covid19-detection-v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "## Import required libraries\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd \n",
    "import io\n",
    "import cv2\n",
    "import random\n",
    "from tensorflow.keras.applications import InceptionV3\n",
    "from tensorflow.keras.layers import Input, Dense, Dropout, Activation, Add, MaxPooling2D, \\\n",
    "                                    Conv2D, Flatten, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.metrics import Recall, Precision\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from skimage import io \n",
    "from skimage import data, color\n",
    "from skimage.transform import rotate, rescale, resize\n",
    "from skimage.util import random_noise\n",
    "\n",
    "# path = 'C:/Users/user/Jupyter/image-processing/COVID-19_Radiography_Database'\n",
    "path = '/kaggle/input/covid19-radiography-database/COVID-19 Radiography Database'\n",
    "\n",
    "# for dirname, _, filenames in os.walk(path):\n",
    "#     for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1024, 1024, 3)\n"
     ]
    }
   ],
   "source": [
    "imagePaths = []\n",
    "imageCovidPaths = []\n",
    "imageNormalPaths = []\n",
    "imagePneumoniaPaths = []\n",
    "for dirname, _, filenames in os.walk(path):\n",
    "    for filename in filenames:\n",
    "        if (filename[-3:] == 'png'):\n",
    "            imagePaths.append(os.path.join(dirname, filename))\n",
    "            if 'COVID' in filename:\n",
    "                imageCovidPaths.append(os.path.join(dirname, filename))\n",
    "            if 'NORMAL' in filename:\n",
    "                imageNormalPaths.append(os.path.join(dirname, filename))\n",
    "            if 'Pneumonia' in filename:\n",
    "                imagePneumoniaPaths.append(os.path.join(dirname, filename))\n",
    "\n",
    "image = cv2.imread(imagePaths[0])\n",
    "print(image.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.1\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n",
    "SEED = 1234\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "tf.random.set_seed(SEED)\n",
    "labels = [\"COVID-19\", \"NORMAL\", \"Viral Pneumonia\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Visualizing Different Classes\n",
    "plt.style.use(\"dark_background\")\n",
    "# Randomly select 5 images in each category\n",
    "fig, axs = plt.subplots(len(labels), 5, figsize = (15, 15))\n",
    "\n",
    "class_len = {}\n",
    "for i, c in enumerate(labels):\n",
    "    class_path = os.path.join(path, c)\n",
    "    all_images = os.listdir(class_path)\n",
    "    sample_images = random.sample(all_images, 5)\n",
    "    class_len[c] = len(all_images)\n",
    "    \n",
    "    for j, image in enumerate(sample_images):\n",
    "        img_path = os.path.join(class_path, image)\n",
    "        img = cv2.imread(img_path)\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "        \n",
    "        axs[i, j].imshow(img)\n",
    "        axs[i, j].set(xlabel = c, xticks = [], yticks = [])\n",
    "        \n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Inspect the the percentage contribution of each category.\n",
    "fig, ax = plt.subplots()\n",
    "ax.pie(\n",
    "    class_len.values(),\n",
    "    labels = class_len.keys(),\n",
    "    autopct = \"%1.1f%%\",\n",
    "    colors = [\"pink\", \"teal\", \"orange\"]\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3064 images belonging to 3 classes.\n",
      "Found 765 images belonging to 3 classes.\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 32\n",
    "\n",
    "datagen_train = ImageDataGenerator(\n",
    "    rescale = 1./255, \n",
    "    validation_split = 0.2,\n",
    "    rotation_range = 5,\n",
    "    width_shift_range = 0.05,\n",
    "    height_shift_range = 0.05,\n",
    "    zoom_range = 0.01\n",
    ")\n",
    "\n",
    "datagen_val = ImageDataGenerator(\n",
    "    rescale = 1./255, \n",
    "    validation_split = 0.2 \n",
    ")    \n",
    "\n",
    "train_generator = datagen_train.flow_from_directory(\n",
    "    directory = path,\n",
    "    classes = labels,\n",
    "    seed = SEED,\n",
    "    batch_size = BATCH_SIZE, \n",
    "    shuffle = True,\n",
    "    interpolation=\"bilinear\",\n",
    "    follow_links=False,\n",
    "    subset = 'training'\n",
    ")\n",
    "\n",
    "valid_generator = datagen_val.flow_from_directory(\n",
    "    directory = path,\n",
    "    classes = labels,\n",
    "    seed = SEED,\n",
    "    batch_size = BATCH_SIZE, \n",
    "    shuffle = True,\n",
    "    interpolation=\"bilinear\",\n",
    "    follow_links=False,\n",
    "    subset = 'validation'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'COVID-19': 0, 'NORMAL': 1, 'Viral Pneumonia': 2}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out labeling\n",
    "valid_generator.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "87916544/87910968 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "base_model = InceptionV3(\n",
    "    include_top = False, \n",
    "    weights = 'imagenet', \n",
    "    input_tensor = Input((256, 256, 3)),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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, 256, 256, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 127, 127, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 127, 127, 32) 96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 127, 127, 32) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 125, 125, 32) 9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 125, 125, 32) 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 125, 125, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 125, 125, 64) 18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 125, 125, 64) 192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 125, 125, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 62, 62, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 62, 62, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 62, 62, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 62, 62, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 60, 60, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 60, 60, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 60, 60, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 29, 29, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 29, 29, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 29, 29, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 29, 29, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 29, 29, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 29, 29, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 29, 29, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 29, 29, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 29, 29, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 29, 29, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 29, 29, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 29, 29, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 29, 29, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 29, 29, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 29, 29, 32)   6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 29, 29, 64)   192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 29, 29, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 29, 29, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 29, 29, 32)   96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 29, 29, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 29, 29, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 29, 29, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 