] Batch ID = 20000, loss = 0.0204207, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0408133 Best loss: 0.04133\n",
      "[TOTAL Validation] Batch ID = 20000, loss = 0.0408133, acc = 0.969387755102\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.10832479924019728\n",
      "[Train] Batch ID = 20010, loss = 0.0121621, acc = 1.0\n",
      "[Validation] Batch ID = 20010, loss = 0.0591486, acc = 0.96\n",
      "[Train] Batch ID = 20020, loss = 0.00480423, acc = 1.0\n",
      "[Validation] Batch ID = 20020, loss = 0.0150694, acc = 1.0\n",
      "[Train] Batch ID = 20030, loss = 0.00492804, acc = 1.0\n",
      "[Validation] Batch ID = 20030, loss = 0.0209972, acc = 1.0\n",
      "[Train] Batch ID = 20040, loss = 0.00766971, acc = 1.0\n",
      "[Validation] Batch ID = 20040, loss = 0.0376517, acc = 0.98\n",
      "[Train] Batch ID = 20050, loss = 0.00550816, acc = 1.0\n",
      "[Validation] Batch ID = 20050, loss = 0.0319362, acc = 1.0\n",
      "[Train] Batch ID = 20060, loss = 0.011221, acc = 1.0\n",
      "[Validation] Batch ID = 20060, loss = 0.0436221, acc = 0.98\n",
      "[Train] Batch ID = 20070, loss = 0.00795804, acc = 1.0\n",
      "[Validation] Batch ID = 20070, loss = 0.0520479, acc = 0.96\n",
      "[Train] Batch ID = 20080, loss = 0.00787, acc = 1.0\n",
      "[Validation] Batch ID = 20080, loss = 0.0704649, acc = 0.92\n",
      "[Train] Batch ID = 20090, loss = 0.00772897, acc = 1.0\n",
      "[Validation] Batch ID = 20090, loss = 0.0586643, acc = 0.94\n",
      "[Train] Batch ID = 20100, loss = 0.00774926, acc = 1.0\n",
      "[Validation] Batch ID = 20100, loss = 0.0289221, acc = 1.0\n",
      "[Train] Batch ID = 20110, loss = 0.00551302, acc = 1.0\n",
      "[Validation] Batch ID = 20110, loss = 0.049123, acc = 0.96\n",
      "[Train] Batch ID = 20120, loss = 0.00561796, acc = 1.0\n",
      "[Validation] Batch ID = 20120, loss = 0.0725273, acc = 0.92\n",
      "[Train] Batch ID = 20130, loss = 0.00634792, acc = 1.0\n",
      "[Validation] Batch ID = 20130, loss = 0.0231948, acc = 0.98\n",
      "[Train] Batch ID = 20140, loss = 0.00705518, acc = 1.0\n",
      "[Validation] Batch ID = 20140, loss = 0.0348869, acc = 0.98\n",
      "[Train] Batch ID = 20150, loss = 0.00427323, acc = 1.0\n",
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      "[Train] Batch ID = 20160, loss = 0.00735411, acc = 1.0\n",
      "[Validation] Batch ID = 20160, loss = 0.0435082, acc = 0.98\n",
      "[Train] Batch ID = 20170, loss = 0.00650522, acc = 1.0\n",
      "[Validation] Batch ID = 20170, loss = 0.0352256, acc = 0.96\n",
      "[Train] Batch ID = 20180, loss = 0.00547402, acc = 1.0\n",
      "[Validation] Batch ID = 20180, loss = 0.035118, acc = 1.0\n",
      "[Train] Batch ID = 20190, loss = 0.15671, acc = 0.9\n",
      "[Validation] Batch ID = 20190, loss = 0.0343312, acc = 0.96\n",
      "[Train] Batch ID = 20200, loss = 0.00999631, acc = 1.0\n",
      "[Validation] Batch ID = 20200, loss = 0.0285852, acc = 0.98\n",
      "[Train] Batch ID = 20210, loss = 0.00336887, acc = 1.0\n",
      "[Validation] Batch ID = 20210, loss = 0.0198216, acc = 1.0\n",
      "[Train] Batch ID = 20220, loss = 0.00530345, acc = 1.0\n",
      "[Validation] Batch ID = 20220, loss = 0.0456875, acc = 0.98\n",
      "[Train] Batch ID = 20230, loss = 0.00605041, acc = 1.0\n",
      "[Validation] Batch ID = 20230, loss = 0.0300056, acc = 0.96\n",
      "[Train] Batch ID = 20240, loss = 0.00620132, acc = 1.0\n",
      "[Validation] Batch ID = 20240, loss = 0.0413816, acc = 0.98\n",
      "[Train] Batch ID = 20250, loss = 0.00831628, acc = 1.0\n",
      "[Validation] Batch ID = 20250, loss = 0.0414614, acc = 0.96\n",
      "[Train] Batch ID = 20260, loss = 0.0050357, acc = 1.0\n",
      "[Validation] Batch ID = 20260, loss = 0.0247127, acc = 0.98\n",
      "[Train] Batch ID = 20270, loss = 0.00407688, acc = 1.0\n",
      "[Validation] Batch ID = 20270, loss = 0.0390399, acc = 0.96\n",
      "[Train] Batch ID = 20280, loss = 0.00334446, acc = 1.0\n",
      "[Validation] Batch ID = 20280, loss = 0.0212297, acc = 1.0\n",
      "[Train] Batch ID = 20290, loss = 0.222736, acc = 0.8\n",
      "[Validation] Batch ID = 20290, loss = 0.0459547, acc = 0.96\n",
      "[Train] Batch ID = 20300, loss = 0.194667, acc = 0.8\n",
      "[Validation] Batch ID = 20300, loss = 0.0214653, acc = 1.0\n",
      "[Train] Batch ID = 20310, loss = 0.00975947, acc = 1.0\n",
      "[Validation] Batch ID = 20310, loss = 0.0414093, acc = 0.96\n",
      "[Train] Batch ID = 20320, loss = 0.00802601, acc = 1.0\n",
      "[Validation] Batch ID = 20320, loss = 0.0223646, acc = 1.0\n",
      "[Train] Batch ID = 20330, loss = 0.00492601, acc = 1.0\n",
      "[Validation] Batch ID = 20330, loss = 0.0472553, acc = 0.96\n",
      "[Train] Batch ID = 20340, loss = 0.00417772, acc = 1.0\n",
      "[Validation] Batch ID = 20340, loss = 0.0310175, acc = 0.96\n",
      "[Train] Batch ID = 20350, loss = 0.191255, acc = 0.86\n",
      "[Validation] Batch ID = 20350, loss = 0.0371954, acc = 0.98\n",
      "[Train] Batch ID = 20360, loss = 0.00456744, acc = 1.0\n",
      "[Validation] Batch ID = 20360, loss = 0.0228792, acc = 1.0\n",
      "[Train] Batch ID = 20370, loss = 0.198478, acc = 0.78\n",
      "[Validation] Batch ID = 20370, loss = 0.0282234, acc = 0.98\n",
      "[Train] Batch ID = 20380, loss = 0.00521678, acc = 1.0\n",
      "[Validation] Batch ID = 20380, loss = 0.0126037, acc = 1.0\n",
      "[Train] Batch ID = 20390, loss = 0.00319034, acc = 1.0\n",
      "[Validation] Batch ID = 20390, loss = 0.0265952, acc = 0.98\n",
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      "[Validation] Batch ID = 20400, loss = 0.0362417, acc = 0.98\n",
      "[Train] Batch ID = 20410, loss = 0.00902145, acc = 1.0\n",
      "[Validation] Batch ID = 20410, loss = 0.0570795, acc = 0.94\n",
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      "[Train] Batch ID = 20700, loss = 0.00655903, acc = 1.0\n",
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      "[Validation] Batch ID = 20720, loss = 0.0206049, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 20730, loss = 0.00851594, acc = 1.0\n",
      "[Validation] Batch ID = 20730, loss = 0.0396499, acc = 0.98\n",
      "[Train] Batch ID = 20740, loss = 0.0115291, acc = 1.0\n",
      "[Validation] Batch ID = 20740, loss = 0.048915, acc = 0.94\n",
      "[Train] Batch ID = 20750, loss = 0.00939869, acc = 1.0\n",
      "[Validation] Batch ID = 20750, loss = 0.0433844, acc = 0.98\n",
      "[Train] Batch ID = 20760, loss = 0.189632, acc = 0.88\n",
      "[Validation] Batch ID = 20760, loss = 0.0395905, acc = 0.98\n",
      "[Train] Batch ID = 20770, loss = 0.00678353, acc = 1.0\n",
      "[Validation] Batch ID = 20770, loss = 0.0294514, acc = 0.98\n",
      "[Train] Batch ID = 20780, loss = 0.00929747, acc = 1.0\n",
      "[Validation] Batch ID = 20780, loss = 0.0319454, acc = 0.98\n",
      "[Train] Batch ID = 20790, loss = 0.00438841, acc = 1.0\n",
      "[Validation] Batch ID = 20790, loss = 0.0323237, acc = 0.98\n",
      "[Train] Batch ID = 20800, loss = 0.00659886, acc = 1.0\n",
      "[Validation] Batch ID = 20800, loss = 0.0405213, acc = 0.98\n",
      "[Train] Batch ID = 20810, loss = 0.010327, acc = 1.0\n",
      "[Validation] Batch ID = 20810, loss = 0.0420512, acc = 0.96\n",
      "[Train] Batch ID = 20820, loss = 0.19306, acc = 0.84\n",
      "[Validation] Batch ID = 20820, loss = 0.053638, acc = 0.94\n",
      "[Train] Batch ID = 20830, loss = 0.00845104, acc = 1.0\n",
      "[Validation] Batch ID = 20830, loss = 0.0292836, acc = 0.98\n",
      "[Train] Batch ID = 20840, loss = 0.00874292, acc = 1.0\n",
      "[Validation] Batch ID = 20840, loss = 0.0370622, acc = 0.94\n",
      "[Train] Batch ID = 20850, loss = 0.00738273, acc = 1.0\n",
      "[Validation] Batch ID = 20850, loss = 0.017642, acc = 0.98\n",
      "[Train] Batch ID = 20860, loss = 0.00575704, acc = 1.0\n",
      "[Validation] Batch ID = 20860, loss = 0.0482965, acc = 0.98\n",
      "[Train] Batch ID = 20870, loss = 0.00539798, acc = 1.0\n",
      "[Validation] Batch ID = 20870, loss = 0.0752459, acc = 0.92\n",
      "[Train] Batch ID = 20880, loss = 0.19607, acc = 0.82\n",
      "[Validation] Batch ID = 20880, loss = 0.0252192, acc = 0.98\n",
      "[Train] Batch ID = 20890, loss = 0.00597661, acc = 1.0\n",
      "[Validation] Batch ID = 20890, loss = 0.043592, acc = 0.98\n",
      "[Train] Batch ID = 20900, loss = 0.0101745, acc = 1.0\n",
      "[Validation] Batch ID = 20900, loss = 0.0232427, acc = 1.0\n",
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      "[Validation] Batch ID = 20910, loss = 0.0352304, acc = 0.96\n",
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      "[Validation] Batch ID = 20920, loss = 0.0706945, acc = 0.9\n",
      "[Train] Batch ID = 20930, loss = 0.00519587, acc = 1.0\n",
      "[Validation] Batch ID = 20930, loss = 0.0287194, acc = 0.98\n",
      "[Train] Batch ID = 20940, loss = 0.00789449, acc = 1.0\n",
      "[Validation] Batch ID = 20940, loss = 0.0279124, acc = 0.98\n",
      "[Train] Batch ID = 20950, loss = 0.00591379, acc = 1.0\n",
      "[Validation] Batch ID = 20950, loss = 0.0503473, acc = 0.96\n",
      "[Train] Batch ID = 20960, loss = 0.00515597, acc = 1.0\n",
      "[Validation] Batch ID = 20960, loss = 0.0435386, acc = 0.94\n",
      "[Train] Batch ID = 20970, loss = 0.231183, acc = 0.76\n",
      "[Validation] Batch ID = 20970, loss = 0.0418741, acc = 0.98\n",
      "[Train] Batch ID = 20980, loss = 0.00501432, acc = 1.0\n",
      "[Validation] Batch ID = 20980, loss = 0.0235779, acc = 1.0\n",
      "[Train] Batch ID = 20990, loss = 0.0125432, acc = 1.0\n",
      "[Validation] Batch ID = 20990, loss = 0.0442443, acc = 0.94\n",
      "[Train] Batch ID = 21000, loss = 0.00539237, acc = 1.0\n",
      "[Validation] Batch ID = 21000, loss = 0.0342983, acc = 0.98\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0431974 Best loss: 0.0408133\n",
      "[TOTAL Validation] Batch ID = 21000, loss = 0.0431974, acc = 0.97074829932\n",
      "Augmented Factor = 0.09749231931617755\n",
      "[Train] Batch ID = 21010, loss = 0.00592436, acc = 1.0\n",
      "[Validation] Batch ID = 21010, loss = 0.0442082, acc = 0.96\n",
      "[Train] Batch ID = 21020, loss = 0.00853046, acc = 1.0\n",
      "[Validation] Batch ID = 21020, loss = 0.021082, acc = 1.0\n",
      "[Train] Batch ID = 21030, loss = 0.00612076, acc = 1.0\n",
      "[Validation] Batch ID = 21030, loss = 0.0386014, acc = 1.0\n",
      "[Train] Batch ID = 21040, loss = 0.00284419, acc = 1.0\n",
      "[Validation] Batch ID = 21040, loss = 0.050859, acc = 0.94\n",
      "[Train] Batch ID = 21050, loss = 0.215822, acc = 0.8\n",
      "[Validation] Batch ID = 21050, loss = 0.0445488, acc = 0.98\n",
      "[Train] Batch ID = 21060, loss = 0.00784949, acc = 1.0\n",
      "[Validation] Batch ID = 21060, loss = 0.0266466, acc = 1.0\n",
      "[Train] Batch ID = 21070, loss = 0.00580367, acc = 1.0\n",
      "[Validation] Batch ID = 21070, loss = 0.0348349, acc = 0.98\n",
      "[Train] Batch ID = 21080, loss = 0.00470474, acc = 1.0\n",
      "[Validation] Batch ID = 21080, loss = 0.0411613, acc = 0.98\n",
      "[Train] Batch ID = 21090, loss = 0.00392595, acc = 1.0\n",
      "[Validation] Batch ID = 21090, loss = 0.0277769, acc = 0.98\n",
      "[Train] Batch ID = 21100, loss = 0.00685797, acc = 1.0\n",
      "[Validation] Batch ID = 21100, loss = 0.0348169, acc = 0.98\n",
      "[Train] Batch ID = 21110, loss = 0.010224, acc = 1.0\n",
      "[Validation] Batch ID = 21110, loss = 0.0382668, acc = 0.96\n",
      "[Train] Batch ID = 21120, loss = 0.0101437, acc = 1.0\n",
      "[Validation] Batch ID = 21120, loss = 0.0196411, acc = 1.0\n",
      "[Train] Batch ID = 21130, loss = 0.00685346, acc = 1.0\n",
      "[Validation] Batch ID = 21130, loss = 0.0361198, acc = 1.0\n",
      "[Train] Batch ID = 21140, loss = 0.00427253, acc = 1.0\n",
      "[Validation] Batch ID = 21140, loss = 0.0258955, acc = 1.0\n",
      "[Train] Batch ID = 21150, loss = 0.00844198, acc = 1.0\n",
      "[Validation] Batch ID = 21150, loss = 0.0191694, acc = 0.98\n",
      "[Train] Batch ID = 21160, loss = 0.00512653, acc = 1.0\n",
      "[Validation] Batch ID = 21160, loss = 0.0374565, acc = 0.98\n",
      "[Train] Batch ID = 21170, loss = 0.00696004, acc = 1.0\n",
      "[Validation] Batch ID = 21170, loss = 0.0454637, acc = 0.96\n",
      "[Train] Batch ID = 21180, loss = 0.00375915, acc = 1.0\n",
      "[Validation] Batch ID = 21180, loss = 0.046177, acc = 0.96\n",
      "[Train] Batch ID = 21190, loss = 0.00579243, acc = 1.0\n",
      "[Validation] Batch ID = 21190, loss = 0.0327552, acc = 0.98\n",
      "[Train] Batch ID = 21200, loss = 0.00328368, acc = 1.0\n",
      "[Validation] Batch ID = 21200, loss = 0.0422119, acc = 0.96\n",
      "[Train] Batch ID = 21210, loss = 0.0100822, acc = 1.0\n",
      "[Validation] Batch ID = 21210, loss = 0.0453153, acc = 0.96\n",
      "[Train] Batch ID = 21220, loss = 0.300039, acc = 0.64\n",
      "[Validation] Batch ID = 21220, loss = 0.0481353, acc = 0.94\n",
      "[Train] Batch ID = 21230, loss = 0.00564377, acc = 1.0\n",
      "[Validation] Batch ID = 21230, loss = 0.018417, acc = 1.0\n",
      "[Train] Batch ID = 21240, loss = 0.00638278, acc = 1.0\n",
      "[Validation] Batch ID = 21240, loss = 0.0289168, acc = 1.0\n",
      "[Train] Batch ID = 21250, loss = 0.0151131, acc = 1.0\n",
      "[Validation] Batch ID = 21250, loss = 0.0152897, acc = 1.0\n",
      "[Train] Batch ID = 21260, loss = 0.00540018, acc = 1.0\n",
      "[Validation] Batch ID = 21260, loss = 0.0486957, acc = 0.92\n",
      "[Train] Batch ID = 21270, loss = 0.00416173, acc = 1.0\n",
      "[Validation] Batch ID = 21270, loss = 0.02185, acc = 1.0\n",
      "[Train] Batch ID = 21280, loss = 0.00424879, acc = 1.0\n",
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      "[Validation] Batch ID = 21430, loss = 0.0587397, acc = 0.94\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 21440, loss = 0.00819353, acc = 1.0\n",
      "[Validation] Batch ID = 21440, loss = 0.0722991, acc = 0.94\n",
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      "[Validation] Batch ID = 21930, loss = 0.0304635, acc = 0.96\n",
      "[Train] Batch ID = 21940, loss = 0.00887312, acc = 1.0\n",
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      "[Train] Batch ID = 21950, loss = 0.00834389, acc = 1.0\n",
      "[Validation] Batch ID = 21950, loss = 0.0461208, acc = 0.96\n",
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      "[Validation] Batch ID = 21960, loss = 0.0254629, acc = 0.98\n",
      "[Train] Batch ID = 21970, loss = 0.00934333, acc = 1.0\n",
      "[Validation] Batch ID = 21970, loss = 0.0391716, acc = 0.96\n",
      "[Train] Batch ID = 21980, loss = 0.00504281, acc = 1.0\n",
      "[Validation] Batch ID = 21980, loss = 0.0285056, acc = 1.0\n",
      "[Train] Batch ID = 21990, loss = 0.00461622, acc = 1.0\n",
      "[Validation] Batch ID = 21990, loss = 0.0301025, acc = 0.98\n",
      "[Train] Batch ID = 22000, loss = 0.275685, acc = 0.7\n",
      "[Validation] Batch ID = 22000, loss = 0.0314925, acc = 0.98\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0364358 Best loss: 0.0408133\n",
      "[TOTAL Validation] Batch ID = 22000, loss = 0.0364358, acc = 0.971201814059\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.0877430873845598\n",
      "[Train] Batch ID = 22010, loss = 0.00551307, acc = 1.0\n",
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      "[Validation] Batch ID = 22720, loss = 0.0248042, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 22730, loss = 0.00520167, acc = 1.0\n",
      "[Validation] Batch ID = 22730, loss = 0.0372026, acc = 0.96\n",
      "[Train] Batch ID = 22740, loss = 0.00261571, acc = 1.0\n",
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      "[Train] Batch ID = 22830, loss = 0.00446882, acc = 1.0\n",
      "[Validation] Batch ID = 22830, loss = 0.0190036, acc = 0.98\n",
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      "[Validation] Batch ID = 22890, loss = 0.0455365, acc = 0.94\n",
      "[Train] Batch ID = 22900, loss = 0.00530616, acc = 1.0\n",
      "[Validation] Batch ID = 22900, loss = 0.052123, acc = 0.96\n",
      "[Train] Batch ID = 22910, loss = 0.00610637, acc = 1.0\n",
      "[Validation] Batch ID = 22910, loss = 0.0244052, acc = 0.98\n",
      "[Train] Batch ID = 22920, loss = 0.00387799, acc = 1.0\n",
      "[Validation] Batch ID = 22920, loss = 0.0544203, acc = 0.94\n",
      "[Train] Batch ID = 22930, loss = 0.00690842, acc = 1.0\n",
      "[Validation] Batch ID = 22930, loss = 0.029389, acc = 1.0\n",
      "[Train] Batch ID = 22940, loss = 0.00701489, acc = 1.0\n",
      "[Validation] Batch ID = 22940, loss = 0.0221939, acc = 0.98\n",
      "[Train] Batch ID = 22950, loss = 0.00764623, acc = 1.0\n",
      "[Validation] Batch ID = 22950, loss = 0.0285962, acc = 0.98\n",
      "[Train] Batch ID = 22960, loss = 0.00337209, acc = 1.0\n",
      "[Validation] Batch ID = 22960, loss = 0.0340476, acc = 0.98\n",
      "[Train] Batch ID = 22970, loss = 0.00475364, acc = 1.0\n",
      "[Validation] Batch ID = 22970, loss = 0.0300634, acc = 0.98\n",
      "[Train] Batch ID = 22980, loss = 0.00324629, acc = 1.0\n",
      "[Validation] Batch ID = 22980, loss = 0.0355735, acc = 0.98\n",
      "[Train] Batch ID = 22990, loss = 0.214933, acc = 0.76\n",
      "[Validation] Batch ID = 22990, loss = 0.0280351, acc = 0.98\n",
      "[Train] Batch ID = 23000, loss = 0.0091363, acc = 1.0\n",
      "[Validation] Batch ID = 23000, loss = 0.0264136, acc = 0.98\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0357922 Best loss: 0.0364358\n",
      "[TOTAL Validation] Batch ID = 23000, loss = 0.0357922, acc = 0.971655328798\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.07896877864610383\n",
      "[Train] Batch ID = 23010, loss = 0.00660331, acc = 1.0\n",
      "[Validation] Batch ID = 23010, loss = 0.0485754, acc = 0.98\n",
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      "[Validation] Batch ID = 23020, loss = 0.0418112, acc = 0.96\n",
      "[Train] Batch ID = 23030, loss = 0.0066666, acc = 1.0\n",
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      "[Validation] Batch ID = 23040, loss = 0.0514943, acc = 0.94\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 23730, loss = 0.00578118, acc = 1.0\n",
      "[Validation] Batch ID = 23730, loss = 0.0261535, acc = 1.0\n",
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      "[Train] Batch ID = 23930, loss = 0.174914, acc = 0.9\n",
      "[Validation] Batch ID = 23930, loss = 0.0535488, acc = 0.96\n",
      "[Train] Batch ID = 23940, loss = 0.00701756, acc = 1.0\n",
      "[Validation] Batch ID = 23940, loss = 0.0354619, acc = 1.0\n",
      "[Train] Batch ID = 23950, loss = 0.00323051, acc = 1.0\n",
      "[Validation] Batch ID = 23950, loss = 0.0490318, acc = 0.94\n",
      "[Train] Batch ID = 23960, loss = 0.00730346, acc = 1.0\n",
      "[Validation] Batch ID = 23960, loss = 0.0443647, acc = 0.96\n",
      "[Train] Batch ID = 23970, loss = 0.00872781, acc = 1.0\n",
      "[Validation] Batch ID = 23970, loss = 0.0220032, acc = 1.0\n",
      "[Train] Batch ID = 23980, loss = 0.18005, acc = 0.86\n",
      "[Validation] Batch ID = 23980, loss = 0.0303295, acc = 1.0\n",
      "[Train] Batch ID = 23990, loss = 0.20618, acc = 0.84\n",
      "[Validation] Batch ID = 23990, loss = 0.0363862, acc = 0.96\n",
      "[Train] Batch ID = 24000, loss = 0.00449326, acc = 1.0\n",
      "[Validation] Batch ID = 24000, loss = 0.0423407, acc = 0.98\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0367299 Best loss: 0.0357922\n",
      "[TOTAL Validation] Batch ID = 24000, loss = 0.0367299, acc = 0.97619047619\n",
      "Augmented Factor = 0.07107190078149345\n",
      "[Train] Batch ID = 24010, loss = 0.00352486, acc = 1.0\n",
      "[Validation] Batch ID = 24010, loss = 0.0334499, acc = 0.96\n",
      "[Train] Batch ID = 24020, loss = 0.005968, acc = 1.0\n",
      "[Validation] Batch ID = 24020, loss = 0.032706, acc = 0.98\n",
      "[Train] Batch ID = 24030, loss = 0.0057694, acc = 1.0\n",
      "[Validation] Batch ID = 24030, loss = 0.0322401, acc = 0.98\n",
      "[Train] Batch ID = 24040, loss = 0.004735, acc = 1.0\n",
      "[Validation] Batch ID = 24040, loss = 0.035968, acc = 0.98\n",
      "[Train] Batch ID = 24050, loss = 0.00428464, acc = 1.0\n",
      "[Validation] Batch ID = 24050, loss = 0.0484945, acc = 0.96\n",
      "[Train] Batch ID = 24060, loss = 0.00583416, acc = 1.0\n",
      "[Validation] Batch ID = 24060, loss = 0.0413706, acc = 0.96\n",
      "[Train] Batch ID = 24070, loss = 0.00213506, acc = 1.0\n",
      "[Validation] Batch ID = 24070, loss = 0.042967, acc = 0.94\n",
      "[Train] Batch ID = 24080, loss = 0.00687046, acc = 1.0\n",
      "[Validation] Batch ID = 24080, loss = 0.0495778, acc = 0.96\n",
      "[Train] Batch ID = 24090, loss = 0.00750339, acc = 1.0\n",
      "[Validation] Batch ID = 24090, loss = 0.0225326, acc = 1.0\n",
      "[Train] Batch ID = 24100, loss = 0.0035057, acc = 1.0\n",
      "[Validation] Batch ID = 24100, loss = 0.022375, acc = 1.0\n",
      "[Train] Batch ID = 24110, loss = 0.00570989, acc = 1.0\n",
      "[Validation] Batch ID = 24110, loss = 0.0286189, acc = 0.98\n",
      "[Train] Batch ID = 24120, loss = 0.00629952, acc = 1.0\n",
      "[Validation] Batch ID = 24120, loss = 0.0216584, acc = 0.98\n",
      "[Train] Batch ID = 24130, loss = 0.00520103, acc = 1.0\n",
      "[Validation] Batch ID = 24130, loss = 0.0443877, acc = 0.96\n",
      "[Train] Batch ID = 24140, loss = 0.223145, acc = 0.72\n",
      "[Validation] Batch ID = 24140, loss = 0.0372075, acc = 0.98\n",
      "[Train] Batch ID = 24150, loss = 0.00567802, acc = 1.0\n",
      "[Validation] Batch ID = 24150, loss = 0.0517043, acc = 0.94\n",
      "[Train] Batch ID = 24160, loss = 0.00600864, acc = 1.0\n",
      "[Validation] Batch ID = 24160, loss = 0.0243798, acc = 0.98\n",
      "[Train] Batch ID = 24170, loss = 0.00395606, acc = 1.0\n",
      "[Validation] Batch ID = 24170, loss = 0.0345373, acc = 0.98\n",
      "[Train] Batch ID = 24180, loss = 0.00185226, acc = 1.0\n",
      "[Validation] Batch ID = 24180, loss = 0.0144064, acc = 1.0\n",
      "[Train] Batch ID = 24190, loss = 0.00225131, acc = 1.0\n",
      "[Validation] Batch ID = 24190, loss = 0.0281935, acc = 1.0\n",
      "[Train] Batch ID = 24200, loss = 0.178447, acc = 0.94\n",
      "[Validation] Batch ID = 24200, loss = 0.0412097, acc = 0.94\n",
      "[Train] Batch ID = 24210, loss = 0.00513449, acc = 1.0\n",
      "[Validation] Batch ID = 24210, loss = 0.0167854, acc = 1.0\n",
      "[Train] Batch ID = 24220, loss = 0.00358039, acc = 1.0\n",
      "[Validation] Batch ID = 24220, loss = 0.0322637, acc = 0.96\n",
      "[Train] Batch ID = 24230, loss = 0.00973643, acc = 1.0\n",
      "[Validation] Batch ID = 24230, loss = 0.0245861, acc = 0.98\n",
      "[Train] Batch ID = 24240, loss = 0.00385076, acc = 1.0\n",
      "[Validation] Batch ID = 24240, loss = 0.0240778, acc = 1.0\n",
      "[Train] Batch ID = 24250, loss = 0.00662441, acc = 1.0\n",
      "[Validation] Batch ID = 24250, loss = 0.0597829, acc = 0.92\n",
      "[Train] Batch ID = 24260, loss = 0.00625236, acc = 1.0\n",
      "[Validation] Batch ID = 24260, loss = 0.0430678, acc = 0.96\n",
      "[Train] Batch ID = 24270, loss = 0.00745797, acc = 1.0\n",
      "[Validation] Batch ID = 24270, loss = 0.0274199, acc = 0.98\n",
      "[Train] Batch ID = 24280, loss = 0.00653947, acc = 1.0\n",
      "[Validation] Batch ID = 24280, loss = 0.029208, acc = 0.98\n",
      "[Train] Batch ID = 24290, loss = 0.00401116, acc = 1.0\n",
      "[Validation] Batch ID = 24290, loss = 0.012159, acc = 1.0\n",
      "[Train] Batch ID = 24300, loss = 0.00492428, acc = 1.0\n",
      "[Validation] Batch ID = 24300, loss = 0.0304341, acc = 0.98\n",
      "[Train] Batch ID = 24310, loss = 0.00364542, acc = 1.0\n",
