2_training-face-mask-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model\n",
    "Using a <b>transfer learning</b> (InceptionV3) model from keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"inception_v3\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 100, 100, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 49, 49, 32)   864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 49, 49, 32)   96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 49, 49, 32)   0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 47, 47, 32)   9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 47, 47, 32)   96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 47, 47, 32)   0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 47, 47, 64)   18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 47, 47, 64)   192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 47, 47, 64)   0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 23, 23, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 23, 23, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 23, 23, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 23, 23, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 21, 21, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 21, 21, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 21, 21, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 10, 10, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 10, 10, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 10, 10, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 10, 10, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 10, 10, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 10, 10, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 10, 10, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 10, 10, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 10, 10, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 10, 10, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 10, 10, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 10, 10, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 10, 10, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 10, 10, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 10, 10, 32)   6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 10, 10, 64)   192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 10, 10, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 10, 10, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 10, 10, 32)   96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 10, 10, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 10, 10, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 10, 10, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 10, 10, 32)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 10, 10, 256)  0           activation_5[0][0]               \n",
      "                                                                 activation_7[0][0]               \n",
      "                                                                 activation_10[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 10, 10, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 10, 10, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 10, 10, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 10, 10, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 10, 10, 96)   55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 10, 10, 48)   144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 10, 10, 96)   288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 10, 10, 48)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 10, 10, 96)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 10, 10, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 10, 10, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 10, 10, 64)   76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 10, 10, 96)   82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 10, 10, 64)   16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 10, 10, 64)   192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 10, 10, 64)   192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 10, 10, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 10, 10, 64)   192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 10, 10, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 10, 10, 64)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 10, 10, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 10, 10, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 10, 10, 288)  0           activation_12[0][0]              \n",
      "                                                                 activation_14[0][0]              \n",
      "                                                                 activation_17[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 10, 10, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 10, 10, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 10, 10, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 10, 10, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 10, 10, 96)   55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 10, 10, 48)   144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 10, 10, 96)   288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 10, 10, 48)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 10, 10, 96)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 10, 10, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 10, 10, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 10, 10, 64)   76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 10, 10, 96)   82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 10, 10, 64)   18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 10, 10, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 10, 10, 64)   192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 10, 10, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 10, 10, 64)   192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 10, 10, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 10, 10, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 10, 10, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 10, 10, 64)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 10, 10, 288)  0           activation_19[0][0]              \n",
      "                                                                 activation_21[0][0]              \n",
      "                                                                 activation_24[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 10, 10, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 10, 10, 64)   192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 10, 10, 64)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 10, 10, 96)   55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 10, 10, 96)   288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 10, 10, 96)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 4, 4, 384)    995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 4, 4, 96)     82944       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 4, 4, 384)    1152        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 4, 4, 96)     288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 4, 4, 384)    0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 4, 4, 96)     0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 4, 4, 288)    0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 4, 4, 768)    0           activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 4, 4, 128)    98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 4, 4, 128)    384         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 4, 4, 128)    0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 4, 4, 128)    114688      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 4, 4, 128)    384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 4, 4, 128)    0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 4, 4, 128)    98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 4, 4, 128)    