notebook.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "# Load the dataset\n",
    "data_dir = \"cell_images\"\n",
    "infected_dir = os.path.join(data_dir, \"Parasitized\")\n",
    "uninfected_dir = os.path.join(data_dir, \"Uninfected\")\n",
    "infected_files = os.listdir(infected_dir)\n",
    "uninfected_files = os.listdir(uninfected_dir)\n",
    "directories = ['Uninfected', 'Parasitized']\n",
    "\n",
    "# Create a dataframe containing the file paths and labels\n",
    "df1 = pd.DataFrame({\n",
    "    \"file_path\": [os.path.join(infected_dir, file) for file in infected_files] + [os.path.join(uninfected_dir, file) for file in uninfected_files],\n",
    "    \"label\": [\"Parasitized\" for _ in infected_files] + [\"Uninfected\" for _ in uninfected_files]\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "file_path    27558\n",
       "label        27558\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parasitized    13779\n",
      "Uninfected     13779\n",
      "Name: label, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Count the number of files for each label\n",
    "count_by_label = df1[\"label\"].value_counts()\n",
    "print(count_by_label)\n",
    "\n",
    "# Plot the distribution of labels\n",
    "plt.figure(figsize=(5,5))\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "sns.countplot(x=\"label\", data=df1)\n",
    "plt.title(\"Distribution of Labels\")\n",
    "plt.show( )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preperation and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 # computer vision package we've used before\n",
    "import numpy as np\n",
    "\n",
    "# Loop through the label subdirectories and collect the data in two lists, \n",
    "# one list for labels themselves and one list for the images inside those label folders.\n",
    "data=[]\n",
    "for labels in directories:\n",
    "    path = os.path.join(data_dir, labels)\n",
    "    class_num = directories.index(labels)\n",
    "    for img in os.listdir(path):\n",
    "        img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_COLOR)\n",
    "        new_array = cv2.resize(img_array, (64, 64))\n",
    "        data.append([new_array, class_num])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import np_utils\n",
    "\n",
    "#append feature and label\n",
    "X = []\n",
    "y = []\n",
    "\n",
    "for feature, label in data:\n",
    "    X.append(feature)\n",
    "    y.append(label)\n",
    "    \n",
    "#converting to categorical for one hot encoding\n",
    "num_classes = 2\n",
    "y = np_utils.to_categorical(y, num_classes = num_classes )\n",
    "\n",
    "\n",
    "#Reshaping data to rgb\n",
    "X = np.array(X)\n",
    "y = np.array(y)\n",
    "\n",
    "X = X.reshape(-1, 64, 64, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#data splitting\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "#data augmentation\n",
    "train_generator = ImageDataGenerator(rescale = 1/255,\n",
    "                                     zoom_range = 0.3,\n",
    "                                     horizontal_flip = True,\n",
    "                                     rotation_range = 30)\n",
    "\n",
    "test_generator = ImageDataGenerator(rescale = 1/255)\n",
    "\n",
    "train_generator = train_generator.flow(np.array(X_train),\n",
    "                                       y_train,\n",
    "                                       batch_size = 64,\n",
    "                                       shuffle = False)\n",
    "\n",
    "test_generator = test_generator.flow(np.array(X_test),\n",
    "                                     y_test,\n",
    "                                     batch_size = 64,\n",
    "                                     shuffle = False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build, Train and Evaluate Model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VGG16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 64, 64, 3)]       0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 64, 64, 64)        1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 64, 64, 64)        36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 32, 32, 64)        0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 32, 32, 128)       73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 32, 32, 128)       147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 16, 16, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 16, 16, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 16, 16, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 16, 16, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 8, 8, 256)         0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 8, 8, 512)         1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 2, 2, 512)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2048)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 256)               524544    \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 256)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 128)               32896     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 2)                 258       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 15,272,386\n",
      "Trainable params: 557,698\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.layers import Flatten, Dense, Dropout\n",
    "from keras.models import Model\n",
    "\n",
    "vg16 = VGG16(input_shape=[64, 64] + [3], weights=\"imagenet\", include_top=False)\n",
    "\n",
    "for layer in vg16.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# Add new layers to the model\n",
    "x = Flatten()(vg16.output)\n",
    "x = Dense(256, activation=\"relu\")(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(128, activation=\"relu\")(x)\n",
    "prediction = Dense(len(directories), activation=\"softmax\")(x)\n",
    "\n",
    "model = Model(inputs=vg16.input, outputs=prediction)\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", metrics=[\"accuracy\"], optimizer=\"adam\")\n",
    "\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "301/301 [==============================] - 40s 102ms/step - loss: 0.3758 - accuracy: 0.8302 - val_loss: 0.2458 - val_accuracy: 0.9032\n",
      "Epoch 2/15\n",
      "301/301 [==============================] - 27s 91ms/step - loss: 0.3188 - accuracy: 0.8678 - val_loss: 0.2253 - val_accuracy: 0.9156\n",
      "Epoch 3/15\n",
      "301/301 [==============================] - 27s 90ms/step - loss: 0.3072 - accuracy: 0.8724 - val_loss: 0.2201 - val_accuracy: 0.9172\n",
      "Epoch 4/15\n",
      "301/301 [==============================] - 27s 90ms/step - loss: 0.2995 - accuracy: 0.8763 - val_loss: 0.2449 - val_accuracy: 0.8989\n",
      "Epoch 5/15\n",
      "301/301 [==============================] - 27s 91ms/step - loss: 0.2896 - accuracy: 0.8828 - val_loss: 0.2152 - val_accuracy: 0.9193\n",
      "Epoch 6/15\n",
      "301/301 [==============================] - 27s 90ms/step - loss: 0.2856 - accuracy: 0.8836 - val_loss: 0.2038 - val_accuracy: 0.9227\n",
      "Epoch 7/15\n",
      "301/301 [==============================] - 27s 91ms/step - loss: 0.2791 - accuracy: 0.8875 - val_loss: 0.2295 - val_accuracy: 0.9228\n",
      "Epoch 8/15\n",
      "301/301 [==============================] - 27s 91ms/step - loss: 0.2779 - accuracy: 0.8851 - val_loss: 0.2258 - val_accuracy: 0.9095\n",
      "Epoch 9/15\n",
      "301/301 [==============================] - 27s 90ms/step - loss: 0.2739 - accuracy: 0.8884 - val_loss: 0.2136 - val_accuracy: 0.9221\n",
      "Epoch 10/15\n",
      "301/301 [==============================] - 27s 90ms/step - loss: 0.2674 - accuracy: 0.8918 - val_loss: 0.2071 - val_accuracy: 0.9240\n",
      "Epoch 11/15\n",
      "301/301 [==============================] - 27s 90ms/step - loss: 0.2749 - accuracy: 0.8877 - val_loss: 0.2242 - val_accuracy: 0.9144\n",
      "Epoch 12/15\n",
      "301/301 [==============================] - 27s 91ms/step - loss: 0.2723 - accuracy: 0.8887 - val_loss: 0.2263 - val_accuracy: 0.9128\n",
      "Epoch 13/15\n",
      "301/301 [==============================] - 27s 90ms/step - loss: 0.2738 - accuracy: 0.8878 - val_loss: 0.2210 - val_accuracy: 0.9240\n",
      "Epoch 14/15\n",
      "301/301 [==============================] - 27s 91ms/step - loss: 0.2687 - accuracy: 0.8911 - val_loss: 0.2385 - val_accuracy: 0.9188\n",
      "Epoch 15/15\n",
      "301/301 [==============================] - 27s 91ms/step - loss: 0.2663 - accuracy: 0.8902 - val_loss: 0.2305 - val_accuracy: 0.9213\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_generator,\n",
    "                    steps_per_epoch = len(X_train)/64,\n",
    "                    epochs = 15,\n",
    "                    shuffle = False,\n",
    "                    validation_data=test_generator, validation_steps=len(test_generator))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "259/259 [==============================] - 9s 34ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Uninfected       0.97      0.65      0.78      4146\n",
      " Parasitized       0.74      0.98      0.84      4122\n",
      "\n",
      "    accuracy                           0.82      8268\n",
      "   macro avg       0.85      0.82      0.81      8268\n",
      "weighted avg       0.86      0.82      0.81      8268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "print(classification_report(np.argmax(y_test, axis=1), y_pred, target_names=directories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report = classification_report(np.argmax(y_test, axis=1), y_pred, target_names=directories, output_dict=True)\n",
    "\n",
    "# Convert report to a dataframe\n",
    "df = pd.DataFrame(report).transpose()\n",
    "\n",
    "# Generate heatmap\n",
    "sns.heatmap(df.drop(['macro avg', 'weighted avg'], axis=0).iloc[:, :3], annot=True, cmap='Blues')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "259/259 [==============================] - 9s 34ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "y_score = model.predict(X_test) # get the prediction probabilities\n",
    "num_classes = 2 # number of classes\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(num_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "# Plot of a ROC curve for a specific class\n",
    "for i in range(num_classes):\n",
    "    plt.figure()\n",
    "    plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f)' % roc_auc[i])\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EfficientNetB7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_5\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_6 (InputLayer)           [(None, 64, 64, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " rescaling_10 (Rescaling)       (None, 64, 64, 3)    0           ['input_6[0][0]']                \n",
      "                                                                                                  \n",
      " normalization_5 (Normalization  (None, 64, 64, 3)   7           ['rescaling_10[0][0]']           \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " rescaling_11 (Rescaling)       (None, 64, 64, 3)    0           ['normalization_5[0][0]']        \n",
      "                                                                                                  \n",
      " stem_conv_pad (ZeroPadding2D)  (None, 65, 65, 3)    0           ['rescaling_11[0][0]']           \n",
      "                                                                                                  \n",
      " stem_conv (Conv2D)             (None, 32, 32, 64)   1728        ['stem_conv_pad[0][0]']          \n",
      "                                                                                                  \n",
      " stem_bn (BatchNormalization)   (None, 32, 32, 64)   256         ['stem_conv[0][0]']              \n",
      "                                                                                                  \n",
      " stem_activation (Activation)   (None, 32, 32, 64)   0           ['stem_bn[0][0]']                \n",
      "                                                                                                  \n",
      " block1a_dwconv (DepthwiseConv2  (None, 32, 32, 64)  576         ['stem_activation[0][0]']        \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block1a_bn (BatchNormalization  (None, 32, 32, 64)  256         ['block1a_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block1a_activation (Activation  (None, 32, 32, 64)  0           ['block1a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block1a_se_squeeze (GlobalAver  (None, 64)          0           ['block1a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block1a_se_reshape (Reshape)   (None, 1, 1, 64)     0           ['block1a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block1a_se_reduce (Conv2D)     (None, 1, 1, 16)     1040        ['block1a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block1a_se_expand (Conv2D)     (None, 1, 1, 64)     1088        ['block1a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block1a_se_excite (Multiply)   (None, 32, 32, 64)   0           ['block1a_activation[0][0]',     \n",
      "                                                                  'block1a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block1a_project_conv (Conv2D)  (None, 32, 32, 32)   2048        ['block1a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block1a_project_bn (BatchNorma  (None, 32, 32, 32)  128         ['block1a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block1b_dwconv (DepthwiseConv2  (None, 32, 32, 32)  288         ['block1a_project_bn[0][0]']     \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block1b_bn (BatchNormalization  (None, 32, 32, 32)  128         ['block1b_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block1b_activation (Activation  (None, 32, 32, 32)  0           ['block1b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block1b_se_squeeze (GlobalAver  (None, 32)          0           ['block1b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block1b_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block1b_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block1b_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block1b_se_excite (Multiply)   (None, 32, 32, 32)   0           ['block1b_activation[0][0]',     \n",
      "                                                                  'block1b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block1b_project_conv (Conv2D)  (None, 32, 32, 32)   1024        ['block1b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block1b_project_bn (BatchNorma  (None, 32, 32, 32)  128         ['block1b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block1b_drop (Dropout)         (None, 32, 32, 32)   0           ['block1b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block1b_add (Add)              (None, 32, 32, 32)   0           ['block1b_drop[0][0]',           \n",
      "                                                                  'block1a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block1c_dwconv (DepthwiseConv2  (None, 32, 32, 32)  288         ['block1b_add[0][0]']            \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block1c_bn (BatchNormalization  (None, 32, 32, 32)  128         ['block1c_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block1c_activation (Activation  (None, 32, 32, 32)  0           ['block1c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block1c_se_squeeze (GlobalAver  (None, 32)          0           ['block1c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block1c_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block1c_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block1c_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block1c_se_excite (Multiply)   (None, 32, 32, 32)   0           ['block1c_activation[0][0]',     \n",
      "                                                                  'block1c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block1c_project_conv (Conv2D)  (None, 32, 32, 32)   1024        ['block1c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block1c_project_bn (BatchNorma  (None, 32, 32, 32)  128         ['block1c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block1c_drop (Dropout)         (None, 32, 32, 32)   0           ['block1c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block1c_add (Add)              (None, 32, 32, 32)   0           ['block1c_drop[0][0]',           \n",
      "                                                                  'block1b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block1d_dwconv (DepthwiseConv2  (None, 32, 32, 32)  288         ['block1c_add[0][0]']            \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block1d_bn (BatchNormalization  (None, 32, 32, 32)  128         ['block1d_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block1d_activation (Activation  (None, 32, 32, 32)  0           ['block1d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block1d_se_squeeze (GlobalAver  (None, 32)          0           ['block1d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block1d_se_reshape (Reshape)   (None, 1, 1, 32)     0           ['block1d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block1d_se_reduce (Conv2D)     (None, 1, 1, 8)      264         ['block1d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block1d_se_expand (Conv2D)     (None, 1, 1, 32)     288         ['block1d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block1d_se_excite (Multiply)   (None, 32, 32, 32)   0           ['block1d_activation[0][0]',     \n",
      "                                                                  'block1d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block1d_project_conv (Conv2D)  (None, 32, 32, 32)   1024        ['block1d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block1d_project_bn (BatchNorma  (None, 32, 32, 32)  128         ['block1d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block1d_drop (Dropout)         (None, 32, 32, 32)   0           ['block1d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block1d_add (Add)              (None, 32, 32, 32)   0           ['block1d_drop[0][0]',           \n",
      "                                                                  'block1c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2a_expand_conv (Conv2D)   (None, 32, 32, 192)  6144        ['block1d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2a_expand_bn (BatchNormal  (None, 32, 32, 192)  768        ['block2a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2a_expand_activation (Act  (None, 32, 32, 192)  0          ['block2a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2a_dwconv_pad (ZeroPaddin  (None, 33, 33, 192)  0          ['block2a_expand_activation[0][0]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\gauta\\anaconda3\\lib\\site-packages\\keras\\optimizers\\optimizer_v2\\adam.py:114: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " g2D)                                                            ']                               \n",
      "                                                                                                  \n",
      " block2a_dwconv (DepthwiseConv2  (None, 16, 16, 192)  1728       ['block2a_dwconv_pad[0][0]']     \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block2a_bn (BatchNormalization  (None, 16, 16, 192)  768        ['block2a_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2a_activation (Activation  (None, 16, 16, 192)  0          ['block2a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2a_se_squeeze (GlobalAver  (None, 192)         0           ['block2a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2a_se_reshape (Reshape)   (None, 1, 1, 192)    0           ['block2a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2a_se_reduce (Conv2D)     (None, 1, 1, 8)      1544        ['block2a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2a_se_expand (Conv2D)     (None, 1, 1, 192)    1728        ['block2a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2a_se_excite (Multiply)   (None, 16, 16, 192)  0           ['block2a_activation[0][0]',     \n",
      "                                                                  'block2a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2a_project_conv (Conv2D)  (None, 16, 16, 48)   9216        ['block2a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block2a_project_bn (BatchNorma  (None, 16, 16, 48)  192         ['block2a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2b_expand_conv (Conv2D)   (None, 16, 16, 288)  13824       ['block2a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2b_expand_bn (BatchNormal  (None, 16, 16, 288)  1152       ['block2b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2b_expand_activation (Act  (None, 16, 16, 288)  0          ['block2b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2b_dwconv (DepthwiseConv2  (None, 16, 16, 288)  2592       ['block2b_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block2b_bn (BatchNormalization  (None, 16, 16, 288)  1152       ['block2b_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2b_activation (Activation  (None, 16, 16, 288)  0          ['block2b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2b_se_squeeze (GlobalAver  (None, 288)         0           ['block2b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2b_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2b_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2b_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2b_se_excite (Multiply)   (None, 16, 16, 288)  0           ['block2b_activation[0][0]',     \n",