      "[Validation] Batch ID = 24310, loss = 0.0369927, acc = 0.96\n",
      "[Train] Batch ID = 24320, loss = 0.00292067, acc = 1.0\n",
      "[Validation] Batch ID = 24320, loss = 0.0210507, acc = 1.0\n",
      "[Train] Batch ID = 24330, loss = 0.00395551, acc = 1.0\n",
      "[Validation] Batch ID = 24330, loss = 0.0267802, acc = 0.98\n",
      "[Train] Batch ID = 24340, loss = 0.00802353, acc = 1.0\n",
      "[Validation] Batch ID = 24340, loss = 0.0112831, acc = 1.0\n",
      "[Train] Batch ID = 24350, loss = 0.00469307, acc = 1.0\n",
      "[Validation] Batch ID = 24350, loss = 0.0127294, acc = 1.0\n",
      "[Train] Batch ID = 24360, loss = 0.00981909, acc = 1.0\n",
      "[Validation] Batch ID = 24360, loss = 0.0298972, acc = 1.0\n",
      "[Train] Batch ID = 24370, loss = 0.00593166, acc = 1.0\n",
      "[Validation] Batch ID = 24370, loss = 0.0196194, acc = 1.0\n",
      "[Train] Batch ID = 24380, loss = 0.0048641, acc = 1.0\n",
      "[Validation] Batch ID = 24380, loss = 0.0133046, acc = 1.0\n",
      "[Train] Batch ID = 24390, loss = 0.00301852, acc = 1.0\n",
      "[Validation] Batch ID = 24390, loss = 0.0251501, acc = 0.98\n",
      "[Train] Batch ID = 24400, loss = 0.00410531, acc = 1.0\n",
      "[Validation] Batch ID = 24400, loss = 0.026933, acc = 1.0\n",
      "[Train] Batch ID = 24410, loss = 0.00392793, acc = 1.0\n",
      "[Validation] Batch ID = 24410, loss = 0.0256165, acc = 0.96\n",
      "[Train] Batch ID = 24420, loss = 0.00416732, acc = 1.0\n",
      "[Validation] Batch ID = 24420, loss = 0.0273996, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 24430, loss = 0.00578181, acc = 1.0\n",
      "[Validation] Batch ID = 24430, loss = 0.0380136, acc = 0.98\n",
      "[Train] Batch ID = 24440, loss = 0.00579406, acc = 1.0\n",
      "[Validation] Batch ID = 24440, loss = 0.0539137, acc = 0.96\n",
      "[Train] Batch ID = 24450, loss = 0.00885114, acc = 1.0\n",
      "[Validation] Batch ID = 24450, loss = 0.0381758, acc = 0.98\n",
      "[Train] Batch ID = 24460, loss = 0.00778931, acc = 1.0\n",
      "[Validation] Batch ID = 24460, loss = 0.0217391, acc = 1.0\n",
      "[Train] Batch ID = 24470, loss = 0.193765, acc = 0.92\n",
      "[Validation] Batch ID = 24470, loss = 0.041658, acc = 0.98\n",
      "[Train] Batch ID = 24480, loss = 0.00529989, acc = 1.0\n",
      "[Validation] Batch ID = 24480, loss = 0.0308775, acc = 0.98\n",
      "[Train] Batch ID = 24490, loss = 0.00640004, acc = 1.0\n",
      "[Validation] Batch ID = 24490, loss = 0.0175122, acc = 0.98\n",
      "[Train] Batch ID = 24500, loss = 0.00694058, acc = 1.0\n",
      "[Validation] Batch ID = 24500, loss = 0.0322712, acc = 0.98\n",
      "[Train] Batch ID = 24510, loss = 0.00552657, acc = 1.0\n",
      "[Validation] Batch ID = 24510, loss = 0.0194014, acc = 1.0\n",
      "[Train] Batch ID = 24520, loss = 0.00286053, acc = 1.0\n",
      "[Validation] Batch ID = 24520, loss = 0.0430332, acc = 0.98\n",
      "[Train] Batch ID = 24530, loss = 0.0150452, acc = 1.0\n",
      "[Validation] Batch ID = 24530, loss = 0.0275533, acc = 1.0\n",
      "[Train] Batch ID = 24540, loss = 0.00293442, acc = 1.0\n",
      "[Validation] Batch ID = 24540, loss = 0.0446893, acc = 0.96\n",
      "[Train] Batch ID = 24550, loss = 0.00932711, acc = 1.0\n",
      "[Validation] Batch ID = 24550, loss = 0.0112964, acc = 1.0\n",
      "[Train] Batch ID = 24560, loss = 0.00550307, acc = 1.0\n",
      "[Validation] Batch ID = 24560, loss = 0.0194865, acc = 0.98\n",
      "[Train] Batch ID = 24570, loss = 0.00969744, acc = 1.0\n",
      "[Validation] Batch ID = 24570, loss = 0.0400699, acc = 0.96\n",
      "[Train] Batch ID = 24580, loss = 0.0103241, acc = 1.0\n",
      "[Validation] Batch ID = 24580, loss = 0.0575275, acc = 0.94\n",
      "[Train] Batch ID = 24590, loss = 0.204736, acc = 0.86\n",
      "[Validation] Batch ID = 24590, loss = 0.026332, acc = 0.98\n",
      "[Train] Batch ID = 24600, loss = 0.186629, acc = 0.84\n",
      "[Validation] Batch ID = 24600, loss = 0.0205656, acc = 1.0\n",
      "[Train] Batch ID = 24610, loss = 0.00900012, acc = 1.0\n",
      "[Validation] Batch ID = 24610, loss = 0.0312581, acc = 0.96\n",
      "[Train] Batch ID = 24620, loss = 0.00410627, acc = 1.0\n",
      "[Validation] Batch ID = 24620, loss = 0.04332, acc = 0.96\n",
      "[Train] Batch ID = 24630, loss = 0.00727379, acc = 1.0\n",
      "[Validation] Batch ID = 24630, loss = 0.03893, acc = 0.98\n",
      "[Train] Batch ID = 24640, loss = 0.00247582, acc = 1.0\n",
      "[Validation] Batch ID = 24640, loss = 0.0158418, acc = 1.0\n",
      "[Train] Batch ID = 24650, loss = 0.00353379, acc = 1.0\n",
      "[Validation] Batch ID = 24650, loss = 0.0304606, acc = 0.98\n",
      "[Train] Batch ID = 24660, loss = 0.00429027, acc = 1.0\n",
      "[Validation] Batch ID = 24660, loss = 0.0252816, acc = 1.0\n",
      "[Train] Batch ID = 24670, loss = 0.00526662, acc = 1.0\n",
      "[Validation] Batch ID = 24670, loss = 0.0178285, acc = 1.0\n",
      "[Train] Batch ID = 24680, loss = 0.00707979, acc = 1.0\n",
      "[Validation] Batch ID = 24680, loss = 0.048138, acc = 0.96\n",
      "[Train] Batch ID = 24690, loss = 0.00580515, acc = 1.0\n",
      "[Validation] Batch ID = 24690, loss = 0.0355851, acc = 0.98\n",
      "[Train] Batch ID = 24700, loss = 0.00231191, acc = 1.0\n",
      "[Validation] Batch ID = 24700, loss = 0.038812, acc = 0.96\n",
      "[Train] Batch ID = 24710, loss = 0.0043974, acc = 1.0\n",
      "[Validation] Batch ID = 24710, loss = 0.0373893, acc = 0.96\n",
      "[Train] Batch ID = 24720, loss = 0.0029025, acc = 1.0\n",
      "[Validation] Batch ID = 24720, loss = 0.0465178, acc = 0.96\n",
      "[Train] Batch ID = 24730, loss = 0.00647107, acc = 1.0\n",
      "[Validation] Batch ID = 24730, loss = 0.0322894, acc = 0.98\n",
      "[Train] Batch ID = 24740, loss = 0.00485711, acc = 1.0\n",
      "[Validation] Batch ID = 24740, loss = 0.0520731, acc = 0.94\n",
      "[Train] Batch ID = 24750, loss = 0.00324136, acc = 1.0\n",
      "[Validation] Batch ID = 24750, loss = 0.0290797, acc = 0.98\n",
      "[Train] Batch ID = 24760, loss = 0.00346774, acc = 1.0\n",
      "[Validation] Batch ID = 24760, loss = 0.0474462, acc = 0.94\n",
      "[Train] Batch ID = 24770, loss = 0.0089573, acc = 1.0\n",
      "[Validation] Batch ID = 24770, loss = 0.0485758, acc = 0.94\n",
      "[Train] Batch ID = 24780, loss = 0.00987406, acc = 1.0\n",
      "[Validation] Batch ID = 24780, loss = 0.0370981, acc = 0.96\n",
      "[Train] Batch ID = 24790, loss = 0.00806436, acc = 1.0\n",
      "[Validation] Batch ID = 24790, loss = 0.0454963, acc = 1.0\n",
      "[Train] Batch ID = 24800, loss = 0.00577064, acc = 1.0\n",
      "[Validation] Batch ID = 24800, loss = 0.044444, acc = 0.96\n",
      "[Train] Batch ID = 24810, loss = 0.159194, acc = 0.88\n",
      "[Validation] Batch ID = 24810, loss = 0.0312028, acc = 1.0\n",
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      "[Validation] Batch ID = 24820, loss = 0.0466342, acc = 0.96\n",
      "[Train] Batch ID = 24830, loss = 0.00417545, acc = 1.0\n",
      "[Validation] Batch ID = 24830, loss = 0.0965958, acc = 0.8\n",
      "[Train] Batch ID = 24840, loss = 0.00264482, acc = 1.0\n",
      "[Validation] Batch ID = 24840, loss = 0.0378908, acc = 0.98\n",
      "[Train] Batch ID = 24850, loss = 0.00336255, acc = 1.0\n",
      "[Validation] Batch ID = 24850, loss = 0.0324793, acc = 0.98\n",
      "[Train] Batch ID = 24860, loss = 0.00567321, acc = 1.0\n",
      "[Validation] Batch ID = 24860, loss = 0.0796566, acc = 0.92\n",
      "[Train] Batch ID = 24870, loss = 0.0054318, acc = 1.0\n",
      "[Validation] Batch ID = 24870, loss = 0.0339976, acc = 0.94\n",
      "[Train] Batch ID = 24880, loss = 0.0064051, acc = 1.0\n",
      "[Validation] Batch ID = 24880, loss = 0.0337051, acc = 0.96\n",
      "[Train] Batch ID = 24890, loss = 0.00642756, acc = 1.0\n",
      "[Validation] Batch ID = 24890, loss = 0.0206096, acc = 1.0\n",
      "[Train] Batch ID = 24900, loss = 0.00491377, acc = 1.0\n",
      "[Validation] Batch ID = 24900, loss = 0.0325694, acc = 0.98\n",
      "[Train] Batch ID = 24910, loss = 0.00598879, acc = 1.0\n",
      "[Validation] Batch ID = 24910, loss = 0.0184886, acc = 1.0\n",
      "[Train] Batch ID = 24920, loss = 0.00784798, acc = 1.0\n",
      "[Validation] Batch ID = 24920, loss = 0.0289651, acc = 0.98\n",
      "[Train] Batch ID = 24930, loss = 0.00403902, acc = 1.0\n",
      "[Validation] Batch ID = 24930, loss = 0.0106058, acc = 1.0\n",
      "[Train] Batch ID = 24940, loss = 0.00668513, acc = 1.0\n",
      "[Validation] Batch ID = 24940, loss = 0.0147243, acc = 1.0\n",
      "[Train] Batch ID = 24950, loss = 0.00278266, acc = 1.0\n",
      "[Validation] Batch ID = 24950, loss = 0.035463, acc = 0.96\n",
      "[Train] Batch ID = 24960, loss = 0.00522582, acc = 1.0\n",
      "[Validation] Batch ID = 24960, loss = 0.0327158, acc = 0.98\n",
      "[Train] Batch ID = 24970, loss = 0.222803, acc = 0.76\n",
      "[Validation] Batch ID = 24970, loss = 0.0254868, acc = 1.0\n",
      "[Train] Batch ID = 24980, loss = 0.00747713, acc = 1.0\n",
      "[Validation] Batch ID = 24980, loss = 0.0340503, acc = 0.98\n",
      "[Train] Batch ID = 24990, loss = 0.00232613, acc = 1.0\n",
      "[Validation] Batch ID = 24990, loss = 0.0392842, acc = 0.94\n",
      "[Train] Batch ID = 25000, loss = 0.0035359, acc = 1.0\n",
      "[Validation] Batch ID = 25000, loss = 0.0661726, acc = 0.92\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0344537 Best loss: 0.0357922\n",
      "[TOTAL Validation] Batch ID = 25000, loss = 0.0344537, acc = 0.973242630385\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.0639647107033441\n",
      "[Train] Batch ID = 25010, loss = 0.00639635, acc = 1.0\n",
      "[Validation] Batch ID = 25010, loss = 0.0449188, acc = 0.96\n",
      "[Train] Batch ID = 25020, loss = 0.00420539, acc = 1.0\n",
      "[Validation] Batch ID = 25020, loss = 0.027596, acc = 0.98\n",
      "[Train] Batch ID = 25030, loss = 0.00459945, acc = 1.0\n",
      "[Validation] Batch ID = 25030, loss = 0.00974723, acc = 1.0\n",
      "[Train] Batch ID = 25040, loss = 0.0034657, acc = 1.0\n",
      "[Validation] Batch ID = 25040, loss = 0.0230413, acc = 0.98\n",
      "[Train] Batch ID = 25050, loss = 0.00362659, acc = 1.0\n",
      "[Validation] Batch ID = 25050, loss = 0.0260174, acc = 0.98\n",
      "[Train] Batch ID = 25060, loss = 0.0032951, acc = 1.0\n",
      "[Validation] Batch ID = 25060, loss = 0.0320628, acc = 0.96\n",
      "[Train] Batch ID = 25070, loss = 0.210708, acc = 0.82\n",
      "[Validation] Batch ID = 25070, loss = 0.0326852, acc = 0.98\n",
      "[Train] Batch ID = 25080, loss = 0.00708059, acc = 1.0\n",
      "[Validation] Batch ID = 25080, loss = 0.0180377, acc = 0.98\n",
      "[Train] Batch ID = 25090, loss = 0.00390868, acc = 1.0\n",
      "[Validation] Batch ID = 25090, loss = 0.0409415, acc = 0.96\n",
      "[Train] Batch ID = 25100, loss = 0.00501257, acc = 1.0\n",
      "[Validation] Batch ID = 25100, loss = 0.0196659, acc = 0.98\n",
      "[Train] Batch ID = 25110, loss = 0.00551312, acc = 1.0\n",
      "[Validation] Batch ID = 25110, loss = 0.0655031, acc = 0.94\n",
      "[Train] Batch ID = 25120, loss = 0.0080233, acc = 1.0\n",
      "[Validation] Batch ID = 25120, loss = 0.0378634, acc = 0.96\n",
      "[Train] Batch ID = 25130, loss = 0.00290751, acc = 1.0\n",
      "[Validation] Batch ID = 25130, loss = 0.0284173, acc = 1.0\n",
      "[Train] Batch ID = 25140, loss = 0.00486272, acc = 1.0\n",
      "[Validation] Batch ID = 25140, loss = 0.0596583, acc = 0.92\n",
      "[Train] Batch ID = 25150, loss = 0.00660316, acc = 1.0\n",
      "[Validation] Batch ID = 25150, loss = 0.0335855, acc = 0.98\n",
      "[Train] Batch ID = 25160, loss = 0.00487435, acc = 1.0\n",
      "[Validation] Batch ID = 25160, loss = 0.0176225, acc = 0.98\n",
      "[Train] Batch ID = 25170, loss = 0.00379892, acc = 1.0\n",
      "[Validation] Batch ID = 25170, loss = 0.0265738, acc = 0.98\n",
      "[Train] Batch ID = 25180, loss = 0.00209184, acc = 1.0\n",
      "[Validation] Batch ID = 25180, loss = 0.0495574, acc = 0.94\n",
      "[Train] Batch ID = 25190, loss = 0.00273337, acc = 1.0\n",
      "[Validation] Batch ID = 25190, loss = 0.0283798, acc = 0.98\n",
      "[Train] Batch ID = 25200, loss = 0.00353634, acc = 1.0\n",
      "[Validation] Batch ID = 25200, loss = 0.0542001, acc = 0.92\n",
      "[Train] Batch ID = 25210, loss = 0.00700817, acc = 1.0\n",
      "[Validation] Batch ID = 25210, loss = 0.0185768, acc = 1.0\n",
      "[Train] Batch ID = 25220, loss = 0.00763123, acc = 1.0\n",
      "[Validation] Batch ID = 25220, loss = 0.0338626, acc = 0.96\n",
      "[Train] Batch ID = 25230, loss = 0.0051291, acc = 1.0\n",
      "[Validation] Batch ID = 25230, loss = 0.0368175, acc = 0.96\n",
      "[Train] Batch ID = 25240, loss = 0.00337264, acc = 1.0\n",
      "[Validation] Batch ID = 25240, loss = 0.0319448, acc = 0.98\n",
      "[Train] Batch ID = 25250, loss = 0.00593421, acc = 1.0\n",
      "[Validation] Batch ID = 25250, loss = 0.0259666, acc = 1.0\n",
      "[Train] Batch ID = 25260, loss = 0.162756, acc = 0.86\n",
      "[Validation] Batch ID = 25260, loss = 0.0151578, acc = 1.0\n",
      "[Train] Batch ID = 25270, loss = 0.0049257, acc = 1.0\n",
      "[Validation] Batch ID = 25270, loss = 0.0197664, acc = 1.0\n",
      "[Train] Batch ID = 25280, loss = 0.00463759, acc = 1.0\n",
      "[Validation] Batch ID = 25280, loss = 0.021977, acc = 0.98\n",
      "[Train] Batch ID = 25290, loss = 0.00465225, acc = 1.0\n",
      "[Validation] Batch ID = 25290, loss = 0.0479826, acc = 0.98\n",
      "[Train] Batch ID = 25300, loss = 0.00332115, acc = 1.0\n",
      "[Validation] Batch ID = 25300, loss = 0.0698038, acc = 0.94\n",
      "[Train] Batch ID = 25310, loss = 0.0049848, acc = 1.0\n",
      "[Validation] Batch ID = 25310, loss = 0.0351909, acc = 1.0\n",
      "[Train] Batch ID = 25320, loss = 0.00539496, acc = 1.0\n",
      "[Validation] Batch ID = 25320, loss = 0.0426134, acc = 0.96\n",
      "[Train] Batch ID = 25330, loss = 0.173396, acc = 0.8\n",
      "[Validation] Batch ID = 25330, loss = 0.0190018, acc = 1.0\n",
      "[Train] Batch ID = 25340, loss = 0.0039035, acc = 1.0\n",
      "[Validation] Batch ID = 25340, loss = 0.0289742, acc = 0.96\n",
      "[Train] Batch ID = 25350, loss = 0.00565386, acc = 1.0\n",
      "[Validation] Batch ID = 25350, loss = 0.0464901, acc = 0.94\n",
      "[Train] Batch ID = 25360, loss = 0.00457536, acc = 1.0\n",
      "[Validation] Batch ID = 25360, loss = 0.0356388, acc = 0.96\n",
      "[Train] Batch ID = 25370, loss = 0.00369901, acc = 1.0\n",
      "[Validation] Batch ID = 25370, loss = 0.0343777, acc = 1.0\n",
      "[Train] Batch ID = 25380, loss = 0.180355, acc = 0.88\n",
      "[Validation] Batch ID = 25380, loss = 0.0190102, acc = 1.0\n",
      "[Train] Batch ID = 25390, loss = 0.00403948, acc = 1.0\n",
      "[Validation] Batch ID = 25390, loss = 0.028232, acc = 0.98\n",
      "[Train] Batch ID = 25400, loss = 0.193305, acc = 0.88\n",
      "[Validation] Batch ID = 25400, loss = 0.035811, acc = 0.98\n",
      "[Train] Batch ID = 25410, loss = 0.00609214, acc = 1.0\n",
      "[Validation] Batch ID = 25410, loss = 0.0647143, acc = 0.92\n",
      "[Train] Batch ID = 25420, loss = 0.00308192, acc = 1.0\n",
      "[Validation] Batch ID = 25420, loss = 0.0310692, acc = 0.98\n",
      "[Train] Batch ID = 25430, loss = 0.19626, acc = 0.8\n",
      "[Validation] Batch ID = 25430, loss = 0.0129089, acc = 1.0\n",
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      "[Train] Batch ID = 25490, loss = 0.00867511, acc = 1.0\n",
      "[Validation] Batch ID = 25490, loss = 0.0461688, acc = 0.96\n",
      "[Train] Batch ID = 25500, loss = 0.00511404, acc = 1.0\n",
      "[Validation] Batch ID = 25500, loss = 0.0377125, acc = 0.94\n",
      "[Train] Batch ID = 25510, loss = 0.00521858, acc = 1.0\n",
      "[Validation] Batch ID = 25510, loss = 0.0253516, acc = 1.0\n",
      "[Train] Batch ID = 25520, loss = 0.00641138, acc = 1.0\n",
      "[Validation] Batch ID = 25520, loss = 0.0476274, acc = 0.98\n",
      "[Train] Batch ID = 25530, loss = 0.00449936, acc = 1.0\n",
      "[Validation] Batch ID = 25530, loss = 0.0245405, acc = 0.98\n",
      "[Train] Batch ID = 25540, loss = 0.00409921, acc = 1.0\n",
      "[Validation] Batch ID = 25540, loss = 0.0515231, acc = 0.96\n",
      "[Train] Batch ID = 25550, loss = 0.00419816, acc = 1.0\n",
      "[Validation] Batch ID = 25550, loss = 0.0396991, acc = 0.94\n",
      "[Train] Batch ID = 25560, loss = 0.00483512, acc = 1.0\n",
      "[Validation] Batch ID = 25560, loss = 0.0252135, acc = 0.98\n",
      "[Train] Batch ID = 25570, loss = 0.00738272, acc = 1.0\n",
      "[Validation] Batch ID = 25570, loss = 0.0450878, acc = 0.98\n",
      "[Train] Batch ID = 25580, loss = 0.00627595, acc = 1.0\n",
      "[Validation] Batch ID = 25580, loss = 0.0144401, acc = 1.0\n",
      "[Train] Batch ID = 25590, loss = 0.00204619, acc = 1.0\n",
      "[Validation] Batch ID = 25590, loss = 0.0351245, acc = 0.96\n",
      "[Train] Batch ID = 25600, loss = 0.153193, acc = 0.94\n",
      "[Validation] Batch ID = 25600, loss = 0.0326385, acc = 1.0\n",
      "[Train] Batch ID = 25610, loss = 0.00663038, acc = 1.0\n",
      "[Validation] Batch ID = 25610, loss = 0.0232786, acc = 1.0\n",
      "[Train] Batch ID = 25620, loss = 0.00416398, acc = 1.0\n",
      "[Validation] Batch ID = 25620, loss = 0.0339643, acc = 0.96\n",
      "[Train] Batch ID = 25630, loss = 0.00577202, acc = 1.0\n",
      "[Validation] Batch ID = 25630, loss = 0.0429454, acc = 0.96\n",
      "[Train] Batch ID = 25640, loss = 0.00381897, acc = 1.0\n",
      "[Validation] Batch ID = 25640, loss = 0.0234658, acc = 0.98\n",
      "[Train] Batch ID = 25650, loss = 0.00503051, acc = 1.0\n",
      "[Validation] Batch ID = 25650, loss = 0.0512088, acc = 0.96\n",
      "[Train] Batch ID = 25660, loss = 0.00575399, acc = 1.0\n",
      "[Validation] Batch ID = 25660, loss = 0.0181857, acc = 0.98\n",
      "[Train] Batch ID = 25670, loss = 0.00437739, acc = 1.0\n",
      "[Validation] Batch ID = 25670, loss = 0.0374583, acc = 0.96\n",
      "[Train] Batch ID = 25680, loss = 0.00392275, acc = 1.0\n",
      "[Validation] Batch ID = 25680, loss = 0.0643692, acc = 0.9\n",
      "[Train] Batch ID = 25690, loss = 0.00262643, acc = 1.0\n",
      "[Validation] Batch ID = 25690, loss = 0.0221178, acc = 0.98\n",
      "[Train] Batch ID = 25700, loss = 0.0066677, acc = 1.0\n",
      "[Validation] Batch ID = 25700, loss = 0.0535426, acc = 0.92\n",
      "[Train] Batch ID = 25710, loss = 0.0064405, acc = 1.0\n",
      "[Validation] Batch ID = 25710, loss = 0.0438907, acc = 0.96\n",
      "[Train] Batch ID = 25720, loss = 0.00399391, acc = 1.0\n",
      "[Validation] Batch ID = 25720, loss = 0.00851319, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 25730, loss = 0.00533695, acc = 1.0\n",
      "[Validation] Batch ID = 25730, loss = 0.0134686, acc = 1.0\n",
      "[Train] Batch ID = 25740, loss = 0.00425786, acc = 1.0\n",
      "[Validation] Batch ID = 25740, loss = 0.0144386, acc = 1.0\n",
      "[Train] Batch ID = 25750, loss = 0.00332391, acc = 1.0\n",
      "[Validation] Batch ID = 25750, loss = 0.0175227, acc = 1.0\n",
      "[Train] Batch ID = 25760, loss = 0.00227195, acc = 1.0\n",
      "[Validation] Batch ID = 25760, loss = 0.0430957, acc = 0.96\n",
      "[Train] Batch ID = 25770, loss = 0.00387504, acc = 1.0\n",
      "[Validation] Batch ID = 25770, loss = 0.0271849, acc = 0.98\n",
      "[Train] Batch ID = 25780, loss = 0.00297543, acc = 1.0\n",
      "[Validation] Batch ID = 25780, loss = 0.0315987, acc = 0.98\n",
      "[Train] Batch ID = 25790, loss = 0.00331605, acc = 1.0\n",
      "[Validation] Batch ID = 25790, loss = 0.0251438, acc = 1.0\n",
      "[Train] Batch ID = 25800, loss = 0.00301288, acc = 1.0\n",
      "[Validation] Batch ID = 25800, loss = 0.0361691, acc = 0.96\n",
      "[Train] Batch ID = 25810, loss = 0.00305112, acc = 1.0\n",
      "[Validation] Batch ID = 25810, loss = 0.0370814, acc = 0.98\n",
      "[Train] Batch ID = 25820, loss = 0.00353276, acc = 1.0\n",
      "[Validation] Batch ID = 25820, loss = 0.0361633, acc = 1.0\n",
      "[Train] Batch ID = 25830, loss = 0.00934081, acc = 1.0\n",
      "[Validation] Batch ID = 25830, loss = 0.0271243, acc = 0.98\n",
      "[Train] Batch ID = 25840, loss = 0.00581993, acc = 1.0\n",
      "[Validation] Batch ID = 25840, loss = 0.0441077, acc = 0.96\n",
      "[Train] Batch ID = 25850, loss = 0.00750682, acc = 1.0\n",
      "[Validation] Batch ID = 25850, loss = 0.0347248, acc = 0.98\n",
      "[Train] Batch ID = 25860, loss = 0.00838789, acc = 1.0\n",
      "[Validation] Batch ID = 25860, loss = 0.0270179, acc = 1.0\n",
      "[Train] Batch ID = 25870, loss = 0.006683, acc = 1.0\n",
      "[Validation] Batch ID = 25870, loss = 0.0347112, acc = 0.98\n",
      "[Train] Batch ID = 25880, loss = 0.00275969, acc = 1.0\n",
      "[Validation] Batch ID = 25880, loss = 0.0367162, acc = 0.96\n",
      "[Train] Batch ID = 25890, loss = 0.00343216, acc = 1.0\n",
      "[Validation] Batch ID = 25890, loss = 0.0538582, acc = 0.92\n",
      "[Train] Batch ID = 25900, loss = 0.00772962, acc = 1.0\n",
      "[Validation] Batch ID = 25900, loss = 0.0259975, acc = 0.98\n",
      "[Train] Batch ID = 25910, loss = 0.00433735, acc = 1.0\n",
      "[Validation] Batch ID = 25910, loss = 0.0115889, acc = 1.0\n",
      "[Train] Batch ID = 25920, loss = 0.00337505, acc = 1.0\n",
      "[Validation] Batch ID = 25920, loss = 0.0115876, acc = 1.0\n",
      "[Train] Batch ID = 25930, loss = 0.00403224, acc = 1.0\n",
      "[Validation] Batch ID = 25930, loss = 0.0147867, acc = 1.0\n",
      "[Train] Batch ID = 25940, loss = 0.185585, acc = 0.82\n",
      "[Validation] Batch ID = 25940, loss = 0.0270805, acc = 0.98\n",
      "[Train] Batch ID = 25950, loss = 0.00718152, acc = 1.0\n",
      "[Validation] Batch ID = 25950, loss = 0.0189938, acc = 1.0\n",
      "[Train] Batch ID = 25960, loss = 0.00611538, acc = 1.0\n",
      "[Validation] Batch ID = 25960, loss = 0.0266209, acc = 0.98\n",
      "[Train] Batch ID = 25970, loss = 0.0044575, acc = 1.0\n",
      "[Validation] Batch ID = 25970, loss = 0.0418553, acc = 0.94\n",
      "[Train] Batch ID = 25980, loss = 0.00801087, acc = 1.0\n",
      "[Validation] Batch ID = 25980, loss = 0.0621258, acc = 0.96\n",
      "[Train] Batch ID = 25990, loss = 0.197612, acc = 0.82\n",
      "[Validation] Batch ID = 25990, loss = 0.0345656, acc = 0.96\n",
      "[Train] Batch ID = 26000, loss = 0.00912309, acc = 1.0\n",
      "[Validation] Batch ID = 26000, loss = 0.0419714, acc = 0.96\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0366307 Best loss: 0.0344537\n",
      "[TOTAL Validation] Batch ID = 26000, loss = 0.0366307, acc = 0.97664399093\n",
      "Augmented Factor = 0.057568239633009693\n",
      "[Train] Batch ID = 26010, loss = 0.00578859, acc = 1.0\n",
      "[Validation] Batch ID = 26010, loss = 0.0370199, acc = 0.94\n",
      "[Train] Batch ID = 26020, loss = 0.00342464, acc = 1.0\n",
      "[Validation] Batch ID = 26020, loss = 0.0341786, acc = 0.96\n",
      "[Train] Batch ID = 26030, loss = 0.00453978, acc = 1.0\n",
      "[Validation] Batch ID = 26030, loss = 0.0197331, acc = 1.0\n",
      "[Train] Batch ID = 26040, loss = 0.00256224, acc = 1.0\n",
      "[Validation] Batch ID = 26040, loss = 0.0196859, acc = 1.0\n",
      "[Train] Batch ID = 26050, loss = 0.00340631, acc = 1.0\n",
      "[Validation] Batch ID = 26050, loss = 0.0452105, acc = 0.94\n",
      "[Train] Batch ID = 26060, loss = 0.00225872, acc = 1.0\n",
      "[Validation] Batch ID = 26060, loss = 0.0155485, acc = 1.0\n",
      "[Train] Batch ID = 26070, loss = 0.00560962, acc = 1.0\n",
      "[Validation] Batch ID = 26070, loss = 0.0266955, acc = 1.0\n",
      "[Train] Batch ID = 26080, loss = 0.00393269, acc = 1.0\n",
