trainPneumonia.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "trainPneumonia.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MIvRpSDJ-_i"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGlPdYKkLxti",
        "outputId": "4e1fe314-996c-42c1-e0cd-a3182c2daa68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Jwu5nwbL1UM",
        "outputId": "f319af02-4d7d-4776-995f-963b4e0877c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!unzip '/content/drive/My Drive/pneumonia/pneumonia_lungXray.zip'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DH9-eEc_MImp",
        "outputId": "430bea06-4ab5-4eb6-958b-fcf8d2a93a42",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "base_model = InceptionV3(input_shape = (150,150,3), include_top=False, weights='imagenet')\n",
        "\n",
        "for layer in base_model.layers:\n",
        "\tlayer.trainable=False\n",
        "\t\n",
        "base_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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",
            "Model: \"inception_v3\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 150, 150, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 74, 74, 32)   864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 74, 74, 32)   96          conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, 74, 74, 32)   0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 72, 72, 32)   9216        activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 72, 72, 32)   96          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 72, 72, 32)   0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 72, 72, 64)   18432       activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 72, 72, 64)   192         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 72, 72, 64)   0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 35, 35, 64)   0           activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 35, 35, 80)   5120        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 35, 35, 80)   240         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 35, 35, 80)   0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 33, 33, 192)  138240      activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 33, 33, 192)  576         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 33, 33, 192)  0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 16, 16, 192)  0           activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 16, 16, 64)   192         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 16, 16, 64)   0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 16, 16, 48)   9216        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 16, 16, 96)   55296       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 16, 16, 48)   144         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 16, 16, 96)   288         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 16, 16, 48)   0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 16, 16, 96)   0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, 16, 16, 192)  0           max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 16, 16, 64)   12288       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 16, 16, 64)   76800       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 16, 16, 96)   82944       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 16, 16, 32)   6144        average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 16, 16, 64)   192         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 16, 16, 64)   192         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 16, 16, 96)   288         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 16, 16, 32)   96          conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 16, 16, 64)   0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 16, 16, 64)   0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 16, 16, 96)   0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 16, 16, 32)   0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, 16, 16, 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, 16, 16, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 16, 16, 64)   192         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 16, 16, 64)   0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 16, 16, 48)   12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 16, 16, 96)   55296       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 16, 16, 48)   144         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 16, 16, 96)   288         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 16, 16, 48)   0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 16, 16, 96)   0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 16, 16, 256)  0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 16, 16, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 16, 16, 64)   76800       activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 16, 16, 96)   82944       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 16, 16, 64)   16384       average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 16, 16, 64)   192         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 16, 16, 64)   192         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 16, 16, 96)   288         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 16, 16, 64)   192         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 16, 16, 64)   0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 16, 16, 64)   0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 16, 16, 96)   0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 16, 16, 64)   0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, 16, 16, 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, 16, 16, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 16, 16, 64)   192         conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 16, 16, 64)   0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 16, 16, 48)   13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 16, 16, 96)   55296       activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 16, 16, 48)   144         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 16, 16, 96)   288         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 16, 16, 48)   0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 16, 16, 96)   0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_2 (AveragePoo (None, 16, 16, 288)  0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 16, 16, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 16, 16, 64)   76800       activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 16, 16, 96)   82944       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 16, 16, 64)   18432       average_pooling2d_2[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 16, 16, 64)   192         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 16, 16, 64)   192         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 16, 16, 96)   288         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 16, 16, 64)   192         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 16, 16, 64)   0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 16, 16, 64)   0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 16, 16, 96)   0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 16, 16, 64)   0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, 16, 16, 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, 16, 16, 64)   18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 16, 16, 64)   192         conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 16, 16, 64)   0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 16, 16, 96)   55296       activation_27[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 16, 16, 96)   288         conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 16, 16, 96)   0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 7, 7, 384)    995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 7, 7, 96)     82944       activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 7, 7, 384)    1152        conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 7, 7, 96)     288         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 7, 7, 384)    0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 7, 7, 96)     0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 7, 7, 288)    0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, 7, 7, 768)    0           activation_26[0][0]              \n",
            "                                                                 activation_29[0][0]              \n",
            "                                                                 max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 7, 7, 128)    384         conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 7, 7, 128)    0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 7, 7, 128)    114688      activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 7, 7, 128)    384         conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 7, 7, 128)    0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 7, 7, 128)    98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 7, 7, 128)    114688      activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 7, 7, 128)    384         conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 7, 7, 128)    384         conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 7, 7, 128)    0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 7, 7, 128)    0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 7, 7, 128)    114688      activation_31[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 7, 7, 128)    114688      activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 7, 7, 128)    384         conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 7, 7, 128)    384         conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 7, 7, 128)    0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 7, 7, 128)    0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_3 (AveragePoo (None, 7, 7, 768)    0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 7, 7, 192)    147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 7, 7, 192)    172032      activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 7, 7, 192)    172032      activation_37[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_3[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 7, 7, 192)    576         conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 7, 7, 192)    576         conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 7, 7, 192)    576         conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 7, 7, 192)    576         conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 7, 7, 192)    0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 7, 7, 192)    0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 7, 7, 192)    0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 7, 7, 192)    0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, 7, 7, 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, 7, 7, 160)    122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 7, 7, 160)    480         conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 7, 7, 160)    0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 7, 7, 160)    179200      activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 7, 7, 160)    480         conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 7, 7, 160)    0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 7, 7, 160)    122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 7, 7, 160)    179200      activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 7, 7, 160)    480         conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 7, 7, 160)    480         conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 7, 7, 160)    0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 7, 7, 160)    0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 7, 7, 160)    179200      activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 7, 7, 160)    179200      activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 7, 7, 160)    480         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 7, 7, 160)    480         conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 7, 7, 160)    0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 7, 7, 160)    0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_4 (AveragePoo (None, 7, 7, 768)    0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 7, 7, 192)    147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 7, 7, 192)    215040      activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 7, 7, 192)    215040      activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_4[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 7, 7, 192)    576         conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 7, 7, 192)    576         conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 7, 7, 192)    576         conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 7, 7, 192)    576         conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 7, 7, 192)    0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 7, 7, 192)    0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 7, 7, 192)    0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 7, 7, 192)    0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, 7, 7, 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, 7, 7, 160)    122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, 7, 7, 160)    480         conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, 7, 7, 160)    0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 7, 7, 160)    179200      activation_54[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, 7, 7, 160)    480         conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_55 (Activation)      (None, 7, 7, 160)    0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 7, 7, 160)    122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 7, 7, 160)    179200      activation_55[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 7, 7, 160)    480         conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, 7, 7, 160)    480         conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, 7, 7, 160)    0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_56 (Activation)      (None, 7, 7, 160)    0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 7, 7, 160)    179200      activation_51[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 7, 7, 160)    179200      activation_56[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 7, 7, 160)    480         conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, 7, 7, 160)    480         conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, 7, 7, 160)    0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_57 (Activation)      (None, 7, 7, 160)    0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_5 (AveragePoo (None, 7, 7, 768)    0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 7, 7, 192)    147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 7, 7, 192)    215040      activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, 7, 7, 192)    215040      activation_57[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_5[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 7, 7, 192)    576         conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 7, 7, 192)    576         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, 7, 7, 192)    576         conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, 7, 7, 192)    576         conv2d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, 7, 7, 192)    0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, 7, 7, 192)    0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_58 (Activation)      (None, 7, 7, 192)    0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_59 (Activation)      (None, 7, 7, 192)    0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, 7, 7, 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, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_64 (BatchNo (None, 7, 7, 192)    576         conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_64 (Activation)      (None, 7, 7, 192)    0           batch_normalization_64[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, 7, 7, 192)    258048      activation_64[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_65 (BatchNo (None, 7, 7, 192)    576         conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_65 (Activation)      (None, 7, 7, 192)    0           batch_normalization_65[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, 7, 7, 192)    258048      activation_65[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, 7, 7, 192)    576         conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_66 (BatchNo (None, 7, 7, 192)    576         conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_61 (Activation)      (None, 7, 7, 192)    0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_66 (Activation)      (None, 7, 7, 192)    0           batch_normalization_66[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, 7, 7, 192)    258048      activation_61[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, 7, 7, 192)    258048      activation_66[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, 7, 7, 192)    576         conv2d_62[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_67 (BatchNo (None, 7, 7, 192)    576         conv2d_67[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_62 (Activation)      (None, 7, 7, 192)    0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_67 (Activation)      (None, 7, 7, 192)    0           batch_normalization_67[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_6 (AveragePoo (None, 7, 7, 768)    0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, 7, 7, 192)    147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, 7, 7, 192)    258048      activation_62[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, 7, 7, 192)    258048      activation_67[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, 7, 7, 192)    147456      average_pooling2d_6[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, 7, 7, 192)    576         conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_63 (BatchNo (None, 7, 7, 192)    576         conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_68 (BatchNo (None, 7, 7, 192)    576         conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_69 (BatchNo (None, 7, 7, 192)    576         conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_60 (Activation)      (None, 7, 7, 192)    0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_63 (Activation)      (None, 7, 7, 192)    0           batch_normalization_63[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_68 (Activation)      (None, 7, 7, 192)    0           batch_normalization_68[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_69 (Activation)      (None, 7, 7, 192)    0           batch_normalization_69[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, 7, 7, 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, 7, 7, 192)    147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_72 (BatchNo (None, 7, 7, 192)    576         conv2d_72[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_72 (Activation)      (None, 7, 7, 192)    0           batch_normalization_72[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_73 (Conv2D)              (None, 7, 7, 192)    258048      activation_72[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_73 (BatchNo (None, 7, 7, 192)    576         conv2d_73[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_73 (Activation)      (None, 7, 7, 192)    0           batch_normalization_73[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, 7, 7, 192)    147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_74 (Conv2D)              (None, 7, 7, 192)    258048      activation_73[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_70 (BatchNo (None, 7, 7, 192)    576         conv2d_70[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_74 (BatchNo (None, 7, 7, 192)    576         conv2d_74[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_70 (Activation)      (None, 7, 7, 192)    0           batch_normalization_70[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_74 (Activation)      (None, 7, 7, 192)    0           batch_normalization_74[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_71 (Conv2D)              (None, 3, 3, 320)    552960      activation_70[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_75 (Conv2D)              (None, 3, 3, 192)    331776      activation_74[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_71 (BatchNo (None, 3, 3, 320)    960         conv2d_71[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_75 (BatchNo (None, 3, 3, 192)    576         conv2d_75[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_71 (Activation)      (None, 3, 3, 320)    0           batch_normalization_71[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_75 (Activation)      (None, 3, 3, 192)    0           batch_normalization_75[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 3, 3, 768)    0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed8 (Concatenate)            (None, 3, 3, 1280)   0           activation_71[0][0]              \n",
            "                                                                 activation_75[0][0]              \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_80 (Conv2D)              (None, 3, 3, 448)    573440      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_80 (BatchNo (None, 3, 3, 448)    1344        conv2d_80[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_80 (Activation)      (None, 3, 3, 448)    0           batch_normalization_80[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_77 (Conv2D)              (None, 3, 3, 384)    491520      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_81 (Conv2D)              (None, 3, 3, 384)    1548288     activation_80[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_77 (BatchNo (None, 3, 3, 384)    1152        conv2d_77[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_81 (BatchNo (None, 3, 3, 384)    1152        conv2d_81[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_77 (Activation)      (None, 3, 3, 384)    0           batch_normalization_77[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_81 (Activation)      (None, 3, 3, 384)    0           batch_normalization_81[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_78 (Conv2D)              (None, 3, 3, 384)    442368      activation_77[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_79 (Conv2D)              (None, 3, 3, 384)    442368      activation_77[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_82 (Conv2D)              (None, 3, 3, 384)    442368      activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_83 (Conv2D)              (None, 3, 3, 384)    442368      activation_81[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_7 (AveragePoo (None, 3, 3, 1280)   0           mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_76 (Conv2D)              (None, 3, 3, 320)    409600      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_78 (BatchNo (None, 3, 3, 384)    1152        conv2d_78[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_79 (BatchNo (None, 3, 3, 384)    1152        conv2d_79[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_82 (BatchNo (None, 3, 3, 384)    1152        conv2d_82[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_83 (BatchNo (None, 3, 3, 384)    1152        conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_84 (Conv2D)              (None, 3, 3, 192)    245760      average_pooling2d_7[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_76 (BatchNo (None, 3, 3, 320)    960         conv2d_76[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_78 (Activation)      (None, 3, 3, 384)    0           batch_normalization_78[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_79 (Activation)      (None, 3, 3, 384)    0           batch_normalization_79[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_82 (Activation)      (None, 3, 3, 384)    0           batch_normalization_82[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_83 (Activation)      (None, 3, 3, 384)    0           batch_normalization_83[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_84 (BatchNo (None, 3, 3, 192)    576         conv2d_84[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_76 (Activation)      (None, 3, 3, 320)    0           batch_normalization_76[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_0 (Concatenate)          (None, 3, 3, 768)    0           activation_78[0][0]              \n",
            "                                                                 activation_79[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 3, 3, 768)    0           activation_82[0][0]              \n",
            "                                                                 activation_83[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_84 (Activation)      (None, 3, 3, 192)    0           batch_normalization_84[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9 (Concatenate)            (None, 3, 3, 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, 3, 3, 448)    917504      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_89 (BatchNo (None, 3, 3, 448)    1344        conv2d_89[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_89 (Activation)      (None, 3, 3, 448)    0           batch_normalization_89[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_86 (Conv2D)              (None, 3, 3, 384)    786432      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_90 (Conv2D)              (None, 3, 3, 384)    1548288     activation_89[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_86 (BatchNo (None, 3, 3, 384)    1152        conv2d_86[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_90 (BatchNo (None, 3, 3, 384)    1152        conv2d_90[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_86 (Activation)      (None, 3, 3, 384)    0           batch_normalization_86[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_90 (Activation)      (None, 3, 3, 384)    0           batch_normalization_90[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_87 (Conv2D)              (None, 3, 3, 384)    442368      activation_86[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_88 (Conv2D)              (None, 3, 3, 384)    442368      activation_86[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_91 (Conv2D)              (None, 3, 3, 384)    442368      activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_92 (Conv2D)              (None, 3, 3, 384)    442368      activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_8 (AveragePoo (None, 3, 3, 2048)   0           mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_85 (Conv2D)              (None, 3, 3, 320)    655360      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_87 (BatchNo (None, 3, 3, 384)    1152        conv2d_87[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_88 (BatchNo (None, 3, 3, 384)    1152        conv2d_88[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_91 (BatchNo (None, 3, 3, 384)    1152        conv2d_91[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_92 (BatchNo (None, 3, 3, 384)    1152        conv2d_92[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_93 (Conv2D)              (None, 3, 3, 192)    393216      average_pooling2d_8[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_85 (BatchNo (None, 3, 3, 320)    960         conv2d_85[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_87 (Activation)      (None, 3, 3, 384)    0           batch_normalization_87[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_88 (Activation)      (None, 3, 3, 384)    0           batch_normalization_88[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_91 (Activation)      (None, 3, 3, 384)    0           batch_normalization_91[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_92 (Activation)      (None, 3, 3, 384)    0           batch_normalization_92[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_93 (BatchNo (None, 3, 3, 192)    576         conv2d_93[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_85 (Activation)      (None, 3, 3, 320)    0           batch_normalization_85[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_1 (Concatenate)          (None, 3, 3, 768)    0           activation_87[0][0]              \n",
            "                                                                 activation_88[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 3, 3, 768)    0           activation_91[0][0]              \n",
            "                                                                 activation_92[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_93 (Activation)      (None, 3, 3, 192)    0           batch_normalization_93[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed10 (Concatenate)           (None, 3, 3, 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: 0\n",
            "Non-trainable params: 21,802,784\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kMdLePT-NAal",
        "outputId": "23922df1-b42f-4647-9254-0ebe03f07ede",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "last_layer = base_model.get_layer('mixed7')\n",
        "last_output = last_layer.output\n",
        "print('last layer output shape: ', last_layer.output_shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "last layer output shape:  (None, 7, 7, 768)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q73ZmT2MNBOj"
      },
      "source": [
        "x = layers.Flatten()(last_output)\n",
        "x = layers.Dense(512, activation='relu')(x)\n",
        "x = layers.Dense(1, activation='sigmoid')(x)           \n",
        "\n",
        "pneumoniaModel = Model(base_model.input, x)\n",
        "\n",
        "pneumoniaModel.compile (optimizer = 'rmsprop', loss = tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StZD_XNGNXMg",
        "outputId": "50992888-c91b-4691-8ed9-b259942ad85a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "train = '/content/pneumonia_lungXray/train'\n",
        "validation = '/content/pneumonia_lungXray/val'\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory (train, batch_size=64, class_mode='binary', target_size=(150,150))\n",
        "validation_generator =  validation_datagen.flow_from_directory (validation, batch_size=64, class_mode='binary', target_size=(150,150))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 4350 images belonging to 2 classes.\n",
            "Found 689 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NU7qRKj_PoWj",
        "outputId": "25969d19-125e-496d-86cb-3a9d2ec555fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history = pneumoniaModel.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=68,\n",
        "    epochs=35,\n",
        "    verbose=1,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=11)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/35\n",
            "68/68 [==============================] - 230s 3s/step - loss: 0.4579 - accuracy: 0.8883 - val_loss: 0.5113 - val_accuracy: 0.8984\n",
            "Epoch 2/35\n",
            "68/68 [==============================] - 229s 3s/step - loss: 0.4275 - accuracy: 0.9467 - val_loss: 0.4882 - val_accuracy: 0.9013\n",
            "Epoch 3/35\n",
            "68/68 [==============================] - 226s 3s/step - loss: 0.4254 - accuracy: 0.9540 - val_loss: 0.5691 - val_accuracy: 0.8070\n",
            "Epoch 4/35\n",
            "68/68 [==============================] - 226s 3s/step - loss: 0.4178 - accuracy: 0.9653 - val_loss: 0.4901 - val_accuracy: 0.9332\n",
            "Epoch 5/35\n",
            "68/68 [==============================] - 226s 3s/step - loss: 0.4187 - accuracy: 0.9621 - val_loss: 0.4877 - val_accuracy: 0.9390\n",
            "Epoch 6/35\n",
            "68/68 [==============================] - 223s 3s/step - loss: 0.4169 - accuracy: 0.9683 - val_loss: 0.4795 - val_accuracy: 0.9521\n",
            "Epoch 7/35\n",
            "68/68 [==============================] - 225s 3s/step - loss: 0.4135 - accuracy: 0.9756 - val_loss: 0.5198 - val_accuracy: 0.8868\n",
            "Epoch 8/35\n",
            "68/68 [==============================] - 223s 3s/step - loss: 0.4145 - accuracy: 0.9745 - val_loss: 0.4865 - val_accuracy: 0.9390\n",
            "Epoch 9/35\n",
            "68/68 [==============================] - 223s 3s/step - loss: 0.4117 - accuracy: 0.9807 - val_loss: 0.4991 - val_accuracy: 0.9202\n",
            "Epoch 10/35\n",