      "                                                                  'block2b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2b_project_conv (Conv2D)  (None, 16, 16, 48)   13824       ['block2b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block2b_project_bn (BatchNorma  (None, 16, 16, 48)  192         ['block2b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2b_drop (Dropout)         (None, 16, 16, 48)   0           ['block2b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2b_add (Add)              (None, 16, 16, 48)   0           ['block2b_drop[0][0]',           \n",
      "                                                                  'block2a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2c_expand_conv (Conv2D)   (None, 16, 16, 288)  13824       ['block2b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2c_expand_bn (BatchNormal  (None, 16, 16, 288)  1152       ['block2c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2c_expand_activation (Act  (None, 16, 16, 288)  0          ['block2c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2c_dwconv (DepthwiseConv2  (None, 16, 16, 288)  2592       ['block2c_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block2c_bn (BatchNormalization  (None, 16, 16, 288)  1152       ['block2c_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2c_activation (Activation  (None, 16, 16, 288)  0          ['block2c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2c_se_squeeze (GlobalAver  (None, 288)         0           ['block2c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2c_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2c_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2c_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2c_se_excite (Multiply)   (None, 16, 16, 288)  0           ['block2c_activation[0][0]',     \n",
      "                                                                  'block2c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2c_project_conv (Conv2D)  (None, 16, 16, 48)   13824       ['block2c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block2c_project_bn (BatchNorma  (None, 16, 16, 48)  192         ['block2c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2c_drop (Dropout)         (None, 16, 16, 48)   0           ['block2c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2c_add (Add)              (None, 16, 16, 48)   0           ['block2c_drop[0][0]',           \n",
      "                                                                  'block2b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2d_expand_conv (Conv2D)   (None, 16, 16, 288)  13824       ['block2c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2d_expand_bn (BatchNormal  (None, 16, 16, 288)  1152       ['block2d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2d_expand_activation (Act  (None, 16, 16, 288)  0          ['block2d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2d_dwconv (DepthwiseConv2  (None, 16, 16, 288)  2592       ['block2d_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block2d_bn (BatchNormalization  (None, 16, 16, 288)  1152       ['block2d_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2d_activation (Activation  (None, 16, 16, 288)  0          ['block2d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2d_se_squeeze (GlobalAver  (None, 288)         0           ['block2d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2d_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2d_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2d_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2d_se_excite (Multiply)   (None, 16, 16, 288)  0           ['block2d_activation[0][0]',     \n",
      "                                                                  'block2d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2d_project_conv (Conv2D)  (None, 16, 16, 48)   13824       ['block2d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block2d_project_bn (BatchNorma  (None, 16, 16, 48)  192         ['block2d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2d_drop (Dropout)         (None, 16, 16, 48)   0           ['block2d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2d_add (Add)              (None, 16, 16, 48)   0           ['block2d_drop[0][0]',           \n",
      "                                                                  'block2c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2e_expand_conv (Conv2D)   (None, 16, 16, 288)  13824       ['block2d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2e_expand_bn (BatchNormal  (None, 16, 16, 288)  1152       ['block2e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2e_expand_activation (Act  (None, 16, 16, 288)  0          ['block2e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2e_dwconv (DepthwiseConv2  (None, 16, 16, 288)  2592       ['block2e_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block2e_bn (BatchNormalization  (None, 16, 16, 288)  1152       ['block2e_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2e_activation (Activation  (None, 16, 16, 288)  0          ['block2e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2e_se_squeeze (GlobalAver  (None, 288)         0           ['block2e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2e_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2e_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2e_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2e_se_excite (Multiply)   (None, 16, 16, 288)  0           ['block2e_activation[0][0]',     \n",
      "                                                                  'block2e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2e_project_conv (Conv2D)  (None, 16, 16, 48)   13824       ['block2e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block2e_project_bn (BatchNorma  (None, 16, 16, 48)  192         ['block2e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2e_drop (Dropout)         (None, 16, 16, 48)   0           ['block2e_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2e_add (Add)              (None, 16, 16, 48)   0           ['block2e_drop[0][0]',           \n",
      "                                                                  'block2d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2f_expand_conv (Conv2D)   (None, 16, 16, 288)  13824       ['block2e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2f_expand_bn (BatchNormal  (None, 16, 16, 288)  1152       ['block2f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2f_expand_activation (Act  (None, 16, 16, 288)  0          ['block2f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2f_dwconv (DepthwiseConv2  (None, 16, 16, 288)  2592       ['block2f_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block2f_bn (BatchNormalization  (None, 16, 16, 288)  1152       ['block2f_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2f_activation (Activation  (None, 16, 16, 288)  0          ['block2f_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2f_se_squeeze (GlobalAver  (None, 288)         0           ['block2f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2f_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2f_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2f_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2f_se_excite (Multiply)   (None, 16, 16, 288)  0           ['block2f_activation[0][0]',     \n",
      "                                                                  'block2f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2f_project_conv (Conv2D)  (None, 16, 16, 48)   13824       ['block2f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block2f_project_bn (BatchNorma  (None, 16, 16, 48)  192         ['block2f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2f_drop (Dropout)         (None, 16, 16, 48)   0           ['block2f_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2f_add (Add)              (None, 16, 16, 48)   0           ['block2f_drop[0][0]',           \n",
      "                                                                  'block2e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2g_expand_conv (Conv2D)   (None, 16, 16, 288)  13824       ['block2f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block2g_expand_bn (BatchNormal  (None, 16, 16, 288)  1152       ['block2g_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block2g_expand_activation (Act  (None, 16, 16, 288)  0          ['block2g_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block2g_dwconv (DepthwiseConv2  (None, 16, 16, 288)  2592       ['block2g_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block2g_bn (BatchNormalization  (None, 16, 16, 288)  1152       ['block2g_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2g_activation (Activation  (None, 16, 16, 288)  0          ['block2g_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block2g_se_squeeze (GlobalAver  (None, 288)         0           ['block2g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block2g_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block2g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block2g_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block2g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block2g_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block2g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block2g_se_excite (Multiply)   (None, 16, 16, 288)  0           ['block2g_activation[0][0]',     \n",
      "                                                                  'block2g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block2g_project_conv (Conv2D)  (None, 16, 16, 48)   13824       ['block2g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block2g_project_bn (BatchNorma  (None, 16, 16, 48)  192         ['block2g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block2g_drop (Dropout)         (None, 16, 16, 48)   0           ['block2g_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block2g_add (Add)              (None, 16, 16, 48)   0           ['block2g_drop[0][0]',           \n",
      "                                                                  'block2f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3a_expand_conv (Conv2D)   (None, 16, 16, 288)  13824       ['block2g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3a_expand_bn (BatchNormal  (None, 16, 16, 288)  1152       ['block3a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3a_expand_activation (Act  (None, 16, 16, 288)  0          ['block3a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3a_dwconv_pad (ZeroPaddin  (None, 19, 19, 288)  0          ['block3a_expand_activation[0][0]\n",
      " g2D)                                                            ']                               \n",
      "                                                                                                  \n",
      " block3a_dwconv (DepthwiseConv2  (None, 8, 8, 288)   7200        ['block3a_dwconv_pad[0][0]']     \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block3a_bn (BatchNormalization  (None, 8, 8, 288)   1152        ['block3a_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3a_activation (Activation  (None, 8, 8, 288)   0           ['block3a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3a_se_squeeze (GlobalAver  (None, 288)         0           ['block3a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3a_se_reshape (Reshape)   (None, 1, 1, 288)    0           ['block3a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3a_se_reduce (Conv2D)     (None, 1, 1, 12)     3468        ['block3a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3a_se_expand (Conv2D)     (None, 1, 1, 288)    3744        ['block3a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3a_se_excite (Multiply)   (None, 8, 8, 288)    0           ['block3a_activation[0][0]',     \n",
      "                                                                  'block3a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3a_project_conv (Conv2D)  (None, 8, 8, 80)     23040       ['block3a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3a_project_bn (BatchNorma  (None, 8, 8, 80)    320         ['block3a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3b_expand_conv (Conv2D)   (None, 8, 8, 480)    38400       ['block3a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_expand_bn (BatchNormal  (None, 8, 8, 480)   1920        ['block3b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3b_expand_activation (Act  (None, 8, 8, 480)   0           ['block3b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3b_dwconv (DepthwiseConv2  (None, 8, 8, 480)   12000       ['block3b_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3b_bn (BatchNormalization  (None, 8, 8, 480)   1920        ['block3b_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3b_activation (Activation  (None, 8, 8, 480)   0           ['block3b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3b_se_squeeze (GlobalAver  (None, 480)         0           ['block3b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3b_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3b_se_excite (Multiply)   (None, 8, 8, 480)    0           ['block3b_activation[0][0]',     \n",
      "                                                                  'block3b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3b_project_conv (Conv2D)  (None, 8, 8, 80)     38400       ['block3b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3b_project_bn (BatchNorma  (None, 8, 8, 80)    320         ['block3b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3b_drop (Dropout)         (None, 8, 8, 80)     0           ['block3b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3b_add (Add)              (None, 8, 8, 80)     0           ['block3b_drop[0][0]',           \n",
      "                                                                  'block3a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3c_expand_conv (Conv2D)   (None, 8, 8, 480)    38400       ['block3b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3c_expand_bn (BatchNormal  (None, 8, 8, 480)   1920        ['block3c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3c_expand_activation (Act  (None, 8, 8, 480)   0           ['block3c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3c_dwconv (DepthwiseConv2  (None, 8, 8, 480)   12000       ['block3c_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3c_bn (BatchNormalization  (None, 8, 8, 480)   1920        ['block3c_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3c_activation (Activation  (None, 8, 8, 480)   0           ['block3c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3c_se_squeeze (GlobalAver  (None, 480)         0           ['block3c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3c_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3c_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3c_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3c_se_excite (Multiply)   (None, 8, 8, 480)    0           ['block3c_activation[0][0]',     \n",
      "                                                                  'block3c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3c_project_conv (Conv2D)  (None, 8, 8, 80)     38400       ['block3c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3c_project_bn (BatchNorma  (None, 8, 8, 80)    320         ['block3c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3c_drop (Dropout)         (None, 8, 8, 80)     0           ['block3c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3c_add (Add)              (None, 8, 8, 80)     0           ['block3c_drop[0][0]',           \n",
      "                                                                  'block3b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3d_expand_conv (Conv2D)   (None, 8, 8, 480)    38400       ['block3c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3d_expand_bn (BatchNormal  (None, 8, 8, 480)   1920        ['block3d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3d_expand_activation (Act  (None, 8, 8, 480)   0           ['block3d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3d_dwconv (DepthwiseConv2  (None, 8, 8, 480)   12000       ['block3d_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3d_bn (BatchNormalization  (None, 8, 8, 480)   1920        ['block3d_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3d_activation (Activation  (None, 8, 8, 480)   0           ['block3d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3d_se_squeeze (GlobalAver  (None, 480)         0           ['block3d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3d_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3d_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3d_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3d_se_excite (Multiply)   (None, 8, 8, 480)    0           ['block3d_activation[0][0]',     \n",
      "                                                                  'block3d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3d_project_conv (Conv2D)  (None, 8, 8, 80)     38400       ['block3d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3d_project_bn (BatchNorma  (None, 8, 8, 80)    320         ['block3d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3d_drop (Dropout)         (None, 8, 8, 80)     0           ['block3d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3d_add (Add)              (None, 8, 8, 80)     0           ['block3d_drop[0][0]',           \n",
      "                                                                  'block3c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3e_expand_conv (Conv2D)   (None, 8, 8, 480)    38400       ['block3d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3e_expand_bn (BatchNormal  (None, 8, 8, 480)   1920        ['block3e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3e_expand_activation (Act  (None, 8, 8, 480)   0           ['block3e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3e_dwconv (DepthwiseConv2  (None, 8, 8, 480)   12000       ['block3e_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3e_bn (BatchNormalization  (None, 8, 8, 480)   1920        ['block3e_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3e_activation (Activation  (None, 8, 8, 480)   0           ['block3e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3e_se_squeeze (GlobalAver  (None, 480)         0           ['block3e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3e_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3e_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3e_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3e_se_excite (Multiply)   (None, 8, 8, 480)    0           ['block3e_activation[0][0]',     \n",
      "                                                                  'block3e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3e_project_conv (Conv2D)  (None, 8, 8, 80)     38400       ['block3e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3e_project_bn (BatchNorma  (None, 8, 8, 80)    320         ['block3e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3e_drop (Dropout)         (None, 8, 8, 80)     0           ['block3e_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3e_add (Add)              (None, 8, 8, 80)     0           ['block3e_drop[0][0]',           \n",
      "                                                                  'block3d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3f_expand_conv (Conv2D)   (None, 8, 8, 480)    38400       ['block3e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3f_expand_bn (BatchNormal  (None, 8, 8, 480)   1920        ['block3f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3f_expand_activation (Act  (None, 8, 8, 480)   0           ['block3f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3f_dwconv (DepthwiseConv2  (None, 8, 8, 480)   12000       ['block3f_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3f_bn (BatchNormalization  (None, 8, 8, 480)   1920        ['block3f_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3f_activation (Activation  (None, 8, 8, 480)   0           ['block3f_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3f_se_squeeze (GlobalAver  (None, 480)         0           ['block3f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3f_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3f_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3f_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3f_se_excite (Multiply)   (None, 8, 8, 480)    0           ['block3f_activation[0][0]',     \n",
      "                                                                  'block3f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3f_project_conv (Conv2D)  (None, 8, 8, 80)     38400       ['block3f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3f_project_bn (BatchNorma  (None, 8, 8, 80)    320         ['block3f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3f_drop (Dropout)         (None, 8, 8, 80)     0           ['block3f_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3f_add (Add)              (None, 8, 8, 80)     0           ['block3f_drop[0][0]',           \n",