      "[Validation] Batch ID = 26080, loss = 0.0303223, acc = 1.0\n",
      "[Train] Batch ID = 26090, loss = 0.0048605, acc = 1.0\n",
      "[Validation] Batch ID = 26090, loss = 0.0414392, acc = 0.98\n",
      "[Train] Batch ID = 26100, loss = 0.00979519, acc = 1.0\n",
      "[Validation] Batch ID = 26100, loss = 0.0479835, acc = 0.94\n",
      "[Train] Batch ID = 26110, loss = 0.00481187, acc = 1.0\n",
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      "[Train] Batch ID = 26120, loss = 0.00537806, acc = 1.0\n",
      "[Validation] Batch ID = 26120, loss = 0.0250419, acc = 0.98\n",
      "[Train] Batch ID = 26130, loss = 0.00388486, acc = 1.0\n",
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      "[Train] Batch ID = 26140, loss = 0.0059843, acc = 1.0\n",
      "[Validation] Batch ID = 26140, loss = 0.034666, acc = 0.98\n",
      "[Train] Batch ID = 26150, loss = 0.0045862, acc = 1.0\n",
      "[Validation] Batch ID = 26150, loss = 0.0215482, acc = 1.0\n",
      "[Train] Batch ID = 26160, loss = 0.00302086, acc = 1.0\n",
      "[Validation] Batch ID = 26160, loss = 0.0185783, acc = 0.98\n",
      "[Train] Batch ID = 26170, loss = 0.00269747, acc = 1.0\n",
      "[Validation] Batch ID = 26170, loss = 0.0086549, acc = 1.0\n",
      "[Train] Batch ID = 26180, loss = 0.00266846, acc = 1.0\n",
      "[Validation] Batch ID = 26180, loss = 0.0519928, acc = 0.94\n",
      "[Train] Batch ID = 26190, loss = 0.00209302, acc = 1.0\n",
      "[Validation] Batch ID = 26190, loss = 0.0202654, acc = 0.98\n",
      "[Train] Batch ID = 26200, loss = 0.00907939, acc = 1.0\n",
      "[Validation] Batch ID = 26200, loss = 0.0224886, acc = 0.98\n",
      "[Train] Batch ID = 26210, loss = 0.0026054, acc = 1.0\n",
      "[Validation] Batch ID = 26210, loss = 0.0332879, acc = 0.98\n",
      "[Train] Batch ID = 26220, loss = 0.00269661, acc = 1.0\n",
      "[Validation] Batch ID = 26220, loss = 0.0238039, acc = 0.98\n",
      "[Train] Batch ID = 26230, loss = 0.0035253, acc = 1.0\n",
      "[Validation] Batch ID = 26230, loss = 0.034603, acc = 0.98\n",
      "[Train] Batch ID = 26240, loss = 0.00399141, acc = 1.0\n",
      "[Validation] Batch ID = 26240, loss = 0.0519859, acc = 0.94\n",
      "[Train] Batch ID = 26250, loss = 0.00169044, acc = 1.0\n",
      "[Validation] Batch ID = 26250, loss = 0.0365988, acc = 0.98\n",
      "[Train] Batch ID = 26260, loss = 0.00282942, acc = 1.0\n",
      "[Validation] Batch ID = 26260, loss = 0.0296884, acc = 0.98\n",
      "[Train] Batch ID = 26270, loss = 0.18625, acc = 0.86\n",
      "[Validation] Batch ID = 26270, loss = 0.0567259, acc = 0.92\n",
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      "[Train] Batch ID = 26290, loss = 0.189436, acc = 0.77551\n",
      "[Validation] Batch ID = 26290, loss = 0.0218903, acc = 1.0\n",
      "[Train] Batch ID = 26300, loss = 0.00955191, acc = 1.0\n",
      "[Validation] Batch ID = 26300, loss = 0.0240006, acc = 0.98\n",
      "[Train] Batch ID = 26310, loss = 0.00536634, acc = 1.0\n",
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      "[Train] Batch ID = 26320, loss = 0.0076938, acc = 1.0\n",
      "[Validation] Batch ID = 26320, loss = 0.0237479, acc = 0.98\n",
      "[Train] Batch ID = 26330, loss = 0.00301295, acc = 1.0\n",
      "[Validation] Batch ID = 26330, loss = 0.0271867, acc = 1.0\n",
      "[Train] Batch ID = 26340, loss = 0.00397235, acc = 1.0\n",
      "[Validation] Batch ID = 26340, loss = 0.0459381, acc = 0.94\n",
      "[Train] Batch ID = 26350, loss = 0.00645607, acc = 1.0\n",
      "[Validation] Batch ID = 26350, loss = 0.028852, acc = 1.0\n",
      "[Train] Batch ID = 26360, loss = 0.00282513, acc = 1.0\n",
      "[Validation] Batch ID = 26360, loss = 0.0278333, acc = 0.98\n",
      "[Train] Batch ID = 26370, loss = 0.00559427, acc = 1.0\n",
      "[Validation] Batch ID = 26370, loss = 0.0239033, acc = 1.0\n",
      "[Train] Batch ID = 26380, loss = 0.00523777, acc = 1.0\n",
      "[Validation] Batch ID = 26380, loss = 0.0280567, acc = 0.98\n",
      "[Train] Batch ID = 26390, loss = 0.0033703, acc = 1.0\n",
      "[Validation] Batch ID = 26390, loss = 0.0260484, acc = 0.98\n",
      "[Train] Batch ID = 26400, loss = 0.00215853, acc = 1.0\n",
      "[Validation] Batch ID = 26400, loss = 0.00590714, acc = 1.0\n",
      "[Train] Batch ID = 26410, loss = 0.00703167, acc = 1.0\n",
      "[Validation] Batch ID = 26410, loss = 0.0243345, acc = 1.0\n",
      "[Train] Batch ID = 26420, loss = 0.00475593, acc = 1.0\n",
      "[Validation] Batch ID = 26420, loss = 0.0320021, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 26430, loss = 0.00396824, acc = 1.0\n",
      "[Validation] Batch ID = 26430, loss = 0.0541277, acc = 0.94\n",
      "[Train] Batch ID = 26440, loss = 0.00328267, acc = 1.0\n",
      "[Validation] Batch ID = 26440, loss = 0.0369022, acc = 0.96\n",
      "[Train] Batch ID = 26450, loss = 0.00263134, acc = 1.0\n",
      "[Validation] Batch ID = 26450, loss = 0.0278014, acc = 0.98\n",
      "[Train] Batch ID = 26460, loss = 0.00421793, acc = 1.0\n",
      "[Validation] Batch ID = 26460, loss = 0.0217491, acc = 0.98\n",
      "[Train] Batch ID = 26470, loss = 0.200859, acc = 0.84\n",
      "[Validation] Batch ID = 26470, loss = 0.0306074, acc = 0.98\n",
      "[Train] Batch ID = 26480, loss = 0.00522908, acc = 1.0\n",
      "[Validation] Batch ID = 26480, loss = 0.0188927, acc = 1.0\n",
      "[Train] Batch ID = 26490, loss = 0.00466273, acc = 1.0\n",
      "[Validation] Batch ID = 26490, loss = 0.0113572, acc = 1.0\n",
      "[Train] Batch ID = 26500, loss = 0.00286973, acc = 1.0\n",
      "[Validation] Batch ID = 26500, loss = 0.0316676, acc = 1.0\n",
      "[Train] Batch ID = 26510, loss = 0.00175133, acc = 1.0\n",
      "[Validation] Batch ID = 26510, loss = 0.0251474, acc = 0.98\n",
      "[Train] Batch ID = 26520, loss = 0.00279293, acc = 1.0\n",
      "[Validation] Batch ID = 26520, loss = 0.0336185, acc = 0.98\n",
      "[Train] Batch ID = 26530, loss = 0.00484142, acc = 1.0\n",
      "[Validation] Batch ID = 26530, loss = 0.0672743, acc = 0.92\n",
      "[Train] Batch ID = 26540, loss = 0.00493622, acc = 1.0\n",
      "[Validation] Batch ID = 26540, loss = 0.0370524, acc = 0.96\n",
      "[Train] Batch ID = 26550, loss = 0.168307, acc = 0.86\n",
      "[Validation] Batch ID = 26550, loss = 0.0385802, acc = 0.96\n",
      "[Train] Batch ID = 26560, loss = 0.00593387, acc = 1.0\n",
      "[Validation] Batch ID = 26560, loss = 0.0115282, acc = 1.0\n",
      "[Train] Batch ID = 26570, loss = 0.00370316, acc = 1.0\n",
      "[Validation] Batch ID = 26570, loss = 0.0406835, acc = 0.96\n",
      "[Train] Batch ID = 26580, loss = 0.00291659, acc = 1.0\n",
      "[Validation] Batch ID = 26580, loss = 0.0204569, acc = 0.98\n",
      "[Train] Batch ID = 26590, loss = 0.00291656, acc = 1.0\n",
      "[Validation] Batch ID = 26590, loss = 0.0260496, acc = 1.0\n",
      "[Train] Batch ID = 26600, loss = 0.00436872, acc = 1.0\n",
      "[Validation] Batch ID = 26600, loss = 0.0369429, acc = 0.96\n",
      "[Train] Batch ID = 26610, loss = 0.00292887, acc = 1.0\n",
      "[Validation] Batch ID = 26610, loss = 0.052785, acc = 0.9\n",
      "[Train] Batch ID = 26620, loss = 0.182779, acc = 0.84\n",
      "[Validation] Batch ID = 26620, loss = 0.0529491, acc = 0.96\n",
      "[Train] Batch ID = 26630, loss = 0.00462629, acc = 1.0\n",
      "[Validation] Batch ID = 26630, loss = 0.0496139, acc = 0.94\n",
      "[Train] Batch ID = 26640, loss = 0.00323106, acc = 1.0\n",
      "[Validation] Batch ID = 26640, loss = 0.0357215, acc = 1.0\n",
      "[Train] Batch ID = 26650, loss = 0.00396145, acc = 1.0\n",
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      "[Train] Batch ID = 26660, loss = 0.00480894, acc = 1.0\n",
      "[Validation] Batch ID = 26660, loss = 0.0358499, acc = 0.98\n",
      "[Train] Batch ID = 26670, loss = 0.00657377, acc = 1.0\n",
      "[Validation] Batch ID = 26670, loss = 0.0281682, acc = 0.98\n",
      "[Train] Batch ID = 26680, loss = 0.00332897, acc = 1.0\n",
      "[Validation] Batch ID = 26680, loss = 0.0234043, acc = 0.96\n",
      "[Train] Batch ID = 26690, loss = 0.00337948, acc = 1.0\n",
      "[Validation] Batch ID = 26690, loss = 0.0234602, acc = 0.96\n",
      "[Train] Batch ID = 26700, loss = 0.00346916, acc = 1.0\n",
      "[Validation] Batch ID = 26700, loss = 0.023408, acc = 1.0\n",
      "[Train] Batch ID = 26710, loss = 0.00478993, acc = 1.0\n",
      "[Validation] Batch ID = 26710, loss = 0.0358729, acc = 0.96\n",
      "[Train] Batch ID = 26720, loss = 0.00496879, acc = 1.0\n",
      "[Validation] Batch ID = 26720, loss = 0.0274833, acc = 0.98\n",
      "[Train] Batch ID = 26730, loss = 0.00416248, acc = 1.0\n",
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      "[Train] Batch ID = 26740, loss = 0.00582328, acc = 1.0\n",
      "[Validation] Batch ID = 26740, loss = 0.060164, acc = 0.92\n",
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      "[Validation] Batch ID = 26750, loss = 0.0254673, acc = 0.98\n",
      "[Train] Batch ID = 26760, loss = 0.174635, acc = 0.94\n",
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      "[Validation] Batch ID = 26770, loss = 0.0337866, acc = 0.96\n",
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      "[Validation] Batch ID = 26800, loss = 0.0131908, acc = 1.0\n",
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      "[Validation] Batch ID = 26820, loss = 0.0508425, acc = 0.94\n",
      "[Train] Batch ID = 26830, loss = 0.00483907, acc = 1.0\n",
      "[Validation] Batch ID = 26830, loss = 0.0384995, acc = 0.96\n",
      "[Train] Batch ID = 26840, loss = 0.00542458, acc = 1.0\n",
      "[Validation] Batch ID = 26840, loss = 0.0316136, acc = 0.98\n",
      "[Train] Batch ID = 26850, loss = 0.00505711, acc = 1.0\n",
      "[Validation] Batch ID = 26850, loss = 0.0617723, acc = 0.94\n",
      "[Train] Batch ID = 26860, loss = 0.00412953, acc = 1.0\n",
      "[Validation] Batch ID = 26860, loss = 0.0155764, acc = 1.0\n",
      "[Train] Batch ID = 26870, loss = 0.00201616, acc = 1.0\n",
      "[Validation] Batch ID = 26870, loss = 0.0493092, acc = 0.94\n",
      "[Train] Batch ID = 26880, loss = 0.23393, acc = 0.78\n",
      "[Validation] Batch ID = 26880, loss = 0.05607, acc = 0.96\n",
      "[Train] Batch ID = 26890, loss = 0.00445797, acc = 1.0\n",
      "[Validation] Batch ID = 26890, loss = 0.0248622, acc = 0.98\n",
      "[Train] Batch ID = 26900, loss = 0.00403341, acc = 1.0\n",
      "[Validation] Batch ID = 26900, loss = 0.0247395, acc = 0.98\n",
      "[Train] Batch ID = 26910, loss = 0.00226296, acc = 1.0\n",
      "[Validation] Batch ID = 26910, loss = 0.00926258, acc = 1.0\n",
      "[Train] Batch ID = 26920, loss = 0.00215366, acc = 1.0\n",
      "[Validation] Batch ID = 26920, loss = 0.0303627, acc = 0.98\n",
      "[Train] Batch ID = 26930, loss = 0.00133493, acc = 1.0\n",
      "[Validation] Batch ID = 26930, loss = 0.014699, acc = 1.0\n",
      "[Train] Batch ID = 26940, loss = 0.00125113, acc = 1.0\n",
      "[Validation] Batch ID = 26940, loss = 0.0439244, acc = 0.98\n",
      "[Train] Batch ID = 26950, loss = 0.00288408, acc = 1.0\n",
      "[Validation] Batch ID = 26950, loss = 0.0448899, acc = 0.94\n",
      "[Train] Batch ID = 26960, loss = 0.00600772, acc = 1.0\n",
      "[Validation] Batch ID = 26960, loss = 0.0274735, acc = 0.98\n",
      "[Train] Batch ID = 26970, loss = 0.00206654, acc = 1.0\n",
      "[Validation] Batch ID = 26970, loss = 0.0220249, acc = 1.0\n",
      "[Train] Batch ID = 26980, loss = 0.00154794, acc = 1.0\n",
      "[Validation] Batch ID = 26980, loss = 0.0188516, acc = 1.0\n",
      "[Train] Batch ID = 26990, loss = 0.00463967, acc = 1.0\n",
      "[Validation] Batch ID = 26990, loss = 0.0280851, acc = 0.98\n",
      "[Train] Batch ID = 27000, loss = 0.00306024, acc = 1.0\n",
      "[Validation] Batch ID = 27000, loss = 0.0241877, acc = 0.98\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0288538 Best loss: 0.0344537\n",
      "[TOTAL Validation] Batch ID = 27000, loss = 0.0288538, acc = 0.975283446712\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.051811415669708726\n",
      "[Train] Batch ID = 27010, loss = 0.00511028, acc = 1.0\n",
      "[Validation] Batch ID = 27010, loss = 0.0165535, acc = 1.0\n",
      "[Train] Batch ID = 27020, loss = 0.00426955, acc = 1.0\n",
      "[Validation] Batch ID = 27020, loss = 0.0351558, acc = 0.98\n",
      "[Train] Batch ID = 27030, loss = 0.00411893, acc = 1.0\n",
      "[Validation] Batch ID = 27030, loss = 0.0209745, acc = 0.98\n",
      "[Train] Batch ID = 27040, loss = 0.00332543, acc = 1.0\n",
      "[Validation] Batch ID = 27040, loss = 0.0352275, acc = 0.96\n",
      "[Train] Batch ID = 27050, loss = 0.00368007, acc = 1.0\n",
      "[Validation] Batch ID = 27050, loss = 0.0459675, acc = 0.96\n",
      "[Train] Batch ID = 27060, loss = 0.00379479, acc = 1.0\n",
      "[Validation] Batch ID = 27060, loss = 0.025172, acc = 0.98\n",
      "[Train] Batch ID = 27070, loss = 0.0038915, acc = 1.0\n",
      "[Validation] Batch ID = 27070, loss = 0.0343857, acc = 0.96\n",
      "[Train] Batch ID = 27080, loss = 0.00317512, acc = 1.0\n",
      "[Validation] Batch ID = 27080, loss = 0.0245631, acc = 1.0\n",
      "[Train] Batch ID = 27090, loss = 0.00547263, acc = 1.0\n",
      "[Validation] Batch ID = 27090, loss = 0.054059, acc = 0.92\n",
      "[Train] Batch ID = 27100, loss = 0.00289482, acc = 1.0\n",
      "[Validation] Batch ID = 27100, loss = 0.0119446, acc = 1.0\n",
      "[Train] Batch ID = 27110, loss = 0.00858852, acc = 1.0\n",
      "[Validation] Batch ID = 27110, loss = 0.0456612, acc = 0.96\n",
      "[Train] Batch ID = 27120, loss = 0.00525914, acc = 1.0\n",
      "[Validation] Batch ID = 27120, loss = 0.0401797, acc = 0.96\n",
      "[Train] Batch ID = 27130, loss = 0.00853301, acc = 1.0\n",
      "[Validation] Batch ID = 27130, loss = 0.031492, acc = 0.96\n",
      "[Train] Batch ID = 27140, loss = 0.00509303, acc = 1.0\n",
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      "[Train] Batch ID = 27170, loss = 0.00213509, acc = 1.0\n",
      "[Validation] Batch ID = 27170, loss = 0.0141826, acc = 1.0\n",
      "[Train] Batch ID = 27180, loss = 0.00329272, acc = 1.0\n",
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      "[Train] Batch ID = 27190, loss = 0.00468888, acc = 1.0\n",
      "[Validation] Batch ID = 27190, loss = 0.0308753, acc = 1.0\n",
      "[Train] Batch ID = 27200, loss = 0.00497168, acc = 1.0\n",
      "[Validation] Batch ID = 27200, loss = 0.0155546, acc = 1.0\n",
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      "[Train] Batch ID = 27220, loss = 0.00439152, acc = 1.0\n",
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      "[Train] Batch ID = 27230, loss = 0.162589, acc = 0.84\n",
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      "[Train] Batch ID = 27240, loss = 0.00438648, acc = 1.0\n",
      "[Validation] Batch ID = 27240, loss = 0.0274206, acc = 1.0\n",
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      "[Train] Batch ID = 27260, loss = 0.00240711, acc = 1.0\n",
      "[Validation] Batch ID = 27260, loss = 0.0264643, acc = 1.0\n",
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      "[Validation] Batch ID = 27720, loss = 0.0314516, acc = 0.96\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 27730, loss = 0.00876324, acc = 1.0\n",
      "[Validation] Batch ID = 27730, loss = 0.0208891, acc = 1.0\n",
      "[Train] Batch ID = 27740, loss = 0.00735486, acc = 1.0\n",
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      "[Validation] Batch ID = 27750, loss = 0.0072705, acc = 1.0\n",
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      "[Validation] Batch ID = 27760, loss = 0.0170011, acc = 1.0\n",
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      "[Validation] Batch ID = 27770, loss = 0.0403789, acc = 0.96\n",
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      "[Validation] Batch ID = 27790, loss = 0.0424083, acc = 0.96\n",
      "[Train] Batch ID = 27800, loss = 0.00346044, acc = 1.0\n",
      "[Validation] Batch ID = 27800, loss = 0.0487666, acc = 0.98\n",
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      "[Validation] Batch ID = 27830, loss = 0.0383321, acc = 0.98\n",
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      "[Validation] Batch ID = 27840, loss = 0.00681413, acc = 1.0\n",
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      "[Validation] Batch ID = 27850, loss = 0.00989431, acc = 1.0\n",
      "[Train] Batch ID = 27860, loss = 0.177231, acc = 0.82\n",
      "[Validation] Batch ID = 27860, loss = 0.034446, acc = 0.98\n",
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      "[Validation] Batch ID = 27880, loss = 0.0245813, acc = 1.0\n",
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      "[Validation] Batch ID = 27890, loss = 0.0108975, acc = 1.0\n",
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      "[Validation] Batch ID = 27900, loss = 0.0318729, acc = 0.98\n",
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      "[Validation] Batch ID = 27930, loss = 0.0375703, acc = 0.96\n",
      "[Train] Batch ID = 27940, loss = 0.00207326, acc = 1.0\n",
      "[Validation] Batch ID = 27940, loss = 0.0245635, acc = 0.98\n",
      "[Train] Batch ID = 27950, loss = 0.00254769, acc = 1.0\n",
      "[Validation] Batch ID = 27950, loss = 0.0272671, acc = 0.98\n",
      "[Train] Batch ID = 27960, loss = 0.00293592, acc = 1.0\n",
      "[Validation] Batch ID = 27960, loss = 0.0281415, acc = 0.96\n",
      "[Train] Batch ID = 27970, loss = 0.00211629, acc = 1.0\n",
      "[Validation] Batch ID = 27970, loss = 0.0138597, acc = 1.0\n",
      "[Train] Batch ID = 27980, loss = 0.00623275, acc = 1.0\n",
      "[Validation] Batch ID = 27980, loss = 0.0294323, acc = 0.96\n",
      "[Train] Batch ID = 27990, loss = 0.00163982, acc = 1.0\n",
      "[Validation] Batch ID = 27990, loss = 0.0236676, acc = 0.98\n",
      "[Train] Batch ID = 28000, loss = 0.00254689, acc = 1.0\n",
      "[Validation] Batch ID = 28000, loss = 0.0199397, acc = 1.0\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.030012 Best loss: 0.0288538\n",
      "[TOTAL Validation] Batch ID = 28000, loss = 0.030012, acc = 0.975736961451\n",
      "Augmented Factor = 0.04663027410273785\n",
      "[Train] Batch ID = 28010, loss = 0.00187517, acc = 1.0\n",
      "[Validation] Batch ID = 28010, loss = 0.0148295, acc = 0.98\n",
      "[Train] Batch ID = 28020, loss = 0.00282962, acc = 1.0\n",
      "[Validation] Batch ID = 28020, loss = 0.0294613, acc = 0.96\n",
      "[Train] Batch ID = 28030, loss = 0.00181198, acc = 1.0\n",
      "[Validation] Batch ID = 28030, loss = 0.0198276, acc = 0.98\n",
      "[Train] Batch ID = 28040, loss = 0.0029496, acc = 1.0\n",
      "[Validation] Batch ID = 28040, loss = 0.0201372, acc = 1.0\n",
      "[Train] Batch ID = 28050, loss = 0.00334896, acc = 1.0\n",
      "[Validation] Batch ID = 28050, loss = 0.0251114, acc = 0.98\n",
      "[Train] Batch ID = 28060, loss = 0.00249863, acc = 1.0\n",
      "[Validation] Batch ID = 28060, loss = 0.0112194, acc = 1.0\n",
      "[Train] Batch ID = 28070, loss = 0.00246355, acc = 1.0\n",
      "[Validation] Batch ID = 28070, loss = 0.0610709, acc = 0.94\n",
      "[Train] Batch ID = 28080, loss = 0.00649246, acc = 1.0\n",
      "[Validation] Batch ID = 28080, loss = 0.026216, acc = 0.98\n",
      "[Train] Batch ID = 28090, loss = 0.00303462, acc = 1.0\n",
      "[Validation] Batch ID = 28090, loss = 0.0378549, acc = 0.98\n",
      "[Train] Batch ID = 28100, loss = 0.00347731, acc = 1.0\n",
      "[Validation] Batch ID = 28100, loss = 0.0222453, acc = 0.98\n",
      "[Train] Batch ID = 28110, loss = 0.00421006, acc = 1.0\n",
      "[Validation] Batch ID = 28110, loss = 0.0195487, acc = 1.0\n",
      "[Train] Batch ID = 28120, loss = 0.00320211, acc = 1.0\n",
      "[Validation] Batch ID = 28120, loss = 0.0396301, acc = 0.96\n",
      "[Train] Batch ID = 28130, loss = 0.000717742, acc = 1.0\n",
      "[Validation] Batch ID = 28130, loss = 0.0516516, acc = 0.94\n",
      "[Train] Batch ID = 28140, loss = 0.00242869, acc = 1.0\n",
      "[Validation] Batch ID = 28140, loss = 0.0381379, acc = 0.96\n",
      "[Train] Batch ID = 28150, loss = 0.00196508, acc = 1.0\n",
      "[Validation] Batch ID = 28150, loss = 0.0192463, acc = 0.98\n",
      "[Train] Batch ID = 28160, loss = 0.00260866, acc = 1.0\n",
      "[Validation] Batch ID = 28160, loss = 0.0447866, acc = 0.96\n",
      "[Train] Batch ID = 28170, loss = 0.00851993, acc = 1.0\n",
      "[Validation] Batch ID = 28170, loss = 0.0234473, acc = 1.0\n",
      "[Train] Batch ID = 28180, loss = 0.00495754, acc = 1.0\n",
      "[Validation] Batch ID = 28180, loss = 0.0296174, acc = 0.98\n",
      "[Train] Batch ID = 28190, loss = 0.00353983, acc = 1.0\n",
      "[Validation] Batch ID = 28190, loss = 0.0131416, acc = 1.0\n",
      "[Train] Batch ID = 28200, loss = 0.00157515, acc = 1.0\n",
      "[Validation] Batch ID = 28200, loss = 0.0171649, acc = 1.0\n",
      "[Train] Batch ID = 28210, loss = 0.00253486, acc = 1.0\n",
      "[Validation] Batch ID = 28210, loss = 0.0307121, acc = 0.98\n",
      "[Train] Batch ID = 28220, loss = 0.00335755, acc = 1.0\n",
      "[Validation] Batch ID = 28220, loss = 0.0493128, acc = 0.96\n",
      "[Train] Batch ID = 28230, loss = 0.00275743, acc = 1.0\n",
      "[Validation] Batch ID = 28230, loss = 0.0510425, acc = 0.94\n",
      "[Train] Batch ID = 28240, loss = 0.00629263, acc = 1.0\n",
      "[Validation] Batch ID = 28240, loss = 0.0141851, acc = 1.0\n",
      "[Train] Batch ID = 28250, loss = 0.00417819, acc = 1.0\n",
      "[Validation] Batch ID = 28250, loss = 0.0299346, acc = 0.98\n",
      "[Train] Batch ID = 28260, loss = 0.00310623, acc = 1.0\n",
      "[Validation] Batch ID = 28260, loss = 0.0284007, acc = 1.0\n",
      "[Train] Batch ID = 28270, loss = 0.00471785, acc = 1.0\n",
      "[Validation] Batch ID = 28270, loss = 0.0341935, acc = 0.98\n",
      "[Train] Batch ID = 28280, loss = 0.00593985, acc = 1.0\n",
      "[Validation] Batch ID = 28280, loss = 0.014572, acc = 1.0\n",
      "[Train] Batch ID = 28290, loss = 0.00565017, acc = 1.0\n",
      "[Validation] Batch ID = 28290, loss = 0.0380004, acc = 1.0\n",
      "[Train] Batch ID = 28300, loss = 0.00331818, acc = 1.0\n",
      "[Validation] Batch ID = 28300, loss = 0.029631, acc = 0.98\n",
      "[Train] Batch ID = 28310, loss = 0.00217559, acc = 1.0\n",
      "[Validation] Batch ID = 28310, loss = 0.0203245, acc = 0.98\n",
      "[Train] Batch ID = 28320, loss = 0.00309005, acc = 1.0\n",
      "[Validation] Batch ID = 28320, loss = 0.0155592, acc = 1.0\n",
      "[Train] Batch ID = 28330, loss = 0.0029702, acc = 1.0\n",
      "[Validation] Batch ID = 28330, loss = 0.0617228, acc = 0.92\n",
      "[Train] Batch ID = 28340, loss = 0.000899298, acc = 1.0\n",
      "[Validation] Batch ID = 28340, loss = 0.0127443, acc = 1.0\n",
      "[Train] Batch ID = 28350, loss = 0.00355056, acc = 1.0\n",
      "[Validation] Batch ID = 28350, loss = 0.0372019, acc = 0.96\n",
      "[Train] Batch ID = 28360, loss = 0.00291447, acc = 1.0\n",
      "[Validation] Batch ID = 28360, loss = 0.0447046, acc = 0.94\n",
      "[Train] Batch ID = 28370, loss = 0.00308209, acc = 1.0\n",
      "[Validation] Batch ID = 28370, loss = 0.022357, acc = 0.98\n",
      "[Train] Batch ID = 28380, loss = 0.00271913, acc = 1.0\n",
      "[Validation] Batch ID = 28380, loss = 0.024885, acc = 0.98\n",
      "[Train] Batch ID = 28390, loss = 0.208916, acc = 0.78\n",
      "[Validation] Batch ID = 28390, loss = 0.0711173, acc = 0.9\n",
      "[Train] Batch ID = 28400, loss = 0.00296322, acc = 1.0\n",
      "[Validation] Batch ID = 28400, loss = 0.0191428, acc = 1.0\n",
      "[Train] Batch ID = 28410, loss = 0.00235889, acc = 1.0\n",
      "[Validation] Batch ID = 28410, loss = 0.0272752, acc = 0.98\n",
      "[Train] Batch ID = 28420, loss = 0.00465191, acc = 1.0\n",
