Notebooks/TransferLearningInceptionV3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import pickle as pkl\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from PIL import Image\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import sklearn.metrics as metrics\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras.models import Model, Input, Sequential, load_model\n",
    "from keras.layers import Dense, Flatten, GlobalAveragePooling2D\n",
    "import keras.backend as K\n",
    "import keras.applications as KA\n",
    "import keras.preprocessing as KP\n",
    "from keras.utils import to_categorical\n",
    "from keras.metrics import categorical_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../assets/datasets/X_train.pkl\", 'rb') as X_tr_file:\n",
    "    X_train = pkl.load(X_tr_file)\n",
    "with open(\"../assets/datasets/y_train.pkl\", 'rb') as y_tr_file:\n",
    "    y_train = pkl.load(y_tr_file)\n",
    "with open(\"../assets/datasets/X_test.pkl\", 'rb') as X_te_file:\n",
    "    X_test = pkl.load(X_te_file)\n",
    "with open(\"../assets/datasets/y_test.pkl\", 'rb') as y_te_file:\n",
    "    y_test = pkl.load(y_te_file)\n",
    "\n",
    "labels = [\"Agaricus_arvensis\", \"Agaricus_xanthodermus\", \"Amanita_muscaria\", \n",
    "          \"Amanita_phalloides\", \"Boletus_edulis\", \"Calocybe_gambosa\",\n",
    "         \"Marasmius_oreades\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (2958, 224, 224, 3)\n",
      "y_train (2958, 7)\n",
      "X_test (162, 224, 224, 3)\n",
      "y_test (162, 7)\n"
     ]
    }
   ],
   "source": [
    "X_train = X_train.reshape(-1,224,224,3)\n",
    "X_test = X_test.reshape(-1,224,224,3)\n",
    "\n",
    "labelencoder = LabelEncoder()\n",
    "y_train_encoded = labelencoder.fit_transform(y_train)\n",
    "y_test_encoded = labelencoder.transform(y_test)\n",
    "\n",
    "y_train = to_categorical(y_train_encoded)\n",
    "y_test = to_categorical(y_test_encoded)\n",
    "\n",
    "print('X_train', X_train.shape)\n",
    "print('y_train', y_train.shape)\n",
    "print('X_test', X_test.shape)\n",
    "print('y_test', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7F22E2A7ED30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img0 = X_train[0]\n",
    "x_4d = np.expand_dims(img0, axis=0).astype('float32')\n",
    "Image.fromarray(img0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "####################################\n",
    "# Load the model\n",
    "####################################\n",
    "\n",
    "# InceptionV3\n",
    "inception_model = KA.inception_v3.InceptionV3()\n",
    "preprocess = KA.inception_v3.preprocess_input\n",
    "decoder = KA.inception_v3.decode_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, None, None, 3 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, None, None, 3 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, None, None, 3 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, None, None, 3 9216        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, None, None, 3 96          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, None, None, 3 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, None, None, 6 18432       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, None, None, 6 192         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, None, None, 6 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 6 0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, None, None, 8 5120        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, None, None, 8 240         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, None, None, 8 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, None, None, 1 138240      activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, None, None, 1 576         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, None, None, 1 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, None, None, 1 0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, None, None, 6 192         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, None, None, 6 0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, None, None, 4 9216        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, None, None, 9 55296       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, None, None, 4 144         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, None, None, 9 288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, None, None, 4 0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, None, None, 9 0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, None, None, 1 0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, None, None, 6 76800       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, None, None, 9 82944       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, None, None, 3 6144        average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, None, None, 6 192         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, None, None, 6 192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, None, None, 9 288         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, None, None, 3 96          conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, None, None, 6 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, None, None, 6 0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, None, None, 9 0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, None, None, 3 0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, None, None, 2 0           activation_6[0][0]               \n",
      "                                                                 activation_8[0][0]               \n",
      "                                                                 activation_11[0][0]              \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, None, None, 6 192         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, None, None, 6 0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, None, None, 4 12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, None, None, 9 55296       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, None, None, 4 144         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, None, None, 9 288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, None, None, 4 0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, None, None, 9 0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, None, None, 2 0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, None, None, 6 76800       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, None, None, 9 82944       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, None, None, 6 16384       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, None, None, 6 192         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, None, None, 6 192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, None, None, 9 288         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, None, None, 6 192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, None, None, 6 0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, None, None, 6 0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, None, None, 9 0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, None, None, 6 0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, None, None, 2 0           activation_13[0][0]              \n",
      "                                                                 activation_15[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, None, None, 6 192         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, None, None, 6 0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, None, None, 4 13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, None, None, 9 55296       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, None, None, 4 144         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, None, None, 9 288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, None, None, 4 0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, None, None, 9 0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, None, None, 2 0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, None, None, 6 76800       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, None, None, 9 82944       activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, None, None, 6 18432       average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, None, None, 6 192         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, None, None, 6 192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, None, None, 9 288         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, None, None, 6 192         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, None, None, 6 0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, None, None, 6 0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, None, None, 9 0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, None, None, 6 0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, None, None, 2 0           activation_20[0][0]              \n",