29, 29, 32)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 29, 29, 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, 29, 29, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 29, 29, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 29, 29, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 29, 29, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 29, 29, 96)   55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 29, 29, 48)   144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 29, 29, 96)   288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 29, 29, 48)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 29, 29, 96)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 29, 29, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 29, 29, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 29, 29, 64)   76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 29, 29, 96)   82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 29, 29, 64)   16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 29, 29, 64)   192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 29, 29, 64)   192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 29, 29, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 29, 29, 64)   192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 29, 29, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 29, 29, 64)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 29, 29, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 29, 29, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 29, 29, 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, 29, 29, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 29, 29, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 29, 29, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 29, 29, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 29, 29, 96)   55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 29, 29, 48)   144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 29, 29, 96)   288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 29, 29, 48)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 29, 29, 96)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 29, 29, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 29, 29, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 29, 29, 64)   76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 29, 29, 96)   82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 29, 29, 64)   18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 29, 29, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 29, 29, 64)   192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 29, 29, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 29, 29, 64)   192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 29, 29, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 29, 29, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 29, 29, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 29, 29, 64)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 29, 29, 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, 29, 29, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 29, 29, 64)   192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 29, 29, 64)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 29, 29, 96)   55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 29, 29, 96)   288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 29, 29, 96)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 14, 14, 384)  995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 14, 14, 96)   82944       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 14, 14, 384)  1152        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 14, 14, 96)   288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 384)  0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 96)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 14, 14, 288)  0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 14, 14, 768)  0           activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 14, 14, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 14, 14, 128)  384         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 128)  0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 14, 14, 128)  114688      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 14, 14, 128)  384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 128)  0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 14, 14, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 14, 14, 128)  114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 14, 14, 128)  384         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 14, 14, 128)  384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 128)  0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 128)  0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 14, 14, 128)  114688      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 14, 14, 128)  114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 14, 14, 128)  384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 14, 14, 128)  384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 128)  0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 128)  0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 14, 14, 768)  0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 14, 14, 192)  147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 14, 14, 192)  172032      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 14, 14, 192)  172032      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 14, 14, 192)  576         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 14, 14, 192)  576         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 14, 14, 192)  576         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 14, 14, 192)  576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 192)  0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 192)  0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 192)  0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 192)  0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 14, 14, 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, 14, 14, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 14, 14, 160)  480         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 14, 14, 160)  0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 14, 14, 160)  179200      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 14, 14, 160)  480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 14, 14, 160)  0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 14, 14, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 14, 14, 160)  179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 14, 14, 160)  480         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 14, 14, 160)  480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 14, 14, 160)  0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 14, 14, 160)  0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 14, 14, 160)  179200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 14, 14, 160)  179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 14, 14, 160)  480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 14, 14, 160)  480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 14, 14, 160)  0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 14, 14, 160)  0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 14, 14, 768)  0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 14, 14, 192)  147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 14, 14, 192)  215040      