114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 4, 4, 128)    384         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 4, 4, 128)    384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 4, 4, 128)    0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 4, 4, 128)    0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 4, 4, 128)    114688      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 4, 4, 128)    114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 4, 4, 128)    384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 4, 4, 128)    384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 4, 4, 128)    0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 4, 4, 128)    0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 4, 4, 768)    0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 4, 4, 192)    147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 4, 4, 192)    172032      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 4, 4, 192)    172032      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 4, 4, 192)    147456      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 4, 4, 192)    576         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 4, 4, 192)    576         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 4, 4, 192)    576         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 4, 4, 192)    576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 4, 4, 192)    0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 4, 4, 192)    0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 4, 4, 192)    0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 4, 4, 192)    0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 4, 4, 768)    0           activation_30[0][0]              \n",
      "                                                                 activation_33[0][0]              \n",
      "                                                                 activation_38[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 4, 4, 160)    122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 4, 4, 160)    480         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 4, 4, 160)    0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 4, 4, 160)    179200      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 4, 4, 160)    480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 4, 4, 160)    0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 4, 4, 160)    122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 4, 4, 160)    179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 4, 4, 160)    480         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 4, 4, 160)    480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 4, 4, 160)    0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 4, 4, 160)    0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 4, 4, 160)    179200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 4, 4, 160)    179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 4, 4, 160)    480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 4, 4, 160)    480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 4, 4, 160)    0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 4, 4, 160)    0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 4, 4, 768)    0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 4, 4, 192)    147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 4, 4, 192)    215040      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 4, 4, 192)    215040      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 4, 4, 192)    147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 4, 4, 192)    576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 4, 4, 192)    576         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 4, 4, 192)    576         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 4, 4, 192)    576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 4, 4, 192)    0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 4, 4, 192)    0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 4, 4, 192)    0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 4, 4, 192)    0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 4, 4, 768)    0           activation_40[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "                                                                 activation_48[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 4, 4, 160)    122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 4, 4, 160)    480         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 4, 4, 160)    0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 4, 4, 160)    179200      activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 4, 4, 160)    480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 4, 4, 160)    0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 4, 4, 160)    122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 4, 4, 160)    179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 4, 4, 160)    480         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 4, 4, 160)    480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 4, 4, 160)    0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 4, 4, 160)    0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 4, 4, 160)    179200      activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 4, 4, 160)    179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 4, 4, 160)    480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 4, 4, 160)    480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 4, 4, 160)    0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 4, 4, 160)    0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 4, 4, 768)    0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 4, 4, 192)    147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 4, 4, 192)    215040      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 4, 4, 192)    215040      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 4, 4, 192)    147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 4, 4, 192)    576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 4, 4, 192)    576         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 4, 4, 192)    576         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 4, 4, 192)    576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 4, 4, 192)    0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 4, 4, 192)    0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 4, 4, 192)    0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 4, 4, 192)    0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 4, 4, 768)    0           activation_50[0][0]              \n",
      "                                                                 activation_53[0][0]              \n",
      "                                                                 activation_58[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 4, 4, 192)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 4, 4, 192)    576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 4, 4, 192)    0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 4, 4, 192)    258048      activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 4, 4, 192)    576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 4, 4, 192)    0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 4, 4, 192)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 4, 4, 192)    258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 4, 4, 192)    576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 4, 4, 192)    576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 4, 4, 192)    0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 4, 4, 192)    0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 4, 4, 192)    258048      activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 4, 4, 192)    258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 4, 4, 192)    576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 4, 4, 192)    576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 4, 4, 192)    0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 4, 4, 192)    0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 4, 4, 768)    0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 4, 4, 192)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 4, 4, 192)    258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 4, 4, 192)    258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 4, 4, 192)    147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 4, 4, 192)    576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 4, 4, 192)    576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 4, 4, 192)    576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 4, 4, 192)    576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 4, 4, 192)    0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 4, 4, 192)    0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 4, 4, 192)    0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 4, 4, 192)    0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 4, 4, 768)    0           activation_60[0][0]              \n",