      "                                                                 activation_22[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "                                                                 activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, None, None, 6 18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, None, None, 6 192         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, None, None, 6 0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, None, None, 9 55296       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, None, None, 9 288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, None, None, 9 0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, None, None, 3 995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, None, None, 9 82944       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, None, None, 3 1152        conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, None, None, 9 288         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, None, None, 3 0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, None, None, 9 0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, None, None, 2 0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, None, None, 7 0           activation_27[0][0]              \n",
      "                                                                 activation_30[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, None, None, 1 384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, None, None, 1 0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, None, None, 1 114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, None, None, 1 384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, None, None, 1 0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, None, None, 1 114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, None, None, 1 384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, None, None, 1 384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, None, None, 1 0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, None, None, 1 0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, None, None, 1 114688      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, None, None, 1 114688      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, None, None, 1 384         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, None, None, 1 384         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, None, None, 1 0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, None, None, 1 0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, None, None, 7 0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, None, None, 1 147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, None, None, 1 172032      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, None, None, 1 172032      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, None, None, 1 576         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, None, None, 1 576         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, None, None, 1 576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, None, None, 1 576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, None, None, 1 0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, None, None, 1 0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, None, None, 1 0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, None, None, 1 0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, None, None, 7 0           activation_31[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "                                                                 activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, None, None, 1 480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, None, None, 1 0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, None, None, 1 179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, None, None, 1 480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, None, None, 1 0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, None, None, 1 179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, None, None, 1 480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, None, None, 1 480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, None, None, 1 0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, None, None, 1 0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, None, None, 1 179200      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, None, None, 1 179200      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, None, None, 1 480         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, None, None, 1 480         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, None, None, 1 0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, None, None, 1 0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, None, None, 7 0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, None, None, 1 147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, None, None, 1 215040      activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, None, None, 1 215040      activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, None, None, 1 576         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, None, None, 1 576         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, None, None, 1 576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, None, None, 1 576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, None, None, 1 0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, None, None, 1 0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, None, None, 1 0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, None, None, 1 0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, None, None, 7 0           activation_41[0][0]              \n",
      "                                                                 activation_44[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "                                                                 activation_50[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, None, None, 1 480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, None, None, 1 0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, None, None, 1 179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, None, None, 1 480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, None, None, 1 0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, None, None, 1 179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, None, None, 1 480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, None, None, 1 480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, None, None, 1 0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, None, None, 1 0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, None, None, 1 179200      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, None, None, 1 179200      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, None, None, 1 480         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, None, None, 1 480         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, None, None, 1 0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, None, None, 1 0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, None, None, 7 0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, None, None, 1 147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, None, None, 1 215040      activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, None, None, 1 215040      activation_58[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, None, None, 1 576         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, None, None, 1 576         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, None, None, 1 576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, None, None, 1 576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, None, None, 1 0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, None, None, 1 0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, None, None, 1 0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, None, None, 1 0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, None, None, 7 0           activation_51[0][0]              \n",