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 14, 14, 192)  215040      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 14, 14, 192)  576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 14, 14, 192)  576         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 14, 14, 192)  576         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 14, 14, 192)  576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 192)  0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 14, 14, 192)  0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 14, 14, 192)  0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 14, 14, 192)  0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 14, 14, 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, 14, 14, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 14, 14, 160)  480         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 14, 14, 160)  0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 14, 14, 160)  179200      activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 14, 14, 160)  480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 14, 14, 160)  0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 14, 14, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 14, 14, 160)  179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 14, 14, 160)  480         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 14, 14, 160)  480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 14, 14, 160)  0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 14, 14, 160)  0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 14, 14, 160)  179200      activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 14, 14, 160)  179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 14, 14, 160)  480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 14, 14, 160)  480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 14, 14, 160)  0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 14, 14, 160)  0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 14, 14, 768)  0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 14, 14, 192)  147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 14, 14, 192)  215040      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 14, 14, 192)  215040      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 14, 14, 192)  576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 14, 14, 192)  576         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 14, 14, 192)  576         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 14, 14, 192)  576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 14, 14, 192)  0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 14, 14, 192)  0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 14, 14, 192)  0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 14, 14, 192)  0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 14, 14, 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, 14, 14, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 14, 14, 192)  576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 14, 14, 192)  0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 14, 14, 192)  258048      activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 14, 14, 192)  576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 14, 14, 192)  0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 14, 14, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 14, 14, 192)  258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 14, 14, 192)  576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 14, 14, 192)  576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 14, 14, 192)  0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 14, 14, 192)  0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 14, 14, 192)  258048      activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 14, 14, 192)  258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 14, 14, 192)  576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 14, 14, 192)  576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 14, 14, 192)  0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 14, 14, 192)  0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 14, 14, 768)  0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 14, 14, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 14, 14, 192)  258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 14, 14, 192)  258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 14, 14, 192)  576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 14, 14, 192)  576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 14, 14, 192)  576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 14, 14, 192)  576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 14, 14, 192)  0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 14, 14, 192)  0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 14, 14, 192)  0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 14, 14, 192)  0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 14, 14, 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, 14, 14, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 14, 14, 192)  576         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 14, 14, 192)  0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 14, 14, 192)  258048      activation_72[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 14, 14, 192)  576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 14, 14, 192)  0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 14, 14, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 14, 14, 192)  258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 14, 14, 192)  576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 14, 14, 192)  576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 14, 14, 192)  0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 14, 14, 192)  0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 6, 6, 320)    552960      activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 6, 6, 192)    331776      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 6, 6, 320)    960         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 6, 6, 192)    576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 6, 6, 320)    0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 6, 6, 192)    0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 6, 6, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 6, 6, 1280)   0           activation_71[0][0]              \n",
      "                                                                 activation_75[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 6, 6, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 6, 6, 448)    1344        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 6, 6, 448)    0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 6, 6, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 6, 6, 384)    1548288     activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 6, 6, 384)    1152        conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 6, 6, 384)    1152        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 6, 6, 384)    0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 6, 6, 384)    0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 6, 6, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 6, 6, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 6, 6, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 6, 6, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, 6, 6, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 6, 6, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 6, 6, 384)    1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 6, 6, 384)    1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 6, 6, 384)    1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 6, 6, 384)    1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 6, 6, 192)    245760      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 6, 6, 320)    960         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 6, 6, 384)    0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 6, 6, 384)    0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 6, 6, 384)    0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 6, 6, 384)    0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 6, 6, 192)    576         conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 6, 6, 320)    0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 6, 6, 768)    0           activation_78[0][0]              \n",