      "                                                                  'block3e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3g_expand_conv (Conv2D)   (None, 8, 8, 480)    38400       ['block3f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block3g_expand_bn (BatchNormal  (None, 8, 8, 480)   1920        ['block3g_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block3g_expand_activation (Act  (None, 8, 8, 480)   0           ['block3g_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block3g_dwconv (DepthwiseConv2  (None, 8, 8, 480)   12000       ['block3g_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block3g_bn (BatchNormalization  (None, 8, 8, 480)   1920        ['block3g_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3g_activation (Activation  (None, 8, 8, 480)   0           ['block3g_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block3g_se_squeeze (GlobalAver  (None, 480)         0           ['block3g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block3g_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block3g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block3g_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block3g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block3g_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block3g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block3g_se_excite (Multiply)   (None, 8, 8, 480)    0           ['block3g_activation[0][0]',     \n",
      "                                                                  'block3g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block3g_project_conv (Conv2D)  (None, 8, 8, 80)     38400       ['block3g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block3g_project_bn (BatchNorma  (None, 8, 8, 80)    320         ['block3g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block3g_drop (Dropout)         (None, 8, 8, 80)     0           ['block3g_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block3g_add (Add)              (None, 8, 8, 80)     0           ['block3g_drop[0][0]',           \n",
      "                                                                  'block3f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4a_expand_conv (Conv2D)   (None, 8, 8, 480)    38400       ['block3g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4a_expand_bn (BatchNormal  (None, 8, 8, 480)   1920        ['block4a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4a_expand_activation (Act  (None, 8, 8, 480)   0           ['block4a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4a_dwconv_pad (ZeroPaddin  (None, 9, 9, 480)   0           ['block4a_expand_activation[0][0]\n",
      " g2D)                                                            ']                               \n",
      "                                                                                                  \n",
      " block4a_dwconv (DepthwiseConv2  (None, 4, 4, 480)   4320        ['block4a_dwconv_pad[0][0]']     \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block4a_bn (BatchNormalization  (None, 4, 4, 480)   1920        ['block4a_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4a_activation (Activation  (None, 4, 4, 480)   0           ['block4a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4a_se_squeeze (GlobalAver  (None, 480)         0           ['block4a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4a_se_reshape (Reshape)   (None, 1, 1, 480)    0           ['block4a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_se_reduce (Conv2D)     (None, 1, 1, 20)     9620        ['block4a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4a_se_expand (Conv2D)     (None, 1, 1, 480)    10080       ['block4a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_se_excite (Multiply)   (None, 4, 4, 480)    0           ['block4a_activation[0][0]',     \n",
      "                                                                  'block4a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_project_conv (Conv2D)  (None, 4, 4, 160)    76800       ['block4a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4a_project_bn (BatchNorma  (None, 4, 4, 160)   640         ['block4a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4b_expand_conv (Conv2D)   (None, 4, 4, 960)    153600      ['block4a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_expand_bn (BatchNormal  (None, 4, 4, 960)   3840        ['block4b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4b_expand_activation (Act  (None, 4, 4, 960)   0           ['block4b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4b_dwconv (DepthwiseConv2  (None, 4, 4, 960)   8640        ['block4b_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4b_bn (BatchNormalization  (None, 4, 4, 960)   3840        ['block4b_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4b_activation (Activation  (None, 4, 4, 960)   0           ['block4b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4b_se_squeeze (GlobalAver  (None, 960)         0           ['block4b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4b_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_se_excite (Multiply)   (None, 4, 4, 960)    0           ['block4b_activation[0][0]',     \n",
      "                                                                  'block4b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_project_conv (Conv2D)  (None, 4, 4, 160)    153600      ['block4b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4b_project_bn (BatchNorma  (None, 4, 4, 160)   640         ['block4b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4b_drop (Dropout)         (None, 4, 4, 160)    0           ['block4b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4b_add (Add)              (None, 4, 4, 160)    0           ['block4b_drop[0][0]',           \n",
      "                                                                  'block4a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_expand_conv (Conv2D)   (None, 4, 4, 960)    153600      ['block4b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4c_expand_bn (BatchNormal  (None, 4, 4, 960)   3840        ['block4c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4c_expand_activation (Act  (None, 4, 4, 960)   0           ['block4c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4c_dwconv (DepthwiseConv2  (None, 4, 4, 960)   8640        ['block4c_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4c_bn (BatchNormalization  (None, 4, 4, 960)   3840        ['block4c_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4c_activation (Activation  (None, 4, 4, 960)   0           ['block4c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4c_se_squeeze (GlobalAver  (None, 960)         0           ['block4c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4c_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_se_excite (Multiply)   (None, 4, 4, 960)    0           ['block4c_activation[0][0]',     \n",
      "                                                                  'block4c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_project_conv (Conv2D)  (None, 4, 4, 160)    153600      ['block4c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4c_project_bn (BatchNorma  (None, 4, 4, 160)   640         ['block4c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4c_drop (Dropout)         (None, 4, 4, 160)    0           ['block4c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4c_add (Add)              (None, 4, 4, 160)    0           ['block4c_drop[0][0]',           \n",
      "                                                                  'block4b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4d_expand_conv (Conv2D)   (None, 4, 4, 960)    153600      ['block4c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4d_expand_bn (BatchNormal  (None, 4, 4, 960)   3840        ['block4d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4d_expand_activation (Act  (None, 4, 4, 960)   0           ['block4d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4d_dwconv (DepthwiseConv2  (None, 4, 4, 960)   8640        ['block4d_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4d_bn (BatchNormalization  (None, 4, 4, 960)   3840        ['block4d_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4d_activation (Activation  (None, 4, 4, 960)   0           ['block4d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4d_se_squeeze (GlobalAver  (None, 960)         0           ['block4d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4d_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4d_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4d_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4d_se_excite (Multiply)   (None, 4, 4, 960)    0           ['block4d_activation[0][0]',     \n",
      "                                                                  'block4d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4d_project_conv (Conv2D)  (None, 4, 4, 160)    153600      ['block4d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4d_project_bn (BatchNorma  (None, 4, 4, 160)   640         ['block4d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4d_drop (Dropout)         (None, 4, 4, 160)    0           ['block4d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4d_add (Add)              (None, 4, 4, 160)    0           ['block4d_drop[0][0]',           \n",
      "                                                                  'block4c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4e_expand_conv (Conv2D)   (None, 4, 4, 960)    153600      ['block4d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4e_expand_bn (BatchNormal  (None, 4, 4, 960)   3840        ['block4e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4e_expand_activation (Act  (None, 4, 4, 960)   0           ['block4e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4e_dwconv (DepthwiseConv2  (None, 4, 4, 960)   8640        ['block4e_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4e_bn (BatchNormalization  (None, 4, 4, 960)   3840        ['block4e_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4e_activation (Activation  (None, 4, 4, 960)   0           ['block4e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4e_se_squeeze (GlobalAver  (None, 960)         0           ['block4e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4e_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4e_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4e_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4e_se_excite (Multiply)   (None, 4, 4, 960)    0           ['block4e_activation[0][0]',     \n",
      "                                                                  'block4e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4e_project_conv (Conv2D)  (None, 4, 4, 160)    153600      ['block4e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4e_project_bn (BatchNorma  (None, 4, 4, 160)   640         ['block4e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4e_drop (Dropout)         (None, 4, 4, 160)    0           ['block4e_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4e_add (Add)              (None, 4, 4, 160)    0           ['block4e_drop[0][0]',           \n",
      "                                                                  'block4d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4f_expand_conv (Conv2D)   (None, 4, 4, 960)    153600      ['block4e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4f_expand_bn (BatchNormal  (None, 4, 4, 960)   3840        ['block4f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4f_expand_activation (Act  (None, 4, 4, 960)   0           ['block4f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4f_dwconv (DepthwiseConv2  (None, 4, 4, 960)   8640        ['block4f_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4f_bn (BatchNormalization  (None, 4, 4, 960)   3840        ['block4f_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4f_activation (Activation  (None, 4, 4, 960)   0           ['block4f_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4f_se_squeeze (GlobalAver  (None, 960)         0           ['block4f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4f_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4f_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4f_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4f_se_excite (Multiply)   (None, 4, 4, 960)    0           ['block4f_activation[0][0]',     \n",
      "                                                                  'block4f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4f_project_conv (Conv2D)  (None, 4, 4, 160)    153600      ['block4f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4f_project_bn (BatchNorma  (None, 4, 4, 160)   640         ['block4f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4f_drop (Dropout)         (None, 4, 4, 160)    0           ['block4f_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4f_add (Add)              (None, 4, 4, 160)    0           ['block4f_drop[0][0]',           \n",
      "                                                                  'block4e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4g_expand_conv (Conv2D)   (None, 4, 4, 960)    153600      ['block4f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4g_expand_bn (BatchNormal  (None, 4, 4, 960)   3840        ['block4g_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4g_expand_activation (Act  (None, 4, 4, 960)   0           ['block4g_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4g_dwconv (DepthwiseConv2  (None, 4, 4, 960)   8640        ['block4g_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4g_bn (BatchNormalization  (None, 4, 4, 960)   3840        ['block4g_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4g_activation (Activation  (None, 4, 4, 960)   0           ['block4g_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4g_se_squeeze (GlobalAver  (None, 960)         0           ['block4g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4g_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4g_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4g_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4g_se_excite (Multiply)   (None, 4, 4, 960)    0           ['block4g_activation[0][0]',     \n",
      "                                                                  'block4g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4g_project_conv (Conv2D)  (None, 4, 4, 160)    153600      ['block4g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4g_project_bn (BatchNorma  (None, 4, 4, 160)   640         ['block4g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4g_drop (Dropout)         (None, 4, 4, 160)    0           ['block4g_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4g_add (Add)              (None, 4, 4, 160)    0           ['block4g_drop[0][0]',           \n",
      "                                                                  'block4f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4h_expand_conv (Conv2D)   (None, 4, 4, 960)    153600      ['block4g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4h_expand_bn (BatchNormal  (None, 4, 4, 960)   3840        ['block4h_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4h_expand_activation (Act  (None, 4, 4, 960)   0           ['block4h_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4h_dwconv (DepthwiseConv2  (None, 4, 4, 960)   8640        ['block4h_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4h_bn (BatchNormalization  (None, 4, 4, 960)   3840        ['block4h_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4h_activation (Activation  (None, 4, 4, 960)   0           ['block4h_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4h_se_squeeze (GlobalAver  (None, 960)         0           ['block4h_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4h_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4h_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4h_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4h_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4h_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4h_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4h_se_excite (Multiply)   (None, 4, 4, 960)    0           ['block4h_activation[0][0]',     \n",
      "                                                                  'block4h_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4h_project_conv (Conv2D)  (None, 4, 4, 160)    153600      ['block4h_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4h_project_bn (BatchNorma  (None, 4, 4, 160)   640         ['block4h_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4h_drop (Dropout)         (None, 4, 4, 160)    0           ['block4h_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4h_add (Add)              (None, 4, 4, 160)    0           ['block4h_drop[0][0]',           \n",
      "                                                                  'block4g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4i_expand_conv (Conv2D)   (None, 4, 4, 960)    153600      ['block4h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4i_expand_bn (BatchNormal  (None, 4, 4, 960)   3840        ['block4i_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4i_expand_activation (Act  (None, 4, 4, 960)   0           ['block4i_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4i_dwconv (DepthwiseConv2  (None, 4, 4, 960)   8640        ['block4i_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4i_bn (BatchNormalization  (None, 4, 4, 960)   3840        ['block4i_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4i_activation (Activation  (None, 4, 4, 960)   0           ['block4i_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4i_se_squeeze (GlobalAver  (None, 960)         0           ['block4i_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4i_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4i_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4i_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4i_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4i_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4i_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4i_se_excite (Multiply)   (None, 4, 4, 960)    0           ['block4i_activation[0][0]',     \n",
      "                                                                  'block4i_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4i_project_conv (Conv2D)  (None, 4, 4, 160)    153600      ['block4i_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4i_project_bn (BatchNorma  (None, 4, 4, 160)   640         ['block4i_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4i_drop (Dropout)         (None, 4, 4, 160)    0           ['block4i_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4i_add (Add)              (None, 4, 4, 160)    0           ['block4i_drop[0][0]',           \n",
      "                                                                  'block4h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4j_expand_conv (Conv2D)   (None, 4, 4, 960)    153600      ['block4i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block4j_expand_bn (BatchNormal  (None, 4, 4, 960)   3840        ['block4j_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block4j_expand_activation (Act  (None, 4, 4, 960)   0           ['block4j_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block4j_dwconv (DepthwiseConv2  (None, 4, 4, 960)   8640        ['block4j_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block4j_bn (BatchNormalization  (None, 4, 4, 960)   3840        ['block4j_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4j_activation (Activation  (None, 4, 4, 960)   0           ['block4j_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block4j_se_squeeze (GlobalAver  (None, 960)         0           ['block4j_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block4j_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block4j_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block4j_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block4j_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block4j_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block4j_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block4j_se_excite (Multiply)   (None, 4, 4, 960)    0           ['block4j_activation[0][0]',     \n",
      "                                                                  'block4j_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block4j_project_conv (Conv2D)  (None, 4, 4, 160)    153600      ['block4j_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block4j_project_bn (BatchNorma  (None, 4, 4, 160)   640         ['block4j_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block4j_drop (Dropout)         (None, 4, 4, 160)    0           ['block4j_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block4j_add (Add)              (None, 4, 4, 160)    0           ['block4j_drop[0][0]',           \n",