      "[Validation] Batch ID = 28420, loss = 0.0346188, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 28430, loss = 0.00310973, acc = 1.0\n",
      "[Validation] Batch ID = 28430, loss = 0.0433816, acc = 0.94\n",
      "[Train] Batch ID = 28440, loss = 0.217337, acc = 0.8\n",
      "[Validation] Batch ID = 28440, loss = 0.020323, acc = 1.0\n",
      "[Train] Batch ID = 28450, loss = 0.0018264, acc = 1.0\n",
      "[Validation] Batch ID = 28450, loss = 0.0367968, acc = 0.96\n",
      "[Train] Batch ID = 28460, loss = 0.00391045, acc = 1.0\n",
      "[Validation] Batch ID = 28460, loss = 0.0493477, acc = 0.96\n",
      "[Train] Batch ID = 28470, loss = 0.00269479, acc = 1.0\n",
      "[Validation] Batch ID = 28470, loss = 0.0227373, acc = 0.98\n",
      "[Train] Batch ID = 28480, loss = 0.00397583, acc = 1.0\n",
      "[Validation] Batch ID = 28480, loss = 0.0238048, acc = 1.0\n",
      "[Train] Batch ID = 28490, loss = 0.00300744, acc = 1.0\n",
      "[Validation] Batch ID = 28490, loss = 0.0544896, acc = 0.96\n",
      "[Train] Batch ID = 28500, loss = 0.00680725, acc = 1.0\n",
      "[Validation] Batch ID = 28500, loss = 0.0505459, acc = 0.94\n",
      "[Train] Batch ID = 28510, loss = 0.00250771, acc = 1.0\n",
      "[Validation] Batch ID = 28510, loss = 0.0273664, acc = 0.98\n",
      "[Train] Batch ID = 28520, loss = 0.00225257, acc = 1.0\n",
      "[Validation] Batch ID = 28520, loss = 0.014909, acc = 1.0\n",
      "[Train] Batch ID = 28530, loss = 0.00351722, acc = 1.0\n",
      "[Validation] Batch ID = 28530, loss = 0.0119792, acc = 1.0\n",
      "[Train] Batch ID = 28540, loss = 0.00299136, acc = 1.0\n",
      "[Validation] Batch ID = 28540, loss = 0.025234, acc = 1.0\n",
      "[Train] Batch ID = 28550, loss = 0.00511427, acc = 1.0\n",
      "[Validation] Batch ID = 28550, loss = 0.0379574, acc = 0.96\n",
      "[Train] Batch ID = 28560, loss = 0.00287795, acc = 1.0\n",
      "[Validation] Batch ID = 28560, loss = 0.0251482, acc = 1.0\n",
      "[Train] Batch ID = 28570, loss = 0.00487774, acc = 1.0\n",
      "[Validation] Batch ID = 28570, loss = 0.0444929, acc = 0.96\n",
      "[Train] Batch ID = 28580, loss = 0.00217892, acc = 1.0\n",
      "[Validation] Batch ID = 28580, loss = 0.0191939, acc = 0.98\n",
      "[Train] Batch ID = 28590, loss = 0.0060878, acc = 1.0\n",
      "[Validation] Batch ID = 28590, loss = 0.0197076, acc = 1.0\n",
      "[Train] Batch ID = 28600, loss = 0.00638748, acc = 1.0\n",
      "[Validation] Batch ID = 28600, loss = 0.0273514, acc = 0.98\n",
      "[Train] Batch ID = 28610, loss = 0.00305316, acc = 1.0\n",
      "[Validation] Batch ID = 28610, loss = 0.037881, acc = 0.96\n",
      "[Train] Batch ID = 28620, loss = 0.00238016, acc = 1.0\n",
      "[Validation] Batch ID = 28620, loss = 0.0586842, acc = 0.92\n",
      "[Train] Batch ID = 28630, loss = 0.00160888, acc = 1.0\n",
      "[Validation] Batch ID = 28630, loss = 0.0184048, acc = 1.0\n",
      "[Train] Batch ID = 28640, loss = 0.00374865, acc = 1.0\n",
      "[Validation] Batch ID = 28640, loss = 0.0218343, acc = 1.0\n",
      "[Train] Batch ID = 28650, loss = 0.00298201, acc = 1.0\n",
      "[Validation] Batch ID = 28650, loss = 0.0714884, acc = 0.9\n",
      "[Train] Batch ID = 28660, loss = 0.00555242, acc = 1.0\n",
      "[Validation] Batch ID = 28660, loss = 0.0381605, acc = 0.98\n",
      "[Train] Batch ID = 28670, loss = 0.00354916, acc = 1.0\n",
      "[Validation] Batch ID = 28670, loss = 0.0427044, acc = 0.96\n",
      "[Train] Batch ID = 28680, loss = 0.00249881, acc = 1.0\n",
      "[Validation] Batch ID = 28680, loss = 0.0395053, acc = 0.96\n",
      "[Train] Batch ID = 28690, loss = 0.00208115, acc = 1.0\n",
      "[Validation] Batch ID = 28690, loss = 0.0358692, acc = 0.98\n",
      "[Train] Batch ID = 28700, loss = 0.0015764, acc = 1.0\n",
      "[Validation] Batch ID = 28700, loss = 0.060289, acc = 0.92\n",
      "[Train] Batch ID = 28710, loss = 0.00121999, acc = 1.0\n",
      "[Validation] Batch ID = 28710, loss = 0.0121361, acc = 1.0\n",
      "[Train] Batch ID = 28720, loss = 0.00561682, acc = 1.0\n",
      "[Validation] Batch ID = 28720, loss = 0.0401307, acc = 0.98\n",
      "[Train] Batch ID = 28730, loss = 0.00820093, acc = 1.0\n",
      "[Validation] Batch ID = 28730, loss = 0.0293135, acc = 0.98\n",
      "[Train] Batch ID = 28740, loss = 0.00329881, acc = 1.0\n",
      "[Validation] Batch ID = 28740, loss = 0.0198598, acc = 0.98\n",
      "[Train] Batch ID = 28750, loss = 0.00204191, acc = 1.0\n",
      "[Validation] Batch ID = 28750, loss = 0.0497055, acc = 0.96\n",
      "[Train] Batch ID = 28760, loss = 0.00207496, acc = 1.0\n",
      "[Validation] Batch ID = 28760, loss = 0.0242623, acc = 0.98\n",
      "[Train] Batch ID = 28770, loss = 0.00220595, acc = 1.0\n",
      "[Validation] Batch ID = 28770, loss = 0.0106344, acc = 1.0\n",
      "[Train] Batch ID = 28780, loss = 0.00307546, acc = 1.0\n",
      "[Validation] Batch ID = 28780, loss = 0.0420062, acc = 0.96\n",
      "[Train] Batch ID = 28790, loss = 0.00332108, acc = 1.0\n",
      "[Validation] Batch ID = 28790, loss = 0.0400945, acc = 0.94\n",
      "[Train] Batch ID = 28800, loss = 0.00481934, acc = 1.0\n",
      "[Validation] Batch ID = 28800, loss = 0.0378716, acc = 0.98\n",
      "[Train] Batch ID = 28810, loss = 0.00714701, acc = 1.0\n",
      "[Validation] Batch ID = 28810, loss = 0.0172037, acc = 0.98\n",
      "[Train] Batch ID = 28820, loss = 0.00726843, acc = 1.0\n",
      "[Validation] Batch ID = 28820, loss = 0.0420491, acc = 0.98\n",
      "[Train] Batch ID = 28830, loss = 0.174289, acc = 0.86\n",
      "[Validation] Batch ID = 28830, loss = 0.0172853, acc = 0.98\n",
      "[Train] Batch ID = 28840, loss = 0.00381695, acc = 1.0\n",
      "[Validation] Batch ID = 28840, loss = 0.0463148, acc = 0.94\n",
      "[Train] Batch ID = 28850, loss = 0.00689381, acc = 1.0\n",
      "[Validation] Batch ID = 28850, loss = 0.0582483, acc = 0.94\n",
      "[Train] Batch ID = 28860, loss = 0.00313813, acc = 1.0\n",
      "[Validation] Batch ID = 28860, loss = 0.0322532, acc = 0.98\n",
      "[Train] Batch ID = 28870, loss = 0.00181361, acc = 1.0\n",
      "[Validation] Batch ID = 28870, loss = 0.0611829, acc = 0.92\n",
      "[Train] Batch ID = 28880, loss = 0.00223306, acc = 1.0\n",
      "[Validation] Batch ID = 28880, loss = 0.0278901, acc = 0.98\n",
      "[Train] Batch ID = 28890, loss = 0.00206606, acc = 1.0\n",
      "[Validation] Batch ID = 28890, loss = 0.0358936, acc = 0.98\n",
      "[Train] Batch ID = 28900, loss = 0.00286588, acc = 1.0\n",
      "[Validation] Batch ID = 28900, loss = 0.0331103, acc = 0.98\n",
      "[Train] Batch ID = 28910, loss = 0.00308549, acc = 1.0\n",
      "[Validation] Batch ID = 28910, loss = 0.0230195, acc = 1.0\n",
      "[Train] Batch ID = 28920, loss = 0.00227246, acc = 1.0\n",
      "[Validation] Batch ID = 28920, loss = 0.0594105, acc = 0.94\n",
      "[Train] Batch ID = 28930, loss = 0.00381859, acc = 1.0\n",
      "[Validation] Batch ID = 28930, loss = 0.0461507, acc = 0.94\n",
      "[Train] Batch ID = 28940, loss = 0.0056247, acc = 1.0\n",
      "[Validation] Batch ID = 28940, loss = 0.0358883, acc = 0.98\n",
      "[Train] Batch ID = 28950, loss = 0.00230527, acc = 1.0\n",
      "[Validation] Batch ID = 28950, loss = 0.0357706, acc = 0.98\n",
      "[Train] Batch ID = 28960, loss = 0.0050136, acc = 1.0\n",
      "[Validation] Batch ID = 28960, loss = 0.0150801, acc = 0.98\n",
      "[Train] Batch ID = 28970, loss = 0.165283, acc = 0.86\n",
      "[Validation] Batch ID = 28970, loss = 0.0466333, acc = 0.98\n",
      "[Train] Batch ID = 28980, loss = 0.00816504, acc = 1.0\n",
      "[Validation] Batch ID = 28980, loss = 0.0410329, acc = 0.96\n",
      "[Train] Batch ID = 28990, loss = 0.00268747, acc = 1.0\n",
      "[Validation] Batch ID = 28990, loss = 0.0654232, acc = 0.92\n",
      "[Train] Batch ID = 29000, loss = 0.00296797, acc = 1.0\n",
      "[Validation] Batch ID = 29000, loss = 0.0252606, acc = 0.96\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0305534 Best loss: 0.0288538\n",
      "[TOTAL Validation] Batch ID = 29000, loss = 0.0305534, acc = 0.974376417234\n",
      "Augmented Factor = 0.041967246692464065\n",
      "[Train] Batch ID = 29010, loss = 0.0038844, acc = 1.0\n",
      "[Validation] Batch ID = 29010, loss = 0.031116, acc = 0.96\n",
      "[Train] Batch ID = 29020, loss = 0.00256857, acc = 1.0\n",
      "[Validation] Batch ID = 29020, loss = 0.0513856, acc = 0.96\n",
      "[Train] Batch ID = 29030, loss = 0.00246887, acc = 1.0\n",
      "[Validation] Batch ID = 29030, loss = 0.0114498, acc = 1.0\n",
      "[Train] Batch ID = 29040, loss = 0.00251095, acc = 1.0\n",
      "[Validation] Batch ID = 29040, loss = 0.0544572, acc = 0.94\n",
      "[Train] Batch ID = 29050, loss = 0.00212128, acc = 1.0\n",
      "[Validation] Batch ID = 29050, loss = 0.0284784, acc = 0.96\n",
      "[Train] Batch ID = 29060, loss = 0.00353372, acc = 1.0\n",
      "[Validation] Batch ID = 29060, loss = 0.0485321, acc = 0.96\n",
      "[Train] Batch ID = 29070, loss = 0.00553347, acc = 1.0\n",
      "[Validation] Batch ID = 29070, loss = 0.0411138, acc = 0.96\n",
      "[Train] Batch ID = 29080, loss = 0.00240105, acc = 1.0\n",
      "[Validation] Batch ID = 29080, loss = 0.0532665, acc = 0.94\n",
      "[Train] Batch ID = 29090, loss = 0.00532016, acc = 1.0\n",
      "[Validation] Batch ID = 29090, loss = 0.0231304, acc = 0.96\n",
      "[Train] Batch ID = 29100, loss = 0.00451271, acc = 1.0\n",
      "[Validation] Batch ID = 29100, loss = 0.0361954, acc = 0.98\n",
      "[Train] Batch ID = 29110, loss = 0.00430247, acc = 1.0\n",
      "[Validation] Batch ID = 29110, loss = 0.0303963, acc = 0.98\n",
      "[Train] Batch ID = 29120, loss = 0.00430035, acc = 1.0\n",
      "[Validation] Batch ID = 29120, loss = 0.0549169, acc = 0.94\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 29130, loss = 0.00229102, acc = 1.0\n",
      "[Validation] Batch ID = 29130, loss = 0.0567443, acc = 0.94\n",
      "[Train] Batch ID = 29140, loss = 0.00287827, acc = 1.0\n",
      "[Validation] Batch ID = 29140, loss = 0.0258817, acc = 1.0\n",
      "[Train] Batch ID = 29150, loss = 0.00324381, acc = 1.0\n",
      "[Validation] Batch ID = 29150, loss = 0.0108777, acc = 1.0\n",
      "[Train] Batch ID = 29160, loss = 0.00439642, acc = 1.0\n",
      "[Validation] Batch ID = 29160, loss = 0.0146374, acc = 1.0\n",
      "[Train] Batch ID = 29170, loss = 0.00489323, acc = 1.0\n",
      "[Validation] Batch ID = 29170, loss = 0.0232258, acc = 1.0\n",
      "[Train] Batch ID = 29180, loss = 0.215129, acc = 0.82\n",
      "[Validation] Batch ID = 29180, loss = 0.016429, acc = 1.0\n",
      "[Train] Batch ID = 29190, loss = 0.00769889, acc = 1.0\n",
      "[Validation] Batch ID = 29190, loss = 0.0509269, acc = 0.96\n",
      "[Train] Batch ID = 29200, loss = 0.00232387, acc = 1.0\n",
      "[Validation] Batch ID = 29200, loss = 0.0245043, acc = 0.98\n",
      "[Train] Batch ID = 29210, loss = 0.00237136, acc = 1.0\n",
      "[Validation] Batch ID = 29210, loss = 0.029129, acc = 1.0\n",
      "[Train] Batch ID = 29220, loss = 0.0021763, acc = 1.0\n",
      "[Validation] Batch ID = 29220, loss = 0.0208607, acc = 1.0\n",
      "[Train] Batch ID = 29230, loss = 0.00385343, acc = 1.0\n",
      "[Validation] Batch ID = 29230, loss = 0.0227971, acc = 1.0\n",
      "[Train] Batch ID = 29240, loss = 0.00287054, acc = 1.0\n",
      "[Validation] Batch ID = 29240, loss = 0.0193502, acc = 1.0\n",
      "[Train] Batch ID = 29250, loss = 0.00488642, acc = 1.0\n",
      "[Validation] Batch ID = 29250, loss = 0.0148413, acc = 1.0\n",
      "[Train] Batch ID = 29260, loss = 0.00536227, acc = 1.0\n",
      "[Validation] Batch ID = 29260, loss = 0.0235412, acc = 0.98\n",
      "[Train] Batch ID = 29270, loss = 0.0036957, acc = 1.0\n",
      "[Validation] Batch ID = 29270, loss = 0.0155122, acc = 1.0\n",
      "[Train] Batch ID = 29280, loss = 0.00438094, acc = 1.0\n",
      "[Validation] Batch ID = 29280, loss = 0.0599261, acc = 0.9\n",
      "[Train] Batch ID = 29290, loss = 0.00301456, acc = 1.0\n",
      "[Validation] Batch ID = 29290, loss = 0.0354964, acc = 0.98\n",
      "[Train] Batch ID = 29300, loss = 0.00131375, acc = 1.0\n",
      "[Validation] Batch ID = 29300, loss = 0.0121265, acc = 1.0\n",
      "[Train] Batch ID = 29310, loss = 0.00527284, acc = 1.0\n",
      "[Validation] Batch ID = 29310, loss = 0.0292488, acc = 0.98\n",
      "[Train] Batch ID = 29320, loss = 0.00360234, acc = 1.0\n",
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      "[Train] Batch ID = 29640, loss = 0.00527555, acc = 1.0\n",
      "[Validation] Batch ID = 29640, loss = 0.0249677, acc = 1.0\n",
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      "[Validation] Batch ID = 29670, loss = 0.052216, acc = 0.96\n",
      "[Train] Batch ID = 29680, loss = 0.000613342, acc = 1.0\n",
      "[Validation] Batch ID = 29680, loss = 0.0300524, acc = 0.96\n",
      "[Train] Batch ID = 29690, loss = 0.00156731, acc = 1.0\n",
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      "[Train] Batch ID = 29700, loss = 0.00267004, acc = 1.0\n",
      "[Validation] Batch ID = 29700, loss = 0.0427525, acc = 0.96\n",
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      "[Validation] Batch ID = 29730, loss = 0.0221981, acc = 1.0\n",
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      "[Validation] Batch ID = 29740, loss = 0.0228101, acc = 0.98\n",
      "[Train] Batch ID = 29750, loss = 0.00277805, acc = 1.0\n",
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      "[Validation] Batch ID = 29760, loss = 0.0120123, acc = 1.0\n",
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      "[Validation] Batch ID = 29770, loss = 0.044132, acc = 0.94\n",
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      "[Validation] Batch ID = 29790, loss = 0.0306247, acc = 0.98\n",
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      "[Validation] Batch ID = 29820, loss = 0.0408857, acc = 0.96\n",
      "[Train] Batch ID = 29830, loss = 0.000998118, acc = 1.0\n",
      "[Validation] Batch ID = 29830, loss = 0.039212, acc = 0.98\n",
      "[Train] Batch ID = 29840, loss = 0.00367502, acc = 1.0\n",
      "[Validation] Batch ID = 29840, loss = 0.0188063, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 29850, loss = 0.000793363, acc = 1.0\n",
      "[Validation] Batch ID = 29850, loss = 0.0151321, acc = 1.0\n",
      "[Train] Batch ID = 29860, loss = 0.00624411, acc = 1.0\n",
      "[Validation] Batch ID = 29860, loss = 0.0587721, acc = 0.96\n",
      "[Train] Batch ID = 29870, loss = 0.00866576, acc = 1.0\n",
      "[Validation] Batch ID = 29870, loss = 0.0466332, acc = 0.96\n",
      "[Train] Batch ID = 29880, loss = 0.00274511, acc = 1.0\n",
      "[Validation] Batch ID = 29880, loss = 0.0189194, acc = 1.0\n",
      "[Train] Batch ID = 29890, loss = 0.0014106, acc = 1.0\n",
      "[Validation] Batch ID = 29890, loss = 0.0257046, acc = 0.98\n",
      "[Train] Batch ID = 29900, loss = 0.00186409, acc = 1.0\n",
      "[Validation] Batch ID = 29900, loss = 0.0350293, acc = 0.98\n",
      "[Train] Batch ID = 29910, loss = 0.00321869, acc = 1.0\n",
      "[Validation] Batch ID = 29910, loss = 0.0376301, acc = 0.96\n",
      "[Train] Batch ID = 29920, loss = 0.00143117, acc = 1.0\n",
      "[Validation] Batch ID = 29920, loss = 0.0411825, acc = 0.96\n",
      "[Train] Batch ID = 29930, loss = 0.000999447, acc = 1.0\n",
      "[Validation] Batch ID = 29930, loss = 0.0343564, acc = 0.98\n",
      "[Train] Batch ID = 29940, loss = 0.00127585, acc = 1.0\n",
      "[Validation] Batch ID = 29940, loss = 0.0254181, acc = 1.0\n",
      "[Train] Batch ID = 29950, loss = 0.00336109, acc = 1.0\n",
      "[Validation] Batch ID = 29950, loss = 0.0471523, acc = 0.94\n",
      "[Train] Batch ID = 29960, loss = 0.0029882, acc = 1.0\n",
      "[Validation] Batch ID = 29960, loss = 0.0176365, acc = 1.0\n",
      "[Train] Batch ID = 29970, loss = 0.00244312, acc = 1.0\n",
      "[Validation] Batch ID = 29970, loss = 0.0292141, acc = 0.98\n",
      "[Train] Batch ID = 29980, loss = 0.00348194, acc = 1.0\n",
      "[Validation] Batch ID = 29980, loss = 0.0214035, acc = 0.98\n",
      "[Train] Batch ID = 29990, loss = 0.00290159, acc = 1.0\n",
      "[Validation] Batch ID = 29990, loss = 0.0328107, acc = 0.96\n",
      "[Train] Batch ID = 30000, loss = 0.0033496, acc = 1.0\n",
      "[Validation] Batch ID = 30000, loss = 0.013939, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0281227 Best loss: 0.0288538\n",
      "[TOTAL Validation] Batch ID = 30000, loss = 0.0281227, acc = 0.978458049887\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.03777052202321766\n",
      "[Train] Batch ID = 30010, loss = 0.00308558, acc = 1.0\n",
      "[Validation] Batch ID = 30010, loss = 0.0298162, acc = 0.98\n",
      "[Train] Batch ID = 30020, loss = 0.00409182, acc = 1.0\n",
      "[Validation] Batch ID = 30020, loss = 0.0267626, acc = 0.98\n",
      "[Train] Batch ID = 30030, loss = 0.00184304, acc = 1.0\n",
      "[Validation] Batch ID = 30030, loss = 0.0167693, acc = 1.0\n",
      "[Train] Batch ID = 30040, loss = 0.00163594, acc = 1.0\n",
      "[Validation] Batch ID = 30040, loss = 0.0353338, acc = 0.96\n",
      "[Train] Batch ID = 30050, loss = 0.00188912, acc = 1.0\n",
      "[Validation] Batch ID = 30050, loss = 0.0216687, acc = 0.98\n",
      "[Train] Batch ID = 30060, loss = 0.00255838, acc = 1.0\n",
      "[Validation] Batch ID = 30060, loss = 0.013865, acc = 1.0\n",
      "[Train] Batch ID = 30070, loss = 0.00305114, acc = 1.0\n",
      "[Validation] Batch ID = 30070, loss = 0.0242632, acc = 0.98\n",
      "[Train] Batch ID = 30080, loss = 0.00290655, acc = 1.0\n",
      "[Validation] Batch ID = 30080, loss = 0.0201221, acc = 1.0\n",
      "[Train] Batch ID = 30090, loss = 0.0027923, acc = 1.0\n",
      "[Validation] Batch ID = 30090, loss = 0.0542704, acc = 0.96\n",
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      "[Validation] Batch ID = 30100, loss = 0.0388422, acc = 0.96\n",
      "[Train] Batch ID = 30110, loss = 0.00511713, acc = 1.0\n",
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      "[Train] Batch ID = 30120, loss = 0.165509, acc = 0.92\n",
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      "[Validation] Batch ID = 30430, loss = 0.052466, acc = 0.94\n",
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      "[Validation] Batch ID = 30450, loss = 0.0245095, acc = 0.98\n",
      "[Train] Batch ID = 30460, loss = 0.00153847, acc = 1.0\n",
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      "[Validation] Batch ID = 30470, loss = 0.0311099, acc = 0.98\n",
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      "[Validation] Batch ID = 30480, loss = 0.0403023, acc = 0.98\n",
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      "[Validation] Batch ID = 30490, loss = 0.0298248, acc = 1.0\n",
      "[Train] Batch ID = 30500, loss = 0.00260245, acc = 1.0\n",
      "[Validation] Batch ID = 30500, loss = 0.0398998, acc = 0.96\n",
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      "[Validation] Batch ID = 30510, loss = 0.020265, acc = 0.98\n",
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      "[Validation] Batch ID = 30570, loss = 0.0621308, acc = 0.94\n",
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      "[Validation] Batch ID = 30580, loss = 0.0495532, acc = 0.96\n",
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      "[Validation] Batch ID = 30590, loss = 0.0223394, acc = 0.98\n",
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      "[Validation] Batch ID = 30620, loss = 0.0290416, acc = 0.98\n",
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      "[Train] Batch ID = 30650, loss = 0.00174651, acc = 1.0\n",
      "[Validation] Batch ID = 30650, loss = 0.0422654, acc = 0.96\n",
      "[Train] Batch ID = 30660, loss = 0.00273615, acc = 1.0\n",
      "[Validation] Batch ID = 30660, loss = 0.0535341, acc = 0.9\n",
      "[Train] Batch ID = 30670, loss = 0.00278145, acc = 1.0\n",
      "[Validation] Batch ID = 30670, loss = 0.00938919, acc = 1.0\n",
      "[Train] Batch ID = 30680, loss = 0.00272183, acc = 1.0\n",
      "[Validation] Batch ID = 30680, loss = 0.0198699, acc = 0.98\n",
      "[Train] Batch ID = 30690, loss = 0.174083, acc = 0.82\n",
      "[Validation] Batch ID = 30690, loss = 0.0409344, acc = 0.96\n",
      "[Train] Batch ID = 30700, loss = 0.00441883, acc = 1.0\n",
      "[Validation] Batch ID = 30700, loss = 0.0370503, acc = 1.0\n",
      "[Train] Batch ID = 30710, loss = 0.0120226, acc = 1.0\n",
      "[Validation] Batch ID = 30710, loss = 0.0770121, acc = 0.94\n",
      "[Train] Batch ID = 30720, loss = 0.00324649, acc = 1.0\n",
      "[Validation] Batch ID = 30720, loss = 0.019695, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 30730, loss = 0.00726951, acc = 1.0\n",
      "[Validation] Batch ID = 30730, loss = 0.0457008, acc = 0.94\n",
      "[Train] Batch ID = 30740, loss = 0.00262897, acc = 1.0\n",
      "[Validation] Batch ID = 30740, loss = 0.0351115, acc = 0.98\n",
      "[Train] Batch ID = 30750, loss = 0.00153887, acc = 1.0\n",
      "[Validation] Batch ID = 30750, loss = 0.0211766, acc = 1.0\n",
      "[Train] Batch ID = 30760, loss = 0.00299434, acc = 1.0\n",
      "[Validation] Batch ID = 30760, loss = 0.00991762, acc = 1.0\n",
      "[Train] Batch ID = 30770, loss = 0.00735433, acc = 1.0\n",
      "[Validation] Batch ID = 30770, loss = 0.0222274, acc = 0.98\n",
      "[Train] Batch ID = 30780, loss = 0.00576768, acc = 1.0\n",
      "[Validation] Batch ID = 30780, loss = 0.030316, acc = 0.96\n",
      "[Train] Batch ID = 30790, loss = 0.00307421, acc = 1.0\n",
      "[Validation] Batch ID = 30790, loss = 0.0150448, acc = 1.0\n",
      "[Train] Batch ID = 30800, loss = 0.00522711, acc = 1.0\n",
      "[Validation] Batch ID = 30800, loss = 0.0102159, acc = 1.0\n",
      "[Train] Batch ID = 30810, loss = 0.00159104, acc = 1.0\n",
      "[Validation] Batch ID = 30810, loss = 0.018668, acc = 1.0\n",
      "[Train] Batch ID = 30820, loss = 0.00209257, acc = 1.0\n",
      "[Validation] Batch ID = 30820, loss = 0.0143923, acc = 0.98\n",
      "[Train] Batch ID = 30830, loss = 0.00293544, acc = 1.0\n",
      "[Validation] Batch ID = 30830, loss = 0.0195162, acc = 0.98\n",
      "[Train] Batch ID = 30840, loss = 0.00101926, acc = 1.0\n",
      "[Validation] Batch ID = 30840, loss = 0.0301813, acc = 0.96\n",
      "[Train] Batch ID = 30850, loss = 0.00348647, acc = 1.0\n",
      "[Validation] Batch ID = 30850, loss = 0.0279274, acc = 0.98\n",
      "[Train] Batch ID = 30860, loss = 0.00116152, acc = 1.0\n",
      "[Validation] Batch ID = 30860, loss = 0.0505418, acc = 0.94\n",
      "[Train] Batch ID = 30870, loss = 0.00126153, acc = 1.0\n",
      "[Validation] Batch ID = 30870, loss = 0.0473751, acc = 0.96\n",
      "[Train] Batch ID = 30880, loss = 0.00305089, acc = 1.0\n",
      "[Validation] Batch ID = 30880, loss = 0.0415256, acc = 0.96\n",
      "[Train] Batch ID = 30890, loss = 0.0011835, acc = 1.0\n",
      "[Validation] Batch ID = 30890, loss = 0.0265887, acc = 1.0\n",
      "[Train] Batch ID = 30900, loss = 0.00317623, acc = 1.0\n",
      "[Validation] Batch ID = 30900, loss = 0.0416649, acc = 0.98\n",
      "[Train] Batch ID = 30910, loss = 0.00629843, acc = 1.0\n",
      "[Validation] Batch ID = 30910, loss = 0.0445942, acc = 0.96\n",
      "[Train] Batch ID = 30920, loss = 0.00561427, acc = 1.0\n",
      "[Validation] Batch ID = 30920, loss = 0.0307548, acc = 0.98\n",
      "[Train] Batch ID = 30930, loss = 0.00464124, acc = 1.0\n",
      "[Validation] Batch ID = 30930, loss = 0.0290602, acc = 0.96\n",
      "[Train] Batch ID = 30940, loss = 0.00261114, acc = 1.0\n",
      "[Validation] Batch ID = 30940, loss = 0.0383824, acc = 0.94\n",
      "[Train] Batch ID = 30950, loss = 0.00281591, acc = 1.0\n",
      "[Validation] Batch ID = 30950, loss = 0.0231211, acc = 0.98\n",