      "                                                                 activation_63[0][0]              \n",
      "                                                                 activation_68[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 4, 4, 192)    147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 4, 4, 192)    576         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 4, 4, 192)    0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 4, 4, 192)    258048      activation_72[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 4, 4, 192)    576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 4, 4, 192)    0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 4, 4, 192)    147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 4, 4, 192)    258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 4, 4, 192)    576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 4, 4, 192)    576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 4, 4, 192)    0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 4, 4, 192)    0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 1, 1, 320)    552960      activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 1, 1, 192)    331776      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 1, 1, 320)    960         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 1, 1, 192)    576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 1, 1, 320)    0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 1, 1, 192)    0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 1, 1, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 1, 1, 1280)   0           activation_71[0][0]              \n",
      "                                                                 activation_75[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 1, 1, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 1, 1, 448)    1344        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 1, 1, 448)    0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 1, 1, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 1, 1, 384)    1548288     activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 1, 1, 384)    1152        conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 1, 1, 384)    1152        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 1, 1, 384)    0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 1, 1, 384)    0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 1, 1, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 1, 1, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 1, 1, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 1, 1, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, 1, 1, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 1, 1, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 1, 1, 384)    1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 1, 1, 384)    1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 1, 1, 384)    1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 1, 1, 384)    1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 1, 1, 192)    245760      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 1, 1, 320)    960         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 1, 1, 384)    0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 1, 1, 384)    0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 1, 1, 384)    0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 1, 1, 384)    0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 1, 1, 192)    576         conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 1, 1, 320)    0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 1, 1, 768)    0           activation_78[0][0]              \n",
      "                                                                 activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 1, 1, 768)    0           activation_82[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 1, 1, 192)    0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 1, 1, 2048)   0           activation_76[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate[0][0]                \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 1, 1, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 1, 1, 448)    1344        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 1, 1, 448)    0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 1, 1, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 1, 1, 384)    1548288     activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 1, 1, 384)    1152        conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 1, 1, 384)    1152        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 1, 1, 384)    0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 1, 1, 384)    0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 1, 1, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 1, 1, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 1, 1, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 1, 1, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, 1, 1, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 1, 1, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 1, 1, 384)    1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 1, 1, 384)    1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 1, 1, 384)    1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 1, 1, 384)    1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 1, 1, 192)    393216      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 1, 1, 320)    960         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 1, 1, 384)    0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 1, 1, 384)    0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 1, 1, 384)    0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 1, 1, 384)    0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 1, 1, 192)    576         conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 1, 1, 320)    0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 1, 1, 768)    0           activation_87[0][0]              \n",
      "                                                                 activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 1, 1, 768)    0           activation_91[0][0]              \n",
      "                                                                 activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 1, 1, 192)    0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 1, 1, 2048)   0           activation_85[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "==================================================================================================\n",
      "Total params: 21,802,784\n",
      "Trainable params: 21,768,352\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "pre_trained_model = InceptionV3(include_top=False, weights='imagenet', input_shape=(100, 100, 3))\n",