            "68/68 [==============================] - 225s 3s/step - loss: 0.4123 - accuracy: 0.9772 - val_loss: 0.4823 - val_accuracy: 0.9448\n",
            "Epoch 11/35\n",
            "68/68 [==============================] - 223s 3s/step - loss: 0.4105 - accuracy: 0.9825 - val_loss: 0.4884 - val_accuracy: 0.9347\n",
            "Epoch 12/35\n",
            "68/68 [==============================] - 223s 3s/step - loss: 0.4103 - accuracy: 0.9823 - val_loss: 0.5179 - val_accuracy: 0.8897\n",
            "Epoch 13/35\n",
            "68/68 [==============================] - 225s 3s/step - loss: 0.4147 - accuracy: 0.9756 - val_loss: 0.4734 - val_accuracy: 0.9594\n",
            "Epoch 14/35\n",
            "68/68 [==============================] - 224s 3s/step - loss: 0.4113 - accuracy: 0.9791 - val_loss: 0.4977 - val_accuracy: 0.9216\n",
            "Epoch 15/35\n",
            "68/68 [==============================] - 224s 3s/step - loss: 0.4084 - accuracy: 0.9864 - val_loss: 0.4868 - val_accuracy: 0.9390\n",
            "Epoch 16/35\n",
            "68/68 [==============================] - 224s 3s/step - loss: 0.4083 - accuracy: 0.9864 - val_loss: 0.4973 - val_accuracy: 0.9231\n",
            "Epoch 17/35\n",
            "68/68 [==============================] - 224s 3s/step - loss: 0.4086 - accuracy: 0.9857 - val_loss: 0.4840 - val_accuracy: 0.9419\n",
            "Epoch 18/35\n",
            "68/68 [==============================] - 227s 3s/step - loss: 0.4081 - accuracy: 0.9864 - val_loss: 0.4738 - val_accuracy: 0.9594\n",
            "Epoch 19/35\n",
            "68/68 [==============================] - 224s 3s/step - loss: 0.4090 - accuracy: 0.9846 - val_loss: 0.4976 - val_accuracy: 0.9216\n",
            "Epoch 20/35\n",
            "68/68 [==============================] - 224s 3s/step - loss: 0.4093 - accuracy: 0.9837 - val_loss: 0.4849 - val_accuracy: 0.9419\n",
            "Epoch 21/35\n",
            "68/68 [==============================] - 228s 3s/step - loss: 0.4111 - accuracy: 0.9811 - val_loss: 0.4729 - val_accuracy: 0.9579\n",
            "Epoch 22/35\n",
            "68/68 [==============================] - 226s 3s/step - loss: 0.4068 - accuracy: 0.9890 - val_loss: 0.4829 - val_accuracy: 0.9448\n",
            "Epoch 23/35\n",
            "68/68 [==============================] - 227s 3s/step - loss: 0.4097 - accuracy: 0.9807 - val_loss: 0.4878 - val_accuracy: 0.9376\n",
            "Epoch 24/35\n",
            "68/68 [==============================] - 225s 3s/step - loss: 0.4080 - accuracy: 0.9876 - val_loss: 0.5077 - val_accuracy: 0.9057\n",
            "Epoch 25/35\n",
            "68/68 [==============================] - 226s 3s/step - loss: 0.4064 - accuracy: 0.9906 - val_loss: 0.4876 - val_accuracy: 0.9376\n",
            "Epoch 26/35\n",
            "68/68 [==============================] - 228s 3s/step - loss: 0.4103 - accuracy: 0.9837 - val_loss: 0.4750 - val_accuracy: 0.9361\n",
            "Epoch 27/35\n",
            "68/68 [==============================] - 226s 3s/step - loss: 0.4079 - accuracy: 0.9860 - val_loss: 0.4817 - val_accuracy: 0.9463\n",
            "Epoch 28/35\n",
            "68/68 [==============================] - 227s 3s/step - loss: 0.4059 - accuracy: 0.9910 - val_loss: 0.4954 - val_accuracy: 0.9260\n",
            "Epoch 29/35\n",
            "68/68 [==============================] - 227s 3s/step - loss: 0.4072 - accuracy: 0.9890 - val_loss: 0.5012 - val_accuracy: 0.9158\n",
            "Epoch 30/35\n",
            "68/68 [==============================] - 225s 3s/step - loss: 0.4056 - accuracy: 0.9920 - val_loss: 0.4852 - val_accuracy: 0.9419\n",
            "Epoch 31/35\n",
            "68/68 [==============================] - 227s 3s/step - loss: 0.4083 - accuracy: 0.9864 - val_loss: 0.4807 - val_accuracy: 0.9492\n",
            "Epoch 32/35\n",
            "68/68 [==============================] - 226s 3s/step - loss: 0.4071 - accuracy: 0.9876 - val_loss: 0.5309 - val_accuracy: 0.8708\n",
            "Epoch 33/35\n",
            "68/68 [==============================] - 227s 3s/step - loss: 0.4072 - accuracy: 0.9883 - val_loss: 0.4983 - val_accuracy: 0.9216\n",
            "Epoch 34/35\n",
            "68/68 [==============================] - 227s 3s/step - loss: 0.4057 - accuracy: 0.9920 - val_loss: 0.4727 - val_accuracy: 0.9608\n",
            "Epoch 35/35\n",
            "68/68 [==============================] - 226s 3s/step - loss: 0.4086 - accuracy: 0.9837 - val_loss: 0.5090 - val_accuracy: 0.9042\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drdJFJ4MOCOv",
        "outputId": "23ef817b-801b-40ae-c517-15a40ee5352a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "history = pneumoniaModel.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=68,\n",
        "    epochs=1,\n",
        "    verbose=1,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=11)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "68/68 [==============================] - 226s 3s/step - loss: 0.4046 - accuracy: 0.9938 - val_loss: 0.4925 - val_accuracy: 0.9289\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFnptDrn_9Yg"
      },
      "source": [
        "pneumoniaModel.save('/content/pneumonia(150x150)2.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3V-pGAMfd-T",
        "outputId": "b6603f6b-9964-400b-af7e-58df929178b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs = range(len(acc))\n",
        "\n",
        "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "plt.show()\n",
        "\n",
        "plt.plot(epochs, loss, 'r', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend(loc=0)\n",
        "plt.figure()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgX6Ov8vferI",
        "outputId": "50709a38-6724-48b6-dec7-c9d91e374194",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test = '/content/pneumonia_lungXray/test'\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_generator = test_datagen.flow_from_directory (test, batch_size=64, class_mode='binary', target_size=(150,150))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 777 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfPLuS3pfhvu",
        "outputId": "054415f5-4d84-4938-c275-da5818bbca5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "pneumoniaModel.evaluate(test_generator, steps=13)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "13/13 [==============================] - 30s 2s/step - loss: 0.5552 - accuracy: 0.8520\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.5552191138267517, 0.8519948720932007]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    }
  ]
}