      "[Train] Batch ID = 30960, loss = 0.00134218, acc = 1.0\n",
      "[Validation] Batch ID = 30960, loss = 0.00769898, acc = 1.0\n",
      "[Train] Batch ID = 30970, loss = 0.00263869, acc = 1.0\n",
      "[Validation] Batch ID = 30970, loss = 0.0480683, acc = 0.92\n",
      "[Train] Batch ID = 30980, loss = 0.00142341, acc = 1.0\n",
      "[Validation] Batch ID = 30980, loss = 0.0273591, acc = 0.98\n",
      "[Train] Batch ID = 30990, loss = 0.00142448, acc = 1.0\n",
      "[Validation] Batch ID = 30990, loss = 0.00866922, acc = 1.0\n",
      "[Train] Batch ID = 31000, loss = 0.00183827, acc = 1.0\n",
      "[Validation] Batch ID = 31000, loss = 0.0293035, acc = 0.98\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0293512 Best loss: 0.0281227\n",
      "[TOTAL Validation] Batch ID = 31000, loss = 0.0293512, acc = 0.973696145125\n",
      "Augmented Factor = 0.03399346982089589\n",
      "[Train] Batch ID = 31010, loss = 0.00109526, acc = 1.0\n",
      "[Validation] Batch ID = 31010, loss = 0.0209888, acc = 0.98\n",
      "[Train] Batch ID = 31020, loss = 0.00149772, acc = 1.0\n",
      "[Validation] Batch ID = 31020, loss = 0.0367371, acc = 0.96\n",
      "[Train] Batch ID = 31030, loss = 0.00287742, acc = 1.0\n",
      "[Validation] Batch ID = 31030, loss = 0.0357544, acc = 0.96\n",
      "[Train] Batch ID = 31040, loss = 0.00622898, acc = 1.0\n",
      "[Validation] Batch ID = 31040, loss = 0.0516094, acc = 0.96\n",
      "[Train] Batch ID = 31050, loss = 0.00314686, acc = 1.0\n",
      "[Validation] Batch ID = 31050, loss = 0.0395477, acc = 0.96\n",
      "[Train] Batch ID = 31060, loss = 0.00232349, acc = 1.0\n",
      "[Validation] Batch ID = 31060, loss = 0.0491235, acc = 0.9\n",
      "[Train] Batch ID = 31070, loss = 0.00257453, acc = 1.0\n",
      "[Validation] Batch ID = 31070, loss = 0.0205726, acc = 1.0\n",
      "[Train] Batch ID = 31080, loss = 0.00108937, acc = 1.0\n",
      "[Validation] Batch ID = 31080, loss = 0.0183345, acc = 1.0\n",
      "[Train] Batch ID = 31090, loss = 0.00246596, acc = 1.0\n",
      "[Validation] Batch ID = 31090, loss = 0.0336431, acc = 0.96\n",
      "[Train] Batch ID = 31100, loss = 0.00278701, acc = 1.0\n",
      "[Validation] Batch ID = 31100, loss = 0.0453568, acc = 0.98\n",
      "[Train] Batch ID = 31110, loss = 0.192478, acc = 0.82\n",
      "[Validation] Batch ID = 31110, loss = 0.0239345, acc = 1.0\n",
      "[Train] Batch ID = 31120, loss = 0.00425275, acc = 1.0\n",
      "[Validation] Batch ID = 31120, loss = 0.00838458, acc = 1.0\n",
      "[Train] Batch ID = 31130, loss = 0.00224531, acc = 1.0\n",
      "[Validation] Batch ID = 31130, loss = 0.0419688, acc = 0.96\n",
      "[Train] Batch ID = 31140, loss = 0.0031795, acc = 1.0\n",
      "[Validation] Batch ID = 31140, loss = 0.0432402, acc = 0.94\n",
      "[Train] Batch ID = 31150, loss = 0.00178239, acc = 1.0\n",
      "[Validation] Batch ID = 31150, loss = 0.0144345, acc = 0.98\n",
      "[Train] Batch ID = 31160, loss = 0.00231957, acc = 1.0\n",
      "[Validation] Batch ID = 31160, loss = 0.0134037, acc = 1.0\n",
      "[Train] Batch ID = 31170, loss = 0.0015889, acc = 1.0\n",
      "[Validation] Batch ID = 31170, loss = 0.0142935, acc = 1.0\n",
      "[Train] Batch ID = 31180, loss = 0.00309462, acc = 1.0\n",
      "[Validation] Batch ID = 31180, loss = 0.0192129, acc = 1.0\n",
      "[Train] Batch ID = 31190, loss = 0.00134566, acc = 1.0\n",
      "[Validation] Batch ID = 31190, loss = 0.0216003, acc = 0.98\n",
      "[Train] Batch ID = 31200, loss = 0.00211744, acc = 1.0\n",
      "[Validation] Batch ID = 31200, loss = 0.0327926, acc = 0.96\n",
      "[Train] Batch ID = 31210, loss = 0.00301934, acc = 1.0\n",
      "[Validation] Batch ID = 31210, loss = 0.0611122, acc = 0.94\n",
      "[Train] Batch ID = 31220, loss = 0.00380254, acc = 1.0\n",
      "[Validation] Batch ID = 31220, loss = 0.0120143, acc = 1.0\n",
      "[Train] Batch ID = 31230, loss = 0.00120407, acc = 1.0\n",
      "[Validation] Batch ID = 31230, loss = 0.00670944, acc = 1.0\n",
      "[Train] Batch ID = 31240, loss = 0.00330565, acc = 1.0\n",
      "[Validation] Batch ID = 31240, loss = 0.0193155, acc = 1.0\n",
      "[Train] Batch ID = 31250, loss = 0.0017795, acc = 1.0\n",
      "[Validation] Batch ID = 31250, loss = 0.02187, acc = 0.98\n",
      "[Train] Batch ID = 31260, loss = 0.000966598, acc = 1.0\n",
      "[Validation] Batch ID = 31260, loss = 0.00780236, acc = 1.0\n",
      "[Train] Batch ID = 31270, loss = 0.00169157, acc = 1.0\n",
      "[Validation] Batch ID = 31270, loss = 0.0066283, acc = 1.0\n",
      "[Train] Batch ID = 31280, loss = 0.00149083, acc = 1.0\n",
      "[Validation] Batch ID = 31280, loss = 0.0183884, acc = 0.98\n",
      "[Train] Batch ID = 31290, loss = 0.00145079, acc = 1.0\n",
      "[Validation] Batch ID = 31290, loss = 0.00428045, acc = 1.0\n",
      "[Train] Batch ID = 31300, loss = 0.000778875, acc = 1.0\n",
      "[Validation] Batch ID = 31300, loss = 0.0280677, acc = 0.98\n",
      "[Train] Batch ID = 31310, loss = 0.00268299, acc = 1.0\n",
      "[Validation] Batch ID = 31310, loss = 0.0393795, acc = 0.96\n",
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      "[Validation] Batch ID = 31320, loss = 0.00898811, acc = 1.0\n",
      "[Train] Batch ID = 31330, loss = 0.00104528, acc = 1.0\n",
      "[Validation] Batch ID = 31330, loss = 0.00902743, acc = 1.0\n",
      "[Train] Batch ID = 31340, loss = 0.00241876, acc = 1.0\n",
      "[Validation] Batch ID = 31340, loss = 0.0196604, acc = 0.98\n",
      "[Train] Batch ID = 31350, loss = 0.00149815, acc = 1.0\n",
      "[Validation] Batch ID = 31350, loss = 0.0299185, acc = 0.98\n",
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      "[Validation] Batch ID = 31360, loss = 0.0428622, acc = 0.96\n",
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      "[Validation] Batch ID = 31370, loss = 0.0282145, acc = 1.0\n",
      "[Train] Batch ID = 31380, loss = 0.00408889, acc = 1.0\n",
      "[Validation] Batch ID = 31380, loss = 0.0378733, acc = 0.94\n",
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      "[Validation] Batch ID = 31390, loss = 0.0219698, acc = 1.0\n",
      "[Train] Batch ID = 31400, loss = 0.00200021, acc = 1.0\n",
      "[Validation] Batch ID = 31400, loss = 0.0274301, acc = 0.98\n",
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      "[Validation] Batch ID = 31410, loss = 0.030923, acc = 0.96\n",
      "[Train] Batch ID = 31420, loss = 0.0025669, acc = 1.0\n",
      "[Validation] Batch ID = 31420, loss = 0.0149037, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 31430, loss = 0.00234606, acc = 1.0\n",
      "[Validation] Batch ID = 31430, loss = 0.0197291, acc = 1.0\n",
      "[Train] Batch ID = 31440, loss = 0.00203573, acc = 1.0\n",
      "[Validation] Batch ID = 31440, loss = 0.0255656, acc = 0.98\n",
      "[Train] Batch ID = 31450, loss = 0.00294161, acc = 1.0\n",
      "[Validation] Batch ID = 31450, loss = 0.01591, acc = 1.0\n",
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      "[Validation] Batch ID = 31460, loss = 0.0287948, acc = 0.98\n",
      "[Train] Batch ID = 31470, loss = 0.00508016, acc = 1.0\n",
      "[Validation] Batch ID = 31470, loss = 0.026645, acc = 0.98\n",
      "[Train] Batch ID = 31480, loss = 0.00196063, acc = 1.0\n",
      "[Validation] Batch ID = 31480, loss = 0.0304203, acc = 0.96\n",
      "[Train] Batch ID = 31490, loss = 0.00179898, acc = 1.0\n",
      "[Validation] Batch ID = 31490, loss = 0.0220754, acc = 0.98\n",
      "[Train] Batch ID = 31500, loss = 0.00227282, acc = 1.0\n",
      "[Validation] Batch ID = 31500, loss = 0.0185636, acc = 1.0\n",
      "[Train] Batch ID = 31510, loss = 0.000770672, acc = 1.0\n",
      "[Validation] Batch ID = 31510, loss = 0.0710147, acc = 0.92\n",
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      "[Validation] Batch ID = 31520, loss = 0.0183172, acc = 1.0\n",
      "[Train] Batch ID = 31530, loss = 0.00145106, acc = 1.0\n",
      "[Validation] Batch ID = 31530, loss = 0.028365, acc = 0.98\n",
      "[Train] Batch ID = 31540, loss = 0.00106641, acc = 1.0\n",
      "[Validation] Batch ID = 31540, loss = 0.0219807, acc = 0.96\n",
      "[Train] Batch ID = 31550, loss = 0.199072, acc = 0.86\n",
      "[Validation] Batch ID = 31550, loss = 0.0395239, acc = 0.98\n",
      "[Train] Batch ID = 31560, loss = 0.00292863, acc = 1.0\n",
      "[Validation] Batch ID = 31560, loss = 0.03267, acc = 0.98\n",
      "[Train] Batch ID = 31570, loss = 0.250853, acc = 0.7\n",
      "[Validation] Batch ID = 31570, loss = 0.0377817, acc = 0.98\n",
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      "[Validation] Batch ID = 31580, loss = 0.030578, acc = 0.98\n",
      "[Train] Batch ID = 31590, loss = 0.00424812, acc = 1.0\n",
      "[Validation] Batch ID = 31590, loss = 0.0219569, acc = 1.0\n",
      "[Train] Batch ID = 31600, loss = 0.00288475, acc = 1.0\n",
      "[Validation] Batch ID = 31600, loss = 0.052804, acc = 0.96\n",
      "[Train] Batch ID = 31610, loss = 0.00174762, acc = 1.0\n",
      "[Validation] Batch ID = 31610, loss = 0.021121, acc = 0.98\n",
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      "[Validation] Batch ID = 31620, loss = 0.0134904, acc = 1.0\n",
      "[Train] Batch ID = 31630, loss = 0.00147968, acc = 1.0\n",
      "[Validation] Batch ID = 31630, loss = 0.0270738, acc = 0.98\n",
      "[Train] Batch ID = 31640, loss = 0.00209516, acc = 1.0\n",
      "[Validation] Batch ID = 31640, loss = 0.0214039, acc = 1.0\n",
      "[Train] Batch ID = 31650, loss = 0.224207, acc = 0.84\n",
      "[Validation] Batch ID = 31650, loss = 0.0110585, acc = 1.0\n",
      "[Train] Batch ID = 31660, loss = 0.00139218, acc = 1.0\n",
      "[Validation] Batch ID = 31660, loss = 0.0202217, acc = 1.0\n",
      "[Train] Batch ID = 31670, loss = 0.0010174, acc = 1.0\n",
      "[Validation] Batch ID = 31670, loss = 0.0339531, acc = 0.98\n",
      "[Train] Batch ID = 31680, loss = 0.00492307, acc = 1.0\n",
      "[Validation] Batch ID = 31680, loss = 0.0264508, acc = 0.98\n",
      "[Train] Batch ID = 31690, loss = 0.00246523, acc = 1.0\n",
      "[Validation] Batch ID = 31690, loss = 0.0255473, acc = 0.98\n",
      "[Train] Batch ID = 31700, loss = 0.00400027, acc = 1.0\n",
      "[Validation] Batch ID = 31700, loss = 0.0270714, acc = 0.98\n",
      "[Train] Batch ID = 31710, loss = 0.00133492, acc = 1.0\n",
      "[Validation] Batch ID = 31710, loss = 0.0315654, acc = 0.96\n",
      "[Train] Batch ID = 31720, loss = 0.00497223, acc = 1.0\n",
      "[Validation] Batch ID = 31720, loss = 0.0239318, acc = 1.0\n",
      "[Train] Batch ID = 31730, loss = 0.00252467, acc = 1.0\n",
      "[Validation] Batch ID = 31730, loss = 0.0173167, acc = 1.0\n",
      "[Train] Batch ID = 31740, loss = 0.00391987, acc = 1.0\n",
      "[Validation] Batch ID = 31740, loss = 0.0176395, acc = 1.0\n",
      "[Train] Batch ID = 31750, loss = 0.00195219, acc = 1.0\n",
      "[Validation] Batch ID = 31750, loss = 0.0252507, acc = 1.0\n",
      "[Train] Batch ID = 31760, loss = 0.00203188, acc = 1.0\n",
      "[Validation] Batch ID = 31760, loss = 0.0429484, acc = 0.94\n",
      "[Train] Batch ID = 31770, loss = 0.00149816, acc = 1.0\n",
      "[Validation] Batch ID = 31770, loss = 0.0743951, acc = 0.88\n",
      "[Train] Batch ID = 31780, loss = 0.00113559, acc = 1.0\n",
      "[Validation] Batch ID = 31780, loss = 0.035306, acc = 0.98\n",
      "[Train] Batch ID = 31790, loss = 0.00168533, acc = 1.0\n",
      "[Validation] Batch ID = 31790, loss = 0.0157123, acc = 0.98\n",
      "[Train] Batch ID = 31800, loss = 0.00204551, acc = 1.0\n",
      "[Validation] Batch ID = 31800, loss = 0.0197226, acc = 1.0\n",
      "[Train] Batch ID = 31810, loss = 0.00149921, acc = 1.0\n",
      "[Validation] Batch ID = 31810, loss = 0.0238835, acc = 0.98\n",
      "[Train] Batch ID = 31820, loss = 0.00323012, acc = 1.0\n",
      "[Validation] Batch ID = 31820, loss = 0.0104428, acc = 1.0\n",
      "[Train] Batch ID = 31830, loss = 0.00295867, acc = 1.0\n",
      "[Validation] Batch ID = 31830, loss = 0.0397866, acc = 0.94\n",
      "[Train] Batch ID = 31840, loss = 0.0016805, acc = 1.0\n",
      "[Validation] Batch ID = 31840, loss = 0.0245933, acc = 0.96\n",
      "[Train] Batch ID = 31850, loss = 0.0013261, acc = 1.0\n",
      "[Validation] Batch ID = 31850, loss = 0.016168, acc = 0.98\n",
      "[Train] Batch ID = 31860, loss = 0.00212478, acc = 1.0\n",
      "[Validation] Batch ID = 31860, loss = 0.039522, acc = 0.96\n",
      "[Train] Batch ID = 31870, loss = 0.00299583, acc = 1.0\n",
      "[Validation] Batch ID = 31870, loss = 0.0145088, acc = 1.0\n",
      "[Train] Batch ID = 31880, loss = 0.00376722, acc = 1.0\n",
      "[Validation] Batch ID = 31880, loss = 0.0287992, acc = 0.98\n",
      "[Train] Batch ID = 31890, loss = 0.00180234, acc = 1.0\n",
      "[Validation] Batch ID = 31890, loss = 0.0359838, acc = 0.96\n",
      "[Train] Batch ID = 31900, loss = 0.00139798, acc = 1.0\n",
      "[Validation] Batch ID = 31900, loss = 0.0201998, acc = 1.0\n",
      "[Train] Batch ID = 31910, loss = 0.00191381, acc = 1.0\n",
      "[Validation] Batch ID = 31910, loss = 0.0290803, acc = 0.98\n",
      "[Train] Batch ID = 31920, loss = 0.0063396, acc = 1.0\n",
      "[Validation] Batch ID = 31920, loss = 0.0322938, acc = 0.98\n",
      "[Train] Batch ID = 31930, loss = 0.00657362, acc = 1.0\n",
      "[Validation] Batch ID = 31930, loss = 0.0325126, acc = 0.98\n",
      "[Train] Batch ID = 31940, loss = 0.00666376, acc = 1.0\n",
      "[Validation] Batch ID = 31940, loss = 0.0350213, acc = 0.98\n",
      "[Train] Batch ID = 31950, loss = 0.00555858, acc = 1.0\n",
      "[Validation] Batch ID = 31950, loss = 0.0405503, acc = 1.0\n",
      "[Train] Batch ID = 31960, loss = 0.00853985, acc = 1.0\n",
      "[Validation] Batch ID = 31960, loss = 0.0285326, acc = 1.0\n",
      "[Train] Batch ID = 31970, loss = 0.00273717, acc = 1.0\n",
      "[Validation] Batch ID = 31970, loss = 0.037582, acc = 0.94\n",
      "[Train] Batch ID = 31980, loss = 0.00391666, acc = 1.0\n",
      "[Validation] Batch ID = 31980, loss = 0.024414, acc = 0.98\n",
      "[Train] Batch ID = 31990, loss = 0.00195356, acc = 1.0\n",
      "[Validation] Batch ID = 31990, loss = 0.0190577, acc = 1.0\n",
      "[Train] Batch ID = 32000, loss = 0.00328198, acc = 1.0\n",
      "[Validation] Batch ID = 32000, loss = 0.0251426, acc = 1.0\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0283315 Best loss: 0.0281227\n",
      "[TOTAL Validation] Batch ID = 32000, loss = 0.0283315, acc = 0.980498866213\n",
      "Augmented Factor = 0.030594122838806304\n",
      "[Train] Batch ID = 32010, loss = 0.00244792, acc = 1.0\n",
      "[Validation] Batch ID = 32010, loss = 0.0152536, acc = 1.0\n",
      "[Train] Batch ID = 32020, loss = 0.00539285, acc = 1.0\n",
      "[Validation] Batch ID = 32020, loss = 0.0192728, acc = 1.0\n",
      "[Train] Batch ID = 32030, loss = 0.00254554, acc = 1.0\n",
      "[Validation] Batch ID = 32030, loss = 0.0483486, acc = 0.94\n",
      "[Train] Batch ID = 32040, loss = 0.226445, acc = 0.78\n",
      "[Validation] Batch ID = 32040, loss = 0.0241564, acc = 0.98\n",
      "[Train] Batch ID = 32050, loss = 0.00198244, acc = 1.0\n",
      "[Validation] Batch ID = 32050, loss = 0.0288919, acc = 0.98\n",
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      "[Validation] Batch ID = 32060, loss = 0.0305794, acc = 0.96\n",
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      "[Validation] Batch ID = 32070, loss = 0.0632883, acc = 0.96\n",
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      "[Validation] Batch ID = 32080, loss = 0.0547151, acc = 0.96\n",
      "[Train] Batch ID = 32090, loss = 0.00352583, acc = 1.0\n",
      "[Validation] Batch ID = 32090, loss = 0.0115315, acc = 1.0\n",
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      "[Validation] Batch ID = 32100, loss = 0.0278435, acc = 0.98\n",
      "[Train] Batch ID = 32110, loss = 0.00183454, acc = 1.0\n",
      "[Validation] Batch ID = 32110, loss = 0.0478019, acc = 0.94\n",
      "[Train] Batch ID = 32120, loss = 0.00186462, acc = 1.0\n",
      "[Validation] Batch ID = 32120, loss = 0.0230001, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 32130, loss = 0.00245284, acc = 1.0\n",
      "[Validation] Batch ID = 32130, loss = 0.021992, acc = 0.98\n",
      "[Train] Batch ID = 32140, loss = 0.0015283, acc = 1.0\n",
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      "[Validation] Batch ID = 32840, loss = 0.00356353, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 32850, loss = 0.00389033, acc = 1.0\n",
      "[Validation] Batch ID = 32850, loss = 0.0164521, acc = 0.98\n",
      "[Train] Batch ID = 32860, loss = 0.00461923, acc = 1.0\n",
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      "[Train] Batch ID = 32900, loss = 0.0010437, acc = 1.0\n",
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      "[Train] Batch ID = 32910, loss = 0.00304987, acc = 1.0\n",
      "[Validation] Batch ID = 32910, loss = 0.0208077, acc = 0.98\n",
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      "[Validation] Batch ID = 32920, loss = 0.0433858, acc = 0.96\n",
      "[Train] Batch ID = 32930, loss = 0.00103577, acc = 1.0\n",
      "[Validation] Batch ID = 32930, loss = 0.0121091, acc = 1.0\n",
      "[Train] Batch ID = 32940, loss = 0.00477809, acc = 1.0\n",
      "[Validation] Batch ID = 32940, loss = 0.0383068, acc = 0.96\n",
      "[Train] Batch ID = 32950, loss = 0.000994055, acc = 1.0\n",
      "[Validation] Batch ID = 32950, loss = 0.0133046, acc = 1.0\n",
      "[Train] Batch ID = 32960, loss = 0.00215455, acc = 1.0\n",
      "[Validation] Batch ID = 32960, loss = 0.0466382, acc = 0.94\n",
      "[Train] Batch ID = 32970, loss = 0.00113158, acc = 1.0\n",
      "[Validation] Batch ID = 32970, loss = 0.0266435, acc = 0.98\n",
      "[Train] Batch ID = 32980, loss = 0.000771765, acc = 1.0\n",
      "[Validation] Batch ID = 32980, loss = 0.0358519, acc = 0.98\n",
      "[Train] Batch ID = 32990, loss = 0.000754133, acc = 1.0\n",
      "[Validation] Batch ID = 32990, loss = 0.00959618, acc = 1.0\n",
      "[Train] Batch ID = 33000, loss = 0.00288395, acc = 1.0\n",
      "[Validation] Batch ID = 33000, loss = 0.0116783, acc = 1.0\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0279286 Best loss: 0.0281227\n",
      "[TOTAL Validation] Batch ID = 33000, loss = 0.0279286, acc = 0.977777777778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.027534710554925675\n",
      "[Train] Batch ID = 33010, loss = 0.00181849, acc = 1.0\n",
      "[Validation] Batch ID = 33010, loss = 0.0340514, acc = 0.98\n",
      "[Train] Batch ID = 33020, loss = 0.00325495, acc = 1.0\n",
      "[Validation] Batch ID = 33020, loss = 0.0228029, acc = 0.98\n",
      "[Train] Batch ID = 33030, loss = 0.00318333, acc = 1.0\n",
      "[Validation] Batch ID = 33030, loss = 0.0273975, acc = 1.0\n",
      "[Train] Batch ID = 33040, loss = 0.00121912, acc = 1.0\n",
      "[Validation] Batch ID = 33040, loss = 0.018543, acc = 1.0\n",
      "[Train] Batch ID = 33050, loss = 0.00053879, acc = 1.0\n",
      "[Validation] Batch ID = 33050, loss = 0.0185162, acc = 1.0\n",
      "[Train] Batch ID = 33060, loss = 0.00249961, acc = 1.0\n",
      "[Validation] Batch ID = 33060, loss = 0.0175863, acc = 0.98\n",
      "[Train] Batch ID = 33070, loss = 0.00226629, acc = 1.0\n",
      "[Validation] Batch ID = 33070, loss = 0.00575258, acc = 1.0\n",
      "[Train] Batch ID = 33080, loss = 0.00122987, acc = 1.0\n",
      "[Validation] Batch ID = 33080, loss = 0.0185106, acc = 0.98\n",
      "[Train] Batch ID = 33090, loss = 0.00130267, acc = 1.0\n",
      "[Validation] Batch ID = 33090, loss = 0.0171537, acc = 1.0\n",
      "[Train] Batch ID = 33100, loss = 0.000840497, acc = 1.0\n",
      "[Validation] Batch ID = 33100, loss = 0.0248239, acc = 1.0\n",
      "[Train] Batch ID = 33110, loss = 0.00497245, acc = 1.0\n",
      "[Validation] Batch ID = 33110, loss = 0.0266441, acc = 1.0\n",
      "[Train] Batch ID = 33120, loss = 0.00246843, acc = 1.0\n",
      "[Validation] Batch ID = 33120, loss = 0.0120394, acc = 0.98\n",
      "[Train] Batch ID = 33130, loss = 0.0030819, acc = 1.0\n",
      "[Validation] Batch ID = 33130, loss = 0.013348, acc = 1.0\n",
      "[Train] Batch ID = 33140, loss = 0.00110325, acc = 1.0\n",
      "[Validation] Batch ID = 33140, loss = 0.0175235, acc = 1.0\n",
      "[Train] Batch ID = 33150, loss = 0.00124824, acc = 1.0\n",
      "[Validation] Batch ID = 33150, loss = 0.0273643, acc = 0.98\n",
      "[Train] Batch ID = 33160, loss = 0.000870531, acc = 1.0\n",
      "[Validation] Batch ID = 33160, loss = 0.0325356, acc = 0.96\n",
      "[Train] Batch ID = 33170, loss = 0.000935222, acc = 1.0\n",
      "[Validation] Batch ID = 33170, loss = 0.0186631, acc = 1.0\n",
      "[Train] Batch ID = 33180, loss = 0.00160773, acc = 1.0\n",
      "[Validation] Batch ID = 33180, loss = 0.0371717, acc = 0.96\n",
      "[Train] Batch ID = 33190, loss = 0.000552651, acc = 1.0\n",
      "[Validation] Batch ID = 33190, loss = 0.0195016, acc = 0.98\n",
      "[Train] Batch ID = 33200, loss = 0.00226886, acc = 1.0\n",
      "[Validation] Batch ID = 33200, loss = 0.0231467, acc = 0.98\n",
      "[Train] Batch ID = 33210, loss = 0.00445881, acc = 1.0\n",
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      "[Train] Batch ID = 33220, loss = 0.00281248, acc = 1.0\n",
      "[Validation] Batch ID = 33220, loss = 0.0155061, acc = 1.0\n",
      "[Train] Batch ID = 33230, loss = 0.00334207, acc = 1.0\n",
      "[Validation] Batch ID = 33230, loss = 0.00982972, acc = 1.0\n",
      "[Train] Batch ID = 33240, loss = 0.00369319, acc = 1.0\n",
      "[Validation] Batch ID = 33240, loss = 0.0407696, acc = 0.98\n",
      "[Train] Batch ID = 33250, loss = 0.00178102, acc = 1.0\n",
      "[Validation] Batch ID = 33250, loss = 0.0148712, acc = 1.0\n",
      "[Train] Batch ID = 33260, loss = 0.0019711, acc = 1.0\n",
      "[Validation] Batch ID = 33260, loss = 0.0283755, acc = 0.98\n",
      "[Train] Batch ID = 33270, loss = 0.00140165, acc = 1.0\n",
      "[Validation] Batch ID = 33270, loss = 0.0285463, acc = 0.98\n",
      "[Train] Batch ID = 33280, loss = 0.00123305, acc = 1.0\n",
      "[Validation] Batch ID = 33280, loss = 0.00876212, acc = 1.0\n",
      "[Train] Batch ID = 33290, loss = 0.00159225, acc = 1.0\n",
      "[Validation] Batch ID = 33290, loss = 0.0250008, acc = 0.98\n",
      "[Train] Batch ID = 33300, loss = 0.00171913, acc = 1.0\n",
      "[Validation] Batch ID = 33300, loss = 0.0219332, acc = 0.98\n",
      "[Train] Batch ID = 33310, loss = 0.00226492, acc = 1.0\n",
      "[Validation] Batch ID = 33310, loss = 0.0258679, acc = 0.98\n",
      "[Train] Batch ID = 33320, loss = 0.00115471, acc = 1.0\n",
      "[Validation] Batch ID = 33320, loss = 0.0182495, acc = 1.0\n",
      "[Train] Batch ID = 33330, loss = 0.0011099, acc = 1.0\n",
      "[Validation] Batch ID = 33330, loss = 0.0129627, acc = 1.0\n",
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      "[Train] Batch ID = 33630, loss = 0.00245096, acc = 1.0\n",
      "[Validation] Batch ID = 33630, loss = 0.0400173, acc = 0.96\n",
      "[Train] Batch ID = 33640, loss = 0.00419924, acc = 1.0\n",
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      "[Train] Batch ID = 33650, loss = 0.00203869, acc = 1.0\n",
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      "[Validation] Batch ID = 33660, loss = 0.0328521, acc = 0.98\n",
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      "[Validation] Batch ID = 33670, loss = 0.068287, acc = 0.9\n",
      "[Train] Batch ID = 33680, loss = 0.0016557, acc = 1.0\n",
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      "[Validation] Batch ID = 33710, loss = 0.0334933, acc = 0.96\n",
      "[Train] Batch ID = 33720, loss = 0.0026075, acc = 1.0\n",