      "                                                                  'block4i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5a_expand_conv (Conv2D)   (None, 4, 4, 960)    153600      ['block4j_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5a_expand_bn (BatchNormal  (None, 4, 4, 960)   3840        ['block5a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5a_expand_activation (Act  (None, 4, 4, 960)   0           ['block5a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5a_dwconv (DepthwiseConv2  (None, 4, 4, 960)   24000       ['block5a_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5a_bn (BatchNormalization  (None, 4, 4, 960)   3840        ['block5a_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5a_activation (Activation  (None, 4, 4, 960)   0           ['block5a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5a_se_squeeze (GlobalAver  (None, 960)         0           ['block5a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5a_se_reshape (Reshape)   (None, 1, 1, 960)    0           ['block5a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5a_se_reduce (Conv2D)     (None, 1, 1, 40)     38440       ['block5a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5a_se_expand (Conv2D)     (None, 1, 1, 960)    39360       ['block5a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_se_excite (Multiply)   (None, 4, 4, 960)    0           ['block5a_activation[0][0]',     \n",
      "                                                                  'block5a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_project_conv (Conv2D)  (None, 4, 4, 224)    215040      ['block5a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5a_project_bn (BatchNorma  (None, 4, 4, 224)   896         ['block5a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5b_expand_conv (Conv2D)   (None, 4, 4, 1344)   301056      ['block5a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_expand_bn (BatchNormal  (None, 4, 4, 1344)  5376        ['block5b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5b_expand_activation (Act  (None, 4, 4, 1344)  0           ['block5b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5b_dwconv (DepthwiseConv2  (None, 4, 4, 1344)  33600       ['block5b_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5b_bn (BatchNormalization  (None, 4, 4, 1344)  5376        ['block5b_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5b_activation (Activation  (None, 4, 4, 1344)  0           ['block5b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5b_se_squeeze (GlobalAver  (None, 1344)        0           ['block5b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5b_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_se_excite (Multiply)   (None, 4, 4, 1344)   0           ['block5b_activation[0][0]',     \n",
      "                                                                  'block5b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_project_conv (Conv2D)  (None, 4, 4, 224)    301056      ['block5b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5b_project_bn (BatchNorma  (None, 4, 4, 224)   896         ['block5b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5b_drop (Dropout)         (None, 4, 4, 224)    0           ['block5b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5b_add (Add)              (None, 4, 4, 224)    0           ['block5b_drop[0][0]',           \n",
      "                                                                  'block5a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_expand_conv (Conv2D)   (None, 4, 4, 1344)   301056      ['block5b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5c_expand_bn (BatchNormal  (None, 4, 4, 1344)  5376        ['block5c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5c_expand_activation (Act  (None, 4, 4, 1344)  0           ['block5c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5c_dwconv (DepthwiseConv2  (None, 4, 4, 1344)  33600       ['block5c_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5c_bn (BatchNormalization  (None, 4, 4, 1344)  5376        ['block5c_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5c_activation (Activation  (None, 4, 4, 1344)  0           ['block5c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5c_se_squeeze (GlobalAver  (None, 1344)        0           ['block5c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5c_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_se_excite (Multiply)   (None, 4, 4, 1344)   0           ['block5c_activation[0][0]',     \n",
      "                                                                  'block5c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_project_conv (Conv2D)  (None, 4, 4, 224)    301056      ['block5c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5c_project_bn (BatchNorma  (None, 4, 4, 224)   896         ['block5c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5c_drop (Dropout)         (None, 4, 4, 224)    0           ['block5c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5c_add (Add)              (None, 4, 4, 224)    0           ['block5c_drop[0][0]',           \n",
      "                                                                  'block5b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5d_expand_conv (Conv2D)   (None, 4, 4, 1344)   301056      ['block5c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5d_expand_bn (BatchNormal  (None, 4, 4, 1344)  5376        ['block5d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5d_expand_activation (Act  (None, 4, 4, 1344)  0           ['block5d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5d_dwconv (DepthwiseConv2  (None, 4, 4, 1344)  33600       ['block5d_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5d_bn (BatchNormalization  (None, 4, 4, 1344)  5376        ['block5d_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5d_activation (Activation  (None, 4, 4, 1344)  0           ['block5d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5d_se_squeeze (GlobalAver  (None, 1344)        0           ['block5d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5d_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5d_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5d_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_se_excite (Multiply)   (None, 4, 4, 1344)   0           ['block5d_activation[0][0]',     \n",
      "                                                                  'block5d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_project_conv (Conv2D)  (None, 4, 4, 224)    301056      ['block5d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5d_project_bn (BatchNorma  (None, 4, 4, 224)   896         ['block5d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5d_drop (Dropout)         (None, 4, 4, 224)    0           ['block5d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5d_add (Add)              (None, 4, 4, 224)    0           ['block5d_drop[0][0]',           \n",
      "                                                                  'block5c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5e_expand_conv (Conv2D)   (None, 4, 4, 1344)   301056      ['block5d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5e_expand_bn (BatchNormal  (None, 4, 4, 1344)  5376        ['block5e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5e_expand_activation (Act  (None, 4, 4, 1344)  0           ['block5e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5e_dwconv (DepthwiseConv2  (None, 4, 4, 1344)  33600       ['block5e_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5e_bn (BatchNormalization  (None, 4, 4, 1344)  5376        ['block5e_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5e_activation (Activation  (None, 4, 4, 1344)  0           ['block5e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5e_se_squeeze (GlobalAver  (None, 1344)        0           ['block5e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5e_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5e_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5e_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_se_excite (Multiply)   (None, 4, 4, 1344)   0           ['block5e_activation[0][0]',     \n",
      "                                                                  'block5e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_project_conv (Conv2D)  (None, 4, 4, 224)    301056      ['block5e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5e_project_bn (BatchNorma  (None, 4, 4, 224)   896         ['block5e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5e_drop (Dropout)         (None, 4, 4, 224)    0           ['block5e_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5e_add (Add)              (None, 4, 4, 224)    0           ['block5e_drop[0][0]',           \n",
      "                                                                  'block5d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5f_expand_conv (Conv2D)   (None, 4, 4, 1344)   301056      ['block5e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5f_expand_bn (BatchNormal  (None, 4, 4, 1344)  5376        ['block5f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5f_expand_activation (Act  (None, 4, 4, 1344)  0           ['block5f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5f_dwconv (DepthwiseConv2  (None, 4, 4, 1344)  33600       ['block5f_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5f_bn (BatchNormalization  (None, 4, 4, 1344)  5376        ['block5f_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5f_activation (Activation  (None, 4, 4, 1344)  0           ['block5f_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5f_se_squeeze (GlobalAver  (None, 1344)        0           ['block5f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5f_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5f_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5f_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5f_se_excite (Multiply)   (None, 4, 4, 1344)   0           ['block5f_activation[0][0]',     \n",
      "                                                                  'block5f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5f_project_conv (Conv2D)  (None, 4, 4, 224)    301056      ['block5f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5f_project_bn (BatchNorma  (None, 4, 4, 224)   896         ['block5f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5f_drop (Dropout)         (None, 4, 4, 224)    0           ['block5f_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5f_add (Add)              (None, 4, 4, 224)    0           ['block5f_drop[0][0]',           \n",
      "                                                                  'block5e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5g_expand_conv (Conv2D)   (None, 4, 4, 1344)   301056      ['block5f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5g_expand_bn (BatchNormal  (None, 4, 4, 1344)  5376        ['block5g_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5g_expand_activation (Act  (None, 4, 4, 1344)  0           ['block5g_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5g_dwconv (DepthwiseConv2  (None, 4, 4, 1344)  33600       ['block5g_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5g_bn (BatchNormalization  (None, 4, 4, 1344)  5376        ['block5g_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5g_activation (Activation  (None, 4, 4, 1344)  0           ['block5g_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5g_se_squeeze (GlobalAver  (None, 1344)        0           ['block5g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5g_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5g_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5g_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5g_se_excite (Multiply)   (None, 4, 4, 1344)   0           ['block5g_activation[0][0]',     \n",
      "                                                                  'block5g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5g_project_conv (Conv2D)  (None, 4, 4, 224)    301056      ['block5g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5g_project_bn (BatchNorma  (None, 4, 4, 224)   896         ['block5g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5g_drop (Dropout)         (None, 4, 4, 224)    0           ['block5g_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5g_add (Add)              (None, 4, 4, 224)    0           ['block5g_drop[0][0]',           \n",
      "                                                                  'block5f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5h_expand_conv (Conv2D)   (None, 4, 4, 1344)   301056      ['block5g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5h_expand_bn (BatchNormal  (None, 4, 4, 1344)  5376        ['block5h_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5h_expand_activation (Act  (None, 4, 4, 1344)  0           ['block5h_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5h_dwconv (DepthwiseConv2  (None, 4, 4, 1344)  33600       ['block5h_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5h_bn (BatchNormalization  (None, 4, 4, 1344)  5376        ['block5h_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5h_activation (Activation  (None, 4, 4, 1344)  0           ['block5h_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5h_se_squeeze (GlobalAver  (None, 1344)        0           ['block5h_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5h_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5h_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5h_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5h_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5h_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5h_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5h_se_excite (Multiply)   (None, 4, 4, 1344)   0           ['block5h_activation[0][0]',     \n",
      "                                                                  'block5h_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5h_project_conv (Conv2D)  (None, 4, 4, 224)    301056      ['block5h_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5h_project_bn (BatchNorma  (None, 4, 4, 224)   896         ['block5h_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5h_drop (Dropout)         (None, 4, 4, 224)    0           ['block5h_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5h_add (Add)              (None, 4, 4, 224)    0           ['block5h_drop[0][0]',           \n",
      "                                                                  'block5g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5i_expand_conv (Conv2D)   (None, 4, 4, 1344)   301056      ['block5h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5i_expand_bn (BatchNormal  (None, 4, 4, 1344)  5376        ['block5i_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5i_expand_activation (Act  (None, 4, 4, 1344)  0           ['block5i_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5i_dwconv (DepthwiseConv2  (None, 4, 4, 1344)  33600       ['block5i_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5i_bn (BatchNormalization  (None, 4, 4, 1344)  5376        ['block5i_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5i_activation (Activation  (None, 4, 4, 1344)  0           ['block5i_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5i_se_squeeze (GlobalAver  (None, 1344)        0           ['block5i_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5i_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5i_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5i_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5i_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5i_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5i_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5i_se_excite (Multiply)   (None, 4, 4, 1344)   0           ['block5i_activation[0][0]',     \n",
      "                                                                  'block5i_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5i_project_conv (Conv2D)  (None, 4, 4, 224)    301056      ['block5i_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5i_project_bn (BatchNorma  (None, 4, 4, 224)   896         ['block5i_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5i_drop (Dropout)         (None, 4, 4, 224)    0           ['block5i_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5i_add (Add)              (None, 4, 4, 224)    0           ['block5i_drop[0][0]',           \n",
      "                                                                  'block5h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5j_expand_conv (Conv2D)   (None, 4, 4, 1344)   301056      ['block5i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block5j_expand_bn (BatchNormal  (None, 4, 4, 1344)  5376        ['block5j_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block5j_expand_activation (Act  (None, 4, 4, 1344)  0           ['block5j_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block5j_dwconv (DepthwiseConv2  (None, 4, 4, 1344)  33600       ['block5j_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block5j_bn (BatchNormalization  (None, 4, 4, 1344)  5376        ['block5j_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5j_activation (Activation  (None, 4, 4, 1344)  0           ['block5j_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block5j_se_squeeze (GlobalAver  (None, 1344)        0           ['block5j_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block5j_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block5j_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block5j_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block5j_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block5j_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block5j_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block5j_se_excite (Multiply)   (None, 4, 4, 1344)   0           ['block5j_activation[0][0]',     \n",
      "                                                                  'block5j_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block5j_project_conv (Conv2D)  (None, 4, 4, 224)    301056      ['block5j_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block5j_project_bn (BatchNorma  (None, 4, 4, 224)   896         ['block5j_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block5j_drop (Dropout)         (None, 4, 4, 224)    0           ['block5j_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block5j_add (Add)              (None, 4, 4, 224)    0           ['block5j_drop[0][0]',           \n",
      "                                                                  'block5i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6a_expand_conv (Conv2D)   (None, 4, 4, 1344)   301056      ['block5j_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6a_expand_bn (BatchNormal  (None, 4, 4, 1344)  5376        ['block6a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6a_expand_activation (Act  (None, 4, 4, 1344)  0           ['block6a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6a_dwconv_pad (ZeroPaddin  (None, 7, 7, 1344)  0           ['block6a_expand_activation[0][0]\n",
      " g2D)                                                            ']                               \n",
      "                                                                                                  \n",
      " block6a_dwconv (DepthwiseConv2  (None, 2, 2, 1344)  33600       ['block6a_dwconv_pad[0][0]']     \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " block6a_bn (BatchNormalization  (None, 2, 2, 1344)  5376        ['block6a_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6a_activation (Activation  (None, 2, 2, 1344)  0           ['block6a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6a_se_squeeze (GlobalAver  (None, 1344)        0           ['block6a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6a_se_reshape (Reshape)   (None, 1, 1, 1344)   0           ['block6a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6a_se_reduce (Conv2D)     (None, 1, 1, 56)     75320       ['block6a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6a_se_expand (Conv2D)     (None, 1, 1, 1344)   76608       ['block6a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_se_excite (Multiply)   (None, 2, 2, 1344)   0           ['block6a_activation[0][0]',     \n",
      "                                                                  'block6a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_project_conv (Conv2D)  (None, 2, 2, 384)    516096      ['block6a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6a_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6b_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6b_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6b_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6b_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6b_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6b_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6b_activation (Activation  (None, 2, 2, 2304)  0           ['block6b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6b_se_squeeze (GlobalAver  (None, 2304)        0           ['block6b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6b_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6b_activation[0][0]',     \n",
      "                                                                  'block6b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6b_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6b_drop (Dropout)         (None, 2, 2, 384)    0           ['block6b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6b_add (Add)              (None, 2, 2, 384)    0           ['block6b_drop[0][0]',           \n",