    "\n",
    "pre_trained_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last layer output shape : (None, 4, 4, 768)\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "for layer in pre_trained_model.layers[:15]:\n",
    "    layer.trainable = False\n",
    "\n",
    "for layer in pre_trained_model.layers[15:]:\n",
    "    layer.trainable = True\n",
    "'''\n",
    "for layer in pre_trained_model.layers:\n",
    "    layer.trainable = False\n",
    "    \n",
    "last_layer = pre_trained_model.get_layer('mixed7')\n",
    "print('Last layer output shape :', last_layer.output_shape)\n",
    "last_output = last_layer.output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 100, 100, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 49, 49, 32)   864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 49, 49, 32)   96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 49, 49, 32)   0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 47, 47, 32)   9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 47, 47, 32)   96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 47, 47, 32)   0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 47, 47, 64)   18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 47, 47, 64)   192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 47, 47, 64)   0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 23, 23, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 23, 23, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 23, 23, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 23, 23, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 21, 21, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 21, 21, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 21, 21, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 10, 10, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 10, 10, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 10, 10, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 10, 10, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 10, 10, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 10, 10, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 10, 10, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 10, 10, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 10, 10, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 10, 10, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 10, 10, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 10, 10, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 10, 10, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 10, 10, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 10, 10, 32)   6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 10, 10, 64)   192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 10, 10, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 10, 10, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 10, 10, 32)   96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 10, 10, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 10, 10, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 10, 10, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 10, 10, 32)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 10, 10, 256)  0           activation_5[0][0]               \n",
      "                                                                 activation_7[0][0]               \n",
      "                                                                 activation_10[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 10, 10, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 10, 10, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 10, 10, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 10, 10, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 10, 10, 96)   55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 10, 10, 48)   144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 10, 10, 96)   288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 10, 10, 48)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 10, 10, 96)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 10, 10, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 10, 10, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 10, 10, 64)   76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 10, 10, 96)   82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 10, 10, 64)   16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 10, 10, 64)   192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 10, 10, 64)   192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 10, 10, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 10, 10, 64)   192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 10, 10, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 10, 10, 64)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 10, 10, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 10, 10, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 10, 10, 288)  0           activation_12[0][0]              \n",
      "                                                                 activation_14[0][0]              \n",
      "                                                                 activation_17[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 10, 10, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 10, 10, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 10, 10, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 10, 10, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 10, 10, 96)   55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 10, 10, 48)   144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 10, 10, 96)   288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 10, 10, 48)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 10, 10, 96)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 10, 10, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 10, 10, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 10, 10, 64)   76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 10, 10, 96)   82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 10, 10, 64)   18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 10, 10, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 10, 10, 64)   192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 10, 10, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 10, 10, 64)   192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 10, 10, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 10, 10, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 10, 10, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 10, 10, 64)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 10, 10, 288)  0           activation_19[0][0]              \n",
      "                                                                 activation_21[0][0]              \n",