      "                                                                 activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 6, 6, 768)    0           activation_82[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 6, 6, 192)    0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 6, 6, 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, 6, 6, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 6, 6, 448)    1344        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 6, 6, 448)    0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 6, 6, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 6, 6, 384)    1548288     activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 6, 6, 384)    1152        conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 6, 6, 384)    1152        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 6, 6, 384)    0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 6, 6, 384)    0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 6, 6, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 6, 6, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 6, 6, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 6, 6, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, 6, 6, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 6, 6, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 6, 6, 384)    1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 6, 6, 384)    1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 6, 6, 384)    1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 6, 6, 384)    1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 6, 6, 192)    393216      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 6, 6, 320)    960         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 6, 6, 384)    0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 6, 6, 384)    0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 6, 6, 384)    0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 6, 6, 384)    0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 6, 6, 192)    576         conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 6, 6, 320)    0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 6, 6, 768)    0           activation_87[0][0]              \n",
      "                                                                 activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 6, 6, 768)    0           activation_91[0][0]              \n",
      "                                                                 activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 6, 6, 192)    0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 6, 6, 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": [
    "base_model.trainable = True\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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, 256, 256, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 127, 127, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 127, 127, 32) 96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 127, 127, 32) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 125, 125, 32) 9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 125, 125, 32) 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 125, 125, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 125, 125, 64) 18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 125, 125, 64) 192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 125, 125, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 62, 62, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 62, 62, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 62, 62, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 62, 62, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 60, 60, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 60, 60, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 60, 60, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 29, 29, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 29, 29, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 29, 29, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 29, 29, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 29, 29, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 29, 29, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 29, 29, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 29, 29, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 29, 29, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 29, 29, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 29, 29, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 29, 29, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 29, 29, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 29, 29, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 29, 29, 32)   6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 29, 29, 64)   192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 29, 29, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 29, 29, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 29, 29, 32)   96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 29, 29, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 29, 29, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 29, 29, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 29, 29, 32)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 29, 29, 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, 29, 29, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 29, 29, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 29, 29, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 29, 29, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 29, 29, 96)   55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 29, 29, 48)   144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 29, 29, 96)   288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 29, 29, 48)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 29, 29, 96)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 29, 29, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 29, 29, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 29, 29, 64)   76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 29, 29, 96)   82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 29, 29, 64)   16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 29, 29, 64)   192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 29, 29, 64)   192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 29, 29, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 29, 29, 64)   192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 29, 29, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 29, 29, 64)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 29, 29, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 29, 29, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 29, 29, 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, 29, 29, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 29, 29, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 29, 29, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 29, 29, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 29, 29, 96)   55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 29, 29, 48)   144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 29, 29, 96)   288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 29, 29, 48)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 29, 29, 96)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 29, 29, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 29, 29, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 29, 29, 64)   76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 29, 29, 96)   82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 29, 29, 64)   18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 29, 29, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 29, 29, 64)   192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 29, 29, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 29, 29, 64)   192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 29, 29, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 29, 29, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 29, 29, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 29, 29, 64)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 29, 29, 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, 29, 29, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 29, 29, 64)   192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 29, 29, 64)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 29, 29, 96)   55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 29, 29, 96)   288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 29, 29, 96)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 14, 14, 384)  995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 14, 14, 96)   82944       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 14, 14, 384)  1152        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 14, 14, 96)   288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 384)  0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 96)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 14, 14, 288)  0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 14, 14, 768)  0           activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 14, 14, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 14, 14, 128)  384         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 128)  0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 14, 14, 128)  114688      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 14, 14, 128)  384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 128)  0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 14, 14, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 14, 14, 128)  114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 14, 14, 128)  384         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 14, 14, 128)  384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 128)  0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 128)  0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 14, 14, 128)  114688      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 14, 14, 128)  114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 14, 14, 128)  384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 14, 14, 128)  384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 128)  0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 128)  0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 14, 14, 768)  0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 14, 14, 192)  147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 14, 14, 192)  172032      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 14, 14, 192)  172032      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 14, 14, 192)  576         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 14, 14, 192)  576         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 14, 14, 192)  576         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 14, 14, 192)  576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 192)  0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 192)  0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 192)  0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 192)  0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 14, 14, 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, 14, 14, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 14, 14, 160)  480         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 14, 14, 160)  0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 14, 14, 160)  179200      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 14, 14, 160)  480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 14, 14, 160)  0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 14, 14, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 14, 14, 160)  179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 14, 14, 160)  480         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 14, 14, 160)  480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 14, 14, 160)  0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 14, 14, 160)  0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 14, 14, 160)  179200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 14, 14, 160)  179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 14, 14, 160)  480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 14, 14, 160)  480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 14, 14, 160)  0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 14, 14, 160)  0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 14, 14, 768)  0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 14, 14, 192)  147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 14, 14, 192)  215040      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 14, 14, 192)  215040      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 14, 14, 192)  576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 14, 14, 192)  576         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 14, 14, 192)  576         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 14, 14, 192)  576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 192)  0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 14, 14, 192)  0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 14, 14, 192)  0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 14, 14, 192)  0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 14, 14, 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, 14, 14, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 14, 14, 160)  480         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 14, 14, 160)  0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 14, 14, 160)  179200      activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 14, 14, 160)  480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 14, 14, 160)  0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 14, 14, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 14, 14, 160)  179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 14, 14, 160)  480         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 14, 14, 160)  480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 14, 14, 