C2W3_Inception_v3_Augmentation_Dropouts_Dogs_vs_Cats.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "C2W3-Inception_v3-Augmentation-Dropouts-Dogs-vs-Cats",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/georgezoto/TensorFlow-in-Practice/blob/master/C2W3_Inception_v3_Augmentation_Dropouts_Dogs_vs_Cats.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ewdKz6PZYlYG",
        "colab_type": "text"
      },
      "source": [
        "## Please use GPU for this notebook!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rX8mhOLljYeM"
      },
      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "BZSlp3DAjdYf",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0wh-hf9hGC4_",
        "colab_type": "code",
        "outputId": "3d753936-9395-494e-d7d0-9c5707d4708f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1xJZ5glPPCRz",
        "outputId": "21a0eab2-0199-4b78-ffe9-477a8a7163de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "import os\n",
        "\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import Model\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \\\n",
        "    -O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
        "  \n",
        "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
        "\n",
        "local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
        "\n",
        "pre_trained_model = InceptionV3(input_shape = (150, 150, 3), \n",
        "                                include_top = False, \n",
        "                                weights = None)\n",
        "\n",
        "pre_trained_model.load_weights(local_weights_file)\n",
        "\n",
        "for layer in pre_trained_model.layers:\n",
        "  layer.trainable = False\n",
        "  \n",
        "#pre_trained_model.summary()\n",
        "\n",
        "last_layer = pre_trained_model.get_layer('mixed7')\n",
        "print('last layer output shape: ', last_layer.output_shape)\n",
        "last_output = last_layer.output"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-17 00:37:49--  https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.142.128, 2607:f8b0:400e:c07::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.142.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 87910968 (84M) [application/x-hdf]\n",
            "Saving to: ‘/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5’\n",
            "\n",
            "/tmp/inception_v3_w 100%[===================>]  83.84M  93.0MB/s    in 0.9s    \n",
            "\n",
            "2020-05-17 00:37:50 (93.0 MB/s) - ‘/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5’ saved [87910968/87910968]\n",
            "\n",
            "last layer output shape:  (None, 7, 7, 768)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "coJ5c89DUAOE",
        "colab_type": "code",
        "outputId": "124f9118-93a6-48bc-da72-73769fad3f9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "pre_trained_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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": {
        "colab_type": "code",
        "id": "BMXb913pbvFg",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.optimizers import RMSprop\n",
        "\n",
        "# Flatten the output layer to 1 dimension\n",
        "x = layers.Flatten()(last_output)\n",
        "# Add a fully connected layer with 1,024 \n",
        "# hidden units and ReLU activation\n",
        "x = layers.Dense(1024, activation='relu')(x)\n",
        "# Add a dropout rate of 0.2\n",
        "x = layers.Dropout(0.2)(x)                  \n",
        "# Add a final sigmoid layer for classification\n",
        "x = layers.Dense(1, activation='sigmoid')(x)           \n",
        "\n",
        "model = Model(pre_trained_model.input, x) \n",
        "\n",
        "model.compile(optimizer = RMSprop(lr=0.0001), \n",
        "              loss = 'binary_crossentropy', \n",
        "              metrics = ['accuracy'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "O4s8HckqGlnb",
        "outputId": "86f464b7-7d33-42c6-8217-aeab33f8711e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "!wget --no-check-certificate \\\n",
        "        https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip \\\n",
        "       -O /tmp/cats_and_dogs_filtered.zip\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '//tmp/cats_and_dogs_filtered.zip'\n",
        "\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n",
        "\n",
        "# Define our example directories and files\n",
        "base_dir = '/tmp/cats_and_dogs_filtered'\n",
        "\n",
        "train_dir = os.path.join( base_dir, 'train')\n",
        "validation_dir = os.path.join( base_dir, 'validation')\n",
        "\n",
        "\n",
        "train_cats_dir = os.path.join(train_dir, 'cats') # Directory with our training cat pictures\n",
        "train_dogs_dir = os.path.join(train_dir, 'dogs') # Directory with our training dog pictures\n",
        "validation_cats_dir = os.path.join(validation_dir, 'cats') # Directory with our validation cat pictures\n",
        "validation_dogs_dir = os.path.join(validation_dir, 'dogs')# Directory with our validation dog pictures\n",
        "\n",
        "train_cat_fnames = os.listdir(train_cats_dir)\n",
        "train_dog_fnames = os.listdir(train_dogs_dir)\n",
        "\n",
        "# Add our data-augmentation parameters to ImageDataGenerator\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255.,\n",
        "                                   rotation_range = 40,\n",
        "                                   width_shift_range = 0.2,\n",
        "                                   height_shift_range = 0.2,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "\n",