      "                                                                 activation_54[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "                                                                 activation_60[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, None, None, 1 576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, None, None, 1 0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, None, None, 1 258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, None, None, 1 576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, None, None, 1 0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, None, None, 1 258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, None, None, 1 576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, None, None, 1 576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, None, None, 1 0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, None, None, 1 0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, None, None, 1 258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, None, None, 1 258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, None, None, 1 576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, None, None, 1 576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, None, None, 1 0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, None, None, 1 0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, None, None, 7 0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, None, None, 1 258048      activation_63[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, None, None, 1 258048      activation_68[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, None, None, 1 576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, None, None, 1 576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, None, None, 1 576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, None, None, 1 576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, None, None, 1 0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, None, None, 1 0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, None, None, 1 0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, None, None, 1 0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, None, None, 7 0           activation_61[0][0]              \n",
      "                                                                 activation_64[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "                                                                 activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, None, None, 1 576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, None, None, 1 0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, None, None, 1 258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, None, None, 1 576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, None, None, 1 0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, None, None, 1 258048      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, None, None, 1 576         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, None, None, 1 576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, None, None, 1 0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, None, None, 1 0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, None, None, 3 552960      activation_71[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, None, None, 1 331776      activation_75[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, None, None, 3 960         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, None, None, 1 576         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, None, None, 3 0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, None, None, 1 0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, None, None, 7 0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, None, None, 1 0           activation_72[0][0]              \n",
      "                                                                 activation_76[0][0]              \n",
      "                                                                 max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, None, None, 4 573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, None, None, 4 1344        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, None, None, 4 0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, None, None, 3 491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, None, None, 3 1548288     activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, None, None, 3 1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, None, None, 3 1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, None, None, 3 0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, None, None, 3 0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, None, None, 3 442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, None, None, 3 442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, None, None, 3 442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, None, None, 3 442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, None, None, 1 0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, None, None, 3 409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, None, None, 3 1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, None, None, 3 1152        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, None, None, 3 1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, None, None, 3 1152        conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, None, None, 1 245760      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, None, None, 3 960         conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, None, None, 3 0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, None, None, 3 0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, None, None, 3 0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, None, None, 3 0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, None, None, 1 576         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, None, None, 3 0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_79[0][0]              \n",
      "                                                                 activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, None, None, 7 0           activation_83[0][0]              \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, None, None, 1 0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, None, None, 2 0           activation_77[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_85[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, None, None, 4 917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, None, None, 4 1344        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, None, None, 4 0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, None, None, 3 786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, None, None, 3 1548288     activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, None, None, 3 1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, None, None, 3 1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, None, None, 3 0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, None, None, 3 0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, None, None, 3 442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, None, None, 3 442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, None, None, 3 442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, None, None, 3 442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePoo (None, None, None, 2 0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, None, None, 3 655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, None, None, 3 1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, None, None, 3 1152        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, None, None, 3 1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, None, None, 3 1152        conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)              (None, None, None, 1 393216      average_pooling2d_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, None, None, 3 960         conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, None, None, 3 0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, None, None, 3 0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, None, None, 3 0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, None, None, 3 0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_94 (BatchNo (None, None, None, 1 576         conv2d_94[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, None, None, 3 0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_88[0][0]              \n",