160)  0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 14, 14, 160)  0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 14, 14, 160)  179200      activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 14, 14, 160)  179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 14, 14, 160)  480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 14, 14, 160)  480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 14, 14, 160)  0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 14, 14, 160)  0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 14, 14, 768)  0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 14, 14, 192)  147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 14, 14, 192)  215040      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 14, 14, 192)  215040      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 14, 14, 192)  576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 14, 14, 192)  576         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 14, 14, 192)  576         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 14, 14, 192)  576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 14, 14, 192)  0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 14, 14, 192)  0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 14, 14, 192)  0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 14, 14, 192)  0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 14, 14, 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, 14, 14, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 14, 14, 192)  576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 14, 14, 192)  0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 14, 14, 192)  258048      activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 14, 14, 192)  576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 14, 14, 192)  0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 14, 14, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 14, 14, 192)  258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 14, 14, 192)  576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 14, 14, 192)  576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 14, 14, 192)  0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 14, 14, 192)  0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 14, 14, 192)  258048      activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 14, 14, 192)  258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 14, 14, 192)  576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 14, 14, 192)  576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 14, 14, 192)  0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 14, 14, 192)  0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 14, 14, 768)  0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 14, 14, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 14, 14, 192)  258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 14, 14, 192)  258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 14, 14, 192)  576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 14, 14, 192)  576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 14, 14, 192)  576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 14, 14, 192)  576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 14, 14, 192)  0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 14, 14, 192)  0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 14, 14, 192)  0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 14, 14, 192)  0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 14, 14, 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, 14, 14, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 14, 14, 192)  576         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 14, 14, 192)  0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 14, 14, 192)  258048      activation_72[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 14, 14, 192)  576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 14, 14, 192)  0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 14, 14, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 14, 14, 192)  258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 14, 14, 192)  576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 14, 14, 192)  576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 14, 14, 192)  0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 14, 14, 192)  0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 6, 6, 320)    552960      activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 6, 6, 192)    331776      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 6, 6, 320)    960         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 6, 6, 192)    576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 6, 6, 320)    0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 6, 6, 192)    0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 6, 6, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 6, 6, 1280)   0           activation_71[0][0]              \n",
      "                                                                 activation_75[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 6, 6, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 6, 6, 448)    1344        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 6, 6, 448)    0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 6, 6, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 6, 6, 384)    1548288     activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 6, 6, 384)    1152        conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 6, 6, 384)    1152        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 6, 6, 384)    0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 6, 6, 384)    0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 6, 6, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 6, 6, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 6, 6, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 6, 6, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, 6, 6, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 6, 6, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 6, 6, 384)    1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 6, 6, 384)    1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 6, 6, 384)    1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 6, 6, 384)    1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 6, 6, 192)    245760      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 6, 6, 320)    960         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 6, 6, 384)    0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 6, 6, 384)    0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 6, 6, 384)    0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 6, 6, 384)    0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 6, 6, 192)    576         conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 6, 6, 320)    0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 6, 6, 768)    0           activation_78[0][0]              \n",