        "# Note that the validation data should not be augmented!\n",
        "test_datagen = ImageDataGenerator( rescale = 1.0/255. )\n",
        "\n",
        "# Flow training images in batches of 20 using train_datagen generator\n",
        "train_generator = train_datagen.flow_from_directory(train_dir,\n",
        "                                                    batch_size = 20,\n",
        "                                                    class_mode = 'binary', \n",
        "                                                    target_size = (150, 150))     \n",
        "\n",
        "# Flow validation images in batches of 20 using test_datagen generator\n",
        "validation_generator =  test_datagen.flow_from_directory( validation_dir,\n",
        "                                                          batch_size  = 20,\n",
        "                                                          class_mode  = 'binary', \n",
        "                                                          target_size = (150, 150))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-05-17 00:38:10--  https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.142.128, 2607:f8b0:400e:c07::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.142.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 68606236 (65M) [application/zip]\n",
            "Saving to: ‘/tmp/cats_and_dogs_filtered.zip’\n",
            "\n",
            "/tmp/cats_and_dogs_ 100%[===================>]  65.43M   156MB/s    in 0.4s    \n",
            "\n",
            "2020-05-17 00:38:11 (156 MB/s) - ‘/tmp/cats_and_dogs_filtered.zip’ saved [68606236/68606236]\n",
            "\n",
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Blhq2MAUeyGA",
        "outputId": "56869a6f-a861-4b88-b938-d803dd447588",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "history = model.fit(\n",
        "            train_generator,\n",
        "            validation_data = validation_generator,\n",
        "            steps_per_epoch = 100,\n",
        "            epochs = 20,\n",
        "            validation_steps = 50,\n",
        "            verbose = 2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "100/100 - 18s - loss: 0.3383 - accuracy: 0.8690 - val_loss: 0.0827 - val_accuracy: 0.9620\n",
            "Epoch 2/20\n",
            "100/100 - 18s - loss: 0.2305 - accuracy: 0.9090 - val_loss: 0.1395 - val_accuracy: 0.9500\n",
            "Epoch 3/20\n",
            "100/100 - 18s - loss: 0.2128 - accuracy: 0.9065 - val_loss: 0.1234 - val_accuracy: 0.9570\n",
            "Epoch 4/20\n",
            "100/100 - 18s - loss: 0.1849 - accuracy: 0.9340 - val_loss: 0.1301 - val_accuracy: 0.9560\n",
            "Epoch 5/20\n",
            "100/100 - 18s - loss: 0.2000 - accuracy: 0.9335 - val_loss: 0.1861 - val_accuracy: 0.9400\n",
            "Epoch 6/20\n",
            "100/100 - 18s - loss: 0.1691 - accuracy: 0.9385 - val_loss: 0.1517 - val_accuracy: 0.9510\n",
            "Epoch 7/20\n",
            "100/100 - 18s - loss: 0.1567 - accuracy: 0.9430 - val_loss: 0.1189 - val_accuracy: 0.9620\n",
            "Epoch 8/20\n",
            "100/100 - 18s - loss: 0.1556 - accuracy: 0.9390 - val_loss: 0.1027 - val_accuracy: 0.9720\n",
            "Epoch 9/20\n",
            "100/100 - 18s - loss: 0.1688 - accuracy: 0.9465 - val_loss: 0.1125 - val_accuracy: 0.9690\n",
            "Epoch 10/20\n",
            "100/100 - 18s - loss: 0.1688 - accuracy: 0.9470 - val_loss: 0.1032 - val_accuracy: 0.9710\n",
            "Epoch 11/20\n",
            "100/100 - 18s - loss: 0.1271 - accuracy: 0.9535 - val_loss: 0.1253 - val_accuracy: 0.9660\n",
            "Epoch 12/20\n",
            "100/100 - 18s - loss: 0.1872 - accuracy: 0.9345 - val_loss: 0.1153 - val_accuracy: 0.9680\n",
            "Epoch 13/20\n",
            "100/100 - 18s - loss: 0.1518 - accuracy: 0.9415 - val_loss: 0.1508 - val_accuracy: 0.9610\n",
            "Epoch 14/20\n",
            "100/100 - 18s - loss: 0.1501 - accuracy: 0.9510 - val_loss: 0.1210 - val_accuracy: 0.9650\n",
            "Epoch 15/20\n",
            "100/100 - 18s - loss: 0.1547 - accuracy: 0.9460 - val_loss: 0.1965 - val_accuracy: 0.9440\n",
            "Epoch 16/20\n",
            "100/100 - 18s - loss: 0.1552 - accuracy: 0.9475 - val_loss: 0.1211 - val_accuracy: 0.9670\n",
            "Epoch 17/20\n",
            "100/100 - 18s - loss: 0.1090 - accuracy: 0.9610 - val_loss: 0.1266 - val_accuracy: 0.9680\n",
            "Epoch 18/20\n",
            "100/100 - 18s - loss: 0.1274 - accuracy: 0.9515 - val_loss: 0.1303 - val_accuracy: 0.9670\n",
            "Epoch 19/20\n",
            "100/100 - 18s - loss: 0.1264 - accuracy: 0.9540 - val_loss: 0.1112 - val_accuracy: 0.9660\n",
            "Epoch 20/20\n",
            "100/100 - 18s - loss: 0.1512 - accuracy: 0.9515 - val_loss: 0.1152 - val_accuracy: 0.9680\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "C2Fp6Se9rKuL",
        "outputId": "9547a4ec-e5b1-427e-916e-284d295c22f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "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();"
      ],
      "execution_count": 0,
      "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": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWM1dHACSl8X",
        "colab_type": "code",
        "outputId": "919345ec-7027-46f6-b7cc-0dadfbc98082",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "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": 0,
      "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": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSOcC-JBVIOW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}