      "[Validation] Batch ID = 33720, loss = 0.0388142, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 33730, loss = 0.0014227, acc = 1.0\n",
      "[Validation] Batch ID = 33730, loss = 0.0465692, acc = 1.0\n",
      "[Train] Batch ID = 33740, loss = 0.00154358, acc = 1.0\n",
      "[Validation] Batch ID = 33740, loss = 0.0217439, acc = 0.98\n",
      "[Train] Batch ID = 33750, loss = 0.00108881, acc = 1.0\n",
      "[Validation] Batch ID = 33750, loss = 0.0153538, acc = 1.0\n",
      "[Train] Batch ID = 33760, loss = 0.00208851, acc = 1.0\n",
      "[Validation] Batch ID = 33760, loss = 0.0187131, acc = 0.98\n",
      "[Train] Batch ID = 33770, loss = 0.00230359, acc = 1.0\n",
      "[Validation] Batch ID = 33770, loss = 0.0193091, acc = 0.98\n",
      "[Train] Batch ID = 33780, loss = 0.00255943, acc = 1.0\n",
      "[Validation] Batch ID = 33780, loss = 0.0253616, acc = 1.0\n",
      "[Train] Batch ID = 33790, loss = 0.00141364, acc = 1.0\n",
      "[Validation] Batch ID = 33790, loss = 0.0381494, acc = 0.94\n",
      "[Train] Batch ID = 33800, loss = 0.000759173, acc = 1.0\n",
      "[Validation] Batch ID = 33800, loss = 0.0206996, acc = 0.98\n",
      "[Train] Batch ID = 33810, loss = 0.00153981, acc = 1.0\n",
      "[Validation] Batch ID = 33810, loss = 0.0403791, acc = 0.96\n",
      "[Train] Batch ID = 33820, loss = 0.00112776, acc = 1.0\n",
      "[Validation] Batch ID = 33820, loss = 0.0104464, acc = 1.0\n",
      "[Train] Batch ID = 33830, loss = 0.000706016, acc = 1.0\n",
      "[Validation] Batch ID = 33830, loss = 0.0349083, acc = 1.0\n",
      "[Train] Batch ID = 33840, loss = 0.00193746, acc = 1.0\n",
      "[Validation] Batch ID = 33840, loss = 0.0152287, acc = 1.0\n",
      "[Train] Batch ID = 33850, loss = 0.00201659, acc = 1.0\n",
      "[Validation] Batch ID = 33850, loss = 0.0305157, acc = 0.98\n",
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      "[Validation] Batch ID = 33860, loss = 0.0289241, acc = 0.98\n",
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      "[Validation] Batch ID = 33870, loss = 0.01571, acc = 0.98\n",
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      "[Validation] Batch ID = 33880, loss = 0.0336432, acc = 0.96\n",
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      "[Validation] Batch ID = 33890, loss = 0.0531919, acc = 0.96\n",
      "[Train] Batch ID = 33900, loss = 0.00429765, acc = 1.0\n",
      "[Validation] Batch ID = 33900, loss = 0.0155319, acc = 1.0\n",
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      "[Validation] Batch ID = 33910, loss = 0.0496658, acc = 0.96\n",
      "[Train] Batch ID = 33920, loss = 0.00597919, acc = 1.0\n",
      "[Validation] Batch ID = 33920, loss = 0.0353993, acc = 0.98\n",
      "[Train] Batch ID = 33930, loss = 0.00523041, acc = 1.0\n",
      "[Validation] Batch ID = 33930, loss = 0.018453, acc = 1.0\n",
      "[Train] Batch ID = 33940, loss = 0.00379052, acc = 1.0\n",
      "[Validation] Batch ID = 33940, loss = 0.0335519, acc = 0.96\n",
      "[Train] Batch ID = 33950, loss = 0.00246781, acc = 1.0\n",
      "[Validation] Batch ID = 33950, loss = 0.0246614, acc = 0.98\n",
      "[Train] Batch ID = 33960, loss = 0.00737513, acc = 1.0\n",
      "[Validation] Batch ID = 33960, loss = 0.0202566, acc = 0.98\n",
      "[Train] Batch ID = 33970, loss = 0.258969, acc = 0.78\n",
      "[Validation] Batch ID = 33970, loss = 0.0498904, acc = 0.96\n",
      "[Train] Batch ID = 33980, loss = 0.00358301, acc = 1.0\n",
      "[Validation] Batch ID = 33980, loss = 0.0265126, acc = 0.98\n",
      "[Train] Batch ID = 33990, loss = 0.00441193, acc = 1.0\n",
      "[Validation] Batch ID = 33990, loss = 0.0612483, acc = 0.94\n",
      "[Train] Batch ID = 34000, loss = 0.00127521, acc = 1.0\n",
      "[Validation] Batch ID = 34000, loss = 0.035544, acc = 0.98\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0281532 Best loss: 0.0279286\n",
      "[TOTAL Validation] Batch ID = 34000, loss = 0.0281532, acc = 0.974376417234\n",
      "Augmented Factor = 0.02478123949943311\n",
      "[Train] Batch ID = 34010, loss = 0.00292716, acc = 1.0\n",
      "[Validation] Batch ID = 34010, loss = 0.0388733, acc = 0.94\n",
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      "[Validation] Batch ID = 34020, loss = 0.0079285, acc = 1.0\n",
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      "[Validation] Batch ID = 34030, loss = 0.054598, acc = 0.94\n",
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      "[Validation] Batch ID = 34040, loss = 0.0246616, acc = 0.98\n",
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      "[Validation] Batch ID = 34070, loss = 0.0265773, acc = 0.98\n",
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      "[Train] Batch ID = 34100, loss = 0.00144634, acc = 1.0\n",
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      "[Train] Batch ID = 34160, loss = 0.00139048, acc = 1.0\n",
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      "[Train] Batch ID = 34190, loss = 0.000957156, acc = 1.0\n",
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      "[Validation] Batch ID = 34420, loss = 0.0388039, acc = 0.96\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 34430, loss = 0.000940373, acc = 1.0\n",
      "[Validation] Batch ID = 34430, loss = 0.00654498, acc = 1.0\n",
      "[Train] Batch ID = 34440, loss = 0.00165389, acc = 1.0\n",
      "[Validation] Batch ID = 34440, loss = 0.0258308, acc = 0.96\n",
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      "[Validation] Batch ID = 34460, loss = 0.0368613, acc = 0.96\n",
      "[Train] Batch ID = 34470, loss = 0.00241067, acc = 1.0\n",
      "[Validation] Batch ID = 34470, loss = 0.0621379, acc = 0.92\n",
      "[Train] Batch ID = 34480, loss = 0.00240353, acc = 1.0\n",
      "[Validation] Batch ID = 34480, loss = 0.0251606, acc = 0.98\n",
      "[Train] Batch ID = 34490, loss = 0.00104062, acc = 1.0\n",
      "[Validation] Batch ID = 34490, loss = 0.0313245, acc = 0.98\n",
      "[Train] Batch ID = 34500, loss = 0.00123432, acc = 1.0\n",
      "[Validation] Batch ID = 34500, loss = 0.0399166, acc = 0.94\n",
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      "[Validation] Batch ID = 34510, loss = 0.0418553, acc = 0.96\n",
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      "[Validation] Batch ID = 34520, loss = 0.020106, acc = 1.0\n",
      "[Train] Batch ID = 34530, loss = 0.000727647, acc = 1.0\n",
      "[Validation] Batch ID = 34530, loss = 0.0168686, acc = 0.98\n",
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      "[Validation] Batch ID = 34540, loss = 0.01112, acc = 1.0\n",
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      "[Validation] Batch ID = 34570, loss = 0.0313396, acc = 0.98\n",
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      "[Validation] Batch ID = 34630, loss = 0.0248081, acc = 1.0\n",
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      "[Validation] Batch ID = 34640, loss = 0.0154217, acc = 0.98\n",
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      "[Validation] Batch ID = 34660, loss = 0.00914493, acc = 1.0\n",
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      "[Validation] Batch ID = 34700, loss = 0.0302443, acc = 0.98\n",
      "[Train] Batch ID = 34710, loss = 0.00427503, acc = 1.0\n",
      "[Validation] Batch ID = 34710, loss = 0.0164207, acc = 1.0\n",
      "[Train] Batch ID = 34720, loss = 0.00319668, acc = 1.0\n",
      "[Validation] Batch ID = 34720, loss = 0.0257829, acc = 0.98\n",
      "[Train] Batch ID = 34730, loss = 0.00244617, acc = 1.0\n",
      "[Validation] Batch ID = 34730, loss = 0.0491925, acc = 0.96\n",
      "[Train] Batch ID = 34740, loss = 0.00130755, acc = 1.0\n",
      "[Validation] Batch ID = 34740, loss = 0.0159836, acc = 1.0\n",
      "[Train] Batch ID = 34750, loss = 0.000922704, acc = 1.0\n",
      "[Validation] Batch ID = 34750, loss = 0.0202928, acc = 0.98\n",
      "[Train] Batch ID = 34760, loss = 0.00113312, acc = 1.0\n",
      "[Validation] Batch ID = 34760, loss = 0.0267652, acc = 0.98\n",
      "[Train] Batch ID = 34770, loss = 0.00113025, acc = 1.0\n",
      "[Validation] Batch ID = 34770, loss = 0.0303798, acc = 0.98\n",
      "[Train] Batch ID = 34780, loss = 0.00107034, acc = 1.0\n",
      "[Validation] Batch ID = 34780, loss = 0.0085439, acc = 1.0\n",
      "[Train] Batch ID = 34790, loss = 0.000568644, acc = 1.0\n",
      "[Validation] Batch ID = 34790, loss = 0.0381623, acc = 0.98\n",
      "[Train] Batch ID = 34800, loss = 0.000406391, acc = 1.0\n",
      "[Validation] Batch ID = 34800, loss = 0.041879, acc = 0.96\n",
      "[Train] Batch ID = 34810, loss = 0.000527877, acc = 1.0\n",
      "[Validation] Batch ID = 34810, loss = 0.0153544, acc = 1.0\n",
      "[Train] Batch ID = 34820, loss = 0.00124814, acc = 1.0\n",
      "[Validation] Batch ID = 34820, loss = 0.022132, acc = 0.98\n",
      "[Train] Batch ID = 34830, loss = 0.00131949, acc = 1.0\n",
      "[Validation] Batch ID = 34830, loss = 0.0181519, acc = 0.98\n",
      "[Train] Batch ID = 34840, loss = 0.00059383, acc = 1.0\n",
      "[Validation] Batch ID = 34840, loss = 0.0282374, acc = 0.98\n",
      "[Train] Batch ID = 34850, loss = 0.00164318, acc = 1.0\n",
      "[Validation] Batch ID = 34850, loss = 0.0201427, acc = 1.0\n",
      "[Train] Batch ID = 34860, loss = 0.00267948, acc = 1.0\n",
      "[Validation] Batch ID = 34860, loss = 0.01531, acc = 1.0\n",
      "[Train] Batch ID = 34870, loss = 0.00182316, acc = 1.0\n",
      "[Validation] Batch ID = 34870, loss = 0.0642087, acc = 0.94\n",
      "[Train] Batch ID = 34880, loss = 0.0020084, acc = 1.0\n",
      "[Validation] Batch ID = 34880, loss = 0.0181483, acc = 1.0\n",
      "[Train] Batch ID = 34890, loss = 0.00117665, acc = 1.0\n",
      "[Validation] Batch ID = 34890, loss = 0.0181139, acc = 0.98\n",
      "[Train] Batch ID = 34900, loss = 0.000585932, acc = 1.0\n",
      "[Validation] Batch ID = 34900, loss = 0.0299241, acc = 0.98\n",
      "[Train] Batch ID = 34910, loss = 0.00195954, acc = 1.0\n",
      "[Validation] Batch ID = 34910, loss = 0.0415075, acc = 0.96\n",
      "[Train] Batch ID = 34920, loss = 0.000746637, acc = 1.0\n",
      "[Validation] Batch ID = 34920, loss = 0.0228915, acc = 0.98\n",
      "[Train] Batch ID = 34930, loss = 0.00301906, acc = 1.0\n",
      "[Validation] Batch ID = 34930, loss = 0.0226889, acc = 1.0\n",
      "[Train] Batch ID = 34940, loss = 0.00361435, acc = 1.0\n",
      "[Validation] Batch ID = 34940, loss = 0.0203312, acc = 1.0\n",
      "[Train] Batch ID = 34950, loss = 0.0013223, acc = 1.0\n",
      "[Validation] Batch ID = 34950, loss = 0.0347867, acc = 0.96\n",
      "[Train] Batch ID = 34960, loss = 0.00272004, acc = 1.0\n",
      "[Validation] Batch ID = 34960, loss = 0.016318, acc = 1.0\n",
      "[Train] Batch ID = 34970, loss = 0.00154643, acc = 1.0\n",
      "[Validation] Batch ID = 34970, loss = 0.0209945, acc = 0.98\n",
      "[Train] Batch ID = 34980, loss = 0.00342527, acc = 1.0\n",
      "[Validation] Batch ID = 34980, loss = 0.0467939, acc = 0.94\n",
      "[Train] Batch ID = 34990, loss = 0.00251763, acc = 1.0\n",
      "[Validation] Batch ID = 34990, loss = 0.0196573, acc = 1.0\n",
      "[Train] Batch ID = 35000, loss = 0.00489313, acc = 1.0\n",
      "[Validation] Batch ID = 35000, loss = 0.0346506, acc = 0.98\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0338471 Best loss: 0.0279286\n",
      "[TOTAL Validation] Batch ID = 35000, loss = 0.0338471, acc = 0.974376417234\n",
      "Augmented Factor = 0.0223031155494898\n",
      "[Train] Batch ID = 35010, loss = 0.00123101, acc = 1.0\n",
      "[Validation] Batch ID = 35010, loss = 0.0211353, acc = 0.98\n",
      "[Train] Batch ID = 35020, loss = 0.00483166, acc = 1.0\n",
      "[Validation] Batch ID = 35020, loss = 0.030276, acc = 0.98\n",
      "[Train] Batch ID = 35030, loss = 0.00303367, acc = 1.0\n",
      "[Validation] Batch ID = 35030, loss = 0.0321597, acc = 0.96\n",
      "[Train] Batch ID = 35040, loss = 0.00195591, acc = 1.0\n",
      "[Validation] Batch ID = 35040, loss = 0.0105399, acc = 1.0\n",
      "[Train] Batch ID = 35050, loss = 0.00064356, acc = 1.0\n",
      "[Validation] Batch ID = 35050, loss = 0.0212917, acc = 0.98\n",
      "[Train] Batch ID = 35060, loss = 0.00151086, acc = 1.0\n",
      "[Validation] Batch ID = 35060, loss = 0.0185054, acc = 1.0\n",
      "[Train] Batch ID = 35070, loss = 0.0019008, acc = 1.0\n",
      "[Validation] Batch ID = 35070, loss = 0.0245975, acc = 0.98\n",
      "[Train] Batch ID = 35080, loss = 0.00178826, acc = 1.0\n",
      "[Validation] Batch ID = 35080, loss = 0.0149673, acc = 1.0\n",
      "[Train] Batch ID = 35090, loss = 0.00141485, acc = 1.0\n",
      "[Validation] Batch ID = 35090, loss = 0.0288711, acc = 0.98\n",
      "[Train] Batch ID = 35100, loss = 0.00148675, acc = 1.0\n",
      "[Validation] Batch ID = 35100, loss = 0.0315895, acc = 0.98\n",
      "[Train] Batch ID = 35110, loss = 0.00496753, acc = 1.0\n",
      "[Validation] Batch ID = 35110, loss = 0.0291774, acc = 1.0\n",
      "[Train] Batch ID = 35120, loss = 0.00148082, acc = 1.0\n",
      "[Validation] Batch ID = 35120, loss = 0.0237277, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 35130, loss = 0.00146676, acc = 1.0\n",
      "[Validation] Batch ID = 35130, loss = 0.0198125, acc = 1.0\n",
      "[Train] Batch ID = 35140, loss = 0.00168412, acc = 1.0\n",
      "[Validation] Batch ID = 35140, loss = 0.0157836, acc = 1.0\n",
      "[Train] Batch ID = 35150, loss = 0.00223866, acc = 1.0\n",
      "[Validation] Batch ID = 35150, loss = 0.0122051, acc = 1.0\n",
      "[Train] Batch ID = 35160, loss = 0.00195233, acc = 1.0\n",
      "[Validation] Batch ID = 35160, loss = 0.0115557, acc = 1.0\n",
      "[Train] Batch ID = 35170, loss = 0.00185339, acc = 1.0\n",
      "[Validation] Batch ID = 35170, loss = 0.032402, acc = 0.96\n",
      "[Train] Batch ID = 35180, loss = 0.00139078, acc = 1.0\n",
      "[Validation] Batch ID = 35180, loss = 0.0296124, acc = 0.98\n",
      "[Train] Batch ID = 35190, loss = 0.00199218, acc = 1.0\n",
      "[Validation] Batch ID = 35190, loss = 0.0219612, acc = 0.98\n",
      "[Train] Batch ID = 35200, loss = 0.000734279, acc = 1.0\n",
      "[Validation] Batch ID = 35200, loss = 0.0523639, acc = 0.96\n",
      "[Train] Batch ID = 35210, loss = 0.000791422, acc = 1.0\n",
      "[Validation] Batch ID = 35210, loss = 0.0186676, acc = 0.98\n",
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      "[Validation] Batch ID = 35220, loss = 0.0182843, acc = 1.0\n",
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      "[Validation] Batch ID = 35230, loss = 0.0164689, acc = 1.0\n",
      "[Train] Batch ID = 35240, loss = 0.00144483, acc = 1.0\n",
      "[Validation] Batch ID = 35240, loss = 0.0359894, acc = 0.96\n",
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      "[Validation] Batch ID = 35250, loss = 0.0228408, acc = 0.98\n",
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      "[Validation] Batch ID = 35280, loss = 0.0290187, acc = 0.96\n",
      "[Train] Batch ID = 35290, loss = 0.00186773, acc = 1.0\n",
      "[Validation] Batch ID = 35290, loss = 0.0259945, acc = 0.96\n",
      "[Train] Batch ID = 35300, loss = 0.00165797, acc = 1.0\n",
      "[Validation] Batch ID = 35300, loss = 0.0342112, acc = 0.96\n",
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      "[Validation] Batch ID = 35310, loss = 0.019165, acc = 1.0\n",
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      "[Validation] Batch ID = 35320, loss = 0.018348, acc = 1.0\n",
      "[Train] Batch ID = 35330, loss = 0.00112968, acc = 1.0\n",
      "[Validation] Batch ID = 35330, loss = 0.0108394, acc = 1.0\n",
      "[Train] Batch ID = 35340, loss = 0.00133968, acc = 1.0\n",
      "[Validation] Batch ID = 35340, loss = 0.0343262, acc = 0.98\n",
      "[Train] Batch ID = 35350, loss = 0.000579713, acc = 1.0\n",
      "[Validation] Batch ID = 35350, loss = 0.0472246, acc = 0.94\n",
      "[Train] Batch ID = 35360, loss = 0.000474305, acc = 1.0\n",
      "[Validation] Batch ID = 35360, loss = 0.0320105, acc = 0.96\n",
      "[Train] Batch ID = 35370, loss = 0.00151863, acc = 1.0\n",
      "[Validation] Batch ID = 35370, loss = 0.00748708, acc = 1.0\n",
      "[Train] Batch ID = 35380, loss = 0.00241837, acc = 1.0\n",
      "[Validation] Batch ID = 35380, loss = 0.0298927, acc = 0.98\n",
      "[Train] Batch ID = 35390, loss = 0.188076, acc = 0.86\n",
      "[Validation] Batch ID = 35390, loss = 0.0526109, acc = 0.92\n",
      "[Train] Batch ID = 35400, loss = 0.00330111, acc = 1.0\n",
      "[Validation] Batch ID = 35400, loss = 0.0161357, acc = 1.0\n",
      "[Train] Batch ID = 35410, loss = 0.0028664, acc = 1.0\n",
      "[Validation] Batch ID = 35410, loss = 0.0191126, acc = 1.0\n",
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      "[Validation] Batch ID = 35420, loss = 0.0172395, acc = 0.98\n",
      "[Train] Batch ID = 35430, loss = 0.000549528, acc = 1.0\n",
      "[Validation] Batch ID = 35430, loss = 0.013277, acc = 0.98\n",
      "[Train] Batch ID = 35440, loss = 0.00108247, acc = 1.0\n",
      "[Validation] Batch ID = 35440, loss = 0.0432458, acc = 0.94\n",
      "[Train] Batch ID = 35450, loss = 0.00114345, acc = 1.0\n",
      "[Validation] Batch ID = 35450, loss = 0.0327118, acc = 0.94\n",
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      "[Validation] Batch ID = 35460, loss = 0.0619566, acc = 0.92\n",
      "[Train] Batch ID = 35470, loss = 0.000570245, acc = 1.0\n",
      "[Validation] Batch ID = 35470, loss = 0.0112915, acc = 1.0\n",
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      "[Validation] Batch ID = 35480, loss = 0.0247959, acc = 0.98\n",
      "[Train] Batch ID = 35490, loss = 0.00278684, acc = 1.0\n",
      "[Validation] Batch ID = 35490, loss = 0.0375957, acc = 0.96\n",
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      "[Validation] Batch ID = 35500, loss = 0.0348293, acc = 0.96\n",
      "[Train] Batch ID = 35510, loss = 0.219207, acc = 0.82\n",
      "[Validation] Batch ID = 35510, loss = 0.00482722, acc = 1.0\n",
      "[Train] Batch ID = 35520, loss = 0.00254461, acc = 1.0\n",
      "[Validation] Batch ID = 35520, loss = 0.0154841, acc = 1.0\n",
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      "[Validation] Batch ID = 35530, loss = 0.0313105, acc = 0.96\n",
      "[Train] Batch ID = 35540, loss = 0.0028022, acc = 1.0\n",
      "[Validation] Batch ID = 35540, loss = 0.0300512, acc = 0.98\n",
      "[Train] Batch ID = 35550, loss = 0.0009475, acc = 1.0\n",
      "[Validation] Batch ID = 35550, loss = 0.0221287, acc = 1.0\n",
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      "[Validation] Batch ID = 35560, loss = 0.0213457, acc = 0.96\n",
      "[Train] Batch ID = 35570, loss = 0.00231058, acc = 1.0\n",
      "[Validation] Batch ID = 35570, loss = 0.0316365, acc = 0.98\n",
      "[Train] Batch ID = 35580, loss = 0.00255997, acc = 1.0\n",
      "[Validation] Batch ID = 35580, loss = 0.068434, acc = 0.92\n",
      "[Train] Batch ID = 35590, loss = 0.00150198, acc = 1.0\n",
      "[Validation] Batch ID = 35590, loss = 0.0297742, acc = 0.98\n",
      "[Train] Batch ID = 35600, loss = 0.00116091, acc = 1.0\n",
      "[Validation] Batch ID = 35600, loss = 0.00840573, acc = 1.0\n",
      "[Train] Batch ID = 35610, loss = 0.0015711, acc = 1.0\n",
      "[Validation] Batch ID = 35610, loss = 0.0110809, acc = 1.0\n",
      "[Train] Batch ID = 35620, loss = 0.00549141, acc = 1.0\n",
      "[Validation] Batch ID = 35620, loss = 0.0167908, acc = 1.0\n",
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      "[Validation] Batch ID = 35630, loss = 0.0517466, acc = 0.94\n",
      "[Train] Batch ID = 35640, loss = 0.00257538, acc = 1.0\n",
      "[Validation] Batch ID = 35640, loss = 0.0368672, acc = 0.96\n",
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      "[Validation] Batch ID = 35680, loss = 0.0245407, acc = 1.0\n",
      "[Train] Batch ID = 35690, loss = 0.001514, acc = 1.0\n",
      "[Validation] Batch ID = 35690, loss = 0.0452087, acc = 0.92\n",
      "[Train] Batch ID = 35700, loss = 0.000824824, acc = 1.0\n",
      "[Validation] Batch ID = 35700, loss = 0.025087, acc = 0.98\n",
      "[Train] Batch ID = 35710, loss = 0.00133319, acc = 1.0\n",
      "[Validation] Batch ID = 35710, loss = 0.0234307, acc = 0.98\n",
      "[Train] Batch ID = 35720, loss = 0.00197493, acc = 1.0\n",
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      "[Train] Batch ID = 35730, loss = 0.00374448, acc = 1.0\n",
      "[Validation] Batch ID = 35730, loss = 0.0337954, acc = 0.98\n",
      "[Train] Batch ID = 35740, loss = 0.00343965, acc = 1.0\n",
      "[Validation] Batch ID = 35740, loss = 0.0219215, acc = 0.98\n",
      "[Train] Batch ID = 35750, loss = 0.00201432, acc = 1.0\n",
      "[Validation] Batch ID = 35750, loss = 0.011466, acc = 1.0\n",
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      "[Validation] Batch ID = 35760, loss = 0.00873763, acc = 1.0\n",
      "[Train] Batch ID = 35770, loss = 0.000926716, acc = 1.0\n",
      "[Validation] Batch ID = 35770, loss = 0.00837433, acc = 1.0\n",
      "[Train] Batch ID = 35780, loss = 0.00168031, acc = 1.0\n",
      "[Validation] Batch ID = 35780, loss = 0.0322792, acc = 0.96\n",
      "[Train] Batch ID = 35790, loss = 0.0014129, acc = 1.0\n",
      "[Validation] Batch ID = 35790, loss = 0.0144773, acc = 0.98\n",
      "[Train] Batch ID = 35800, loss = 0.00128679, acc = 1.0\n",
      "[Validation] Batch ID = 35800, loss = 0.0263892, acc = 0.98\n",
      "[Train] Batch ID = 35810, loss = 0.00140809, acc = 1.0\n",
      "[Validation] Batch ID = 35810, loss = 0.0217168, acc = 1.0\n",
      "[Train] Batch ID = 35820, loss = 0.00457105, acc = 1.0\n",
      "[Validation] Batch ID = 35820, loss = 0.0217399, acc = 1.0\n",
      "[Train] Batch ID = 35830, loss = 0.00135411, acc = 1.0\n",
      "[Validation] Batch ID = 35830, loss = 0.0276325, acc = 0.98\n",
      "[Train] Batch ID = 35840, loss = 0.0020144, acc = 1.0\n",
      "[Validation] Batch ID = 35840, loss = 0.0280892, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 35850, loss = 0.00229682, acc = 1.0\n",
      "[Validation] Batch ID = 35850, loss = 0.0187131, acc = 0.98\n",
      "[Train] Batch ID = 35860, loss = 0.0031632, acc = 1.0\n",
      "[Validation] Batch ID = 35860, loss = 0.0141907, acc = 1.0\n",
      "[Train] Batch ID = 35870, loss = 0.0016642, acc = 1.0\n",
      "[Validation] Batch ID = 35870, loss = 0.039399, acc = 0.96\n",
      "[Train] Batch ID = 35880, loss = 0.00201166, acc = 1.0\n",
      "[Validation] Batch ID = 35880, loss = 0.0098812, acc = 1.0\n",
      "[Train] Batch ID = 35890, loss = 0.00132046, acc = 1.0\n",
      "[Validation] Batch ID = 35890, loss = 0.0193322, acc = 0.98\n",
      "[Train] Batch ID = 35900, loss = 0.00105584, acc = 1.0\n",
      "[Validation] Batch ID = 35900, loss = 0.00898944, acc = 1.0\n",
      "[Train] Batch ID = 35910, loss = 0.000852348, acc = 1.0\n",
      "[Validation] Batch ID = 35910, loss = 0.0090617, acc = 1.0\n",
      "[Train] Batch ID = 35920, loss = 0.000753168, acc = 1.0\n",
      "[Validation] Batch ID = 35920, loss = 0.0315808, acc = 0.98\n",
      "[Train] Batch ID = 35930, loss = 0.00113977, acc = 1.0\n",
      "[Validation] Batch ID = 35930, loss = 0.0249847, acc = 0.98\n",
      "[Train] Batch ID = 35940, loss = 0.000651954, acc = 1.0\n",
      "[Validation] Batch ID = 35940, loss = 0.0387029, acc = 0.94\n",
      "[Train] Batch ID = 35950, loss = 0.000960406, acc = 1.0\n",
      "[Validation] Batch ID = 35950, loss = 0.00776293, acc = 1.0\n",
      "[Train] Batch ID = 35960, loss = 0.00253778, acc = 1.0\n",
      "[Validation] Batch ID = 35960, loss = 0.0403442, acc = 0.94\n",
      "[Train] Batch ID = 35970, loss = 0.00231554, acc = 1.0\n",
      "[Validation] Batch ID = 35970, loss = 0.022458, acc = 0.98\n",
      "[Train] Batch ID = 35980, loss = 0.00144338, acc = 1.0\n",
      "[Validation] Batch ID = 35980, loss = 0.0207997, acc = 1.0\n",