      "                                                                 activation_24[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 10, 10, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 10, 10, 64)   192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 10, 10, 64)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 10, 10, 96)   55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 10, 10, 96)   288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 10, 10, 96)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 4, 4, 384)    995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 4, 4, 96)     82944       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 4, 4, 384)    1152        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 4, 4, 96)     288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 4, 4, 384)    0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 4, 4, 96)     0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 4, 4, 288)    0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 4, 4, 768)    0           activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 4, 4, 128)    98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 4, 4, 128)    384         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 4, 4, 128)    0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 4, 4, 128)    114688      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 4, 4, 128)    384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 4, 4, 128)    0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 4, 4, 128)    98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 4, 4, 128)    114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 4, 4, 128)    384         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 4, 4, 128)    384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 4, 4, 128)    0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 4, 4, 128)    0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 4, 4, 128)    114688      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 4, 4, 128)    114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 4, 4, 128)    384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 4, 4, 128)    384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 4, 4, 128)    0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 4, 4, 128)    0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 4, 4, 768)    0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 4, 4, 192)    147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 4, 4, 192)    172032      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 4, 4, 192)    172032      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 4, 4, 192)    147456      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 4, 4, 192)    576         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 4, 4, 192)    576         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 4, 4, 192)    576         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 4, 4, 192)    576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 4, 4, 192)    0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 4, 4, 192)    0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 4, 4, 192)    0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 4, 4, 192)    0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 4, 4, 768)    0           activation_30[0][0]              \n",
      "                                                                 activation_33[0][0]              \n",
      "                                                                 activation_38[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 4, 4, 160)    122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 4, 4, 160)    480         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 4, 4, 160)    0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 4, 4, 160)    179200      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 4, 4, 160)    480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 4, 4, 160)    0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 4, 4, 160)    122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 4, 4, 160)    179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 4, 4, 160)    480         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 4, 4, 160)    480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 4, 4, 160)    0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 4, 4, 160)    0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 4, 4, 160)    179200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 4, 4, 160)    179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 4, 4, 160)    480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 4, 4, 160)    480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 4, 4, 160)    0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 4, 4, 160)    0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 4, 4, 768)    0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 4, 4, 192)    147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 4, 4, 192)    215040      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 4, 4, 192)    215040      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 4, 4, 192)    147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 4, 4, 192)    576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 4, 4, 192)    576         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 4, 4, 192)    576         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 4, 4, 192)    576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 4, 4, 192)    0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 4, 4, 192)    0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 4, 4, 192)    0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 4, 4, 192)    0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 4, 4, 768)    0           activation_40[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "                                                                 activation_48[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 4, 4, 160)    122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 4, 4, 160)    480         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 4, 4, 160)    0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 4, 4, 160)    179200      activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 4, 4, 160)    480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 4, 4, 160)    0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 4, 4, 160)    122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 4, 4, 160)    179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 4, 4, 160)    480         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 4, 4, 160)    480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 4, 4, 160)    0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 4, 4, 160)    0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 4, 4, 160)    179200      activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 4, 4, 160)    179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 4, 4, 160)    480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 4, 4, 160)    480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 4, 4, 160)    0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 4, 4, 160)    0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 4, 4, 768)    0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 4, 4, 192)    147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 4, 4, 192)    215040      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 4, 4, 192)    215040      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 4, 4, 192)    147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 4, 4, 192)    576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 4, 4, 192)    576         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 4, 4, 192)    576         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 4, 4, 192)    576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 4, 4, 192)    0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 4, 4, 192)    0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 4, 4, 192)    0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 4, 4, 192)    0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 4, 4, 768)    0           activation_50[0][0]              \n",
      "                                                                 activation_53[0][0]              \n",