      "                                                                  'block6a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6c_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6c_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6c_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6c_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6c_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6c_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6c_activation (Activation  (None, 2, 2, 2304)  0           ['block6c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6c_se_squeeze (GlobalAver  (None, 2304)        0           ['block6c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6c_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6c_activation[0][0]',     \n",
      "                                                                  'block6c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6c_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6c_drop (Dropout)         (None, 2, 2, 384)    0           ['block6c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6c_add (Add)              (None, 2, 2, 384)    0           ['block6c_drop[0][0]',           \n",
      "                                                                  'block6b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6d_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6d_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6d_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6d_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6d_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6d_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6d_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6d_activation (Activation  (None, 2, 2, 2304)  0           ['block6d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6d_se_squeeze (GlobalAver  (None, 2304)        0           ['block6d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6d_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6d_activation[0][0]',     \n",
      "                                                                  'block6d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6d_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6d_drop (Dropout)         (None, 2, 2, 384)    0           ['block6d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6d_add (Add)              (None, 2, 2, 384)    0           ['block6d_drop[0][0]',           \n",
      "                                                                  'block6c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6e_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6e_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6e_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6e_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6e_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6e_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6e_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6e_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6e_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6e_activation (Activation  (None, 2, 2, 2304)  0           ['block6e_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6e_se_squeeze (GlobalAver  (None, 2304)        0           ['block6e_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6e_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6e_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6e_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6e_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6e_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6e_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6e_activation[0][0]',     \n",
      "                                                                  'block6e_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6e_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6e_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6e_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6e_drop (Dropout)         (None, 2, 2, 384)    0           ['block6e_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6e_add (Add)              (None, 2, 2, 384)    0           ['block6e_drop[0][0]',           \n",
      "                                                                  'block6d_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6f_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6f_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6f_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6f_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6f_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6f_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6f_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6f_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6f_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6f_activation (Activation  (None, 2, 2, 2304)  0           ['block6f_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6f_se_squeeze (GlobalAver  (None, 2304)        0           ['block6f_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6f_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6f_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6f_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6f_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6f_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6f_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6f_activation[0][0]',     \n",
      "                                                                  'block6f_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6f_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6f_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6f_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6f_drop (Dropout)         (None, 2, 2, 384)    0           ['block6f_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6f_add (Add)              (None, 2, 2, 384)    0           ['block6f_drop[0][0]',           \n",
      "                                                                  'block6e_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6g_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6g_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6g_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6g_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6g_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6g_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6g_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6g_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6g_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6g_activation (Activation  (None, 2, 2, 2304)  0           ['block6g_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6g_se_squeeze (GlobalAver  (None, 2304)        0           ['block6g_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6g_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6g_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6g_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6g_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6g_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6g_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6g_activation[0][0]',     \n",
      "                                                                  'block6g_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6g_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6g_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6g_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6g_drop (Dropout)         (None, 2, 2, 384)    0           ['block6g_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6g_add (Add)              (None, 2, 2, 384)    0           ['block6g_drop[0][0]',           \n",
      "                                                                  'block6f_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6h_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6h_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6h_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6h_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6h_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6h_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6h_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6h_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6h_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6h_activation (Activation  (None, 2, 2, 2304)  0           ['block6h_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6h_se_squeeze (GlobalAver  (None, 2304)        0           ['block6h_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6h_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6h_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6h_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6h_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6h_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6h_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6h_activation[0][0]',     \n",
      "                                                                  'block6h_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6h_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6h_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6h_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6h_drop (Dropout)         (None, 2, 2, 384)    0           ['block6h_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6h_add (Add)              (None, 2, 2, 384)    0           ['block6h_drop[0][0]',           \n",
      "                                                                  'block6g_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6i_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6i_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6i_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6i_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6i_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6i_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6i_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6i_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6i_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6i_activation (Activation  (None, 2, 2, 2304)  0           ['block6i_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6i_se_squeeze (GlobalAver  (None, 2304)        0           ['block6i_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6i_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6i_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6i_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6i_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6i_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6i_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6i_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6i_activation[0][0]',     \n",
      "                                                                  'block6i_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6i_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6i_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6i_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6i_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6i_drop (Dropout)         (None, 2, 2, 384)    0           ['block6i_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6i_add (Add)              (None, 2, 2, 384)    0           ['block6i_drop[0][0]',           \n",
      "                                                                  'block6h_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6j_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6j_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6j_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6j_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6j_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6j_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6j_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6j_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6j_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6j_activation (Activation  (None, 2, 2, 2304)  0           ['block6j_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6j_se_squeeze (GlobalAver  (None, 2304)        0           ['block6j_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6j_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6j_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6j_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6j_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6j_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6j_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6j_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6j_activation[0][0]',     \n",
      "                                                                  'block6j_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6j_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6j_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6j_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6j_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6j_drop (Dropout)         (None, 2, 2, 384)    0           ['block6j_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6j_add (Add)              (None, 2, 2, 384)    0           ['block6j_drop[0][0]',           \n",
      "                                                                  'block6i_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6k_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6j_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6k_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6k_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6k_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6k_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6k_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6k_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6k_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6k_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6k_activation (Activation  (None, 2, 2, 2304)  0           ['block6k_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6k_se_squeeze (GlobalAver  (None, 2304)        0           ['block6k_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6k_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6k_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6k_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6k_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6k_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6k_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6k_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6k_activation[0][0]',     \n",
      "                                                                  'block6k_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6k_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6k_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6k_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6k_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6k_drop (Dropout)         (None, 2, 2, 384)    0           ['block6k_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6k_add (Add)              (None, 2, 2, 384)    0           ['block6k_drop[0][0]',           \n",
      "                                                                  'block6j_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6l_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6k_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6l_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6l_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6l_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6l_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6l_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6l_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6l_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6l_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6l_activation (Activation  (None, 2, 2, 2304)  0           ['block6l_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6l_se_squeeze (GlobalAver  (None, 2304)        0           ['block6l_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6l_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6l_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6l_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6l_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6l_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6l_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6l_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6l_activation[0][0]',     \n",
      "                                                                  'block6l_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6l_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6l_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6l_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6l_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6l_drop (Dropout)         (None, 2, 2, 384)    0           ['block6l_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6l_add (Add)              (None, 2, 2, 384)    0           ['block6l_drop[0][0]',           \n",
      "                                                                  'block6k_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6m_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6l_add[0][0]']            \n",
      "                                                                                                  \n",
      " block6m_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block6m_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block6m_expand_activation (Act  (None, 2, 2, 2304)  0           ['block6m_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block6m_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  57600       ['block6m_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block6m_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block6m_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6m_activation (Activation  (None, 2, 2, 2304)  0           ['block6m_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block6m_se_squeeze (GlobalAver  (None, 2304)        0           ['block6m_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block6m_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block6m_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block6m_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block6m_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block6m_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block6m_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block6m_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block6m_activation[0][0]',     \n",
      "                                                                  'block6m_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block6m_project_conv (Conv2D)  (None, 2, 2, 384)    884736      ['block6m_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block6m_project_bn (BatchNorma  (None, 2, 2, 384)   1536        ['block6m_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block6m_drop (Dropout)         (None, 2, 2, 384)    0           ['block6m_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block6m_add (Add)              (None, 2, 2, 384)    0           ['block6m_drop[0][0]',           \n",
      "                                                                  'block6l_add[0][0]']            \n",
      "                                                                                                  \n",
      " block7a_expand_conv (Conv2D)   (None, 2, 2, 2304)   884736      ['block6m_add[0][0]']            \n",
      "                                                                                                  \n",
      " block7a_expand_bn (BatchNormal  (None, 2, 2, 2304)  9216        ['block7a_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block7a_expand_activation (Act  (None, 2, 2, 2304)  0           ['block7a_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block7a_dwconv (DepthwiseConv2  (None, 2, 2, 2304)  20736       ['block7a_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block7a_bn (BatchNormalization  (None, 2, 2, 2304)  9216        ['block7a_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block7a_activation (Activation  (None, 2, 2, 2304)  0           ['block7a_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block7a_se_squeeze (GlobalAver  (None, 2304)        0           ['block7a_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block7a_se_reshape (Reshape)   (None, 1, 1, 2304)   0           ['block7a_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block7a_se_reduce (Conv2D)     (None, 1, 1, 96)     221280      ['block7a_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block7a_se_expand (Conv2D)     (None, 1, 1, 2304)   223488      ['block7a_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block7a_se_excite (Multiply)   (None, 2, 2, 2304)   0           ['block7a_activation[0][0]',     \n",