      "[Train] Batch ID = 35990, loss = 0.000867255, acc = 1.0\n",
      "[Validation] Batch ID = 35990, loss = 0.0347783, acc = 0.96\n",
      "[Train] Batch ID = 36000, loss = 0.00100862, acc = 1.0\n",
      "[Validation] Batch ID = 36000, loss = 0.0304484, acc = 0.96\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0264652 Best loss: 0.0279286\n",
      "[TOTAL Validation] Batch ID = 36000, loss = 0.0264652, acc = 0.978004535147\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.02007280399454082\n",
      "[Train] Batch ID = 36010, loss = 0.200188, acc = 0.78\n",
      "[Validation] Batch ID = 36010, loss = 0.050085, acc = 0.94\n",
      "[Train] Batch ID = 36020, loss = 0.00255817, acc = 1.0\n",
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      "[Validation] Batch ID = 36680, loss = 0.0188118, acc = 1.0\n",
      "[Train] Batch ID = 36690, loss = 0.00141413, acc = 1.0\n",
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      "[Validation] Batch ID = 36700, loss = 0.027812, acc = 0.98\n",
      "[Train] Batch ID = 36710, loss = 0.000971419, acc = 1.0\n",
      "[Validation] Batch ID = 36710, loss = 0.027191, acc = 0.98\n",
      "[Train] Batch ID = 36720, loss = 0.000378196, acc = 1.0\n",
      "[Validation] Batch ID = 36720, loss = 0.0105049, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 36730, loss = 0.0025604, acc = 1.0\n",
      "[Validation] Batch ID = 36730, loss = 0.0169361, acc = 1.0\n",
      "[Train] Batch ID = 36740, loss = 0.00218077, acc = 1.0\n",
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      "[Validation] Batch ID = 36760, loss = 0.0445748, acc = 0.96\n",
      "[Train] Batch ID = 36770, loss = 0.000961732, acc = 1.0\n",
      "[Validation] Batch ID = 36770, loss = 0.0351225, acc = 0.98\n",
      "[Train] Batch ID = 36780, loss = 0.00447581, acc = 1.0\n",
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      "[Validation] Batch ID = 36790, loss = 0.039876, acc = 0.96\n",
      "[Train] Batch ID = 36800, loss = 0.00405995, acc = 1.0\n",
      "[Validation] Batch ID = 36800, loss = 0.0196059, acc = 1.0\n",
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      "[Train] Batch ID = 36830, loss = 0.00164828, acc = 1.0\n",
      "[Validation] Batch ID = 36830, loss = 0.0190434, acc = 0.98\n",
      "[Train] Batch ID = 36840, loss = 0.0036785, acc = 1.0\n",
      "[Validation] Batch ID = 36840, loss = 0.0218362, acc = 0.98\n",
      "[Train] Batch ID = 36850, loss = 0.00237001, acc = 1.0\n",
      "[Validation] Batch ID = 36850, loss = 0.0450192, acc = 0.94\n",
      "[Train] Batch ID = 36860, loss = 0.00205953, acc = 1.0\n",
      "[Validation] Batch ID = 36860, loss = 0.0202107, acc = 1.0\n",
      "[Train] Batch ID = 36870, loss = 0.0019133, acc = 1.0\n",
      "[Validation] Batch ID = 36870, loss = 0.0312143, acc = 0.98\n",
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      "[Validation] Batch ID = 36880, loss = 0.0352698, acc = 0.96\n",
      "[Train] Batch ID = 36890, loss = 0.000639507, acc = 1.0\n",
      "[Validation] Batch ID = 36890, loss = 0.0362391, acc = 0.92\n",
      "[Train] Batch ID = 36900, loss = 0.000972909, acc = 1.0\n",
      "[Validation] Batch ID = 36900, loss = 0.0265807, acc = 0.98\n",
      "[Train] Batch ID = 36910, loss = 0.00166517, acc = 1.0\n",
      "[Validation] Batch ID = 36910, loss = 0.0145177, acc = 0.98\n",
      "[Train] Batch ID = 36920, loss = 0.000424803, acc = 1.0\n",
      "[Validation] Batch ID = 36920, loss = 0.024946, acc = 0.98\n",
      "[Train] Batch ID = 36930, loss = 0.00122428, acc = 1.0\n",
      "[Validation] Batch ID = 36930, loss = 0.0103561, acc = 1.0\n",
      "[Train] Batch ID = 36940, loss = 0.000987635, acc = 1.0\n",
      "[Validation] Batch ID = 36940, loss = 0.0216528, acc = 0.96\n",
      "[Train] Batch ID = 36950, loss = 0.00243964, acc = 1.0\n",
      "[Validation] Batch ID = 36950, loss = 0.0215313, acc = 0.98\n",
      "[Train] Batch ID = 36960, loss = 0.00213407, acc = 1.0\n",
      "[Validation] Batch ID = 36960, loss = 0.0195071, acc = 1.0\n",
      "[Train] Batch ID = 36970, loss = 0.00147166, acc = 1.0\n",
      "[Validation] Batch ID = 36970, loss = 0.0198261, acc = 1.0\n",
      "[Train] Batch ID = 36980, loss = 0.00117199, acc = 1.0\n",
      "[Validation] Batch ID = 36980, loss = 0.0270078, acc = 0.98\n",
      "[Train] Batch ID = 36990, loss = 0.000867529, acc = 1.0\n",
      "[Validation] Batch ID = 36990, loss = 0.0206978, acc = 1.0\n",
      "[Train] Batch ID = 37000, loss = 0.000824032, acc = 1.0\n",
      "[Validation] Batch ID = 37000, loss = 0.0259292, acc = 0.98\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0254414 Best loss: 0.0264652\n",
      "[TOTAL Validation] Batch ID = 37000, loss = 0.0254414, acc = 0.975510204082\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.01806552359508674\n",
      "[Train] Batch ID = 37010, loss = 0.00219439, acc = 1.0\n",
      "[Validation] Batch ID = 37010, loss = 0.0213016, acc = 0.96\n",
      "[Train] Batch ID = 37020, loss = 0.000328499, acc = 1.0\n",
      "[Validation] Batch ID = 37020, loss = 0.0340482, acc = 0.98\n",
      "[Train] Batch ID = 37030, loss = 0.000604523, acc = 1.0\n",
      "[Validation] Batch ID = 37030, loss = 0.0299739, acc = 0.98\n",
      "[Train] Batch ID = 37040, loss = 0.00110161, acc = 1.0\n",
      "[Validation] Batch ID = 37040, loss = 0.0181824, acc = 0.98\n",
      "[Train] Batch ID = 37050, loss = 0.00123481, acc = 1.0\n",
      "[Validation] Batch ID = 37050, loss = 0.048744, acc = 0.96\n",
      "[Train] Batch ID = 37060, loss = 0.000347122, acc = 1.0\n",
      "[Validation] Batch ID = 37060, loss = 0.0270024, acc = 0.98\n",
      "[Train] Batch ID = 37070, loss = 0.000961318, acc = 1.0\n",
      "[Validation] Batch ID = 37070, loss = 0.0254172, acc = 0.96\n",
      "[Train] Batch ID = 37080, loss = 0.000748675, acc = 1.0\n",
      "[Validation] Batch ID = 37080, loss = 0.0200109, acc = 0.98\n",
      "[Train] Batch ID = 37090, loss = 0.000891692, acc = 1.0\n",
      "[Validation] Batch ID = 37090, loss = 0.0192072, acc = 0.98\n",
      "[Train] Batch ID = 37100, loss = 0.000955008, acc = 1.0\n",
      "[Validation] Batch ID = 37100, loss = 0.0302344, acc = 0.98\n",
      "[Train] Batch ID = 37110, loss = 0.00153561, acc = 1.0\n",
      "[Validation] Batch ID = 37110, loss = 0.0325721, acc = 1.0\n",
      "[Train] Batch ID = 37120, loss = 0.000750753, acc = 1.0\n",
      "[Validation] Batch ID = 37120, loss = 0.0366266, acc = 0.96\n",
      "[Train] Batch ID = 37130, loss = 0.000712051, acc = 1.0\n",
      "[Validation] Batch ID = 37130, loss = 0.0262635, acc = 0.98\n",
      "[Train] Batch ID = 37140, loss = 0.00163962, acc = 1.0\n",
      "[Validation] Batch ID = 37140, loss = 0.0275794, acc = 0.96\n",
      "[Train] Batch ID = 37150, loss = 0.00433396, acc = 1.0\n",
      "[Validation] Batch ID = 37150, loss = 0.035029, acc = 0.98\n",
      "[Train] Batch ID = 37160, loss = 0.00381715, acc = 1.0\n",
      "[Validation] Batch ID = 37160, loss = 0.0216777, acc = 0.96\n",
      "[Train] Batch ID = 37170, loss = 0.00123481, acc = 1.0\n",
      "[Validation] Batch ID = 37170, loss = 0.0242624, acc = 1.0\n",
      "[Train] Batch ID = 37180, loss = 0.00148104, acc = 1.0\n",
      "[Validation] Batch ID = 37180, loss = 0.0424997, acc = 0.94\n",
      "[Train] Batch ID = 37190, loss = 0.00155893, acc = 1.0\n",
      "[Validation] Batch ID = 37190, loss = 0.0222218, acc = 0.96\n",
      "[Train] Batch ID = 37200, loss = 0.00170277, acc = 1.0\n",
      "[Validation] Batch ID = 37200, loss = 0.0285615, acc = 0.98\n",
      "[Train] Batch ID = 37210, loss = 0.00311964, acc = 1.0\n",
      "[Validation] Batch ID = 37210, loss = 0.0228171, acc = 0.98\n",
      "[Train] Batch ID = 37220, loss = 0.00084114, acc = 1.0\n",
      "[Validation] Batch ID = 37220, loss = 0.0318931, acc = 0.96\n",
      "[Train] Batch ID = 37230, loss = 0.00175938, acc = 1.0\n",
      "[Validation] Batch ID = 37230, loss = 0.0100522, acc = 1.0\n",
      "[Train] Batch ID = 37240, loss = 0.00308574, acc = 1.0\n",
      "[Validation] Batch ID = 37240, loss = 0.016312, acc = 0.98\n",
      "[Train] Batch ID = 37250, loss = 0.000713381, acc = 1.0\n",
      "[Validation] Batch ID = 37250, loss = 0.0522177, acc = 0.96\n",
      "[Train] Batch ID = 37260, loss = 0.000759051, acc = 1.0\n",
      "[Validation] Batch ID = 37260, loss = 0.0436251, acc = 0.96\n",
      "[Train] Batch ID = 37270, loss = 0.000975786, acc = 1.0\n",
      "[Validation] Batch ID = 37270, loss = 0.0101251, acc = 1.0\n",
      "[Train] Batch ID = 37280, loss = 0.0011086, acc = 1.0\n",
      "[Validation] Batch ID = 37280, loss = 0.00910077, acc = 1.0\n",
      "[Train] Batch ID = 37290, loss = 0.00200636, acc = 1.0\n",
      "[Validation] Batch ID = 37290, loss = 0.0358176, acc = 0.9\n",
      "[Train] Batch ID = 37300, loss = 0.00777636, acc = 1.0\n",
      "[Validation] Batch ID = 37300, loss = 0.0117058, acc = 1.0\n",
      "[Train] Batch ID = 37310, loss = 0.0024414, acc = 1.0\n",
      "[Validation] Batch ID = 37310, loss = 0.0204751, acc = 0.98\n",
      "[Train] Batch ID = 37320, loss = 0.000981288, acc = 1.0\n",
      "[Validation] Batch ID = 37320, loss = 0.0188924, acc = 1.0\n",
      "[Train] Batch ID = 37330, loss = 0.000897745, acc = 1.0\n",
      "[Validation] Batch ID = 37330, loss = 0.0130475, acc = 1.0\n",
      "[Train] Batch ID = 37340, loss = 0.00177241, acc = 1.0\n",
      "[Validation] Batch ID = 37340, loss = 0.036657, acc = 0.96\n",
      "[Train] Batch ID = 37350, loss = 0.000939688, acc = 1.0\n",
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      "[Train] Batch ID = 37360, loss = 0.00125714, acc = 1.0\n",
      "[Validation] Batch ID = 37360, loss = 0.0240832, acc = 0.98\n",
      "[Train] Batch ID = 37370, loss = 0.000906425, acc = 1.0\n",
      "[Validation] Batch ID = 37370, loss = 0.0151605, acc = 1.0\n",
      "[Train] Batch ID = 37380, loss = 0.000967521, acc = 1.0\n",
      "[Validation] Batch ID = 37380, loss = 0.0304334, acc = 0.96\n",
      "[Train] Batch ID = 37390, loss = 0.00121941, acc = 1.0\n",
      "[Validation] Batch ID = 37390, loss = 0.0181249, acc = 0.98\n",
      "[Train] Batch ID = 37400, loss = 0.00106544, acc = 1.0\n",
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      "[Train] Batch ID = 37450, loss = 0.00326622, acc = 1.0\n",
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      "[Train] Batch ID = 37480, loss = 0.0012269, acc = 1.0\n",
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      "[Validation] Batch ID = 37500, loss = 0.0371534, acc = 0.98\n",
      "[Train] Batch ID = 37510, loss = 0.00122545, acc = 1.0\n",
      "[Validation] Batch ID = 37510, loss = 0.0218342, acc = 1.0\n",
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      "[Validation] Batch ID = 37520, loss = 0.0196635, acc = 0.98\n",
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      "[Validation] Batch ID = 37540, loss = 0.0343205, acc = 0.96\n",
      "[Train] Batch ID = 37550, loss = 0.000908291, acc = 1.0\n",
      "[Validation] Batch ID = 37550, loss = 0.0288307, acc = 0.96\n",
      "[Train] Batch ID = 37560, loss = 0.00121291, acc = 1.0\n",
      "[Validation] Batch ID = 37560, loss = 0.0269828, acc = 1.0\n",
      "[Train] Batch ID = 37570, loss = 0.000699753, acc = 1.0\n",
      "[Validation] Batch ID = 37570, loss = 0.033114, acc = 0.96\n",
      "[Train] Batch ID = 37580, loss = 0.000472514, acc = 1.0\n",
      "[Validation] Batch ID = 37580, loss = 0.0216205, acc = 1.0\n",
      "[Train] Batch ID = 37590, loss = 0.000877045, acc = 1.0\n",
      "[Validation] Batch ID = 37590, loss = 0.0184613, acc = 1.0\n",
      "[Train] Batch ID = 37600, loss = 0.000992497, acc = 1.0\n",
      "[Validation] Batch ID = 37600, loss = 0.0194417, acc = 0.96\n",
      "[Train] Batch ID = 37610, loss = 0.00095358, acc = 1.0\n",
      "[Validation] Batch ID = 37610, loss = 0.0199265, acc = 0.98\n",
      "[Train] Batch ID = 37620, loss = 0.000848513, acc = 1.0\n",
      "[Validation] Batch ID = 37620, loss = 0.0476511, acc = 0.94\n",
      "[Train] Batch ID = 37630, loss = 0.00115382, acc = 1.0\n",
      "[Validation] Batch ID = 37630, loss = 0.0388899, acc = 0.94\n",
      "[Train] Batch ID = 37640, loss = 0.000717152, acc = 1.0\n",
      "[Validation] Batch ID = 37640, loss = 0.0392804, acc = 0.96\n",
      "[Train] Batch ID = 37650, loss = 0.000750362, acc = 1.0\n",
      "[Validation] Batch ID = 37650, loss = 0.0205194, acc = 0.98\n",
      "[Train] Batch ID = 37660, loss = 0.00191947, acc = 1.0\n",
      "[Validation] Batch ID = 37660, loss = 0.0251928, acc = 0.98\n",
      "[Train] Batch ID = 37670, loss = 0.00217696, acc = 1.0\n",
      "[Validation] Batch ID = 37670, loss = 0.0153992, acc = 1.0\n",
      "[Train] Batch ID = 37680, loss = 0.000975817, acc = 1.0\n",
      "[Validation] Batch ID = 37680, loss = 0.0231079, acc = 0.98\n",
      "[Train] Batch ID = 37690, loss = 0.000572098, acc = 1.0\n",
      "[Validation] Batch ID = 37690, loss = 0.0255703, acc = 0.96\n",
      "[Train] Batch ID = 37700, loss = 0.00079442, acc = 1.0\n",
      "[Validation] Batch ID = 37700, loss = 0.0237417, acc = 0.98\n",
      "[Train] Batch ID = 37710, loss = 0.000269621, acc = 1.0\n",
      "[Validation] Batch ID = 37710, loss = 0.0269557, acc = 0.98\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 37720, loss = 0.00203224, acc = 1.0\n",
      "[Validation] Batch ID = 37720, loss = 0.0248781, acc = 0.98\n",
      "[Train] Batch ID = 37730, loss = 0.00110009, acc = 1.0\n",
      "[Validation] Batch ID = 37730, loss = 0.0469319, acc = 0.96\n",
      "[Train] Batch ID = 37740, loss = 0.000753219, acc = 1.0\n",
      "[Validation] Batch ID = 37740, loss = 0.0232629, acc = 1.0\n",
      "[Train] Batch ID = 37750, loss = 0.000811752, acc = 1.0\n",
      "[Validation] Batch ID = 37750, loss = 0.0154835, acc = 1.0\n",
      "[Train] Batch ID = 37760, loss = 0.0011162, acc = 1.0\n",
      "[Validation] Batch ID = 37760, loss = 0.0184545, acc = 1.0\n",
      "[Train] Batch ID = 37770, loss = 0.00100909, acc = 1.0\n",
      "[Validation] Batch ID = 37770, loss = 0.0357438, acc = 0.96\n",
      "[Train] Batch ID = 37780, loss = 0.000712245, acc = 1.0\n",
      "[Validation] Batch ID = 37780, loss = 0.0192121, acc = 0.98\n",
      "[Train] Batch ID = 37790, loss = 0.00193588, acc = 1.0\n",
      "[Validation] Batch ID = 37790, loss = 0.0311035, acc = 0.98\n",
      "[Train] Batch ID = 37800, loss = 0.00257869, acc = 1.0\n",
      "[Validation] Batch ID = 37800, loss = 0.0499938, acc = 0.94\n",
      "[Train] Batch ID = 37810, loss = 0.00265965, acc = 1.0\n",
      "[Validation] Batch ID = 37810, loss = 0.0277208, acc = 0.98\n",
      "[Train] Batch ID = 37820, loss = 0.00157478, acc = 1.0\n",
      "[Validation] Batch ID = 37820, loss = 0.00830829, acc = 1.0\n",
      "[Train] Batch ID = 37830, loss = 0.00218248, acc = 1.0\n",
      "[Validation] Batch ID = 37830, loss = 0.0103641, acc = 1.0\n",
      "[Train] Batch ID = 37840, loss = 0.00132101, acc = 1.0\n",
      "[Validation] Batch ID = 37840, loss = 0.00902896, acc = 0.98\n",
      "[Train] Batch ID = 37850, loss = 0.000503887, acc = 1.0\n",
      "[Validation] Batch ID = 37850, loss = 0.00674285, acc = 1.0\n",
      "[Train] Batch ID = 37860, loss = 0.000996867, acc = 1.0\n",
      "[Validation] Batch ID = 37860, loss = 0.019344, acc = 0.98\n",
      "[Train] Batch ID = 37870, loss = 0.00140236, acc = 1.0\n",
      "[Validation] Batch ID = 37870, loss = 0.022957, acc = 0.96\n",
      "[Train] Batch ID = 37880, loss = 0.000615535, acc = 1.0\n",
      "[Validation] Batch ID = 37880, loss = 0.0140036, acc = 1.0\n",
      "[Train] Batch ID = 37890, loss = 0.000882416, acc = 1.0\n",
      "[Validation] Batch ID = 37890, loss = 0.0489776, acc = 0.92\n",
      "[Train] Batch ID = 37900, loss = 0.000691616, acc = 1.0\n",
      "[Validation] Batch ID = 37900, loss = 0.0249626, acc = 0.98\n",
      "[Train] Batch ID = 37910, loss = 0.000303196, acc = 1.0\n",
      "[Validation] Batch ID = 37910, loss = 0.0137402, acc = 1.0\n",
      "[Train] Batch ID = 37920, loss = 0.00179201, acc = 1.0\n",
      "[Validation] Batch ID = 37920, loss = 0.0104005, acc = 1.0\n",
      "[Train] Batch ID = 37930, loss = 0.00270238, acc = 1.0\n",
      "[Validation] Batch ID = 37930, loss = 0.0384267, acc = 0.96\n",
      "[Train] Batch ID = 37940, loss = 0.00164577, acc = 1.0\n",
      "[Validation] Batch ID = 37940, loss = 0.00693579, acc = 1.0\n",
      "[Train] Batch ID = 37950, loss = 0.0016245, acc = 1.0\n",
      "[Validation] Batch ID = 37950, loss = 0.0170104, acc = 0.98\n",
      "[Train] Batch ID = 37960, loss = 0.00103855, acc = 1.0\n",
      "[Validation] Batch ID = 37960, loss = 0.0401362, acc = 0.96\n",
      "[Train] Batch ID = 37970, loss = 0.00117575, acc = 1.0\n",
      "[Validation] Batch ID = 37970, loss = 0.0317617, acc = 0.98\n",
      "[Train] Batch ID = 37980, loss = 0.00131384, acc = 1.0\n",
      "[Validation] Batch ID = 37980, loss = 0.0204108, acc = 0.98\n",
      "[Train] Batch ID = 37990, loss = 0.000580951, acc = 1.0\n",
      "[Validation] Batch ID = 37990, loss = 0.0288793, acc = 0.98\n",
      "[Train] Batch ID = 38000, loss = 0.000873868, acc = 1.0\n",
      "[Validation] Batch ID = 38000, loss = 0.0470329, acc = 0.96\n",
      "Evaluate full validation dataset ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Saving model ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current loss: 0.0254155 Best loss: 0.0254414\n",
      "[TOTAL Validation] Batch ID = 38000, loss = 0.0254155, acc = 0.978911564626\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ModelBase::Model successfully saved here: outputs/checkpoints/c1s_9_c1n_256_c2s_6_c2n_64_c2d_0.7_c1vl_16_c1s_5_c1nf_16_c2vl_32_lr_0.0001_rs_1--TrafficSign--1510487290.423481\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Factor = 0.016258971235578068\n",
      "[Train] Batch ID = 38010, loss = 0.00127426, acc = 1.0\n",
      "[Validation] Batch ID = 38010, loss = 0.0350408, acc = 0.98\n",
      "[Train] Batch ID = 38020, loss = 0.000956805, acc = 1.0\n",
      "[Validation] Batch ID = 38020, loss = 0.0209388, acc = 1.0\n",
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      "[Validation] Batch ID = 38030, loss = 0.0118166, acc = 1.0\n",
      "[Train] Batch ID = 38040, loss = 0.00107406, acc = 1.0\n",
      "[Validation] Batch ID = 38040, loss = 0.0138267, acc = 1.0\n",
      "[Train] Batch ID = 38050, loss = 0.000383219, acc = 1.0\n",
      "[Validation] Batch ID = 38050, loss = 0.0214781, acc = 0.98\n",
      "[Train] Batch ID = 38060, loss = 0.000667237, acc = 1.0\n",
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      "[Train] Batch ID = 38080, loss = 0.00136138, acc = 1.0\n",
      "[Validation] Batch ID = 38080, loss = 0.00993024, acc = 1.0\n",
      "[Train] Batch ID = 38090, loss = 0.00140649, acc = 1.0\n",
      "[Validation] Batch ID = 38090, loss = 0.0568554, acc = 0.96\n",
      "[Train] Batch ID = 38100, loss = 0.00168229, acc = 1.0\n",
      "[Validation] Batch ID = 38100, loss = 0.0271529, acc = 0.96\n",
      "[Train] Batch ID = 38110, loss = 0.000772352, acc = 1.0\n",
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      "[Train] Batch ID = 38120, loss = 0.000371287, acc = 1.0\n",
      "[Validation] Batch ID = 38120, loss = 0.0302386, acc = 0.98\n",
      "[Train] Batch ID = 38130, loss = 0.00298079, acc = 1.0\n",
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      "[Train] Batch ID = 38160, loss = 0.00397412, acc = 1.0\n",
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      "[Train] Batch ID = 38170, loss = 0.00158877, acc = 1.0\n",
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      "[Train] Batch ID = 38180, loss = 0.00213705, acc = 1.0\n",
      "[Validation] Batch ID = 38180, loss = 0.0851418, acc = 0.9\n",
      "[Train] Batch ID = 38190, loss = 0.00227948, acc = 1.0\n",
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      "[Train] Batch ID = 38200, loss = 0.00165916, acc = 1.0\n",
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      "[Train] Batch ID = 38210, loss = 0.00104245, acc = 1.0\n",
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      "[Validation] Batch ID = 38230, loss = 0.0170409, acc = 1.0\n",
      "[Train] Batch ID = 38240, loss = 0.00194562, acc = 1.0\n",
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      "[Train] Batch ID = 38250, loss = 0.00147689, acc = 1.0\n",
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      "[Train] Batch ID = 38290, loss = 0.00169602, acc = 1.0\n",
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      "[Train] Batch ID = 38300, loss = 0.00142095, acc = 1.0\n",
      "[Validation] Batch ID = 38300, loss = 0.0200929, acc = 0.98\n",
      "[Train] Batch ID = 38310, loss = 0.000980565, acc = 1.0\n",
      "[Validation] Batch ID = 38310, loss = 0.0424986, acc = 0.96\n",
      "[Train] Batch ID = 38320, loss = 0.00195072, acc = 1.0\n",
      "[Validation] Batch ID = 38320, loss = 0.0197234, acc = 0.98\n",
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      "[Validation] Batch ID = 38330, loss = 0.0224008, acc = 0.98\n",
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      "[Train] Batch ID = 38350, loss = 0.00150395, acc = 1.0\n",
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      "[Validation] Batch ID = 38360, loss = 0.0206325, acc = 1.0\n",
      "[Train] Batch ID = 38370, loss = 0.170731, acc = 0.86\n",
      "[Validation] Batch ID = 38370, loss = 0.0391262, acc = 0.98\n",
      "[Train] Batch ID = 38380, loss = 0.00186632, acc = 1.0\n",
      "[Validation] Batch ID = 38380, loss = 0.0287267, acc = 0.98\n",
      "[Train] Batch ID = 38390, loss = 0.00834115, acc = 1.0\n",
      "[Validation] Batch ID = 38390, loss = 0.0340762, acc = 0.98\n",
      "[Train] Batch ID = 38400, loss = 0.0040914, acc = 1.0\n",
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      "[Validation] Batch ID = 38430, loss = 0.0463568, acc = 0.98\n",
      "[Train] Batch ID = 38440, loss = 0.00068495, acc = 1.0\n",
      "[Validation] Batch ID = 38440, loss = 0.0285424, acc = 0.98\n",
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      "[Validation] Batch ID = 38450, loss = 0.0299334, acc = 0.98\n",
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      "[Validation] Batch ID = 38460, loss = 0.0294329, acc = 0.96\n",
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      "[Validation] Batch ID = 38480, loss = 0.0119642, acc = 1.0\n",
      "[Train] Batch ID = 38490, loss = 0.000964883, acc = 1.0\n",
      "[Validation] Batch ID = 38490, loss = 0.0553975, acc = 0.92\n",
      "[Train] Batch ID = 38500, loss = 0.000534654, acc = 1.0\n",
      "[Validation] Batch ID = 38500, loss = 0.033053, acc = 0.96\n",
      "[Train] Batch ID = 38510, loss = 0.000696871, acc = 1.0\n",
      "[Validation] Batch ID = 38510, loss = 0.0447776, acc = 0.96\n",
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      "[Validation] Batch ID = 38520, loss = 0.0406054, acc = 1.0\n",
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      "[Validation] Batch ID = 38530, loss = 0.024247, acc = 1.0\n",
      "[Train] Batch ID = 38540, loss = 0.00116827, acc = 1.0\n",
      "[Validation] Batch ID = 38540, loss = 0.0186592, acc = 0.98\n",
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      "[Train] Batch ID = 38560, loss = 0.000771803, acc = 1.0\n",
      "[Validation] Batch ID = 38560, loss = 0.0323878, acc = 0.98\n",
      "[Train] Batch ID = 38570, loss = 0.00124786, acc = 1.0\n",
      "[Validation] Batch ID = 38570, loss = 0.0100446, acc = 1.0\n",
      "[Train] Batch ID = 38580, loss = 0.000618459, acc = 1.0\n",
      "[Validation] Batch ID = 38580, loss = 0.0383244, acc = 0.96\n",
      "[Train] Batch ID = 38590, loss = 0.000772228, acc = 1.0\n",
      "[Validation] Batch ID = 38590, loss = 0.0308014, acc = 0.96\n",
      "[Train] Batch ID = 38600, loss = 0.00165526, acc = 1.0\n",