      "                                                                 activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, None, None, 7 0           activation_92[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, None, None, 1 0           batch_normalization_94[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, None, None, 2 0           activation_86[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_2[0][0]              \n",
      "                                                                 activation_94[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "predictions (Dense)             (None, 1000)         2049000     avg_pool[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 23,851,784\n",
      "Trainable params: 23,817,352\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inception_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train a classifier with the features extracted with the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2048)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extract_features_model = Model(inputs=inception_model.input,\n",
    "                                 outputs=inception_model.get_layer(\"avg_pool\").output)\n",
    "features = extract_features_model.predict(preprocess(x_4d.copy()))\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 41min 22s, sys: 1min 14s, total: 42min 37s\n",
      "Wall time: 13min 55s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "features_train = []\n",
    "train_preprocessed = []\n",
    "\n",
    "for X in X_train:\n",
    "    x_4d = np.expand_dims(X, axis=0).astype('float32')\n",
    "    processed = preprocess(x_4d.copy())\n",
    "    features_train.append(extract_features_model.predict(processed))\n",
    "    train_preprocessed.append(processed)\n",
    "\n",
    "features_train = np.array(features_train)\n",
    "    \n",
    "with open(\"../assets/datasets/features_inception_train.pkl\", 'wb') as f:\n",
    "    pkl.dump(features_train, f)\n",
    "with open(\"../assets/datasets/train_inception_processed.pkl\", 'wb') as f:\n",
    "    pkl.dump(train_preprocessed, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 19s, sys: 4.5 s, total: 2min 23s\n",
      "Wall time: 59.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "features_test = []\n",
    "test_preprocessed = []\n",
    "for X in X_test:\n",
    "    x_4d = np.expand_dims(X, axis=0).astype('float32')\n",
    "    processed = preprocess(x_4d.copy())\n",
    "    features_test.append(extract_features_model.predict(processed))\n",
    "    test_preprocessed.append(processed)\n",
    "\n",
    "features_test = np.array(features_test)\n",
    "    \n",
    "with open(\"../assets/datasets/features_inception_test.pkl\", 'wb') as f:\n",
    "    pkl.dump(features_test, f)\n",
    "with open(\"../assets/datasets/test_inception_processed.pkl\", 'wb') as f:\n",
    "    pkl.dump(test_preprocessed, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../assets/datasets/features_inception_train.pkl\", 'rb') as f:\n",
    "    ft_train = pkl.load(f)\n",
    "    features_train = np.array(ft_train)\n",
    "with open(\"../assets/datasets/features_inception_test.pkl\", 'rb') as f:\n",
    "    ft_test = pkl.load(f)\n",
    "    features_test = np.array(ft_test)\n",
    "with open(\"../assets/datasets/train_inception_processed.pkl\", 'rb') as f:\n",
    "    train_preprocessed = pkl.load(f)\n",
    "    X_train_processed = np.array(train_preprocessed).reshape(-1, 224, 224, 3)\n",
    "with open(\"../assets/datasets/test_inception_processed.pkl\", 'rb') as f:\n",
    "    test_preprocessed = pkl.load(f)\n",
    "    X_test_processed = np.array(test_preprocessed).reshape(-1, 224, 224, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_4 (InputLayer)         (None, 1, 2048)           0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1, 256)            524544    \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 100)               25700     \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 7)                 707       \n",
      "=================================================================\n",
      "Total params: 571,151\n",
      "Trainable params: 571,151\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "a = Input(shape=(1, 2048))\n",
    "x = Dense(256, activation=\"tanh\")(a)\n",
    "x = Flatten()(x)\n",
    "x = Dense(100, activation=\"tanh\")(x)\n",
    "x = Dense(100, activation=\"tanh\")(x)\n",
    "x = Dense(100, activation=\"tanh\")(x)\n",
    "x = Dense(7, activation=\"softmax\")(x)\n",
    "\n",
    "classifier = Model(inputs=a, outputs=x)\n",
    "\n",
    "classifier.compile(optimizer = \"adam\" , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "\n",
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "2958/2958 [==============================] - 3s 985us/step - loss: 1.0938 - acc: 0.5727\n",
      "Epoch 2/10\n",
      "2958/2958 [==============================] - 2s 557us/step - loss: 0.6666 - acc: 0.7454\n",
      "Epoch 3/10\n",
      "2958/2958 [==============================] - 2s 549us/step - loss: 0.5022 - acc: 0.8103\n",
      "Epoch 4/10\n",
      "2958/2958 [==============================] - 2s 564us/step - loss: 0.4307 - acc: 0.8448\n",
      "Epoch 5/10\n",
      "2958/2958 [==============================] - 2s 580us/step - loss: 0.3119 - acc: 0.8857\n",
      "Epoch 6/10\n",
      "2958/2958 [==============================] - 2s 524us/step - loss: 0.2824 - acc: 0.9016\n",
      "Epoch 7/10\n",
      "2958/2958 [==============================] - 2s 550us/step - loss: 0.2157 - acc: 0.9195\n",
      "Epoch 8/10\n",
      "2958/2958 [==============================] - 2s 585us/step - loss: 0.1867 - acc: 0.9304\n",
      "Epoch 9/10\n",
      "2958/2958 [==============================] - 2s 557us/step - loss: 0.1245 - acc: 0.9547\n",
      "Epoch 10/10\n",
      "2958/2958 [==============================] - 2s 573us/step - loss: 0.1068 - acc: 0.9648\n",
      "CPU times: user 25.7 s, sys: 3.75 s, total: 29.5 s\n",
      "Wall time: 18.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fdb50226518>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "classifier.fit(features_train, y_train, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162/162 [==============================] - 0s 165us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.38820940913793484, 0.8827160493827161]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.evaluate(features_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.save(\"../assets/models/transfer_inception_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'features_test' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-b33542dae12b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mclassifier_l\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../assets/models/transfer_inception_model.h5\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mclassifier_l\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'features_test' is not defined"
     ]
    }
   ],
   "source": [
    "classifier_l = load_model(\"../assets/models/transfer_inception_model.h5\")\n",
    "classifier_l.evaluate(features_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, None, None, 3 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, None, None, 3 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, None, None, 3 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, None, None, 3 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, None, None, 3 9216        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, None, None, 3 96          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, None, None, 3 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, None, None, 6 18432       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, None, None, 6 192         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, None, None, 6 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 6 0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, None, None, 8 5120        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, None, None, 8 240         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, None, None, 8 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, None, None, 1 138240      activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, None, None, 1 576         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, None, None, 1 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, None, None, 1 0           activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, None, None, 6 192         