      "                                                                 activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 6, 6, 768)    0           activation_82[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 6, 6, 192)    0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 6, 6, 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, 6, 6, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 6, 6, 448)    1344        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 6, 6, 448)    0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 6, 6, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 6, 6, 384)    1548288     activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 6, 6, 384)    1152        conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 6, 6, 384)    1152        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 6, 6, 384)    0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 6, 6, 384)    0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 6, 6, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 6, 6, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 6, 6, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 6, 6, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, 6, 6, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 6, 6, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 6, 6, 384)    1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 6, 6, 384)    1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 6, 6, 384)    1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 6, 6, 384)    1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 6, 6, 192)    393216      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 6, 6, 320)    960         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 6, 6, 384)    0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 6, 6, 384)    0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 6, 6, 384)    0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 6, 6, 384)    0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 6, 6, 192)    576         conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 6, 6, 320)    0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 6, 6, 768)    0           activation_87[0][0]              \n",
      "                                                                 activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 6, 6, 768)    0           activation_91[0][0]              \n",
      "                                                                 activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 6, 6, 192)    0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 6, 6, 2048)   0           activation_85[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 2048)         0           global_average_pooling2d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 256)          524544      flatten[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 256)          0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 128)          32896       dropout[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 128)          0           dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 3)            387         dropout_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 22,360,611\n",
      "Trainable params: 22,326,179\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Add classification head to the model\n",
    "head_model_start = base_model.output\n",
    "head_model = GlobalAveragePooling2D()(head_model_start)\n",
    "head_model = Flatten()(head_model) \n",
    "head_model = Dense(256, activation = \"relu\")(head_model)\n",
    "head_model = Dropout(0.3)(headmodel)\n",
    "head_model = Dense(128, activation = \"relu\")(head_model)\n",
    "head_model = Dropout(0.3)(head_model)\n",
    "head_model = Dense(3, activation = \"softmax\")(head_model) # 3 output classes\n",
    "\n",
    "cnn_model = Model(inputs = base_model.input, outputs = head_model)\n",
    "cnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "\n",
    "# COVID-19 spreads very quickly, it is important that we identify as many cases as possible.\n",
    "# not care a lot about Flase Positives (precision), because it may be okay \n",
    "# to declare normal people as being COVID-19 positive.\n",
    "# However, we should really care about False Negatives (recall), because it is NOT okay \n",
    "# to declare COVID-19 positive people as being normal!\n",
    "\n",
    "metrics_list = [\"accuracy\"]\n",
    "metrics_list += [Recall(class_id = i) for i in range(len(labels))] \n",
    "metrics_list += [Precision(class_id = i) for i in range(len(labels))]\n",
    "\n",
    "cnn_model.compile(\n",
    "    loss = \"categorical_crossentropy\",\n",
    "    optimizer = \"adam\",\n",
    "    metrics = metrics_list\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using early stopping to exit training if recall is \n",
    "# not increase even after certain patience (epochs)\n",
    "earlystopping = EarlyStopping(\n",
    "    monitor = \"recall\",\n",
    "    patience = 20\n",
    ")\n",
    "\n",
    "# save the best model with lower loss\n",
    "checkpointer = ModelCheckpoint(\n",
    "    filepath = \"weights.hdf5\", \n",
    "    save_best_only = True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "32/32 [==============================] - 395s 12s/step - loss: 0.4270 - accuracy: 0.8496 - recall: 0.8488 - recall_1: 0.8201 - recall_2: 0.7756 - precision: 0.9167 - precision_1: 0.8770 - precision_2: 0.8563 - val_loss: 16.6904 - val_accuracy: 0.3516 - val_recall: 0.0000e+00 - val_recall_1: 1.0000 - val_recall_2: 0.0037 - val_precision: 0.0000e+00 - val_precision_1: 0.3531 - val_precision_2: 0.1667\n",
      "Epoch 2/100\n",
      "32/32 [==============================] - 393s 12s/step - loss: 0.3561 - accuracy: 0.9055 - recall: 0.9412 - recall_1: 0.8904 - recall_2: 0.8362 - precision: 0.9474 - precision_1: 0.9135 - precision_2: 0.9136 - val_loss: 2.4342 - val_accuracy: 0.3516 - val_recall: 0.0000e+00 - val_recall_1: 1.0000 - val_recall_2: 0.0037 - val_precision: 0.0000e+00 - val_precision_1: 0.3743 - val_precision_2: 0.0270\n",
      "Epoch 3/100\n",
      "32/32 [==============================] - 394s 12s/step - loss: 0.2342 - accuracy: 0.9365 - recall: 0.9412 - recall_1: 0.9302 - recall_2: 0.9083 - precision: 0.9505 - precision_1: 0.9250 - precision_2: 0.9506 - val_loss: 5.5037 - val_accuracy: 0.4170 - val_recall: 0.1623 - val_recall_1: 1.0000 - val_recall_2: 0.0446 - val_precision: 1.0000 - val_precision_1: 0.3754 - val_precision_2: 1.0000\n",
      "Epoch 4/100\n",
      "32/32 [==============================] - 390s 12s/step - loss: 0.1410 - accuracy: 0.9646 - recall: 0.9707 - recall_1: 0.9451 - recall_2: 0.9565 - precision: 0.9835 - precision_1: 0.9690 - precision_2: 0.9456 - val_loss: 11.9640 - val_accuracy: 0.4784 - val_recall: 0.3202 - val_recall_1: 1.0000 - val_recall_2: 0.0855 - val_precision: 1.0000 - val_precision_1: 0.4042 - val_precision_2: 1.0000\n",
      "Epoch 5/100\n",
      "32/32 [==============================] - 397s 12s/step - loss: 0.1020 - accuracy: 0.9668 - recall: 0.9664 - recall_1: 0.9780 - recall_2: 0.9420 - precision: 0.9829 - precision_1: 0.9596 - precision_2: 0.9688 - val_loss: 0.4853 - val_accuracy: 0.9386 - val_recall: 0.9912 - val_recall_1: 0.9851 - val_recall_2: 0.8476 - val_precision: 0.9912 - val_precision_1: 0.8771 - val_precision_2: 0.9744\n",
      "Epoch 6/100\n",
      "32/32 [==============================] - 396s 12s/step - loss: 0.1456 - accuracy: 0.9453 - recall: 0.9731 - recall_1: 0.9416 - recall_2: 0.9029 - precision: 0.9731 - precision_1: 0.9367 - precision_2: 0.9518 - val_loss: 0.3141 - val_accuracy: 0.9490 - val_recall: 0.9693 - val_recall_1: 0.9627 - val_recall_2: 0.9182 - val_precision: 1.0000 - val_precision_1: 0.9149 - val_precision_2: 0.9427\n",
      "Epoch 7/100\n",
      "32/32 [==============================] - 386s 12s/step - loss: 0.1222 - accuracy: 0.9606 - recall: 0.9706 - recall_1: 0.9652 - recall_2: 0.9351 - precision: 0.9851 - precision_1: 0.9550 - precision_2: 0.9584 - val_loss: 0.3288 - val_accuracy: 0.9242 - val_recall: 0.9825 - val_recall_1: 0.9963 - val_recall_2: 0.7993 - val_precision: 1.0000 - val_precision_1: 0.8292 - val_precision_2: 0.9954\n",