      "                                                                 activation_58[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 4, 4, 192)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 4, 4, 192)    576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 4, 4, 192)    0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 4, 4, 192)    258048      activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 4, 4, 192)    576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 4, 4, 192)    0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 4, 4, 192)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 4, 4, 192)    258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 4, 4, 192)    576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 4, 4, 192)    576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 4, 4, 192)    0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 4, 4, 192)    0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 4, 4, 192)    258048      activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 4, 4, 192)    258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 4, 4, 192)    576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 4, 4, 192)    576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 4, 4, 192)    0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 4, 4, 192)    0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 4, 4, 768)    0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 4, 4, 192)    147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 4, 4, 192)    258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 4, 4, 192)    258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 4, 4, 192)    147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 4, 4, 192)    576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 4, 4, 192)    576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 4, 4, 192)    576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 4, 4, 192)    576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 4, 4, 192)    0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 4, 4, 192)    0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 4, 4, 192)    0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 4, 4, 192)    0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 4, 4, 768)    0           activation_60[0][0]              \n",
      "                                                                 activation_63[0][0]              \n",
      "                                                                 activation_68[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 12288)        0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 1024)         12583936    flatten[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 1024)         0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 3)            3075        dropout[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 21,562,275\n",
      "Trainable params: 12,587,011\n",
      "Non-trainable params: 8,975,264\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "x = tf.keras.layers.Flatten()(last_output)\n",
    "x = tf.keras.layers.Dense(1024, activation='relu')(x)\n",
    "x = tf.keras.layers.Dropout(0.3)(x)\n",
    "#The Final layer with 3 outputs for 3 categories\n",
    "x = tf.keras.layers.Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = tf.keras.models.Model(inputs=pre_trained_model.input, outputs=x)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing data for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4072, 100, 100, 3) (4072, 3)\n"
     ]
    }
   ],
   "source": [
    "X = np.load('data/X.npy')\n",
    "Y = np.load('data/Y.npy')\n",
    "\n",
    "print(X.shape, Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(np.argmax(Y, axis=1), palette=\"Set1\", alpha=0.8)\n",
    "ax.set_xticklabels(['without_mask', 'with_mask', 'mask_weared_incorrect'], rotation=30, ha=\"right\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalizing\n",
    "X = X / 255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "104/104 [==============================] - 61s 589ms/step - loss: 0.5370 - acc: 0.9217 - val_loss: 0.2271 - val_acc: 0.9237\n",
      "Epoch 2/20\n",
      "104/104 [==============================] - 58s 560ms/step - loss: 0.1020 - acc: 0.9694 - val_loss: 0.2287 - val_acc: 0.9373\n",
      "Epoch 3/20\n",
      "104/104 [==============================] - 60s 575ms/step - loss: 0.0570 - acc: 0.9830 - val_loss: 0.1723 - val_acc: 0.9619\n",
      "Epoch 4/20\n",
      "104/104 [==============================] - 57s 549ms/step - loss: 0.0323 - acc: 0.9888 - val_loss: 0.2239 - val_acc: 0.9591\n",
      "Epoch 5/20\n",
      "104/104 [==============================] - 57s 547ms/step - loss: 0.0102 - acc: 0.9970 - val_loss: 0.2269 - val_acc: 0.9619\n",
      "Epoch 6/20\n",
      "104/104 [==============================] - 57s 546ms/step - loss: 0.0232 - acc: 0.9918 - val_loss: 0.2761 - val_acc: 0.9537\n",
      "Epoch 7/20\n",
      "104/104 [==============================] - 57s 547ms/step - loss: 0.0209 - acc: 0.9939 - val_loss: 0.2788 - val_acc: 0.9482\n",
      "Epoch 8/20\n",
      "104/104 [==============================] - 57s 552ms/step - loss: 0.0065 - acc: 0.9985 - val_loss: 0.2553 - val_acc: 0.9564\n",
      "Epoch 9/20\n",
      "104/104 [==============================] - 58s 554ms/step - loss: 0.0092 - acc: 0.9973 - val_loss: 0.3116 - val_acc: 0.9591\n",
      "Epoch 10/20\n",
      "104/104 [==============================] - 57s 548ms/step - loss: 0.0130 - acc: 0.9961 - val_loss: 0.2523 - val_acc: 0.9564\n",
      "Epoch 11/20\n",
      "104/104 [==============================] - 57s 545ms/step - loss: 0.0297 - acc: 0.9909 - val_loss: 0.5270 - val_acc: 0.9510\n",
      "Epoch 12/20\n",
      "104/104 [==============================] - 57s 547ms/step - loss: 0.0605 - acc: 0.9854 - val_loss: 0.3976 - val_acc: 0.9591\n",
      "Epoch 13/20\n",
      "104/104 [==============================] - 57s 544ms/step - loss: 0.0428 - acc: 0.9924 - val_loss: 0.3716 - val_acc: 0.9510\n",
      "Epoch 14/20\n",
      "104/104 [==============================] - 57s 552ms/step - loss: 0.0301 - acc: 0.9906 - val_loss: 0.4507 - val_acc: 0.9510\n",
      "Epoch 15/20\n",
      "104/104 [==============================] - 57s 546ms/step - loss: 0.0280 - acc: 0.9933 - val_loss: 0.3419 - val_acc: 0.9591\n",
      "Epoch 16/20\n",
      "104/104 [==============================] - 57s 547ms/step - loss: 0.0145 - acc: 0.9961 - val_loss: 0.5025 - val_acc: 0.9455\n",
      "Epoch 17/20\n",
      "104/104 [==============================] - 57s 545ms/step - loss: 0.0180 - acc: 0.9939 - val_loss: 0.4111 - val_acc: 0.9564\n",
      "Epoch 18/20\n",
      "104/104 [==============================] - 57s 544ms/step - loss: 0.0362 - acc: 0.9930 - val_loss: 0.4215 - val_acc: 0.9591\n",
      "Epoch 19/20\n",
      "104/104 [==============================] - 57s 548ms/step - loss: 0.0320 - acc: 0.9951 - val_loss: 0.5373 - val_acc: 0.9482\n",
      "Epoch 20/20\n",
      "104/104 [==============================] - 57s 547ms/step - loss: 0.0033 - acc: 0.9985 - val_loss: 0.4813 - val_acc: 0.9564\n"
     ]
    }
   ],
   "source": [
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath='data/model-{epoch:03d}.ckpt',\n",
    "    save_weights_only=True,\n",
    "    monitor='val_acc',\n",
    "    mode='max',\n",
    "    save_best_only=True, \n",
    "    verbose=0)\n",
    "\n",
    "history = model.fit(X_train, \n",
    "                    Y_train, \n",
    "                    epochs=20, \n",
    "                    callbacks=[checkpoint], \n",
    "                    validation_split=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOT LOSS AND ACCURACY\n",
    "acc = history.history['acc']\n",
    "val_acc = history.history['val_acc']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.subplot(121)\n",
    "plt.plot(acc, label='Training')\n",
    "plt.plot(val_acc, label='Validation')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(loss, label='Training')\n",
    "plt.plot(val_loss, label='Validation')\n",
    "plt.title('Training and validation loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('data/inceptionV3-model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13/13 [==============================] - 5s 413ms/step - loss: 0.3194 - acc: 0.9681\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.31944170594215393, 0.968137264251709]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = np.argmax(model.predict(X_test), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test = np.argmax(Y_test, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(Y_test, Y_pred), annot=True, fmt='g', cmap=plt.cm.Blues)\n",
    "plt.xlabel('Predicted label')\n",
    "plt.ylabel('True label')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}