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, None, None, 6 0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, None, None, 4 9216        max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, None, None, 9 55296       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, None, None, 4 144         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, None, None, 9 288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, None, None, 4 0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, None, None, 9 0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, None, None, 1 0           max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, None, None, 6 76800       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, None, None, 9 82944       activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, None, None, 3 6144        average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, None, None, 6 192         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, None, None, 6 192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, None, None, 9 288         conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, None, None, 3 96          conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, None, None, 6 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, None, None, 6 0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, None, None, 9 0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, None, None, 3 0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, None, None, 2 0           activation_6[0][0]               \n",
      "                                                                 activation_8[0][0]               \n",
      "                                                                 activation_11[0][0]              \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, None, None, 6 192         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, None, None, 6 0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, None, None, 4 12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, None, None, 9 55296       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, None, None, 4 144         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, None, None, 9 288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, None, None, 4 0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, None, None, 9 0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, None, None, 2 0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, None, None, 6 76800       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, None, None, 9 82944       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, None, None, 6 16384       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, None, None, 6 192         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, None, None, 6 192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, None, None, 9 288         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, None, None, 6 192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, None, None, 6 0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, None, None, 6 0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, None, None, 9 0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, None, None, 6 0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, None, None, 2 0           activation_13[0][0]              \n",
      "                                                                 activation_15[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "                                                                 activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, None, None, 6 192         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, None, None, 6 0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, None, None, 4 13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, None, None, 9 55296       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, None, None, 4 144         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, None, None, 9 288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, None, None, 4 0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, None, None, 9 0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, None, None, 2 0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, None, None, 6 76800       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, None, None, 9 82944       activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, None, None, 6 18432       average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, None, None, 6 192         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, None, None, 6 192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, None, None, 9 288         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, None, None, 6 192         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, None, None, 6 0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, None, None, 6 0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, None, None, 9 0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, None, None, 6 0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, None, None, 2 0           activation_20[0][0]              \n",
      "                                                                 activation_22[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "                                                                 activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, None, None, 6 18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, None, None, 6 192         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, None, None, 6 0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, None, None, 9 55296       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, None, None, 9 288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, None, None, 9 0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, None, None, 3 995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, None, None, 9 82944       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, None, None, 3 1152        conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, None, None, 9 288         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, None, None, 3 0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, None, None, 9 0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, None, None, 2 0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, None, None, 7 0           activation_27[0][0]              \n",
      "                                                                 activation_30[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, None, None, 1 384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, None, None, 1 0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, None, None, 1 114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, None, None, 1 384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, None, None, 1 0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, None, None, 1 114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, None, None, 1 384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, None, None, 1 384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, None, None, 1 0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, None, None, 1 0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, None, None, 1 114688      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, None, None, 1 114688      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, None, None, 1 384         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, None, None, 1 384         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, None, None, 1 0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, None, None, 1 0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, None, None, 7 0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, None, None, 1 147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, None, None, 1 172032      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, None, None, 1 172032      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, None, None, 1 576         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, None, None, 1 576         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, None, None, 1 576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, None, None, 1 576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, None, None, 1 0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, None, None, 1 0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, None, None, 1 0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, None, None, 1 0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, None, None, 7 0           activation_31[0][0]              \n",
      "                                                                 activation_34[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "                                                                 activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, None, None, 1 480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, None, None, 1 0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, None, None, 1 179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, None, None, 1 480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, None, None, 1 0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, None, None, 1 179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, None, None, 1 480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, None, None, 1 480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, None, None, 1 0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, None, None, 1 0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, None, None, 1 179200      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, None, None, 1 179200      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, None, None, 1 480         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, None, None, 1 480         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, None, None, 1 0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, None, None, 1 0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, None, None, 7 0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, None, None, 1 147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, None, None, 1 215040      activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, None, None, 1 215040      activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, None, None, 1 576         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, None, None, 1 576         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, None, None, 1 576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, None, None, 1 576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, None, None, 1 0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, None, None, 1 0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, None, None, 1 0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, None, None, 1 0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, None, None, 7 0           activation_41[0][0]              \n",
      "                                                                 activation_44[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "                                                                 activation_50[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, None, None, 1 480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, None, None, 1 0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, None, None, 1 179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, None, None, 1 480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, None, None, 1 0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, None, None, 1 179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, None, None, 1 480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, None, None, 1 480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, None, None, 1 0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, None, None, 1 0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, None, None, 1 179200      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, None, None, 1 179200      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, None, None, 1 480         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, None, None, 1 480         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, None, None, 1 0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, None, None, 1 0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, None, None, 7 0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, None, None, 1 147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, None, None, 1 215040      activation_53[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, None, None, 1 215040      activation_58[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, None, None, 1 576         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, None, None, 1 576         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, None, None, 1 576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, None, None, 1 576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, None, None, 1 0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, None, None, 1 0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, None, None, 1 0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, None, None, 1 0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, None, None, 7 0           activation_51[0][0]              \n",
      "                                                                 activation_54[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "                                                                 activation_60[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, None, None, 1 576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, None, None, 1 0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, None, None, 1 258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, None, None, 1 576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, None, None, 1 0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, None, None, 1 258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, None, None, 1 576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, None, None, 1 576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, None, None, 1 0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, None, None, 1 0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, None, None, 1 258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, None, None, 1 258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, None, None, 1 576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, None, None, 1 576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, None, None, 1 0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, None, None, 1 0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, None, None, 7 0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, None, None, 1 258048      activation_63[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, None, None, 1 258048      activation_68[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, None, None, 1 576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, None, None, 1 576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, None, None, 1 576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, None, None, 1 576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, None, None, 1 0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, None, None, 1 0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, None, None, 1 0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, None, None, 1 0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, None, None, 7 0           activation_61[0][0]              \n",
      "                                                                 activation_64[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "                                                                 activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, None, None, 1 576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, None, None, 1 0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, None, None, 1 258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, None, None, 1 576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, None, None, 1 0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, None, None, 1 258048      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, None, None, 1 576         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, None, None, 1 576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, None, None, 1 0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, None, None, 1 0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, None, None, 3 552960      activation_71[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, None, None, 1 331776      activation_75[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, None, None, 3 960         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, None, None, 1 576         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, None, None, 3 0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, None, None, 1 0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, None, None, 7 0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, None, None, 1 0           activation_72[0][0]              \n",