      "                                                                  'block7a_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block7a_project_conv (Conv2D)  (None, 2, 2, 640)    1474560     ['block7a_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block7a_project_bn (BatchNorma  (None, 2, 2, 640)   2560        ['block7a_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block7b_expand_conv (Conv2D)   (None, 2, 2, 3840)   2457600     ['block7a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block7b_expand_bn (BatchNormal  (None, 2, 2, 3840)  15360       ['block7b_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block7b_expand_activation (Act  (None, 2, 2, 3840)  0           ['block7b_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block7b_dwconv (DepthwiseConv2  (None, 2, 2, 3840)  34560       ['block7b_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block7b_bn (BatchNormalization  (None, 2, 2, 3840)  15360       ['block7b_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block7b_activation (Activation  (None, 2, 2, 3840)  0           ['block7b_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block7b_se_squeeze (GlobalAver  (None, 3840)        0           ['block7b_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block7b_se_reshape (Reshape)   (None, 1, 1, 3840)   0           ['block7b_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block7b_se_reduce (Conv2D)     (None, 1, 1, 160)    614560      ['block7b_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block7b_se_expand (Conv2D)     (None, 1, 1, 3840)   618240      ['block7b_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block7b_se_excite (Multiply)   (None, 2, 2, 3840)   0           ['block7b_activation[0][0]',     \n",
      "                                                                  'block7b_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block7b_project_conv (Conv2D)  (None, 2, 2, 640)    2457600     ['block7b_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block7b_project_bn (BatchNorma  (None, 2, 2, 640)   2560        ['block7b_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block7b_drop (Dropout)         (None, 2, 2, 640)    0           ['block7b_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block7b_add (Add)              (None, 2, 2, 640)    0           ['block7b_drop[0][0]',           \n",
      "                                                                  'block7a_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block7c_expand_conv (Conv2D)   (None, 2, 2, 3840)   2457600     ['block7b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block7c_expand_bn (BatchNormal  (None, 2, 2, 3840)  15360       ['block7c_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block7c_expand_activation (Act  (None, 2, 2, 3840)  0           ['block7c_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block7c_dwconv (DepthwiseConv2  (None, 2, 2, 3840)  34560       ['block7c_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block7c_bn (BatchNormalization  (None, 2, 2, 3840)  15360       ['block7c_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block7c_activation (Activation  (None, 2, 2, 3840)  0           ['block7c_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block7c_se_squeeze (GlobalAver  (None, 3840)        0           ['block7c_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block7c_se_reshape (Reshape)   (None, 1, 1, 3840)   0           ['block7c_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block7c_se_reduce (Conv2D)     (None, 1, 1, 160)    614560      ['block7c_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block7c_se_expand (Conv2D)     (None, 1, 1, 3840)   618240      ['block7c_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block7c_se_excite (Multiply)   (None, 2, 2, 3840)   0           ['block7c_activation[0][0]',     \n",
      "                                                                  'block7c_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block7c_project_conv (Conv2D)  (None, 2, 2, 640)    2457600     ['block7c_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block7c_project_bn (BatchNorma  (None, 2, 2, 640)   2560        ['block7c_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block7c_drop (Dropout)         (None, 2, 2, 640)    0           ['block7c_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block7c_add (Add)              (None, 2, 2, 640)    0           ['block7c_drop[0][0]',           \n",
      "                                                                  'block7b_add[0][0]']            \n",
      "                                                                                                  \n",
      " block7d_expand_conv (Conv2D)   (None, 2, 2, 3840)   2457600     ['block7c_add[0][0]']            \n",
      "                                                                                                  \n",
      " block7d_expand_bn (BatchNormal  (None, 2, 2, 3840)  15360       ['block7d_expand_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " block7d_expand_activation (Act  (None, 2, 2, 3840)  0           ['block7d_expand_bn[0][0]']      \n",
      " ivation)                                                                                         \n",
      "                                                                                                  \n",
      " block7d_dwconv (DepthwiseConv2  (None, 2, 2, 3840)  34560       ['block7d_expand_activation[0][0]\n",
      " D)                                                              ']                               \n",
      "                                                                                                  \n",
      " block7d_bn (BatchNormalization  (None, 2, 2, 3840)  15360       ['block7d_dwconv[0][0]']         \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block7d_activation (Activation  (None, 2, 2, 3840)  0           ['block7d_bn[0][0]']             \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " block7d_se_squeeze (GlobalAver  (None, 3840)        0           ['block7d_activation[0][0]']     \n",
      " agePooling2D)                                                                                    \n",
      "                                                                                                  \n",
      " block7d_se_reshape (Reshape)   (None, 1, 1, 3840)   0           ['block7d_se_squeeze[0][0]']     \n",
      "                                                                                                  \n",
      " block7d_se_reduce (Conv2D)     (None, 1, 1, 160)    614560      ['block7d_se_reshape[0][0]']     \n",
      "                                                                                                  \n",
      " block7d_se_expand (Conv2D)     (None, 1, 1, 3840)   618240      ['block7d_se_reduce[0][0]']      \n",
      "                                                                                                  \n",
      " block7d_se_excite (Multiply)   (None, 2, 2, 3840)   0           ['block7d_activation[0][0]',     \n",
      "                                                                  'block7d_se_expand[0][0]']      \n",
      "                                                                                                  \n",
      " block7d_project_conv (Conv2D)  (None, 2, 2, 640)    2457600     ['block7d_se_excite[0][0]']      \n",
      "                                                                                                  \n",
      " block7d_project_bn (BatchNorma  (None, 2, 2, 640)   2560        ['block7d_project_conv[0][0]']   \n",
      " lization)                                                                                        \n",
      "                                                                                                  \n",
      " block7d_drop (Dropout)         (None, 2, 2, 640)    0           ['block7d_project_bn[0][0]']     \n",
      "                                                                                                  \n",
      " block7d_add (Add)              (None, 2, 2, 640)    0           ['block7d_drop[0][0]',           \n",
      "                                                                  'block7c_add[0][0]']            \n",
      "                                                                                                  \n",
      " top_conv (Conv2D)              (None, 2, 2, 2560)   1638400     ['block7d_add[0][0]']            \n",
      "                                                                                                  \n",
      " top_bn (BatchNormalization)    (None, 2, 2, 2560)   10240       ['top_conv[0][0]']               \n",
      "                                                                                                  \n",
      " top_activation (Activation)    (None, 2, 2, 2560)   0           ['top_bn[0][0]']                 \n",
      "                                                                                                  \n",
      " global_average_pooling2d_5 (Gl  (None, 2560)        0           ['top_activation[0][0]']         \n",
      " obalAveragePooling2D)                                                                            \n",
      "                                                                                                  \n",
      " dense_12 (Dense)               (None, 256)          655616      ['global_average_pooling2d_5[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " dense_13 (Dense)               (None, 2)            514         ['dense_12[0][0]']               \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 64,753,817\n",
      "Trainable params: 656,130\n",
      "Non-trainable params: 64,097,687\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.applications import EfficientNetB7\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "# Build the EfficientNetB7 model\n",
    "base_model_ENB7 = EfficientNetB7(weights=\"imagenet\", include_top=False, input_shape=[64, 64] + [3])\n",
    "x1 = base_model_ENB7.output\n",
    "x1 = GlobalAveragePooling2D()(x1)\n",
    "x1 = Dense(256, activation=\"relu\")(x1)\n",
    "predictions = Dense(num_classes, activation=\"softmax\")(x1)\n",
    "model1 = Model(inputs=base_model_ENB7.input, outputs=predictions)\n",
    "\n",
    "# Freeze the layers in the base model\n",
    "for layer in base_model_ENB7.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# Compile the model\n",
    "model1.compile(optimizer=Adam(lr=1e-4), loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "\n",
    "# Print the model summary\n",
    "model1.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "301/301 [==============================] - 120s 330ms/step - loss: 0.6995 - accuracy: 0.4989 - val_loss: 0.6936 - val_accuracy: 0.5010\n",
      "Epoch 2/10\n",
      "301/301 [==============================] - 94s 313ms/step - loss: 0.6937 - accuracy: 0.4949 - val_loss: 0.6931 - val_accuracy: 0.5481\n",
      "Epoch 3/10\n",
      "301/301 [==============================] - 94s 313ms/step - loss: 0.6934 - accuracy: 0.4994 - val_loss: 0.6934 - val_accuracy: 0.4985\n",
      "Epoch 4/10\n",
      "301/301 [==============================] - 94s 312ms/step - loss: 0.6934 - accuracy: 0.4965 - val_loss: 0.6931 - val_accuracy: 0.4983\n",
      "Epoch 5/10\n",
      "301/301 [==============================] - 94s 312ms/step - loss: 0.6933 - accuracy: 0.4984 - val_loss: 0.6932 - val_accuracy: 0.4963\n",
      "Epoch 6/10\n",
      "301/301 [==============================] - 94s 312ms/step - loss: 0.6932 - accuracy: 0.4976 - val_loss: 0.6940 - val_accuracy: 0.4118\n",
      "Epoch 7/10\n",
      "301/301 [==============================] - 94s 312ms/step - loss: 0.6934 - accuracy: 0.4964 - val_loss: 0.6932 - val_accuracy: 0.4985\n",
      "Epoch 8/10\n",
      "301/301 [==============================] - 94s 312ms/step - loss: 0.6932 - accuracy: 0.4920 - val_loss: 0.6931 - val_accuracy: 0.4970\n",
      "Epoch 9/10\n",
      "301/301 [==============================] - 94s 312ms/step - loss: 0.6932 - accuracy: 0.4979 - val_loss: 0.6933 - val_accuracy: 0.4987\n",
      "Epoch 10/10\n",
      "301/301 [==============================] - 94s 312ms/step - loss: 0.6932 - accuracy: 0.5014 - val_loss: 0.6932 - val_accuracy: 0.4988\n"
     ]
    }
   ],
   "source": [
    "history1 = model1.fit(train_generator,\n",
    "                    steps_per_epoch = len(X_train)/64,\n",
    "                    epochs = 10,\n",
    "                    shuffle = False,\n",
    "                    validation_data=(X_test, y_test), validation_steps=len(test_generator))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/hqW9e/dSVlbGsGHDvMciIiJITU1l586dtcpv2bKFiIgILrvsshrlN2/ezPDhwzlx4gTFxcUMHjy4xvP69OnDp59+itPp5NChQ3Tv3p2YmBjv4x07diQ6Otrb+9QSBQcG0CEqGNDmlCIi0r643W6/vn6AP188NzcX8ISV08XHx3Ps2LFa5TMzM0lKSmLjxo0sXbqUvLw8UlNTmTlzJj169CAyMhKLxVLruTk5OTgcDkpKSoiLi6OgoACn04nJZALAarVSXFzM8ePHG3wtbrcbm83W4OefTfVcqvLycjp3CKGwqJz9RwrplhDk89eSczu9PcT/1B4ti9qj8SoqKnC5XDidTpxOp/e42+3G7Ti/ffY8z6nEaXDjMhgaXCeDORDDeT7/rrvu8nZ6bN++nfXr17Nw4ULeffddSktL6dWrF1OnTmXkyJEAOJ1O/vSnP/Hee+9x/PhxunTpwvjx47n99ttZu3Ytjz32GAC9e/fm5ZdfZsiQIfWqh9PpxOVyUV5ejsvlqvW42+2u17X5NSxV/0JZLJYaxwMDAykuLq5V3mq1kpWVxeLFi5k+fToREREsWbKEcePGsX79emJjYxk7dixLliwhNTWVfv36sX37dtasWQN4hv2uu+46XnzxRebMmcNDDz2E0+nkySefxGAwnHWeVH04HI4aE819LTMzk2CT5xflq71H6BSquUv+lJmZ6e8qyGnUHi2L2qNxAgICqKj4Lhi53W5OvjUbx7Fv/VIfc6deRP/08fMKTH/84x+5//77SUhIYObMmcycOZMDBw7w1FNPkZCQwEcffcSvfvUr74KtN954gw0bNjB37lzi4uL46KOP+MMf/kC3bt24/PLLefjhh5k/fz4bN24kMjISu91er3pUVFRQVVXFwYMHz1rm+xnkTPwaloKCPL0jlZWV3u/Bc3HBwcG1ypvNZkpLS0lPT6dHjx4ApKenM3r0aNauXcvEiROZOXMmFRUVjBs3DoBevXoxadIk5s2bR3h4OAkJCSxcuJDf//73vPbaawQFBTF+/HguvvhiwsLCGnwtZrOZnj17Nvj5Z1NeXk5mZibJycn0KzvO//btodwZSJ8+fXz+WnJup7fHmX5GpXmpPVoWtUfjVVRUcPToUQIDA72fi263G6PJf7NmjEYjQUFB5xWWgoKCCAwMJDQ0lMrKSjZs2MBbb73FxRdfDMCFF17IgQMHePXVV7n66qs5duwYISEhdO/enbi4OLp3786FF17IhRdeSFRUFNHR0QB06dLlvOsfEBBA165dCQwMrPXY/v3763eO835VH6oefsvPz6dr167e4/n5+aSkpNQqn5iYSEBAgDcogadBkpKSyM7OBiAsLIx58+Yxe/Zs77Dba6+9RlxcHCEhIQCMHj2aDz/8kIKCAsLDwwkKCmLEiBHccsstDb4Wg8HgPX9TCA4OpmdSBwByCsqa9LXk3IKDg9UGLYjao2VRezSc0WjEaDRiMpm8U0UAOk34v/MehnM6nVRUVBAYGFjjXOerIcNw4PlcNBgM7Nu3D4Cf//znNR53OBxERERgMplIS0tj06ZNXH755Vx88cWMHDmSa665hvj4eMDzvgDnfR0mkwmj0UhwcHCNTpnT61gffg1LKSkphIWFsX37dm9YKikpISMjg7S0tFrlBw0aRFVVFbt27aJv374A2O12jhw5wnXXXQfAY489Rv/+/fnZz35GXFwcABs2bGDUqFEAfPrpp6Snp7Ny5Urv4zt27ODkyZOMGDGiya+5MZISPD1fhcV2bHYHIUFmP9dIRESag8FgwGA5v7mqbqcTgwuMliCMjQhLjVU9Ofu1114jNDS0xmPVISg5OZmNGzeyY8cOPv74YzZt2sSLL77I3Llzufnmm5u9zt/n19VwFouFtLQ05s+fz6ZNm9i7dy8PPvggiYmJXH311TidTgoKCrxjk4MGDWLEiBHMmDGDTz75hP379zN9+nRMJhNjx44FPL1VCxYsYNu2bWRlZfHkk0+SkZHB5MmTAejRowfffvstc+bM4ciRI2zdupWHHnqI22+/naSkJL+9F/URFmIhJsLTjaj9lkREpDXo1asX4Bk16tatm/fP22+/7Z1T/Morr7Bx40ZGjhzJ9OnTeffddxk+fDjr168H6t8D1FT8vinlfffdx6233srjjz/OHXfcgclkYsWKFd5VbZdeeqn3zQJYuHAhQ4YMYcqUKdx6661YrVZeeeUV71YAkydPZuzYsUybNo2xY8eSmZnJqlWr6NatGwBRUVEsXbqUvXv3csMNN/DYY49x++238/jjj/vl+s9X14QIQGFJRERattDQUHJycggPD+fyyy/niSeeYNOmTRw5coQVK1bw5z//2dtJcfz4cf7whz+wadMmcnJy+Oijj8jIyGDgwIEA3mHd3bt313tyty8Z3P7evKAN2LVrF4B3aNCXbDYbe/bsoU+fPoSEhLB03S7e/e9Bbh7Tk3tuuMjnryd1+357iH+pPVoWtUfj2e12Dh06xAUXXHDGOTbnw+l0YrfbCQoKatScpYb6z3/+w4wZM3C73fznP//hT3/6E+vXr6e4uJikpCTuvvtufvrTnwKe+UvVWwcUFhYSFxfHTTfdxJQpUzCZTBQXFzNp0iQyMjKYN28e11xzTb3qcK73s76f336dsyTnLykhHFDPkoiItGxjxoxh+/bt3r8/+uijPProo2csazabmTZtGtOmTTvj45GRkbz11ltNUs/68PswnJyfrqfCUpbCkoiISLNQWGplqnuW8k/YsFdU+bk2IiIibZ/CUisTEWohKsyzIi47X/eIExERaWoKS61QkncorsTPNREREWn7FJZaoerNKbNyNW9JRKQt0kJ13/DV+6iw1Ap19a6I0zCciEhbYjZ77sxgs9n8XJO2ofp9rH5fG0pbB7RCSYnaPkBEpC0ymUxERUWRn58PeDZjbOju1dX3hqs+b3vidrux2Wzk5+cTFRXV6OtXWGqFqnfxzj1RRoXDSaC5ff0SiIi0ZYmJiQDewNRQLpeLqqoqAgICvPdga2+ioqK872djKCy1QpFhFsJDLJTaKsnJt9K9c6S/qyQiIj5iMBjo2LEj8fHxOByOBp+nvLycgwcP0rVrV4KDg31Yw9bBbDb7rEdNYakVMhgMdE0M5+uDx8nKK1VYEhFpg0wmU6M+7F0uFwCBgYGNvnVKe9c+++XaAN32REREpHkoLLVS1dsHKCyJiIg0LYWlVsp7jzjttSQiItKkFJZaqephuGPHy3BUOf1cGxERkbZLYamViokIIjQoAJfLTU5Bmb+rIyIi0mYpLLVSBoPhu0neGooTERFpMgpLrdh3N9RVWBIREWkqCkutWNdEz07eWhEnIiLSdBSWWrGu6lkSERFpcgpLrVj1MNzRAitVTpefayMiItI2KSy1Yh2igggONOF0uTlWqBVxIiIiTUFhqRU7fUWchuJERESahsJSK5eknbxFRESalMJSK9dVN9QVERFpUgpLrVySwpKIiEiTUlhq5arDUna+FadWxImIiPicwlIrFx8dQqDFRJXTRe4Jm7+rIyIi0uYoLLVyRqOBpPgwQJO8RUREmoLCUhugeUsiIiJNR2GpDVBYEhERaToKS22A7hEnIiLSdBSW2oCkxFMr4vJKcbrcfq6NiIhI26Kw1AYkxIRiDjBSWeUiXyviREREfEphqQ0wGQ10ObUiTvOWREREfEthqY3QDXVFRESahsJSG6F7xImIiDQNhaU2omuiepZERESagsJSG+G9R1xeKS6tiBMREfEZhaU2omNsKAEmA/ZKJ4VF5f6ujoiISJuhsNRGmExGOsedukechuJERER8xu9hyeVy8fzzzzNq1Cj69+/PPffcw+HDh89a3uFw8OyzzzJq1CgGDBhAWloae/bs8T7udrtZtWoV11xzDQMGDOC2225j69atNc5RUFDAQw89xNChQxk6dCj3338/ubm5TXaNzUW3PREREfE9v4elxYsX88YbbzB79mzefPNNDAYDkyZNorKy8ozlZ82axerVq3nqqadYs2YNUVFRTJo0idJST0BYtmwZzzzzDHfeeSfr1q3j+uuvZ/LkyezYscN7jgcffJBjx47x0ksv8dJLL5Gbm8uvf/3rZrnepuS97UmuwpKIiIiv+DUsVVZWsnLlSqZOncro0aNJSUkhPT2dvLw8Pvjgg1rljxw5wurVq5k7dy5jxoyhR48ezJkzB4vFwu7duwFYvnw5EyZMIC0tjeTkZMaPH8+NN97IokWLACgpKWHnzp1MmjSJ1NRUUlNTuffee/n66685efJks16/r1Xf9kQ9SyIiIr7j17C0d+9eysrKGDZsmPdYREQEqamp7Ny5s1b5LVu2EBERwWWXXVaj/ObNmxk+fDgnTpyguLiYwYMH13henz59+PTTT3E6nQQGBhISEsK6deuwWq1YrVbeeecdkpOTiYyMbLqLbQanb0zpdmtFnIiIiC8E+PPFq+cJdezYscbx+Ph4jh07Vqt8ZmYmSUlJbNy4kaVLl5KXl0dqaiozZ86kR48eREZGYrFYaj03JycHh8NBSUkJ0dHR/N///R9/+MMfGDRoEAaDgbi4OF599VWMxoZnR7fbjc3m+/uylZeX1/hal+gQI0ajgfKKKrJzTxIbGeTz+rR359Me0vTUHi2L2qNlUXucm9vtxmAwnLOcX8NSdQNaLJYaxwMDAykuLq5V3mq1kpWVxeLFi5k+fToREREsWbKEcePGsX79emJjYxk7dixLliwhNTWVfv36sX37dtasWQN4hv3cbjf79u1j4MCBTJw4EafTSXp6Or/5zW/461//SlhYWIOuxeFw1Jho7muZmZn1KhcTZqKwpIqtn+6hZ0eFpaZS3/aQ5qH2aFnUHi2L2qNu388gZ+LXsBQU5Pkwr6ys9H4PUFFRQXBwcK3yZrOZ0tJS0tPT6dGjBwDp6emMHj2atWvXMnHiRGbOnElFRQXjxo0DoFevXkyaNIl58+YRHh7Oe++9x+uvv86///1vbzB68cUXufzyy1mzZg133XVXg67FbDbTs2fPBj23LuXl5WRmZpKcnHzG9+T7enxRSWFGPsagGPr06ebz+rR359se0rTUHi2L2qNlUXuc2/79++tVzq9hqXr4LT8/n65du3qP5+fnk5KSUqt8YmIiAQEB3qAEnsCVlJREdnY2AGFhYcybN4/Zs2dTUlJCXFwcr732GnFxcYSEhPDpp59ywQUX1OhBioyM5IILLmhU+jYYDISEhDT4+ecSHBxcr/Mnd45ie0Y+eScrmrQ+7V1920Oah9qjZVF7tCxqj7OrzxAc+HmCd0pKCmFhYWzfvt17rKSkhIyMDAYNGlSr/KBBg6iqqmLXrl3eY3a7nSNHjtCtm6cX5bHHHuOtt94iMDCQuLg4ADZs2MCoUaMAT0A7fPgwFRUV3nOUl5eTnZ3tPUdrpu0DREREfMuvYclisZCWlsb8+fPZtGkTe/fu5cEHHyQxMZGrr74ap9NJQUEBdrsd8ISlESNGMGPGDD755BP279/P9OnTMZlMjB07FvCEoQULFrBt2zaysrJ48sknycjIYPLkyQDcdNNNADzwwAPs3bvX+5oWi4VbbrnFL++DL52+MaVWxImIiDSe3zelvO+++7j11lt5/PHHueOOOzCZTKxYscK7qu3SSy9l/fr13vILFy5kyJAhTJkyhVtvvRWr1corr7xCTEwMAJMnT2bs2LFMmzaNsWPHkpmZyapVq7y9RvHx8bz++uu43W7uuusu7r77bsxmM3/961+JiIjwy3vgS53jwjAawFru4GRpxbmfICIiInUyuNX90GjVw4J9+/b1+bltNht79uyhT58+9R5z/uXcf3G0sIzZvxxB/wvjfF6n9qwh7SFNR+3Rsqg9Wha1x7nV9/Pb7z1L4nunb04pIiIijaOw1AZ11W1PREREfEZhqQ1Sz5KIiIjvKCy1QV0T1LMkIiLiKwpLbVDn+DAMBigpq6TYqhVxIiIijaGw1AYFWQJIiPGsfNBQnIiISOMoLLVRSRqKExER8QmFpTZKtz0RERHxDYWlNko9SyIiIr6hsNRGafsAERER31BYaqO6xIcBUFRaQUlZpZ9rIyIi0nopLLVRIUFm4qKDAQ3FiYiINIbCUhumeUsiIiKNp7DUhmknbxERkcZTWGrDumqSt4iISKMpLLVhSYnqWRIREWkshaU2LCneE5aOF9uxljv8XBsREZHWSWGpDQsNNhMbGQRAtnqXREREGkRhqY3T5pQiIiKNo7DUxmlFnIiISOMoLLVx6lkSERFpHIWlNk4bU4qIiDSOwlIb1/XU9gEFJ8ux2bUiTkRE5HwpLLVx4SEWosMDAcjOt/q5NiIiIq2PwlI7oKE4ERGRhlNYage0Ik5ERKThFJbagerbnhzOVVgSERE5XwpL7YCG4URERBpOYakdqB6Gyz9pw15R5efaiIiItC4KS+1AZFggEaEW3G7ILtCKOBERkfOhsNROaChORESkYRSW2gmtiBMREWkYhaV2onon7yytiBMRETkvCkvthIbhREREGkZhqZ2oHobLPV5GpcPp59qIiIi0HgpL7URUeCBhwWZcbsjRijgREZF6U1hqJwwGg3coTvOWRERE6k9hqR2pnuSteUsiIiL1p7DUjnh7lhSWRERE6k1hqR3RijgREZHz5/ew5HK5eP755xk1ahT9+/fnnnvu4fDhw2ct73A4ePbZZxk1ahQDBgwgLS2NPXv2eB93u92sWrWKa665hgEDBnDbbbexdetW7+MLFy6kd+/eZ/zzyCOPNOm1+lv1irijhWU4qlx+ro2IiEjr4PewtHjxYt544w1mz57Nm2++icFgYNKkSVRWVp6x/KxZs1i9ejVPPfUUa9asISoqikmTJlFa6uktWbZsGc888wx33nkn69at4/rrr2fy5Mns2LEDgHvuuYctW7bU+PPAAw8QFBTEXXfd1WzX7Q+xkUGEBAXgcrk5WqgVcSIiIvXh17BUWVnJypUrmTp1KqNHjyYlJYX09HTy8vL44IMPapU/cuQIq1evZu7cuYwZM4YePXowZ84cLBYLu3fvBmD58uVMmDCBtLQ0kpOTGT9+PDfeeCOLFi0CIDQ0lLi4OO+f8vJy/vznPzNz5kxSUlKa9fqb2+kr4jQUJyIiUj9+DUt79+6lrKyMYcOGeY9FRESQmprKzp07a5XfsmULERERXHbZZTXKb968meHDh3PixAmKi4sZPHhwjef16dOHTz/9FKez9maMTz/9NL169eK2227z4ZW1XN57xGn7ABERkXoJ8OeL5+bmAtCxY8cax+Pj4zl27Fit8pmZmSQlJbFx40aWLl1KXl4eqampzJw5kx49ehAZGYnFYqn13JycHBwOByUlJURHR3uP79q1i02bNvGXv/wFo7FxudHtdmOz2Rp1jjMpLy+v8bWxEmICATiYU9Qk9W3rfN0e0jhqj5ZF7dGyqD3Oze12YzAYzlnOr2GpugEtFkuN44GBgRQXF9cqb7VaycrKYvHixUyfPp2IiAiWLFnCuHHjWL9+PbGxsYwdO5YlS5aQmppKv3792L59O2vWrAGoNQ/q5Zdfpn///jV6thrK4XDUmGjua5mZmT45j9vuec8PZB9v0vq2db5qD/ENtUfLovZoWdQedft+BjkTv4aloKAgwBNiqr8HqKioIDg4uFZ5s9lMaWkp6enp9OjRA4D09HRGjx7N2rVrmThxIjNnzqSiooJx48YB0KtXLyZNmsS8efMIDw/3nstms/HBBx/wxBNP+ORazGYzPXv29Mm5TldeXk5mZibJyclnfE/OV4eO5bz2ny2csDrpdWFvAkx+n+Pfqvi6PaRx1B4ti9qjZVF7nNv+/fvrVc6vYal6+C0/P5+uXbt6j+fn559xsnViYiIBAQHeoASewJWUlER2djYAYWFhzJs3j9mzZ1NSUkJcXByvvfYacXFxhISEeJ/33//+F5fLxdVXX+2TazEYDDXO72vBwcE+OX9SUDBBFhP2SifFNjdJCU1X57bMV+0hvqH2aFnUHi2L2uPs6jMEB36e4J2SkkJYWBjbt2/3HispKSEjI4NBgwbVKj9o0CCqqqrYtWuX95jdbufIkSN069YNgMcee4y33nqLwMBA4uLiANiwYQOjRo2qca5PP/2Uiy66iIiIiKa4tBbLaDTQRSviRERE6s2vPUsWi4W0tDTmz59PTEwMnTt3Zt68eSQmJnL11VfjdDo5ceIE4eHhBAUFMWjQIEaMGMGMGTP4wx/+QFRUFM8//zwmk4mxY8cCnt6qBQsWkJSUROfOnXnppZfIyMhg9uzZNV577969XHjhhf64bL/rmhDO/iNFCksiIiL14NewBHDfffdRVVXF448/jt1uZ/DgwaxYsQKLxUJ2djZXXnklc+fO5ZZbbgE8O3DPnz+fKVOmYLfbueSSS3jllVeIiYkBYPLkydhsNqZNm0ZZWRkDBgxg1apV3p6naoWFhfTv37/Zr7cl0D3iRERE6s/vYclkMjFt2jSmTZtW67EuXbqwb9++GsfCwsKYNWsWs2bNOuP5AgICmD59OtOnT6/zddevX9/gOrd2XRM1DCciIlJfWgrVDlVvTJmdb8Xpcvu5NiIiIi2bwlI7FBcdgsVswlHlIu9Emb+rIyIi0qIpLLVDJqOBLvFhAGTpticiIiJ1Ulhqp7pq+wAREZF6UVhqp7QiTkREpH4UltqpJPUsiYiI1IvCUjv13fYBVlxaESciInJWCkvtVGJMCAEmI5UOJ/knbf6ujoiISIulsNROmUxG74o4DcWJiIicncJSO6YVcSIiIuemsNSOJSVqRZyIiMi5KCy1Y97tA7QxpYiIyFkpLLVjpw/Dud1aESciInImCkvtWMcOoZiMBuyVTgqKyv1dHRERkRZJYakdCzAZ6RSnFXEiIiJ1UVhq57QiTkREpG4KS+2cJnmLiIjUTWGpnVPPkoiISN0Ultq5pEStiBMREamLwlI71zkuFKPRQJm9ihMldn9XR0REpMVRWGrnzAEmOsaGApq3JCIiciYKS0LXRM1bEhEROZsGh6WdO3fy2WefAZCdnc29997LDTfcwKJFi3xWOWke3hVxCksiIiK1NCgsvfPOO0yYMIF//etfAMyaNYudO3fSrVs3XnzxRZYuXerTSkrTStKKOBERkbNqUFh66aWXuPnmm5k+fTrHjx9n69atTJkyhRdeeIEHH3yQNWvW+Lqe0oS6nrbXklbEiYiI1NSgsHTw4EHGjh0LwEcffYTb7ebKK68EoG/fvhw7dsx3NZQm1zk+DIMBrOUOiqwV/q6OiIhIi9KgsBQREUFZWRkAH374IZ06dSI5ORmArKwsoqOjfVZBaXqBZhOJMZ4VcRqKExERqalBYWnYsGG88MIL/PnPf+aDDz7g2muvBeCf//wnCxYsYOTIkT6tpDQ977wlbR8gIiJSQ4PC0mOPPUZ0dDSLFi1ixIgR/PKXvwRg7ty5dOrUid/+9rc+raQ0vertA7QiTkREpKaAhjwpOjqaFStW1Dr++uuv06lTp0ZXSprfdyvirH6uiYiISMvS4H2WrFYreXl5AFRWVrJ8+XJWrlzJzp07fVY5aT7eFXF5JX6uiYiISMvSoLD01VdfccUVV7Bq1SoAZs+ezfz58/n73//OXXfdxaZNm3xaSWl6XeLDACi2VlKsFXEiIiJeDQpL6enpdO/endtuuw273c67777LuHHj2LFjB7feeisvvviir+spTSwoMID4mBBAK+JERERO16Cw9OWXX/KrX/2KpKQktm3bht1u9+67dO211/Ltt9/6tJLSPLpqJ28REZFaGhSWjEYjFosF8OyzFBERQb9+/QDPXKagoCDf1VCaje4RJyIiUluDVsNdfPHFrF69mqCgIN5//33GjBmDwWDg+PHjLFu2jIsvvtjX9ZRm0DXBM29JPUsiIiLfaVDP0vTp09m2bRt33HEHJpOJX/3qVwBcf/31ZGZm8sADD/iyjtJMdENdERGR2hrUs5SamsrGjRs5cOAAvXr1IiTEMzF41qxZXHLJJcTFxfm0ktI8qsPSiZIKrLZKwkIsfq6RiIiI/zUoLAGEhYVxwQUX8Mknn1BaWkp0dDQjR44kLCzMl/WTZhQSZKZDVDCFReUcybPS54IYf1dJRETE7xoclpYuXcrixYupqKjA7XYDYDabmTx5Mr/5zW98VkFpXl0TwiksKicrr0RhSUREhAaGpTVr1vDcc89x6623cuONN9KhQwcKCgp45513eOGFF+jUqRM333yzr+sqzSApIZzP9uVrRZyIiMgpDZrg/fLLL3PHHXcwe/ZshgwZQvfu3Rk6dChz5szhjjvu4JVXXqn3uVwuF88//zyjRo2if//+3HPPPRw+fPis5R0OB88++yyjRo1iwIABpKWlsWfPHu/jbrebVatWcc011zBgwABuu+02tm7del7naM+8k7xzFZZERESggWHp8OHDXHXVVWd87Morr+TgwYP1PtfixYt54403mD17Nm+++SYGg4FJkyZRWVl5xvKzZs1i9erVPPXUU6xZs4aoqCgmTZpEaannw33ZsmU888wz3Hnnnaxbt47rr7+eyZMns2PHjnqfoz3TxpQiIiI1NSgsJSQkkJ2dfcbHjhw5Uu9J3pWVlaxcuZKpU6cyevRoUlJSSE9PJy8vjw8++OCM5169ejVz585lzJgx9OjRgzlz5mCxWNi9ezcAy5cvZ8KECaSlpZGcnMz48eO58cYbWbRoUb3P0Z4lndprqbDYjs3u8HNtRERE/K9BYemKK67g+eef54svvqhx/PPPP2fhwoVcccUV9TrP3r17KSsrY9iwYd5jERERpKamsnPnzlrlt2zZQkREBJdddlmN8ps3b2b48OGcOHGC4uJiBg8eXON5ffr04dNPP8XpdJ7zHO1dWIiFmIhAQL1LIiIi0MAJ3lOnTmXr1q3ccccddOrUibi4OAoKCsjJyaFnz5789re/rdd5cnNzAejYsWON4/Hx8Rw7dqxW+czMTJKSkti4cSNLly4lLy+P1NRUZs6cSY8ePYiMjMRisdR6bk5ODg6Hg5KSknOeo6Hcbjc2m63Bzz+b8vLyGl+bQ6cOoZwoqWD/keMkxenWNafzR3vI2ak9Wha1R8ui9jg3t9uNwWA4Z7kGhaWwsDBWr17N22+/zY4dOyguLqZfv3784he/4Cc/+QmBgYH1Ok91A1bfZ65aYGAgxcXFtcpbrVaysrJYvHgx06dPJyIigiVLljBu3DjWr19PbGwsY8eOZcmSJaSmptKvXz+2b9/OmjVrAM+wX33O0RAOh6NJJ4lnZmY22bm/LyTAM1/sq71ZJAaXNNvrtibN2R5ybmqPlkXt0bKoPer2/QxyJvUOS4888sgZjwcFBXlvnLtr1y527dqFwWBgzpw55zxn9fMqKytr3Hy3oqKC4ODgWuXNZjOlpaWkp6d7e4HS09MZPXo0a9euZeLEicycOZOKigrGjRsHQK9evZg0aRLz5s0jPDy8XudoCLPZTM+ePRv03LqUl5eTmZlJcnLyGd+TppBdeoQd3+yl3BlEnz59muU1Wwt/tIecndqjZVF7tCxqj3Pbv39/vcrVOyxt37693i9eny4t+G74LT8/n65du3qP5+fnk5KSUqt8YmIiAQEBNYbLgoKCSEpK8k44DwsLY968ecyePZuSkhLi4uJ47bXXiIuLIyQkpF7naAiDweC97UtTCA4ObtLzn65n1w4A5BSUNdtrtjbN2R5ybmqPlkXt0bKoPc6uvnml3mFp8+bNDa7M2aSkpBAWFsb27du9YamkpISMjAzS0tJqlR80aBBVVVXs2rWLvn37AmC32zly5AjXXXcdAI899hj9+/fnZz/7mfcedRs2bGDUqFH1Pkd7V73XUv7JcsorqggObPBG7yIiIq1eg1bD+YrFYiEtLY358+ezadMm9u7dy4MPPkhiYiJXX301TqeTgoIC7HY74Ak6I0aMYMaMGXzyySfs37+f6dOnYzKZGDt2LODprVqwYAHbtm0jKyuLJ598koyMDCZPnlzvc7R3EaEWosI8886y87UiTkRE2je/dxncd999VFVV8fjjj2O32xk8eDArVqzAYrGQnZ3NlVdeydy5c7nlllsAWLhwIfPnz2fKlCnY7XYuueQSXnnlFWJiPPcxmzx5MjabjWnTplFWVsaAAQNYtWoV3bp1877muc4hnt6lImsFR/JK6ZUU7e/qiIiI+I3BXX0XXGmwXbt2AXiH9XzJZrOxZ88e+vTp06xjzkvWfMn6rZn85PKe/Pz6i5rtdVs6f7WHnJnao2VRe7Qsao9zq+/nt1+H4aTl+u62J1Y/10RERMS/FJbkjJISdY84ERERUFiSs6heEZd7oowKh9PPtREREfEfhSU5o6iwQMJDzLjdkJOvoTgREWm/FJbkjAwGA10TIwDI0lCciIi0YwpLclbVQ3FZubo/nIiItF8KS3JWSQlhgCZ5i4hI+6awJGf13fYBCksiItJ+KSzJWVUPwx0rLMNRpRVxIiLSPiksyVnFRAQRGhSAyw05BWX+ro6IiIhfKCzJWRkMBm/v0pFcDcWJiEj7pLAkdfKuiNO8JRERaacUlqROXXXbExERaecUlqRO6lkSEZH2TmFJ6tQ1wbOL99ECK44ql59rIyIi0vwUlqROHaKCCA404XS5OVaoe8SJiEj7o7AkdaqxIi5PYUlERNofhSU5J81bEhGR9kxhSc5Jtz0REZH2TGFJzilJYUlERNoxhSU5p+qwlJ1vxenUijgREWlfFJbknOKjQ7CYTVQ5XeSesPm7OiIiIs1KYUnOyWg0kJQQBkCW7hEnIiLtjMKS1Mt3K+JK/FwTERGR5qWwJPXiXRGXq72WRESkfVFYknrR9gEiItJeKSxJvSQlVq+IK8Xpcvu5NiIiIs1HYUnqJSEmFHOAkcoqF/laESciIu2IwpLUi8looEu8Z0WchuJERKQ9UViSetM94kREpD1SWJJ60yRvERFpjxSWpN7UsyQiIu2RwpLUm/cecXmluLQiTkRE2gmFJam3Th1CCTAZsFc6KSgq93d1REREmoXCktSbyWSkc5xWxImISPuisCTnxTtvSTfUFRGRdkJhSc6LVsSJiEh7o7Ak56X6ticKSyIi0l4oLMl5OX37ALdbK+JERKTtU1iS89KpQxhGo4HyiiqOF9v9XR0REZEmp7Ak58UcYKRTh1BAm1OKiEj74Pew5HK5eP755xk1ahT9+/fnnnvu4fDhw2ct73A4ePbZZxk1ahQDBgwgLS2NPXv2eB93u92sWrWKa665hgEDBnDbbbexdevWGudYu3YtvXv3rvWnrteV7yRpkreIiLQjfg9Lixcv5o033mD27Nm8+eabGAwGJk2aRGVl5RnLz5o1i9WrV/PUU0+xZs0aoqKimDRpEqWlng/uZcuW8cwzz3DnnXeybt06rr/+eiZPnsyOHTu859i3bx9Dhgxhy5YtNf506dKlWa65teuq7QNERKQd8WtYqqysZOXKlUydOpXRo0eTkpJCeno6eXl5fPDBB7XKHzlyhNWrVzN37lzGjBlDjx49mDNnDhaLhd27dwOwfPlyJkyYQFpaGsnJyYwfP54bb7yRRYsWec/zzTffkJKSQlxcXI0/JpOp2a69NeuqFXEiItKOBPjzxffu3UtZWRnDhg3zHouIiCA1NZWdO3dy3XXX1Si/ZcsWIiIiuOyyy2qU37x5MwAnTpyguLiYwYMH13henz59WLduHU6nE5PJxL59+/jRj37k02txu93YbDafnhOgvLy8xteWIC7SDEBWbgllZWUYDAY/16j5tMT2aM/UHi2L2qNlUXucm9vtrtdnmF/DUm5uLgAdO3ascTw+Pp5jx47VKp+ZmUlSUhIbN25k6dKl5OXlkZqaysyZM+nRoweRkZFYLJZaz83JycHhcFBSUoLb7aawsJCdO3eyatUqioqK6N+/Pw8//DAXXHBBg6/F4XDUmDvla5mZmU127vPlcLoxGKDMXsXOz78mPLj99ci1pPYQtUdLo/ZoWdQedbNYLOcs49ewVJ12v1/RwMBAiouLa5W3Wq1kZWWxePFipk+fTkREBEuWLGHcuHGsX7+e2NhYxo4dy5IlS0hNTaVfv35s376dNWvWAJ5hv0OHDgFgMpl45plnsNlsLF68mHHjxvHuu+/SoUOHBl2L2WymZ8+eDXpuXcrLy8nMzCQ5OZng4GCfn7+hEv91kmPHbQRHdqRPj1h/V6fZtNT2aK/UHi2L2qNlUXuc2/79++tVzq9hKSgoCPCEmOrvASoqKs7YsGazmdLSUtLT0+nRowcA6enpjB49mrVr1zJx4kRmzpxJRUUF48aNA6BXr15MmjSJefPmER4ezrBhw9ixYweRkZHe8y5atIjLL7+ct99+m3vvvbdB12IwGAgJCWnQc+sjODi4Sc9/vromRnDsuI38IkeLqldzaWnt0d6pPVoWtUfLovY4u/pOI/HrBO/q4bf8/Pwax/Pz80lMTKxVPjExkYCAAG9QAk/gSkpKIjs7G4CwsDDmzZvH559/zocffsg777xDcHAwcXFx3h+W04MSQEhICF26dCEvL8+n19eWaZK3iIi0F34NSykpKYSFhbF9+3bvsZKSEjIyMhg0aFCt8oMGDaKqqopdu3Z5j9ntdo4cOUK3bt0AeOyxx3jrrbcIDAwkLi4OgA0bNjBq1CgAXn/9dYYOHYrd/t3u01arlczMzCYZRmurTr/tiYiISFvm17BksVhIS0tj/vz5bNq0ib179/Lggw+SmJjI1VdfjdPppKCgwBtsBg0axIgRI5gxYwaffPIJ+/fvZ/r06ZhMJsaOHQt4eqsWLFjAtm3byMrK4sknnyQjI4PJkycDcPnll+N2u5k+fTrffvstu3btYurUqcTExHDzzTf77b1obbQxpYiItBd+nbMEcN9991FVVcXjjz+O3W5n8ODBrFixAovFQnZ2NldeeSVz587llltuAWDhwoXMnz+fKVOmYLfbueSSS3jllVeIiYkBYPLkydhsNqZNm0ZZWRkDBgxg1apV3p6njh078pe//IX58+dzxx134Ha7GTlyJK+88kqNeVNSty7xYRgMUFJWSVFpBVHhgf6ukoiISJMwuHXr+EarHhbs27evz89ts9nYs2cPffr0aXET9Cb+3wfknbAx51cj6duzYasIW5uW3B7tkdqjZVF7tCxqj3Or7+e33293Iq1X9SRvzVsSEZG2TGFJGqyr5i2JiEg7oLAkDaZJ3iIi0h4oLEmDafsAERFpDxSWpMG6xIcBUFRaQUlZpZ9rIyIi0jQUlqTBQoLMxEV7bkujoTgREWmrFJakUTRvSURE2jqFJWmUrpq3JCIibZzCkjSKt2cpV2FJRETaJoUlaRRtTCkiIm2dwpI0SlK8JyydKLFjLXf4uTYiIiK+p7AkjRIabCY20nMD4mz1LomISBuksCSNps0pRUSkLVNYkkbTPeJERKQtU1iSRlPPkoiItGUKS9Jo2phSRETaMoUlabTqsFRwshybXSviRESkbVFYkkaLCLUQFR4IQHa+1c+1ERER8S2FJfEJ721PtJO3iIi0MQpL4hNaESciIm2VwpL4RJJueyIiIm2UwpL4hFbEiYhIW6WwJD5RPQyXf9KGvaLKz7URERHxHYUl8YnIsEAiQi243ZBdoBVxIiLSdigsic9oKE5ERNoihSXxGa2IExGRtkhhSXwmSXstiYhIG6SwJD7TVTfUFRGRNkhhSXymeq+lvONlVDicfq6NiIiIbygsic9EhwcSFmzG5YajWhEnIiJthMKS+IzBYNC8JRERaXMUlsSnuiZqRZyIiLQtCkviU0ma5C0iIm2MwpL4lDamFBGRtkZhSXyqevuAo4VlOKpcfq6NiIhI4yksiU/FRgYRHBiAy+XWijgREWkTFJbEpwwGgzanFBGRNkVhSXxO85ZERKQtUVgSn6vePkA9SyIi0hYoLInPqWdJRETaEr+HJZfLxfPPP8+oUaPo378/99xzD4cPHz5reYfDwbPPPsuoUaMYMGAAaWlp7Nmzx/u42+1m1apVXHPNNQwYMIDbbruNrVu3nvV87777Lr179yY7O9un19WeeVfEFVipcmpFnIiItG5+D0uLFy/mjTfeYPbs2bz55psYDAYmTZpEZWXlGcvPmjWL1atX89RTT7FmzRqioqKYNGkSpaWeXoxly5bxzDPPcOedd7Ju3Tquv/56Jk+ezI4dO2qdKycnhyeffLJJr6896hAVTJDFRJXTzbHCMn9XR0REpFH8GpYqKytZuXIlU6dOZfTo0aSkpJCenk5eXh4ffPBBrfJHjhxh9erVzJ07lzFjxtCjRw/mzJmDxWJh9+7dACxfvpwJEyaQlpZGcnIy48eP58Ybb2TRokU1zuVyuZg2bRoXXXRRs1xre2I0GuiioTgREWkj/BqW9u7dS1lZGcOGDfMei4iIIDU1lZ07d9Yqv2XLFiIiIrjssstqlN+8eTPDhw/nxIkTFBcXM3jw4BrP69OnD59++ilOp9N77MUXX8ThcPDLX/6yCa5MuiosiYhIGxHgzxfPzc0FoGPHjjWOx8fHc+zYsVrlMzMzSUpKYuPGjSxdupS8vDxSU1OZOXMmPXr0IDIyEovFUuu5OTk5OBwOSkpKiI6O5quvvmLlypWsXr2avLw8n1yL2+3GZrP55FynKy8vr/G1tUiMDgTgUE5Rk7wv/tJa26OtUnu0LGqPlkXtcW5utxuDwXDOcn4NS9UNaLFYahwPDAykuLi4Vnmr1UpWVhaLFy9m+vTpREREsGTJEsaNG8f69euJjY1l7NixLFmyhNTUVPr168f27dtZs2YN4Bn2s9lsPPzwwzz88MMkJyf7LCw5HI4aE819LTMzs8nO3RTcFZ623X/keJO+L/7S2tqjrWvt7WGwl2JwOXGFRPm7Kj7R2tujrVF71O37GeRM/BqWgoKCAE+Iqf4eoKKiguDg4FrlzWYzpaWlpKen06NHDwDS09MZPXo0a9euZeLEicycOZOKigrGjRsHQK9evZg0aRLz5s0jPDyc2bNnk5yczO233+7TazGbzfTs2dOn5wRPoMzMzCQ5OfmM70lLFZ1g468ffcwJq5MLL+yNyeT3tQQ+0Vrbo61qC+1h/2Y7xVuW4nY5CR99JyH9r6rX/+m2RG2hPdoStce57d+/v17l/BqWqoff8vPz6dq1q/d4fn4+KSkptconJiYSEBDgDUrgCVxJSUnepf9hYWHMmzeP2bNnU1JSQlxcHK+99hpxcXGEhISwZs0aLBYLAwcOBPDOY7r++uu58cYb+cMf/tCgazEYDISEhDToufURHBzcpOf3tW5BwVgCjFRWuSi1Q6e41lP3+mht7dHWtcb2cLtdnPzwTYo+Xu09VvrvV3AXHqbDNb/EaA70Y+0apzW2R1um9ji7+v6PiV/DUkpKCmFhYWzfvt0blkpKSsjIyCAtLa1W+UGDBlFVVcWuXbvo27cvAHa7nSNHjnDdddcB8Nhjj9G/f39+9rOfERcXB8CGDRsYNWoUABs3bqxxzi+//JJp06axdOnSGiFMGsd0akXcwZxisvJK6RQX5u8qibQYrgob+e88j+1bz0KWyKE3YAqP4cSmVVh3fUhlfhYJt07DHJXg55qKCPg5LFksFtLS0pg/fz4xMTF07tyZefPmkZiYyNVXX43T6eTEiROEh4cTFBTEoEGDGDFiBDNmzOAPf/gDUVFRPP/885hMJsaOHQt4eqsWLFhAUlISnTt35qWXXiIjI4PZs2cD0K1btxp1qJ5k3qlTJ2JjY5v3DWjjup4KS0fyShl2ccdzP0GkHXCczCX3rbk4CrMxmMx0uHYy4f3GABCY2J28t5+lMu8QOSunE3/Tg4R0H+DX+opIC9iU8r777uPWW2/l8ccf54477sBkMrFixQrvqrZLL72U9evXe8svXLiQIUOGMGXKFG699VasViuvvPIKMTExAEyePJmxY8cybdo0xo4dS2ZmJqtWraoVkqTpVd/2RPeIE/GwHfqSnJUzcBRmYwqLpuP4P3iDEkBwt4vp8ov5BHbqhavcSu5fZ3Py47dxu93+q7SIYHDrt7DRdu3aBeAdGvQlm83Gnj176NOnT6sbc9626xhzXt5Bjy6R/OnBMf6ujk+05vZoi1pLe7jdbkp2vsfxf/0F3C4CO/Ui4dbpBITHnLl8lYPCfy6n9It/ARDSeyjxN0zBGNhyrxFaT3u0F2qPc6vv57dfh+GkbeuaWL0xpRWXy43R2DpX+Ig0hrvKQcH7S7F+tRmAsL6j6XDtZIwBZ1+ubAgwE3fdrwjs1JPCfy7Htm87OYXZJNw6HUuHLs1VdRE5xe/DcNJ2JcaEEGAyUulwkn+y7WxMKVJfVdaTHH31CU9QMhiJueou4m6YWmdQOl3EwKvpNH42pvBYHMdzyHlpJmV7tzdxrUXk+xSWpMmYTEa6xHtWwWnekrQ3FUf3k7NyOhU5+zAGhZJ426NEDb3xvPdQCurciy6/mEdQ14twV5aTt+aPnPj3a7hdznM/WUR8QmFJmlT1JO8juQpL0n6U7v6Io6t+h7P0BObYznT6+dOE9BjY4POZQiPpeOcTRA69AYCirW+T++b/4bTp90qkOSgsSZPSijhpT9wuJ8c3r6LgnQW4qyoJ6fkDOv98LpbYTo0+t8FoIvaqnxN/04MYzIGUH/zS03OVe9AHNReRuigsSZP6bpK3wpK0bU57GblvzaV42zoAokbcQsJPZ2AMCvXp64RddCmd7ppDQHQiVcX5HP3LY5Tu+tCnryEiNSksSZPqmvBdWNIuFdJWVR7P4ejLMyk/8DmGAAvxNz1AzOV3YjCamuT1AhOS6Xz3MwT3uAR3VSUFf3+ewn+uwO2sapLXE2nvFJakSXXsEIrJaMBe6aSgqNzf1RHxOdv+zzj60kwcx49iCo+l04TZhF00qslf1xQcRuJtjxA16mcAlHyynmOvzaLKerLJX1ukvVFYkiYVYDJ67wunoThpS9xuN0Xb1pH75hxcFTYCu6TQ+Z4/Etix+e4xaTAYibnsNhJ+OhNDYAj2I3vIWTENe/a+ZquDSHugsCRN7vShOJG2wOWooODvz3Ni8yrATXj/K+l05ywCwqL8Up/QCwfT5Z5nMHfogtN6kqOrfk/Jpxs09C3iIwpL0uSqV8S99/Eh3vzXPg4dLdY/4tJqVZUc59iq32Hd/REYjMT+8Bd0uO5XGALMfq2XOaYTne9+mtA+w8FVReGGZRT8YxEuR4Vf6yXSFuh2Jy1YRe5BCv+xmKDwJNy9egCt894+A3vH8bdN35B73Mar7+/l1ff3EhcdzJDURIakJtK3ZyzmgKaZCCviS/bsfeSt/iPOsiKMwWEk3PIwwcm+vydkQxktwcTf/FuKO77DiX+/hvWrf1OZn0XCrQ9jjoz3d/VEWi2FpRbMUZRHVd4hgvMOUbhqH4YfT2rUxnb+knpBLCsev5qdGXnsyMjly28KKDhZznsfH+K9jw8RHGhiwIXxDElNZHBqApFhgf6uskgtpV9upuD9P4OzCnNcVxJ/OgNzdKK/q1WLwWAgavhNBCZ2J2/tc1TmHiBnxXTib36QkAv6+7t6Iq2SwlILFtp7GJHX/oaTm/4CRXnkvjGbkN5D6XD13QRExvm7euclNjKYHw9P5sfDk7FXVvHVt4XsyMhlZ0YuJ0oq2LbrGNt2HcNggN5doxlykafXqWti+HnfHkLEl9wuJ8f/9RdKdr4HQMiFQ4i/8T6MgcF+rlndgi/oR+df/JG81fOpzD1A7l9nE3P5nUQOG6vfKZHzpLDUghkMBoJ7DyOzMojOJzKwff5PbPu2c+TgF0RfeiuRQ2/AYPLvPImGCLIEeMLQRYm4XG4O5BSx42tPr9PBnGL2Hj7J3sMneWX9HhJiQhicmsDQixK5qHsHzAGaZifNx1leSv7a5yg/9BUAUZf+lOjLfobB0Dp+Ds2R8XS6azaF7y/D+tVmTmxeRcXR/cRd/5sWH/ZEWhKFpdYgIJCI0eOI+cHVFG5Yhv3IHk78+zVKv/oPHX48qUXNmThfRqOBXknR9EqK5s4fp1BYVM7OjFx2ZOTx5bcF5J2w8Y8th/jHlkOEBAUwsLdnuG5QnwQiQut353aRhqgsyCL3b89QdTIXgzmQuBunEpYy3N/VOm/GAAtx1/+aoE49Kdy4krK926gsPELCrTN8chsWkfZAYakVscR3o+P4p7Du/pATm17BcTyHY6/NIjR1JLFX/ZyA8Bh/V7HROkQFc82IC7hmxAXYK6r44tsCdnydy849eRSVVvDxl0f5+MujGA2QkhzjmSR+USJd4sM0tCA+U/bNTvLf+RPuSjsBkfEk/HQGgQnJ/q5WgxkMBiJ+8CMsCcnkrZmPozCbnJdmEH/jfYReONjf1RNp8RSWWhmDwUB43zGE9BrMyQ//Ssmn/6Qs42Ns+z8l+rLbiBx0LQZT22jWoMAAhl3ckWEXd8TlcvPtkZPsyMhjx9e5ZB4rIePQCTIOneDl9zLoGBvK4IsSGJKayEXdYwkwtY5hEmlZ3G43RR+v4eSHbwBugrpeRMJPHsYUEuHvqvlEUJfedP7FH8l/+1nsR/aQ97enibr0VqJH/azJbs0i0ha0jU/VdsgUFEqHH00kvN8VFG5YSsXRbznxr79g/erfxP5oEsFdU/1dRZ8yGg307hZD724xjL+mD/knbN7huq/2F3LseBl//+ggf//oIKFBAVySksCQ1AR+0CeB8BAN18m5uSrtFPzjBcr2bAMg4gc/Jvbqu9vM/3xUCwiLpuOdszi+6S+U7FxP0ZbVVBw7QPzYBzAFh/m7eiItUtv6V6AdCuzYnU4/n0PpF5s58e9XqczP4tiq3xHWdwwxV4z3247CTS0+JoTrLu3OdZd2x2Z38MU3BezIyOWTPXkUWyv57xc5/PeLHIxGA328w3UJdIkP93fVpQVyFOeT97c/Upl3CIwmOvxoIhGX/NDf1WoyBlMAHX74CwI79qRw/YuUH/icnJXTSbh1eqsebhRpKgpLbYDBYCRi4FWE9h7Kif+8Runn/8K66z/YvtlB9JhxRFzywzbdxR4SZGZEv06M6NcJp8vNt1kn2ZGRy46vczmcW8rXB4/z9cHjvPSPr+kcF8rgU/OcUpNjMGm4rt0rz8ogb808XLYSjCERJPxkWpvrmT2b8L6jscR1JW/1H6kqyuPoy48Qd92vCbu46W8ELNKaKCy1IaaQcOKunUx4/ysp3LCUytyDHP/nckq/2ESHa+4lqPOF/q5ikzMZDaQkx5CSHMOEa1PJPV7m3Qxz94FCcgrKyPnwAOs+PEBYsJkfpCQw5KIELklJICy49W3DII1T8tlGCv+5HFxOLAkXkPjTGa1uD7PGCky8wDOPad2fKD/4Bfnv/An7sf3EXjG+zQ1BijSUfhPaoKDOveh899OUfPYBJz98ncq8Qxx9+RHCB1xFzOV3tpnJqvWRGBvKDaO6c8Moz3Dd5/sKTm2GmUeprZIPP8/mw8+zMRkNXNQ99lSvUwKdOmjuRlvmdlZxfONKSj77JwChfUZ49h6yBPm5Zv5hCg4n8bZHOfnRmxR9vIaSHf+gMvcQ8Tc/1GaH8kXOh8JSG2Uwmogc9GPC+gzn+OZVWL/6N6Vf/Iuyff8j5vI0wgdc2Wo21vOVkCAzI/t3YmR/z3Dd3swTpyaJ53Ikz8pX+wv5an8hK/6+my7xYd5tCVK6RWu4rg1xlhWT9/az2LO+BgxEj7mDqBG3tPutJwxGEzFjxhGY2IP8dxdiz/qanJXTSPjJtHbRKy1SF4WlNs4UGkn8DVOIGHAlhRuWUZl/mML1L3qG5n48icCOPfxdRb+o7km6qHssP7/+Io4VlnnnOX198DjZ+Vay8/fz9n/2Ex5i5gd9EhjWI5TuHCaguBR3lW9ubOx2u3G7we35C27PF8/xU49z6vE6y7rBTc2yAC7XmY+73RBoMREeYm5XNzGuyMsk729PU1VcgMESTPzY+7XP0PeEpgylc4fO5K3+I47jORxd9Ts6/PAXbXrCu8i5GNzV/3pKg+3atQuAvn19v5O2zWZjz5499OnTh5CQxn04u11OSj55nxMfvoG7shzwbFQXPfoOLRk+TVm5g8/25rMjI5ev9xymh+sgAy2Z9ArIxWTw/Lo43CYOuxLY7+rMfmcnjrlicLoNwHeBxn3qPy5Pujlj0GkJLGZPaAoPsRAabP7e9xbCQsyEB3u+hoVUH7MQEhiA0ejf3pjz+f2w7t1Gwd8X4nZUEBCdSOJPZ2KJS2qmmrY+ropy8t9diG3fdgDCB1xF7I9+gTHAsxWHy+XmeLGdowVWcgqt5BRYOZJbQnFJKX26J5CS3IHunSPpHB+Oyc8/J+2VLz8/2qr6fn6rZ6kdMRhNRA65ntA+Izix6RWsX/+Xkk83YN2zldgrJxDWd0y7H4oACMJOf8Neehq3Uh7yFbhd3sdyqqIJM9qJNJbT03SUnqajYAarK5BvHYnsq+rIN1UdOe7y3xYFBoNn81LDqe/B4Dl26sHqz60KhxO3GyodTo4XOzlebD+v1zEaIDTYTFiIhbDqYBV8eqAyExZsITzkVJkQs7ecxdx8vVlut8szF2fLasBzg9n4mx/CFKxtJOpiDAwm/paHyf9wDWXb3qT0i39x9Js9/DviRg6cNHG0wEplleuMzz2Ym8V7W7MATxi/oFME3TtH0qNzFD06R9KtY3i76tGU1k9hqR0KCI8h/qYHCB9wJYX/XI6jMJuCd1+g9ItNxP5oYrvcZ8VZbsX2zQ6se7Z6bprqcnofsyRcQFjqCEL7jCCg3MzXGd8QGheCpfAA7qNf4z62lzCHnYGBhxkYeBgAQ0Q8pi4XEXDqjzE4nOocWh1kMIDx1EGDoWagqQ46p5f9fgAyGmqXPR8ulxtbRRVWWyWltkqsNofnT3klpTYHpbZKyso9X63lnseqv6+odOJyc6qc47zfb4vZdCo4fS9snd57dSqIhZ8WukKCzOfVm+WqKCf/789j+2YHABFDrif2yglteiuNhrDZHRwtLONYQZm3l+hogZWcgjLKyi30DriSu8I+ItyWw+XWlzhsvYzKqkRMRgOJsaF0jgujU1wocZEW8vJyqSCMrLwyDh0txl7pZN/hk+w7fNL7eiajga6J4d4A1b1zJBd0iiAkSCtSpWXSMJwPtJZhuDNxOx0U73iPk/99C7ejAgxGIgZfS8xlt2EMbNvdtk57GbZvdlK2Zyu2g1+Cq8r7mCU+mdDUEYT1GY455rubjZ6pPdzOKiqO7af80FeUH/oKe843NcIWGLAkXkDwBf0ITu5HUFIKRnNgc11mk3BUOb3hqdTmqBGqTg9epeWVlNUoV3lqWLJhDAYIDTo1TBhiJiTQhNthI6lTHB2iQ4kMCyQqLJDIMAvhrmKqNi6gqvAImAKIu+aXhPe/wndvQivjqHKSe9zmDUFHTwtFJ0oq6nxuXHQwvaOd/Kj8H0RU5OI2GDEPu41Ol91MwGk9RN///XC63BwrtHIwp5gD2cWerzlFZwzYBgN06hBK91O9T91P/YkMa92/K/6kYbhzq+/nt8KSD7TmsFStqqSQ4x+8TNlez60eTGHRxF51F6Gpl7apoTlXhY0yb0D6ApynB6SuhPYZSWif4VhiO5/x+fVpD1dFOfasDGyZnvDkKMiq8bjBZCYoKcUbniyJF7Sbng6Xy015RdVpoao6YDk839tq9mRV93JZbZXYK53nfoFTegUc4+6wDwk1VlLiCmaN8cfYwpJOC1OBp763eL6Ge/4eEWpp1fcVdLrcFBaVe0PQ0cIy7/f5J2x1BtXIMAudOoR5e4k6x3m+T+wQSuCpYVOXo4LC9/+MddeHAISmjiTuul97t1yoz++H2+2moKi8VoA62zBwh6hgenSO9AaoHl2iiI0MalP/LjUVhaVzU1hqRm0hLHlf78DnHN+4AseJYwAEdbuYDj+ehKVDlyZ/7abiqijH9u0nWPd8TPmBL3A7v/u/WnOHLoSljiS0z4h6XWND2qOq9CTlh3ed6nn6EmfpiRqPG4PCCE6+mODkfgRf0I+A6ER9EJyBo8qFtbxmr9Xxk1YOZGYTFBqNrcJFUamdrid3cmnlRxhxc7gqlhWll1Psrv/vTniI+bQwFUhEmMUbsKp7raoDVliwudnbyu12U2St4GhBWa1QdKywDMdZ5hEBBAea6BQXRucOYZ6vcaF0ivN8X99NWd1uNyWfbuD4By+By4k5riuJt07HHNOxUf9eFVsrOJBTzIHsolMBqphjhWVnLBsRajk1hHdqHlSXSBJjQ/2+4MAf3G43rvJSHEX5VBXlUVWU5/2+8kQuVWXFmIJCMAWHYwoKxRgUhjHY89X796BQTKcdr/67IaDtD4sqLDWjthSWANxVDor+9w5FH6/BXVUJRhORQ28g+tJbMVqCm6UOjeWqLMf27aeeOUj7P6sZkGI7E9pnBGGpI7DEdT2v8za2PdxuN44TRyk/+CXlmV9Rfvhr3BW2GmUCIuO8wSk4uS+m0Mjzfp324vT2CLaYKdywjNIvNwEQdvFlhF85ieIKN8XWCoqtlRRZKyi2Vni+llZ+9721guKySlznOUZoMhq84almr9WpgBVe81iQpf7TRMvKHaeGyspODZ15QtHRAis2e9VZnxdgMtKxQyidOlTPJfKEos5xYUSFB/os3NmP7CFvzXycZUUYA0OIH/sAdO7j03+vbHYHB3OKveHpYE4xWXmlZ2yn4MAALugUQY8uUXTvFEmPLpEkJYS36p7Cai5HxWkhKB/HqVBU/b278vwWZ9SXIcDiDVemUyGqdriqDl6nPR4c6l012dIpLDWjthaWqjmK8ji+8SVs3+4EwBQeS+wP7ya097AW2fPhqrRj2/+pZ4ht/2eeoHeKOaaTNyCZ47o2uP6+bg+3y0nFsQPfzXfK3ldj7hR4JpgHX9DXM9+pa2qrn+/kS9XtcWHXTpSsX0hF9j4wGIm5Io3IoTeeVzu7XG6s5Y6aAaq0giLr90KV1XOsrPz8J7YHWUy1Q9WpIUBHlatGL1FR6dnnERkMEB8d4glDHUJPBaJTk6yjQ5ptqX5V6Qny3p7ved+BsGE3cyQyhT6pqU3271Wlw8nh3JIaQ3iZR0vOuDLPHGCkW2K4J0Cd6onq1jHivEJrc3C7nFSVHKeqKA/78VzKC49RcTIPZ3E+lBZirCg55zlK3CEcd4ZR4AzjhCuM484wjrvCKXUFEWhwEGKoJNhYSYihkvAAB5FmB2GmKsJMnmNBhkoC3XYsLjsmZwUGGhcNPEHr9DAVijE4rObfq4NXcM0g1pz/xiksNaO2GpaqlX37Ccf/uYKq4nwAgrsPoMOPflFj4rO/uBwV2PZ/Rtmej7F9+2mNgBQQnUhYnxGEpo7EEt/NJwGvqdvDVWnHfmSPNzxV5mfWLGAKIKhLb2/PU2DHHu1mvtOZ2Gw2vvnfZqJ2vYPLesLTw3HzQ4T0GNjkr+2oclJSVklRac1eq++C1Wk9WaUVdQ6PnU10eOB3QcgbikJJjA1t1u0X6uJ2Ojj+wcuUfLoBgKrIzoR36YElLMr7YenpdQj57sPQx8M8TqeL7Hyrt/fpQI5nKO9MPXBGA3RJCK8xjHdB50if3RvSUeWirNxBmd2z8MFa7qDMVkl5yUmqivKhtABjWSHm8hMEVp4ktKqIMLcVE3X/fNhcZo67wjnhCqOwOhC5wjl+6vsqvvt5qN7WIyQoAJfTgZsAbBVVdfZIns6AmyCDJ0QFGyoJMVTUClsRAQ7CTA5CjQ6CDRUEUkGgy06AywdBy2T2/JwEf9eLZYqIJXrUzwgIi27Uub9PYakZtfWwBJ5QUrT1bYq2rfNMijYFEDXsJqJG3tLsPR0uRwXlBz7Humcrtm8/8aziOyUgKsG7zN+ScIHPe8Cauz2qrEXYT813sh36CmdJYY3HjYEhBCX39YYnc0zHFtnr11hutxt3pR2X3YrTXobLXobLbsWWl0XJx6sxuKowx3Yi4aczzzo535/cbs/E9uLv9VJ5Q1VpBSajwTN/qMN3S/Fb01L60i83U/D+UnDWv8ftjL0P3h6GkDMcC/X2SBgsdU/ydrnc5J2wecPTgZxiDmYXU2Q9c49dYmyIdwVej85RJMaGUF5R5Qk+5Z5FCdUhyGqrpKy8qkYgqiy3EVh5kghnMbFGKzEmK7HGUmKNVmJNVgINdQeVKreR46d6hE64wig1RVJmjqbSEo0zJBZzaDihQWZCg6v/BHi21wi2fHcsyHM8ODAAg8FwxtWJ5XZHjWspO7WYouz046euuUbgK3dQXnHusOUJWp4AFXJ64DJWnApenrAVbnIQanIQYqwkmAoC3RWYXfY6g1aH635FxICrzlmH86Gw1IzaQ1iq5jhxlMJ/rqD84BcABETGE/vDe5r8lhGuqsrvAtI3n+B2fDdGHxAV7xli6zMCS2L3Jg0L/mwPt9tN1clj3uBkP7wbl73mBFhTRAeCk/sRckE/gpL7tqiboLrdbtwVtlNhx4rLXobz1FeXvQxX+RmOnRaOam7HUFPgBf3peMtvMQaFNuMVyfeV5Bwga8cm4qMjMDkrcJWX4aoo87RhuRVXRXW72qCRvQ8YjKf1UoXW6LX6rjcrtEYYMwaGUOwIILPQwYFjVu9cqPwTtnO+nBEX0cayU+HHE4JiTgWhWKOVcGPd84bcgN0Ujj0wmqrgWFyhHTCExxEQFU9QTCIhsR0ICw4kNNhMsI92x/f1v1dOpwvbqQBZHaBqBqxTx+y1j5XZHZRX1L2i1YD7uyHD08OW0TNicNXPbmXogORGX8fptIO3NAlzTCcSb3+csn3/4/jGl6gqzifvb08T0msQsT+8B3NUgs9ey1VVSfnBLynbs5Wyb3aeukWLR0Bk3HcBqWOPNtmb8n0GgwFzTCfMMZ2I+MGPT813OuiZKH7oK+zZe3GWFGL9ajPWrzYDnu0QPL1O/Qnq2qfRE/Tdbhcuu6122KkRdL57/Luw4zl++m7oDWIMwBQcijHQ8wHotgRTFNiBhB/fpaDUAgREd6Si6w8IO8eHs9vtwlVR7vm58Aaq7773BCtbzWPVP3PlZZ55fW4XrvJSXOWl1G9w6TuxQAdLMMODQjHFh+LuEoLNZaHEEcAJu4n8MjdldidxAWXEmqxEUUqYuxTjuQJeYCgBkfFYYhIwRyVgjoonICqBgKgEzJFxrX51mclkJDzEQnhIwyZvVzld2OxV34Wq8spavXZl3wthJ+0OsssdGI0GfhId4eMrqj+FJTlvBoOBsJThhHQfyMktf6N4+7vYvv2E8kNfETXiFiKHj23wSgh3lQPbodMC0mkrxUwRHTxzkPqMILBTz3YRkOpiMJoI6tyLoM69iB75E1yOiprznfIOUZmfRWV+FsU7/gHGAIK6XOgdsjOFRNTo5XGVW2v+/XtDXr7qETjTfIQzD72cvgrHU8Zgrrmay2azkbdnDwZj61/x1J4YDEZMp3qEiDq/57rdbtxVlTV6qpw1eq3KvvdzXP2z7Anx1SvH3JXlOCvLvUPbZjwhKhboBVD9T9hpP+6GAAsBkZ7eIHN1CIqKJ+BUKDIpsNcpwGQkItRCRGjrWCl3OoUlaTCjJYjYK8YT3u9yCjcsw354Nyc/eoPSXf+hw48m1nuSrdvpoPzQV54htn07cJ0ekMJjvJO0PQFJH4pnYzQHEtJ9ACHdBwDgLCum/PBu7/5OVcUF2LMysGdlcPKjNxr1Wue3pLjmnBOt5pPGMBgMGMyBp36OYs/7+W5nFa4KT3ByenutvhsmPH3Y1xOMEk4Fo3hMYVH6N6idUliSRrN06ELHO2dRlrGF4//6C1Unc8l9YzahKcOIvfpuAiI61HqO21lFeeYurBlbsX2zvcbcG1NYDKF9hhOWOoLAzhfqH6cGMoVGEpY6krDUkZ75TkV53l6n8sO7cTsdtZbw1pr/Efz9xz1/b+3DCdJ+GUwBmEIiMIVEoJ9iqS+FJfEJg8FA2EWjCOn5A05+9CbFO9dTtvd/2A58TvSlPyVy6PWAgfLDuynL2ErZN9txlVu9zzeFRnn3QQrs0lsByccMBgPm6ETM0YlEXPJDf1dHRKRV8XtYcrlcvPDCC/ztb3+jpKSEH/zgBzzxxBN069btjOUdDgfPP/8869ato7S0lIsvvpjHHnuMPn36AJ7x7FdffZXXX3+dY8eO0bt3b+6//35GjBjhPcfu3buZN28eX331FYGBgfzwhz/k4YcfJiLCf5PH2gpjYAixV99NWL/LOf7P5diP7OHEv1+l5PMPPF3f5aXesqbQKEJThhGaOoKgLinter8gERFpufz+v++LFy/mjTfeYPbs2bz55psYDAYmTZpEZWXlGcvPmjWL1atX89RTT7FmzRqioqKYNGkSpaWeD+Fly5bxzDPPcOedd7Ju3Tquv/56Jk+ezI4dOwDIz8/n7rvvpmvXrqxdu5bFixfz2WefMWPGjGa75vYgMCGZjuOfIu6GKRhDIqgqysNVXooxJIKIS35Ex7Qn6XrfUjr8eBLBXS9SUBIRkRbLrz1LlZWVrFy5kmnTpjF69GgA0tPTGTVqFB988AHXXXddjfJHjhxh9erV/PnPf2bMmDEAzJkzh5tuuondu3czfPhwli9fzoQJE0hLSwMgOTmZffv2sWjRIoYMGUJOTg6jRo3iiSeeICAggOTkZH7605+Snp7erNfeHhgMBsL7XU7IhUMo2/s/zFHxBHVNVTASEZFWxa9hae/evZSVlTFs2DDvsYiICFJTU9m5c2etsLRlyxYiIiK47LLLapTfvNmzp8yJEycoLi5m8OCaGyT26dOHdevW4XQ6GThwIAMHfrdKa//+/axdu5aRI0c26lrcbjc227k3Njtf5eXlNb62TgYCLhyOGyi3n/1+V61B22iPtkPt0bKoPVoWtce5ud3uem1D49ewlJubC0DHjh1rHI+Pj+fYsWO1ymdmZpKUlMTGjRtZunQpeXl5pKamMnPmTHr06EFkZCQWi6XWc3NycnA4HJSUlBAd/d19ZX70ox+RmZlJ586dWbx4caOuxeFwsGfPnkadoy6ZmZlNdm45f2qPlkXt0bKoPVoWtUfdLJZz7/vk17BUnXa/X9HAwECKi4trlbdarWRlZbF48WKmT59OREQES5YsYdy4caxfv57Y2FjGjh3LkiVLSE1NpV+/fmzfvp01a9YA1JoHNX/+fOx2O/Pnz2fChAm88847hIY2bFMxs9lMz549G/TcupSXl5OZmUlycjLBwY3bfVkaT+3Rsqg9Wha1R8ui9ji3/fv316ucX8NSUFAQ4Akx1d8DVFRUnLFhzWYzpaWlpKen06NHD8Azx2n06NGsXbuWiRMnMnPmTCoqKhg3bhwAvXr1YtKkScybN4/w8PAa56u+F8zChQsZPXo0H3zwATfddFODrsVgMDTpvcKCg4NbxL3hxEPt0bKoPVoWtUfLovY4u/reCcKvq+Gqh9/y8/NrHM/PzycxMbFW+cTERAICArxBCTyBKykpiezsbADCwsKYN28en3/+OR9++CHvvPMOwcHBxMXFERISwoEDB/jwww9rnDc+Pp7IyEjy8vJ8fYkiIiLSyvk1LKWkpBAWFsb27du9x0pKSsjIyGDQoEG1yg8aNIiqqirvXYIB7HY7R44c8e7L9Nhjj/HWW28RGBhIXFwcABs2bGDUqFEA/Pe//+X+++/Hav1uQ8SsrCxOnjxZI4SJiIiIgJ/DksViIS0tjfnz57Np0yb27t3Lgw8+SGJiIldffTVOp5OCggLsds+NDwcNGsSIESOYMWMGn3zyCfv372f69OmYTCbGjh0LeHqrFixYwLZt28jKyuLJJ58kIyODyZMnAzB27FjCw8OZNm0a3377LZ988gn33Xcf/fr14/LLL/fbeyEiIiItk9938L7vvvuoqqri8ccfx263M3jwYFasWIHFYiE7O5srr7ySuXPncssttwCe+UXz589nypQp2O12LrnkEl555RViYmIAmDx5MjabjWnTplFWVsaAAQNYtWqVt+cpOjqaV155haeffpo77rgDk8nElVdeycyZMzGZtP+PiIiI1GRwu91uf1eitaseFqyeMO5LNpuNPXv20KdPH03QawHUHi2L2qNlUXu0LGqPc6vv57ffb3ciIiIi0pIpLImIiIjUQWFJREREpA4KSyIiIiJ1UFgSERERqYNWw/nAZ599htvtrtfN+M6X2+3G4XBgNpvrvS27NB21R8ui9mhZ1B4ti9rj3CorKzEYDFxyySV1lvP7PkttQVP+EBoMhiYJYdIwao+WRe3Rsqg9Wha1x7kZDIZ6fYarZ0lERESkDpqzJCIiIlIHhSURERGROigsiYiIiNRBYUlERESkDgpLIiIiInVQWBIRERGpg8KSiIiISB0UlkRERETqoLAkIiIiUgeFJREREZE6KCyJiIiI1EFhSURERKQOCkstlMvl4vnnn2fUqFH079+fe+65h8OHD/u7Wu1WUVERv//977nsssu45JJLuOOOO/jkk0/8XS0BDh06xMCBA3n77bf9XZV2b926dVx77bX07duX6667jvfff9/fVWq3HA4H6enpjBkzhoEDBzJu3Dg+++wzf1er1VJYaqEWL17MG2+8wezZs3nzzTcxGAxMmjSJyspKf1etXXrooYf48ssvee6551i9ejUXXXQRv/jFLzhw4IC/q9auORwOHn74YWw2m7+r0u698847PProo9x222384x//4Nprr+Whhx7i888/93fV2qUlS5awZs0aZs+ezbp16+jevTuTJk0iLy/P31VrlRSWWqDKykpWrlzJ1KlTGT16NCkpKaSnp5OXl8cHH3zg7+q1O4cPH+bjjz/miSeeYNCgQXTv3p3HHnuMhIQE/vGPf/i7eu3awoULCQ0N9Xc12j23282CBQu46667uOuuu+jWrRu/+c1vGDFiBDt27PB39dqlTZs2cf3113PppZfSrVs3Zs6cidVq5YsvvvB31VolhaUWaO/evZSVlTFs2DDvsYiICFJTU9m5c6cfa9Y+RUdHs3TpUi6++GLvMYPBgNvtpri42I81a9927tzJm2++yTPPPOPvqrR7Bw8eJCcnhxtuuKHG8RUrVvDLX/7ST7Vq36Kiovj3v/9NdnY2TqeTN998E4vFQp8+ffxdtVYpwN8VkNpyc3MB6NixY43j8fHxHDt2zB9VatciIiIYPXp0jWPvv/8+WVlZXHrppX6qVftWUlLC9OnTefzxx2v9nkjzy8zMBMBms/GLX/yCjIwMunTpwq9+9SuuuOIK/1aunXrsscd48MEHufLKKzGZTBiNRhYsWEDXrl39XbVWST1LLVB5eTkAFoulxvHAwEAqKir8USU5zaeffsqjjz7KlVdeqQ8CP5k1axYDBgyo1ZMh/mG1WgGYMWMG119/PStXrmTkyJH8+te/Ztu2bX6uXft04MABIiIiWLRoEW+++Sa33HILM2bMYO/evf6uWquknqUWKCgoCPDMXar+HqCiooLg4GB/VUuAf/3rXzz88MP079+f5557zt/VaZfWrVvHJ598wrvvvuvvqsgpZrMZgF/84hfcfPPNAPTp04eMjAxeeuklhg8f7s/qtTs5OTlMmzaNl19+mUGDBgHQt29f9u/fz8KFC1m0aJGfa9j6qGepBaoeVsjPz69xPD8/n8TERH9USYBXX32VqVOnctlll7Fs2bIaQVaaz5o1azh+/Lh3SfTAgQMBeOKJJ7juuuv8XLv2qfrfpQsvvLDG8Z49e5Kdne2PKrVrX331FQ6Hg759+9Y43r9/f++QqZwf9Sy1QCkpKYSFhbF9+3bv+HJJSQkZGRmkpaX5uXbt0+uvv85TTz3F+PHjefTRRzEa9f8Z/jJ//nzsdnuNYz/84Q+57777uPbaa/1Uq/YtNTWV0NBQvvzyS29PBsA333yjOTJ+UP0/3Pv27aNfv37e49988w3dunXzV7VaNYWlFshisZCWlsb8+fOJiYmhc+fOzJs3j8TERK6++mp/V6/dOXToEHPmzOHqq6/ml7/8JcePH/c+FhQURHh4uB9r1/4kJCSc8XhsbCydO3du5toIeH4PJk6cyKJFi0hISKBfv3689957fPzxx7z88sv+rl67069fPwYNGsSMGTN44oknSExMZN26dWzbto3XX3/d39VrlRSWWqj77ruPqqoqHn/8cex2O4MHD2bFihW1Jn1L0/vnP/+Jw+Hggw8+qLXP1c0338zTTz/tp5qJtBy//vWvCQ4O9u4J16NHDxYuXMjQoUP9XbV2x2g0snjxYv70pz/xyCOPUFxczIUXXsjLL7/MgAED/F29Vsngdrvd/q6EiIiISEuliRciIiIidVBYEhEREamDwpKIiIhIHRSWREREROqgsCQiIiJSB4UlERERkTooLImIiIjUQWFJREREpA4KSyIiTeSKK65g5syZ/q6GiDSSwpKIiIhIHRSWREREROqgsCQibc7f/vY3rrvuOi6++GLGjBnDwoULqaqqAmDmzJmMHz+e1atXc/nllzNw4EAmTJhARkZGjXNkZmZy3333MXLkSAYMGMD48eP59NNPa5QpKytj7ty5XHbZZQwYMIBbbrmFzZs31yjjcDj44x//6D3PPffcw+HDh5v2DRARn1JYEpE25c9//jO/+93vGD58OC+++CJ33nkny5Yt4/e//723zJ49e0hPT2fKlCnMmzePoqIixo8fT15eHgD79+/nlltu4ciRIzz++OPMnz8fg8HAXXfdxY4dOwBwuVxMnDiRtWvXcu+997JkyRIuvPBCpkyZwvbt272vtX79er799luefvppfv/737Nr1y4efPDB5n1TRKRRAvxdARERXyktLWXJkiXcdtttPP744wBceumlREVF8fjjj3P33XfXKDd48GAA+vXrx1VXXcXLL7/MjBkzeOGFFzCbzbzyyiuEh4cDMGbMGK6//nrmzZvH3/72Nz766CM+++wzFi9ezJVXXgnAsGHDOHz4MP/73/8YOnQoAAkJCSxevBiz2QzA4cOHefHFF7FarYSFhTXr+yMiDaOwJCJtxueff055eTlXXHGFd9gNPKvSAD7++GMAOnXq5A1KAPHx8QwcONA7zLZjxw4uv/xyb1ACCAgI4LrrrmPRokWUlZXxySefYDabufzyy71lDAYDf/3rX2vUqV+/ft6gBJCUlARASUmJwpJIK6GwJCJtRlFREQD33nvvGR/Pz88HPOHo+2JjY/n6668BKC4upkOHDrXKdOjQAbfbjdVqpaioiKioKIzGumczhISE1Ph7dXmXy1X3xYhIi6GwJCJtRkREBADz588nOTm51uMdOnRgwYIF3lB1usLCQmJjYwGIjIyksLCwVpmCggIAoqOjCQ8Pp6ioCJfLVSMw7dmzh6qqKvr27euDKxKRlkATvEWkzejfvz9ms5m8vDz69u3r/WM2m3n22WfJzs4GICsri/3793ufl5eXxxdffMHw4cMBGDx4MP/+978pLS31lnE6nbz33nv07dsXi8XCoEGDcDgcfPjhh94ybrebxx57jCVLljTTFYtIc1DPkoi0GdHR0UycOJEFCxZgtVoZOnQoeXl5LFiwAIPBQEpKCuAJNb/+9a954IEHMJlMvPDCC0RERDB+/HgApkyZwkcffcSECRO49957sVgsvPrqqxw5coTly5cDngnfAwcO5JFHHuH++++nW7duvPvuu3zzzTf87ne/89t7ICK+p7AkIm3KAw88QFxcHK+//jrLly8nMjKS4cOH89BDD3knbHfq1Im7776bOXPmUF5ezogRI1iyZAlRUVEA9OrVi9dff53nnnuORx99FIPBQL9+/XjllVcYNGgQACaTiWXLlvHss8+ycOFCbDYbKSkpLF++nIEDB/rr8kWkCRjcbrfb35UQEWkuM2fOZMeOHbU2jxQRORvNWRIRERGpg8KSiIiISB00DCciIiJSB/UsiYiIiNRBYUlERESkDgpLIiIiInVQWBIRERGpg8KSiIiISB0UlkRERETqoLAkIiIiUgeFJREREZE6/D+f0ra9LHT+HgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# summarize history for accuracy\n",
    "plt.plot(history1.history['accuracy'])\n",
    "plt.plot(history1.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history1.history['loss'])\n",
    "plt.plot(history1.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Uninfected       1.00      0.00      0.00      4146\n",
      " Parasitized       0.50      1.00      0.67      4122\n",
      "\n",
      "    accuracy                           0.50      8268\n",
      "   macro avg       0.75      0.50      0.33      8268\n",
      "weighted avg       0.75      0.50      0.33      8268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_pred = model1.predict(X_test)\n",
    "y_pred = np.argmax(y_pred, axis=1)\n",
    "print(classification_report(np.argmax(y_test, axis=1), y_pred, target_names=directories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report = classification_report(np.argmax(y_test, axis=1), y_pred, target_names=directories, output_dict=True)\n",
    "\n",
    "# Convert report to a dataframe\n",
    "df = pd.DataFrame(report).transpose()\n",
    "\n",
    "# Generate heatmap\n",
    "sns.heatmap(df.drop(['macro avg', 'weighted avg'], axis=0).iloc[:, :3], annot=True, cmap='Blues')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "259/259 [==============================] - 33s 125ms/step\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "y_score = y_pred # get the prediction probabilities\n",
    " # number of classes\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(num_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "# Plot of a ROC curve for a specific class\n",
    "for i in range(num_classes):\n",
    "    plt.figure()\n",
    "    plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f)' % roc_auc[i])\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 62, 62, 32)        896       \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 62, 62, 32)        0         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 60, 60, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 30, 30, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 28800)             0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 512)               14746112  \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 512)               0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 2)                 1026      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 14,757,282\n",
      "Trainable params: 14,757,282\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.layers import Conv2D, Dense, Dropout, MaxPooling2D, Flatten\n",
    "\n",
    "# Create the model\n",
    "model2 = Sequential()\n",
    "model2.add(Conv2D(filters=32, kernel_size=(3, 3), input_shape=(64, 64, 3), \n",
    "                 activation='relu', kernel_regularizer=l2(0.01)))\n",
    "model2.add(Dropout(0.3))\n",
    "model2.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu', kernel_regularizer=l2(0.01)))\n",
    "model2.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model2.add(Flatten())\n",
    "model2.add(Dense(512, activation='relu', kernel_regularizer=l2(0.01)))\n",
    "model2.add(Dropout(0.4))\n",
    "model2.add(Dense(num_classes, activation='softmax')) # num_classes = 8\n",
    "\n",
    "# Compile model\n",
    "model2.compile(loss='binary_crossentropy', \n",
    "              optimizer='adam', metrics=['accuracy'])\n",
    "print(model2.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "1206/1206 [==============================] - 26s 20ms/step - loss: 10.4715 - accuracy: 0.5500 - val_loss: 2.5409 - val_accuracy: 0.5772\n",
      "Epoch 2/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 1.7966 - accuracy: 0.5785 - val_loss: 1.3114 - val_accuracy: 0.5949\n",
      "Epoch 3/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 1.1923 - accuracy: 0.5882 - val_loss: 1.0301 - val_accuracy: 0.5665\n",
      "Epoch 4/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 1.0543 - accuracy: 0.5947 - val_loss: 1.0232 - val_accuracy: 0.5510\n",
      "Epoch 5/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.9305 - accuracy: 0.5992 - val_loss: 0.8796 - val_accuracy: 0.6032\n",
      "Epoch 6/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.9171 - accuracy: 0.6059 - val_loss: 0.8343 - val_accuracy: 0.6193\n",
      "Epoch 7/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.8220 - accuracy: 0.6089 - val_loss: 0.7743 - val_accuracy: 0.6178\n",
      "Epoch 8/40\n",
      "1206/1206 [==============================] - 25s 20ms/step - loss: 0.8262 - accuracy: 0.6215 - val_loss: 0.7434 - val_accuracy: 0.6082\n",
      "Epoch 9/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.7439 - accuracy: 0.6117 - val_loss: 0.7320 - val_accuracy: 0.6160\n",
      "Epoch 10/40\n",
      "1206/1206 [==============================] - 25s 20ms/step - loss: 0.7302 - accuracy: 0.6110 - val_loss: 0.7020 - val_accuracy: 0.6211\n",
      "Epoch 11/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.7216 - accuracy: 0.6195 - val_loss: 0.6995 - val_accuracy: 0.6208\n",
      "Epoch 12/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.7067 - accuracy: 0.6152 - val_loss: 0.6984 - val_accuracy: 0.6155\n",
      "Epoch 13/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.7019 - accuracy: 0.6074 - val_loss: 0.7120 - val_accuracy: 0.5757\n",
      "Epoch 14/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.6199 - accuracy: 0.7755 - val_loss: 0.4308 - val_accuracy: 0.9311\n",
      "Epoch 15/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.4048 - accuracy: 0.9218 - val_loss: 0.3737 - val_accuracy: 0.9323\n",
      "Epoch 16/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3795 - accuracy: 0.9272 - val_loss: 0.3037 - val_accuracy: 0.9366\n",
      "Epoch 17/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3609 - accuracy: 0.9241 - val_loss: 0.3665 - val_accuracy: 0.9216\n",
      "Epoch 18/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3515 - accuracy: 0.9272 - val_loss: 0.3132 - val_accuracy: 0.9393\n",
      "Epoch 19/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3480 - accuracy: 0.9262 - val_loss: 0.3177 - val_accuracy: 0.9302\n",
      "Epoch 20/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3410 - accuracy: 0.9296 - val_loss: 0.2966 - val_accuracy: 0.9406\n",
      "Epoch 21/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3341 - accuracy: 0.9284 - val_loss: 0.3325 - val_accuracy: 0.9399\n",
      "Epoch 22/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3392 - accuracy: 0.9274 - val_loss: 0.2905 - val_accuracy: 0.9370\n",
      "Epoch 23/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3265 - accuracy: 0.9295 - val_loss: 0.2783 - val_accuracy: 0.9351\n",
      "Epoch 24/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3269 - accuracy: 0.9287 - val_loss: 0.3574 - val_accuracy: 0.9194\n",
      "Epoch 25/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3243 - accuracy: 0.9293 - val_loss: 0.2901 - val_accuracy: 0.9291\n",
      "Epoch 26/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3203 - accuracy: 0.9287 - val_loss: 0.2957 - val_accuracy: 0.9342\n",
      "Epoch 27/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3207 - accuracy: 0.9285 - val_loss: 0.2742 - val_accuracy: 0.9394\n",
      "Epoch 28/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3273 - accuracy: 0.9234 - val_loss: 0.2655 - val_accuracy: 0.9384\n",
      "Epoch 29/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3212 - accuracy: 0.9301 - val_loss: 0.2767 - val_accuracy: 0.9387\n",
      "Epoch 30/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3272 - accuracy: 0.9268 - val_loss: 0.2884 - val_accuracy: 0.9384\n",
      "Epoch 31/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3145 - accuracy: 0.9299 - val_loss: 0.3202 - val_accuracy: 0.9265\n",
      "Epoch 32/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3167 - accuracy: 0.9312 - val_loss: 0.3067 - val_accuracy: 0.9301\n",
      "Epoch 33/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3176 - accuracy: 0.9306 - val_loss: 0.2813 - val_accuracy: 0.9380\n",
      "Epoch 34/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3123 - accuracy: 0.9314 - val_loss: 0.2733 - val_accuracy: 0.9343\n",
      "Epoch 35/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3133 - accuracy: 0.9309 - val_loss: 0.2824 - val_accuracy: 0.9334\n",
      "Epoch 36/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3126 - accuracy: 0.9290 - val_loss: 0.3006 - val_accuracy: 0.9289\n",
      "Epoch 37/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3027 - accuracy: 0.9303 - val_loss: 0.3155 - val_accuracy: 0.9246\n",
      "Epoch 38/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3041 - accuracy: 0.9317 - val_loss: 0.2822 - val_accuracy: 0.9338\n",
      "Epoch 39/40\n",
      "1206/1206 [==============================] - 25s 20ms/step - loss: 0.2992 - accuracy: 0.9331 - val_loss: 0.2807 - val_accuracy: 0.9381\n",
      "Epoch 40/40\n",
      "1206/1206 [==============================] - 24s 20ms/step - loss: 0.3052 - accuracy: 0.9304 - val_loss: 0.2986 - val_accuracy: 0.9328\n",
      "40\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "\n",
    "callback = EarlyStopping(monitor='loss', patience=4)\n",
    "epochs = 40\n",
    "seed = 1234\n",
    "np.random.seed(seed)\n",
    "history = model2.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=16)\n",
    "\n",
    "\n",
    "print(len(history.history['loss']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "259/259 [==============================] - 1s 5ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Uninfected       0.95      0.92      0.93      4146\n",
      " Parasitized       0.92      0.95      0.93      4122\n",
      "\n",
      "    accuracy                           0.93      8268\n",
      "   macro avg       0.93      0.93      0.93      8268\n",
      "weighted avg       0.93      0.93      0.93      8268\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_pred = model2.predict(X_test)\n",
    "y_pred = np.argmax(y_pred, axis=1)\n",
    "print(classification_report(np.argmax(y_test, axis=1), y_pred, target_names=directories))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report = classification_report(np.argmax(y_test, axis=1), y_pred, target_names=directories, output_dict=True)\n",
    "\n",
    "# Convert report to a dataframe\n",
    "df = pd.DataFrame(report).transpose()\n",
    "\n",
    "# Generate heatmap\n",
    "sns.heatmap(df.drop(['macro avg', 'weighted avg'], axis=0).iloc[:, :3], annot=True, cmap='Blues')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "259/259 [==============================] - 1s 5ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "y_score = model2.predict(X_test) # get the prediction probabilities\n",
    " # number of classes\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(num_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "# Plot of a ROC curve for a specific class\n",
    "for i in range(num_classes):\n",
    "    plt.figure()\n",
    "    plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f)' % roc_auc[i])\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic')  \n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras_tuner as kt\n",
    "\n",
    "# Step 1: build the model and compile it with the parametrs you want to optimise and search through using: \n",
    "# hp.choice('units', [8,16,32])\n",
    "\n",
    "def build_model(hp):\n",
    "    # Create the model\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(hp.Choice('filters', [16, 32, 64]), \n",
    "                     kernel_size=(3,3), \n",
    "                     input_shape=(64, 64, 3), \n",
    "                     activation='relu', \n",
    "                     kernel_regularizer=l2(hp.Choice('l2_reg', [0.01, 0.001]))))\n",
    "    model.add(Dropout(hp.Choice('dropout_rate', [0.3, 0.4])))\n",
    "    model.add(Conv2D(filters=32, kernel_size=(3, 3), \n",
    "                     activation='relu', \n",
    "                     kernel_regularizer=l2(hp.Choice('l2_reg', [0.01, 0.001]))))\n",
    "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(hp.Choice('dense_units', [512, 1024]), activation='relu', \n",
    "                    kernel_regularizer=l2(hp.Choice('l2_reg', [0.01, 0.001]))))\n",
    "    model.add(Dropout(hp.Choice('dropout_rate', [0.3, 0.4])))\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: use RandomSearch from keras_tuner\n",
    "tuner = kt.RandomSearch(\n",
    "    build_model,\n",
    "    objective='val_loss',\n",
    "    max_trials=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 5 Complete [00h 08m 50s]\n",
      "val_loss: 1.079538106918335\n",
      "\n",
      "Best val_loss So Far: 0.3074723780155182\n",
      "Total elapsed time: 00h 29m 51s\n",
      "INFO:tensorflow:Oracle triggered exit\n",
      "WARNING:tensorflow:Detecting that an object or model or tf.train.Checkpoint is being deleted with unrestored values. See the following logs for the specific values in question. To silence these warnings, use `status.expect_partial()`. See https://www.tensorflow.org/api_docs/python/tf/train/Checkpoint#restorefor details about the status object returned by the restore function.\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.iter\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_1\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.beta_2\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.decay\n",
      "WARNING:tensorflow:Value in checkpoint could not be found in the restored object: (root).optimizer.learning_rate\n"
     ]
    }
   ],
   "source": [
    "# Step 3: start the search\n",
    "tuner.search(X_train, y_train, epochs=25, validation_data=(X_test, y_test))\n",
    "best_model = tuner.get_best_models()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results summary\n",
      "Results in .\\untitled_project\n",
      "Showing 10 best trials\n",
      "Objective(name=\"val_loss\", direction=\"min\")\n",
      "\n",
      "Trial 0 summary\n",
      "Hyperparameters:\n",
      "filters: 32\n",
      "l2_reg: 0.01\n",
      "dropout_rate: 0.4\n",
      "dense_units: 1024\n",
      "Score: 0.3074723780155182\n",
      "\n",
      "Trial 1 summary\n",
      "Hyperparameters:\n",
      "filters: 32\n",
      "l2_reg: 0.01\n",
      "dropout_rate: 0.3\n",
      "dense_units: 512\n",
      "Score: 0.7009240984916687\n",
      "\n",
      "Trial 3 summary\n",
      "Hyperparameters:\n",
      "filters: 32\n",
      "l2_reg: 0.01\n",
      "dropout_rate: 0.4\n",
      "dense_units: 512\n",
      "Score: 0.7108293175697327\n",
      "\n",
      "Trial 4 summary\n",
      "Hyperparameters:\n",
      "filters: 32\n",
      "l2_reg: 0.001\n",
      "dropout_rate: 0.3\n",
      "dense_units: 1024\n",
      "Score: 1.079538106918335\n",
      "\n",
      "Trial 2 summary\n",
      "Hyperparameters:\n",
      "filters: 16\n",
      "l2_reg: 0.01\n",
      "dropout_rate: 0.4\n",
      "dense_units: 512\n",
      "Traceback (most recent call last):\n",
      "  File \"c:\\Users\\gauta\\anaconda3\\lib\\site-packages\\keras_tuner\\engine\\base_tuner.py\", line 270, in _try_run_and_update_trial\n",
      "    self._run_and_update_trial(trial, *fit_args, **fit_kwargs)\n",
      "  File \"c:\\Users\\gauta\\anaconda3\\lib\\site-packages\\keras_tuner\\engine\\base_tuner.py\", line 235, in _run_and_update_trial\n",
      "    results = self.run_trial(trial, *fit_args, **fit_kwargs)\n",
      "  File \"c:\\Users\\gauta\\anaconda3\\lib\\site-packages\\keras_tuner\\engine\\tuner.py\", line 287, in run_trial\n",
      "    obj_value = self._build_and_fit_model(trial, *args, **copied_kwargs)\n",
      "  File \"c:\\Users\\gauta\\anaconda3\\lib\\site-packages\\keras_tuner\\engine\\tuner.py\", line 214, in _build_and_fit_model\n",
      "    results = self.hypermodel.fit(hp, model, *args, **kwargs)\n",
      "  File \"c:\\Users\\gauta\\anaconda3\\lib\\site-packages\\keras_tuner\\engine\\hypermodel.py\", line 144, in fit\n",
      "    return model.fit(*args, **kwargs)\n",
      "  File \"c:\\Users\\gauta\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n",
      "    raise e.with_traceback(filtered_tb) from None\n",
      "  File \"c:\\Users\\gauta\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\", line 54, in quick_execute\n",
      "    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n",
      "tensorflow.python.framework.errors_impl.UnknownError: {{function_node __wrapped__MergeV2Checkpoints_device_/job:localhost/replica:0/task:0/device:CPU:0}} Failed to rename: .\\untitled_project\\trial_2\\checkpoint_temp/part-00000-of-00001.data-00000-of-00001 to: .\\untitled_project\\trial_2\\checkpoint.data-00000-of-00001 : Access is denied.\n",
      "; Input/output error [Op:MergeV2Checkpoints]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tuner.results_summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 62, 62, 32)        896       \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 62, 62, 32)        0         \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 60, 60, 32)        9248      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 30, 30, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 28800)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1024)              29492224  \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 1024)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 2)                 2050      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 29,504,418\n",
      "Trainable params: 29,504,418\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "best_model.summary()"
   ]
  }
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