      "Epoch 8/100\n",
      "32/32 [==============================] - 390s 12s/step - loss: 0.0957 - accuracy: 0.9636 - recall: 0.9838 - recall_1: 0.9443 - recall_2: 0.9617 - precision: 0.9806 - precision_1: 0.9641 - precision_2: 0.9565 - val_loss: 1.3533 - val_accuracy: 0.8013 - val_recall: 0.9868 - val_recall_1: 1.0000 - val_recall_2: 0.4461 - val_precision: 0.9868 - val_precision_1: 0.6427 - val_precision_2: 1.0000\n",
      "Epoch 9/100\n",
      "32/32 [==============================] - 389s 12s/step - loss: 0.1046 - accuracy: 0.9557 - recall: 0.9709 - recall_1: 0.9536 - recall_2: 0.9443 - precision: 0.9804 - precision_1: 0.9562 - precision_2: 0.9471 - val_loss: 2.2560 - val_accuracy: 0.6052 - val_recall: 0.2281 - val_recall_1: 1.0000 - val_recall_2: 0.4424 - val_precision: 1.0000 - val_precision_1: 0.6036 - val_precision_2: 0.5459\n",
      "Epoch 10/100\n",
      "32/32 [==============================] - 382s 12s/step - loss: 0.0983 - accuracy: 0.9744 - recall: 0.9837 - recall_1: 0.9747 - recall_2: 0.9548 - precision: 0.9741 - precision_1: 0.9747 - precision_2: 0.9797 - val_loss: 0.8754 - val_accuracy: 0.8275 - val_recall: 0.9868 - val_recall_1: 1.0000 - val_recall_2: 0.5167 - val_precision: 1.0000 - val_precision_1: 0.6768 - val_precision_2: 1.0000\n",
      "Epoch 11/100\n",
      "32/32 [==============================] - 390s 12s/step - loss: 0.1165 - accuracy: 0.9600 - recall: 0.9828 - recall_1: 0.9579 - recall_2: 0.9392 - precision: 0.9727 - precision_1: 0.9499 - precision_2: 0.9753 - val_loss: 0.7870 - val_accuracy: 0.8288 - val_recall: 0.9167 - val_recall_1: 1.0000 - val_recall_2: 0.5799 - val_precision: 1.0000 - val_precision_1: 0.6802 - val_precision_2: 0.9811\n",
      "Epoch 12/100\n",
      "32/32 [==============================] - 387s 12s/step - loss: 0.0918 - accuracy: 0.9766 - recall: 0.9786 - recall_1: 0.9820 - recall_2: 0.9663 - precision: 0.9892 - precision_1: 0.9695 - precision_2: 0.9745 - val_loss: 0.3928 - val_accuracy: 0.8837 - val_recall: 0.9956 - val_recall_1: 0.9142 - val_recall_2: 0.7435 - val_precision: 0.8078 - val_precision_1: 0.9074 - val_precision_2: 0.9901\n",
      "Epoch 13/100\n",
      "32/32 [==============================] - 393s 12s/step - loss: 0.0914 - accuracy: 0.9707 - recall: 0.9901 - recall_1: 0.9655 - recall_2: 0.9544 - precision: 0.9934 - precision_1: 0.9545 - precision_2: 0.9674 - val_loss: 9.0064 - val_accuracy: 0.4928 - val_recall: 0.1930 - val_recall_1: 1.0000 - val_recall_2: 0.2342 - val_precision: 1.0000 - val_precision_1: 0.4092 - val_precision_2: 1.0000\n",
      "Epoch 14/100\n",
      "32/32 [==============================] - 394s 12s/step - loss: 0.1261 - accuracy: 0.9678 - recall: 0.9837 - recall_1: 0.9565 - recall_2: 0.9600 - precision: 0.9837 - precision_1: 0.9644 - precision_2: 0.9628 - val_loss: 0.5032 - val_accuracy: 0.8105 - val_recall: 0.9868 - val_recall_1: 0.4664 - val_recall_2: 0.9926 - val_precision: 0.9494 - val_precision_1: 0.9921 - val_precision_2: 0.6742\n",
      "Epoch 15/100\n",
      "32/32 [==============================] - 391s 12s/step - loss: 0.0736 - accuracy: 0.9766 - recall: 0.9900 - recall_1: 0.9731 - recall_2: 0.9660 - precision: 0.9867 - precision_1: 0.9757 - precision_2: 0.9743 - val_loss: 3.2310 - val_accuracy: 0.7412 - val_recall: 0.9956 - val_recall_1: 0.4664 - val_recall_2: 0.7881 - val_precision: 0.5510 - val_precision_1: 1.0000 - val_precision_2: 0.9593\n",
      "Epoch 16/100\n",
      "32/32 [==============================] - 387s 12s/step - loss: 0.1342 - accuracy: 0.9596 - recall: 0.9845 - recall_1: 0.9454 - recall_2: 0.9449 - precision: 0.9845 - precision_1: 0.9481 - precision_2: 0.9504 - val_loss: 1.2570 - val_accuracy: 0.6693 - val_recall: 0.9912 - val_recall_1: 1.0000 - val_recall_2: 0.0446 - val_precision: 0.8659 - val_precision_1: 0.5666 - val_precision_2: 1.0000\n",
      "Epoch 17/100\n",
      "32/32 [==============================] - 389s 12s/step - loss: 0.0570 - accuracy: 0.9803 - recall: 0.9935 - recall_1: 0.9743 - recall_2: 0.9694 - precision: 0.9967 - precision_1: 0.9660 - precision_2: 0.9803 - val_loss: 0.4192 - val_accuracy: 0.9020 - val_recall: 0.9649 - val_recall_1: 1.0000 - val_recall_2: 0.7509 - val_precision: 0.9865 - val_precision_1: 0.7906 - val_precision_2: 0.9951\n",
      "Epoch 18/100\n",
      "32/32 [==============================] - 394s 12s/step - loss: 0.0644 - accuracy: 0.9824 - recall: 0.9932 - recall_1: 0.9830 - recall_2: 0.9707 - precision: 0.9932 - precision_1: 0.9747 - precision_2: 0.9812 - val_loss: 1.9645 - val_accuracy: 0.7242 - val_recall: 0.9518 - val_recall_1: 0.9963 - val_recall_2: 0.2602 - val_precision: 0.7949 - val_precision_1: 0.6388 - val_precision_2: 0.9859\n",
      "Epoch 19/100\n",
      "32/32 [==============================] - 387s 12s/step - loss: 0.1491 - accuracy: 0.9570 - recall: 0.9823 - recall_1: 0.9447 - recall_2: 0.9503 - precision: 0.9858 - precision_1: 0.9599 - precision_2: 0.9425 - val_loss: 21.0511 - val_accuracy: 0.7529 - val_recall: 0.3728 - val_recall_1: 0.9440 - val_recall_2: 0.8848 - val_precision: 0.9551 - val_precision_1: 0.9004 - val_precision_2: 0.6056\n",
      "Epoch 20/100\n",
      "32/32 [==============================] - 389s 12s/step - loss: 0.0895 - accuracy: 0.9717 - recall: 0.9732 - recall_1: 0.9713 - recall_2: 0.9682 - precision: 0.9898 - precision_1: 0.9686 - precision_2: 0.9605 - val_loss: 244.8410 - val_accuracy: 0.3529 - val_recall: 0.0044 - val_recall_1: 0.0000e+00 - val_recall_2: 1.0000 - val_precision: 1.0000 - val_precision_1: 0.0000e+00 - val_precision_2: 0.3521\n",
      "Epoch 21/100\n",
      "32/32 [==============================] - 393s 12s/step - loss: 0.0771 - accuracy: 0.9785 - recall: 0.9831 - recall_1: 0.9703 - recall_2: 0.9721 - precision: 0.9831 - precision_1: 0.9782 - precision_2: 0.9749 - val_loss: 0.4466 - val_accuracy: 0.8732 - val_recall: 0.8860 - val_recall_1: 0.7425 - val_recall_2: 0.9926 - val_precision: 1.0000 - val_precision_1: 0.9522 - val_precision_2: 0.7564\n"
     ]
    }
   ],
   "source": [
    "## Model Training\n",
    "history = cnn_model.fit_generator(\n",
    "    train_generator,\n",
    "    validation_data = valid_generator,\n",
    "    steps_per_epoch = BATCH_SIZE,\n",
    "    epochs = 100, \n",
    "    callbacks = [earlystopping, checkpointer]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training and Validation Loss')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Evaluation\n",
    "# Plot training and validation loss per epoch\n",
    "train_loss = history.history[\"loss\"]\n",
    "valid_loss = history.history[\"val_loss\"]\n",
    "\n",
    "epochs = range(len(train_loss)) \n",
    "\n",
    "plt.plot(epochs, train_loss)\n",
    "plt.plot(epochs, valid_loss)\n",
    "plt.legend([\"Training Loss\", \"Validation Loss\"])\n",
    "plt.title(\"Training and Validation Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Training and Validation Accuracy')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and validation accuracy per epoch\n",
    "train_acc = history.history[\"accuracy\"]\n",
    "valid_acc = history.history[\"val_accuracy\"]\n",
    "\n",
    "epochs = range(len(train_acc)) \n",
    "\n",
    "plt.plot(epochs, train_acc)\n",
    "plt.plot(epochs, valid_acc)\n",
    "plt.legend([\"Training Accuracy\", \"Validation Accuracy\"])\n",
    "plt.title(\"Training and Validation Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model weights as h5 model\n",
    "model_json = cnn_model.to_json()\n",
    "with open(\"model.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "    \n",
    "cnn_model.save(\"cnn_model.h5\")"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}