      "                                                                 activation_76[0][0]              \n",
      "                                                                 max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, None, None, 4 573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, None, None, 4 1344        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, None, None, 4 0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, None, None, 3 491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, None, None, 3 1548288     activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, None, None, 3 1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, None, None, 3 1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, None, None, 3 0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, None, None, 3 0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, None, None, 3 442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, None, None, 3 442368      activation_78[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, None, None, 3 442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, None, None, 3 442368      activation_82[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, None, None, 1 0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, None, None, 3 409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, None, None, 3 1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, None, None, 3 1152        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, None, None, 3 1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, None, None, 3 1152        conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, None, None, 1 245760      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, None, None, 3 960         conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, None, None, 3 0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, None, None, 3 0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, None, None, 3 0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, None, None, 3 0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, None, None, 1 576         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, None, None, 3 0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_79[0][0]              \n",
      "                                                                 activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, None, None, 7 0           activation_83[0][0]              \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, None, None, 1 0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, None, None, 2 0           activation_77[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_85[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, None, None, 4 917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, None, None, 4 1344        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, None, None, 4 0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, None, None, 3 786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, None, None, 3 1548288     activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, None, None, 3 1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, None, None, 3 1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, None, None, 3 0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, None, None, 3 0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, None, None, 3 442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, None, None, 3 442368      activation_87[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, None, None, 3 442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, None, None, 3 442368      activation_91[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePoo (None, None, None, 2 0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, None, None, 3 655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, None, None, 3 1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, None, None, 3 1152        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, None, None, 3 1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, None, None, 3 1152        conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_94 (Conv2D)              (None, None, None, 1 393216      average_pooling2d_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, None, None, 3 960         conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, None, None, 3 0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, None, None, 3 0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, None, None, 3 0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, None, None, 3 0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_94 (BatchNo (None, None, None, 1 576         conv2d_94[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, None, None, 3 0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_88[0][0]              \n",
      "                                                                 activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, None, None, 7 0           activation_92[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_94 (Activation)      (None, None, None, 1 0           batch_normalization_94[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, None, None, 2 0           activation_86[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_2[0][0]              \n",
      "                                                                 activation_94[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "avg_pool (GlobalAveragePooling2 (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 256)          524544      avg_pool[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 100)          25700       dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 100)          10100       dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 100)          10100       dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 7)            707         dense_4[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 22,373,935\n",
      "Trainable params: 571,151\n",
      "Non-trainable params: 21,802,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "a = extract_features_model.output\n",
    "x = Dense(256, activation=\"tanh\")(a)\n",
    "x = Dense(100, activation=\"tanh\")(x)\n",
    "x = Dense(100, activation=\"tanh\")(x)\n",
    "x = Dense(100, activation=\"tanh\")(x)\n",
    "x = Dense(7, activation=\"softmax\")(x)\n",
    "\n",
    "classifier = Model(inputs=extract_features_model.input, outputs=x)\n",
    "\n",
    "for layer in classifier.layers[:-5]:\n",
    "    layer.trainable=False\n",
    "for layer in classifier.layers[-5:]:\n",
    "    layer.trainable=True\n",
    "\n",
    "classifier.compile(optimizer = \"adam\" , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
    "\n",
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "2958/2958 [==============================] - 545s 184ms/step - loss: 1.4774 - acc: 0.4114\n",
      "Epoch 2/5\n",
      "2958/2958 [==============================] - 541s 183ms/step - loss: 0.9222 - acc: 0.6542\n",
      "Epoch 3/5\n",
      "2958/2958 [==============================] - 540s 183ms/step - loss: 0.7475 - acc: 0.7184\n",
      "Epoch 4/5\n",
      "2958/2958 [==============================] - 537s 182ms/step - loss: 0.6536 - acc: 0.7559\n",
      "Epoch 5/5\n",
      "2958/2958 [==============================] - 538s 182ms/step - loss: 0.5990 - acc: 0.7823\n",
      "CPU times: user 2h 32min 17s, sys: 9min 2s, total: 2h 41min 19s\n",
      "Wall time: 45min 2s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f0b6392b208>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "classifier.fit(X_train_processed, y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier.save(\"../assets/models/transfer_inception_model_tfalse.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = load_model(\"../assets/models/transfer_inception_model.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f22e4017a20>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = classifier.predict(X_test)\n",
    "df_cm = pd.DataFrame(metrics.confusion_matrix(y_test.argmax(axis=1), predicted.argmax(axis=1)), index=labels,\n",
    "                  columns=labels)\n",
    "plt.figure(figsize = (10,7))\n",
    "sns.heatmap(df_cm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_____________________________________________________________"
   ]
  }
 ],
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