      "[Validation] Batch ID = 38600, loss = 0.0193809, acc = 0.98\n",
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      "[Validation] Batch ID = 38610, loss = 0.0145645, acc = 0.98\n",
      "[Train] Batch ID = 38620, loss = 0.00219446, acc = 1.0\n",
      "[Validation] Batch ID = 38620, loss = 0.0233388, acc = 1.0\n",
      "[Train] Batch ID = 38630, loss = 0.000707247, acc = 1.0\n",
      "[Validation] Batch ID = 38630, loss = 0.0558707, acc = 0.92\n",
      "[Train] Batch ID = 38640, loss = 0.00153203, acc = 1.0\n",
      "[Validation] Batch ID = 38640, loss = 0.0117385, acc = 1.0\n",
      "[Train] Batch ID = 38650, loss = 0.0016247, acc = 1.0\n",
      "[Validation] Batch ID = 38650, loss = 0.0172804, acc = 1.0\n",
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      "[Validation] Batch ID = 38660, loss = 0.00864521, acc = 1.0\n",
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      "[Validation] Batch ID = 38670, loss = 0.0233263, acc = 1.0\n",
      "[Train] Batch ID = 38680, loss = 0.000736744, acc = 1.0\n",
      "[Validation] Batch ID = 38680, loss = 0.0108782, acc = 1.0\n",
      "[Train] Batch ID = 38690, loss = 0.0014492, acc = 1.0\n",
      "[Validation] Batch ID = 38690, loss = 0.0358421, acc = 1.0\n",
      "[Train] Batch ID = 38700, loss = 0.00154679, acc = 1.0\n",
      "[Validation] Batch ID = 38700, loss = 0.0276107, acc = 0.96\n",
      "[Train] Batch ID = 38710, loss = 0.00532646, acc = 1.0\n",
      "[Validation] Batch ID = 38710, loss = 0.0140915, acc = 1.0\n",
      "[Train] Batch ID = 38720, loss = 0.00223791, acc = 1.0\n",
      "[Validation] Batch ID = 38720, loss = 0.0279973, acc = 1.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Train] Batch ID = 38730, loss = 0.00189701, acc = 1.0\n",
      "[Validation] Batch ID = 38730, loss = 0.00645008, acc = 1.0\n",
      "[Train] Batch ID = 38740, loss = 0.00230352, acc = 1.0\n",
      "[Validation] Batch ID = 38740, loss = 0.0184136, acc = 0.98\n",
      "[Train] Batch ID = 38750, loss = 0.00268211, acc = 1.0\n",
      "[Validation] Batch ID = 38750, loss = 0.0215661, acc = 1.0\n",
      "[Train] Batch ID = 38760, loss = 0.00457277, acc = 1.0\n",
      "[Validation] Batch ID = 38760, loss = 0.0433518, acc = 0.96\n",
      "[Train] Batch ID = 38770, loss = 0.00330429, acc = 1.0\n",
      "[Validation] Batch ID = 38770, loss = 0.0198763, acc = 0.98\n",
      "[Train] Batch ID = 38780, loss = 0.000720369, acc = 1.0\n",
      "[Validation] Batch ID = 38780, loss = 0.0443286, acc = 0.96\n",
      "[Train] Batch ID = 38790, loss = 0.0016142, acc = 1.0\n",
      "[Validation] Batch ID = 38790, loss = 0.0578755, acc = 0.94\n",
      "[Train] Batch ID = 38800, loss = 0.000302942, acc = 1.0\n",
      "[Validation] Batch ID = 38800, loss = 0.0113212, acc = 1.0\n",
      "[Train] Batch ID = 38810, loss = 0.00107272, acc = 1.0\n",
      "[Validation] Batch ID = 38810, loss = 0.0258947, acc = 0.98\n",
      "[Train] Batch ID = 38820, loss = 0.0010258, acc = 1.0\n",
      "[Validation] Batch ID = 38820, loss = 0.0333209, acc = 0.98\n",
      "[Train] Batch ID = 38830, loss = 0.00230507, acc = 1.0\n",
      "[Validation] Batch ID = 38830, loss = 0.0237396, acc = 0.98\n",
      "[Train] Batch ID = 38840, loss = 0.000935915, acc = 1.0\n",
      "[Validation] Batch ID = 38840, loss = 0.0435639, acc = 0.94\n",
      "[Train] Batch ID = 38850, loss = 0.00155814, acc = 1.0\n",
      "[Validation] Batch ID = 38850, loss = 0.0352852, acc = 0.98\n",
      "[Train] Batch ID = 38860, loss = 0.000889952, acc = 1.0\n",
      "[Validation] Batch ID = 38860, loss = 0.0291988, acc = 0.98\n",
      "[Train] Batch ID = 38870, loss = 0.00103695, acc = 1.0\n",
      "[Validation] Batch ID = 38870, loss = 0.0199554, acc = 1.0\n",
      "[Train] Batch ID = 38880, loss = 0.000765492, acc = 1.0\n",
      "[Validation] Batch ID = 38880, loss = 0.0212793, acc = 0.98\n",
      "[Train] Batch ID = 38890, loss = 0.00181694, acc = 1.0\n",
      "[Validation] Batch ID = 38890, loss = 0.0295611, acc = 0.98\n",
      "[Train] Batch ID = 38900, loss = 0.000546289, acc = 1.0\n",
      "[Validation] Batch ID = 38900, loss = 0.0295596, acc = 0.98\n",
      "[Train] Batch ID = 38910, loss = 0.00134746, acc = 1.0\n",
      "[Validation] Batch ID = 38910, loss = 0.0110999, acc = 1.0\n",
      "[Train] Batch ID = 38920, loss = 0.0015986, acc = 1.0\n",
      "[Validation] Batch ID = 38920, loss = 0.0352543, acc = 0.96\n",
      "[Train] Batch ID = 38930, loss = 0.000362096, acc = 1.0\n",
      "[Validation] Batch ID = 38930, loss = 0.0383362, acc = 0.96\n",
      "[Train] Batch ID = 38940, loss = 0.000596293, acc = 1.0\n",
      "[Validation] Batch ID = 38940, loss = 0.0518469, acc = 0.94\n",
      "[Train] Batch ID = 38950, loss = 0.00126123, acc = 1.0\n",
      "[Validation] Batch ID = 38950, loss = 0.0224572, acc = 1.0\n",
      "[Train] Batch ID = 38960, loss = 0.00118702, acc = 1.0\n",
      "[Validation] Batch ID = 38960, loss = 0.0164987, acc = 0.98\n",
      "[Train] Batch ID = 38970, loss = 0.000883441, acc = 1.0\n",
      "[Validation] Batch ID = 38970, loss = 0.00711842, acc = 1.0\n",
      "[Train] Batch ID = 38980, loss = 0.00201727, acc = 1.0\n",
      "[Validation] Batch ID = 38980, loss = 0.0159498, acc = 1.0\n",
      "[Train] Batch ID = 38990, loss = 0.00258629, acc = 1.0\n",
      "[Validation] Batch ID = 38990, loss = 0.0398028, acc = 0.96\n",
      "[Train] Batch ID = 39000, loss = 0.00189284, acc = 1.0\n",
      "[Validation] Batch ID = 39000, loss = 0.0139362, acc = 1.0\n",
      "Evaluate full validation dataset ...\n",
      "Current loss: 0.0271079 Best loss: 0.0254155\n",
      "[TOTAL Validation] Batch ID = 39000, loss = 0.0271079, acc = 0.97641723356\n",
      "Augmented Factor = 0.014633074112020262\n",
      "[Train] Batch ID = 39010, loss = 0.00176929, acc = 1.0\n",
      "[Validation] Batch ID = 39010, loss = 0.0138865, acc = 0.98\n",
      "[Train] Batch ID = 39020, loss = 0.0023623, acc = 1.0\n",
      "[Validation] Batch ID = 39020, loss = 0.0130478, acc = 1.0\n",
      "[Train] Batch ID = 39030, loss = 0.00309093, acc = 1.0\n",
      "[Validation] Batch ID = 39030, loss = 0.00392264, acc = 1.0\n",
      "[Train] Batch ID = 39040, loss = 0.0015213, acc = 1.0\n",
      "[Validation] Batch ID = 39040, loss = 0.019257, acc = 0.98\n",
      "[Train] Batch ID = 39050, loss = 0.000661352, acc = 1.0\n",
      "[Validation] Batch ID = 39050, loss = 0.033629, acc = 0.98\n",
      "[Train] Batch ID = 39060, loss = 0.000923142, acc = 1.0\n",
      "[Validation] Batch ID = 39060, loss = 0.0239484, acc = 0.98\n",
      "[Train] Batch ID = 39070, loss = 0.00057004, acc = 1.0\n",
      "[Validation] Batch ID = 39070, loss = 0.0341158, acc = 1.0\n",
      "[Train] Batch ID = 39080, loss = 0.00189745, acc = 1.0\n",
      "[Validation] Batch ID = 39080, loss = 0.0163904, acc = 0.98\n",
      "[Train] Batch ID = 39090, loss = 0.00148706, acc = 1.0\n",
      "[Validation] Batch ID = 39090, loss = 0.0334534, acc = 0.98\n",
      "[Train] Batch ID = 39100, loss = 0.00128108, acc = 1.0\n",
      "[Validation] Batch ID = 39100, loss = 0.0245813, acc = 0.96\n",
      "[Train] Batch ID = 39110, loss = 0.000524287, acc = 1.0\n",
      "[Validation] Batch ID = 39110, loss = 0.0171096, acc = 0.98\n",
      "[Train] Batch ID = 39120, loss = 0.000790245, acc = 1.0\n",
      "[Validation] Batch ID = 39120, loss = 0.0288631, acc = 0.96\n",
      "[Train] Batch ID = 39130, loss = 0.000615459, acc = 1.0\n",
      "[Validation] Batch ID = 39130, loss = 0.0149825, acc = 1.0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-7df2cfe2a939>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;31m### Training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m     \u001b[0mcost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m     \u001b[0;31m### Validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0mx_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-ab059c574b09>\u001b[0m in \u001b[0;36moptimize\u001b[0;34m(self, images, labels, tb_save)\u001b[0m\n\u001b[1;32m    195\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_images\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mimages\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_labels\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtf_conv_2_dropout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv_2_dropout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m         })\n\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl3-gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    893\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 895\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    896\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl3-gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1122\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1124\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1125\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl3-gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1319\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1320\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1321\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1322\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl3-gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1325\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1327\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1328\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1329\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dl3-gpu/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1304\u001b[0m           return tf_session.TF_Run(session, options,\n\u001b[1;32m   1305\u001b[0m                                    \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1306\u001b[0;31m                                    status, run_metadata)\n\u001b[0m\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "### Train your model here.\n",
    "### Calculate and report the accuracy on the training and validation set.\n",
    "### Once a final model architecture is selected, \n",
    "### the accuracy on the test set should be calculated and reported as well.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "BATCH_SIZE = 50\n",
    "\n",
    "# Utils method to print the current progression\n",
    "def plot_progression(b, cost, acc, label): print(\n",
    "    \"[%s] Batch ID = %s, loss = %s, acc = %s\" % (label, b, cost, acc))\n",
    "\n",
    "# Training pipeline\n",
    "b = 0\n",
    "valid_batch = inference_datagen.flow(X_valid, y_valid, batch_size=BATCH_SIZE)\n",
    "best_validation_loss = None\n",
    "augmented_factor = 0.99\n",
    "decrease_factor = 0.90\n",
    "train_batches = train_datagen.flow(X_train, y_train, batch_size=BATCH_SIZE)\n",
    "augmented_train_batches = train_datagen_augmented.flow(X_train, y_train, batch_size=BATCH_SIZE)\n",
    "\n",
    "while True:\n",
    "    next_batch = next(\n",
    "        augmented_train_batches if random.uniform(0, 1) < augmented_factor else train_batches)\n",
    "    x_batch, y_batch = next_batch\n",
    "\n",
    "    ### Training\n",
    "    cost, acc = model.optimize(x_batch, y_batch)\n",
    "    ### Validation\n",
    "    x_batch, y_batch = next(valid_batch, None)\n",
    "    # Retrieve the cost and acc on this validation batch and save it in tensorboard\n",
    "    cost_val, acc_val = model.evaluate(x_batch, y_batch, tb_test_save=True)\n",
    "\n",
    "    if b % 10 == 0: # Plot the last results\n",
    "        plot_progression(b, cost, acc, \"Train\")\n",
    "        plot_progression(b, cost_val, acc_val, \"Validation\")\n",
    "    if b % 1000 == 0: # Test the model on all the validation\n",
    "        print(\"Evaluate full validation dataset ...\")\n",
    "        loss, acc, _ = model.evaluate_dataset(X_valid, y_valid)\n",
    "        print(\"Current loss: %s Best loss: %s\" % (loss, best_validation_loss))\n",
    "        plot_progression(b, loss, acc, \"TOTAL Validation\")\n",
    "        if best_validation_loss is None or loss < best_validation_loss:\n",
    "            best_validation_loss = loss\n",
    "            model.save()\n",
    "        augmented_factor = augmented_factor * decrease_factor\n",
    "        print(\"Augmented Factor = %s\" % augmented_factor)\n",
    "\n",
    "    b += 1\n",
    "\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy =  0.976009501188\n",
      "Test Loss =  0.0311028\n"
     ]
    }
   ],
   "source": [
    "# Test the model on the test set\n",
    "\n",
    "# Evaluate all the dataset\n",
    "loss, acc, predicted_class = model.evaluate_dataset(X_test, y_test)\n",
    "\n",
    "print(\"Test Accuracy = \", acc)\n",
    "print(\"Test Loss = \", loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 3: Test a Model on New Images\n",
    "\n",
    "To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.\n",
    "\n",
    "You may find `signnames.csv` useful as it contains mappings from the class id (integer) to the actual sign name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and Output the Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febba7795f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec609e7400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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J6IkvhCguh3W1393bATwN4GoAzWY2BQAK/+8jPne6e72716dIFx8hRPEZNvjN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\nhYgpCn4hYoqCX4iYouAXIqYo+IWIKf8X9l5r1hTYBDIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febac482e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IS3jpwHBSytHDvA5eyZDw3yhvDbfxAoAL5vGO4WtXHKFaVxvfjyIPJ820d3bR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feba15d3f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fec60470160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Load the images and plot them here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "import os\n",
    "images = []\n",
    "\n",
    "# Read all image into the folder\n",
    "for filename in os.listdir(\"from_web\"):\n",
    "    img = Image.open(os.path.join(\"from_web\", filename))\n",
    "    img = img.resize((32, 32))\n",
    "    plt.imshow(img)\n",
    "    plt.show()\n",
    "    img = np.array(img) / 255\n",
    "    images.append(img)\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict the Sign Type for Each Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Run the predictions here and use the model to output the prediction for each image.\n",
    "### Make sure to pre-process the images with the same pre-processing pipeline used earlier.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "# Get the prediction\n",
    "predictions = model.predict(images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ne6TMbNBIOgg4GNhF0rya0CTANaTMbMRzImVmg+mPwBJgK+CMmudXArc2aixp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9zM7KdZ/ryZjdnZWDPraWb3mtkr2Z899tb+AGh9KKQAlCQzq5B0haQJkkZJOsPMRjXZbIKkkdnX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\nAADU9P8B0FgRSoIoFl8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7febb43224a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Calculate the accuracy for these 5 new images. \n",
    "### For example, if the model predicted 1 out of 5 signs correctly, it's 20% accurate on these new images.\n",
    "\n",
    "# Plot the result\n",
    "fig, axs = plt.subplots(5, 2, figsize=(10, 25))\n",
    "axs = axs.ravel()\n",
    "for i in range(10):\n",
    "    if i%2 == 0:\n",
    "        axs[i].axis('off')\n",
    "        axs[i].imshow(images[i // 2])\n",
    "        axs[i].set_title(\"Prediction: %s\" % id_to_name[np.argmax(predictions[i // 2])])\n",
    "    else:\n",
    "        axs[i].bar(np.arange(43), predictions[i // 2])\n",
    "        axs[i].set_ylabel(\"Softmax\")\n",
    "        axs[i].set_xlabel(\"Labels\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Output Top 5 Softmax Probabilities For Each Image Found on the Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each of the new images, print out the model's softmax probabilities to show the **certainty** of the model's predictions (limit the output to the top 5 probabilities for each image). [`tf.nn.top_k`](https://www.tensorflow.org/versions/r0.12/api_docs/python/nn.html#top_k) could prove helpful here. \n",
    "\n",
    "The example below demonstrates how tf.nn.top_k can be used to find the top k predictions for each image.\n",
    "\n",
    "`tf.nn.top_k` will return the values and indices (class ids) of the top k predictions. So if k=3, for each sign, it'll return the 3 largest probabilities (out of a possible 43) and the correspoding class ids.\n",
    "\n",
    "Take this numpy array as an example. The values in the array represent predictions. The array contains softmax probabilities for five candidate images with six possible classes. `tf.nn.top_k` is used to choose the three classes with the highest probability:\n",
    "\n",
    "```\n",
    "# (5, 6) array\n",
    "a = np.array([[ 0.24879643,  0.07032244,  0.12641572,  0.34763842,  0.07893497,\n",
    "         0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.08594638,  0.0178669 ,  0.18063401,\n",
    "         0.15899337],\n",
    "       [ 0.26076848,  0.23664738,  0.08020603,  0.07001922,  0.1134371 ,\n",
    "         0.23892179],\n",
    "       [ 0.11943333,  0.29198961,  0.02605103,  0.26234032,  0.1351348 ,\n",
    "         0.16505091],\n",
    "       [ 0.09561176,  0.34396535,  0.0643941 ,  0.16240774,  0.24206137,\n",
    "         0.09155967]])\n",
    "```\n",
    "\n",
    "Running it through `sess.run(tf.nn.top_k(tf.constant(a), k=3))` produces:\n",
    "\n",
    "```\n",
    "TopKV2(values=array([[ 0.34763842,  0.24879643,  0.12789202],\n",
    "       [ 0.28086119,  0.27569815,  0.18063401],\n",
    "       [ 0.26076848,  0.23892179,  0.23664738],\n",
    "       [ 0.29198961,  0.26234032,  0.16505091],\n",
    "       [ 0.34396535,  0.24206137,  0.16240774]]), indices=array([[3, 0, 5],\n",
    "       [0, 1, 4],\n",
    "       [0, 5, 1],\n",
    "       [1, 3, 5],\n",
    "       [1, 4, 3]], dtype=int32))\n",
    "```\n",
    "\n",
    "Looking just at the first row we get `[ 0.34763842,  0.24879643,  0.12789202]`, you can confirm these are the 3 largest probabilities in `a`. You'll also notice `[3, 0, 5]` are the corresponding indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Print out the top five softmax probabilities for the predictions on the German traffic sign images found on the web. \n",
    "### Feel free to use as many code cells as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Project Writeup\n",
    "\n",
    "Once you have completed the code implementation, document your results in a project writeup using this [template](https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md) as a guide. The writeup can be in a markdown or pdf file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to  \\n\",\n",
    "    \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Step 4 (Optional): Visualize the Neural Network's State with Test Images\n",
    "\n",
    " This Section is not required to complete but acts as an additional excersise for understaning the output of a neural network's weights. While neural networks can be a great learning device they are often referred to as a black box. We can understand what the weights of a neural network look like better by plotting their feature maps. After successfully training your neural network you can see what it's feature maps look like by plotting the output of the network's weight layers in response to a test stimuli image. From these plotted feature maps, it's possible to see what characteristics of an image the network finds interesting. For a sign, maybe the inner network feature maps react with high activation to the sign's boundary outline or to the contrast in the sign's painted symbol.\n",
    "\n",
    " Provided for you below is the function code that allows you to get the visualization output of any tensorflow weight layer you want. The inputs to the function should be a stimuli image, one used during training or a new one you provided, and then the tensorflow variable name that represents the layer's state during the training process, for instance if you wanted to see what the [LeNet lab's](https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81) feature maps looked like for it's second convolutional layer you could enter conv2 as the tf_activation variable.\n",
    "\n",
    "For an example of what feature map outputs look like, check out NVIDIA's results in their paper [End-to-End Deep Learning for Self-Driving Cars](https://devblogs.nvidia.com/parallelforall/deep-learning-self-driving-cars/) in the section Visualization of internal CNN State. NVIDIA was able to show that their network's inner weights had high activations to road boundary lines by comparing feature maps from an image with a clear path to one without. Try experimenting with a similar test to show that your trained network's weights are looking for interesting features, whether it's looking at differences in feature maps from images with or without a sign, or even what feature maps look like in a trained network vs a completely untrained one on the same sign image.\n",
    "\n",
    "<figure>\n",
    " <img src=\"visualize_cnn.png\" width=\"380\" alt=\"Combined Image\" />\n",
    " <figcaption>\n",
    " <p></p> \n",
    " <p style=\"text-align: center;\"> Your output should look something like this (above)</p> \n",
    " </figcaption>\n",
    "</figure>\n",
    " <p></p> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "### Visualize your network's feature maps here.\n",
    "### Feel free to use as many code cells as needed.\n",
    "\n",
    "# image_input: the test image being fed into the network to produce the feature maps\n",
    "# tf_activation: should be a tf variable name used during your training procedure that represents the calculated state of a specific weight layer\n",
    "# activation_min/max: can be used to view the activation contrast in more detail, by default matplot sets min and max to the actual min and max values of the output\n",
    "# plt_num: used to plot out multiple different weight feature map sets on the same block, just extend the plt number for each new feature map entry\n",
    "\n",
    "def outputFeatureMap(image_input, tf_activation, activation_min=-1, activation_max=-1 ,plt_num=1):\n",
    "    # Here make sure to preprocess your image_input in a way your network expects\n",
    "    # with size, normalization, ect if needed\n",
    "    # image_input =\n",
    "    # Note: x should be the same name as your network's tensorflow data placeholder variable\n",
    "    # If you get an error tf_activation is not defined it may be having trouble accessing the variable from inside a function\n",
    "    activation = tf_activation.eval(session=sess,feed_dict={x : image_input})\n",
    "    featuremaps = activation.shape[3]\n",
    "    plt.figure(plt_num, figsize=(15,15))\n",
    "    for featuremap in range(featuremaps):\n",
    "        plt.subplot(6,8, featuremap+1) # sets the number of feature maps to show on each row and column\n",
    "        plt.title('FeatureMap ' + str(featuremap)) # displays the feature map number\n",
    "        if activation_min != -1 & activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin =activation_min, vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_max != -1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmax=activation_max, cmap=\"gray\")\n",
    "        elif activation_min !=-1:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", vmin=activation_min, cmap=\"gray\")\n",
    "        else:\n",
    "            plt.imshow(activation[0,:,:, featuremap], interpolation=\"nearest\", cmap=\"gray\")"
   ]
  }
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