Inception_vs_VGG16_in_Tensorflow.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Inception_vs_VGG16_in_Tensorflow.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/akshat-suwalka/Cocoon-Project/blob/main/Inception_vs_VGG16_in_Tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CvlmlxwDtD4C"
      },
      "source": [
        "# Dataset from Google drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qmvW6MpzZgyY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "139749e7-0414-4599-fc9b-4c5fd88b8efe"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOxST-cFZqtr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "494c260c-8ef1-4e1c-d01a-cba2a265e6dc"
      },
      "source": [
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '/content/drive/My Drive/Cocoon Dataset.zip'\n",
        "\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "\n",
        "zip_ref.extractall('/content')\n",
        "zip_ref.close()\n",
        "\n",
        "# Define our example directories and files\n",
        "base_dir = '/content/Cocoon Dataset'\n",
        "\n",
        "train_dir = os.path.join( base_dir, 'Train' )\n",
        "validation_dir = os.path.join( base_dir, 'Test')\n",
        "\n",
        "\n",
        "train_Male_dir = os.path.join(train_dir, 'Male') # Directory with our training cat pictures\n",
        "train_Female_dir = os.path.join(train_dir, 'Female') # Directory with our training dog pictures\n",
        "validation_Male_dir = os.path.join(validation_dir, 'Male') # Directory with our validation cat pictures\n",
        "validation_Female_dir = os.path.join(validation_dir, 'Female')# Directory with our validation dog pictures\n",
        "\n",
        "train_Male_fnames = os.listdir(train_Male_dir)\n",
        "train_Female_fnames = os.listdir(train_Female_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 = 21,\n",
        "                                                    class_mode = 'binary', \n",
        "                                                    target_size = (300, 300))     \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  = 10,\n",
        "                                                          class_mode  = 'binary', \n",
        "                                                          target_size = (300, 300))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 357 images belonging to 2 classes.\n",
            "Found 90 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Djj6O2U81ZHH"
      },
      "source": [
        "## For vgg16"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hq78J_ZIn38P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "9b6d459e-fad6-4a4d-c3f8-118f8fde7636"
      },
      "source": [
        "from keras.applications.vgg16 import VGG16\n",
        "\n",
        "# Load the VGG model\n",
        "pre_trained_model = VGG16(input_shape = (224, 224, 3), \n",
        "                                include_top = False)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 1s 0us/step\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmzA4Js_o4-0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "90dcab64-bbe7-408b-c638-e8b848781bc3"
      },
      "source": [
        "# Freeze the layers except the last 1 layers\n",
        "#for layer in pre_trained_model.layers[:-4]:\n",
        "#  layer.trainable = False\n",
        "  \n",
        "# Check the trainable status of the individual layers\n",
        "for layer in pre_trained_model.layers:\n",
        "  print(layer,layer.trainable)\n",
        "  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<keras.engine.input_layer.InputLayer object at 0x7fa2492b2710> False\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20ea415f8> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20ea41630> True\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7fa20e1ea518> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e1f8f60> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e1fbe80> True\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7fa20e203c50> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e190b00> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e196358> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e19b1d0> True\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7fa20e1a0b70> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e1aea20> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e1b42b0> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e1b95c0> True\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7fa20e1c0a20> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e1cc940> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e152208> True\n",
            "<keras.layers.convolutional.Conv2D object at 0x7fa20e157588> True\n",
            "<keras.layers.pooling.MaxPooling2D object at 0x7fa20e1607f0> True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxMLsbQCp0yg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "1747f773-ae8a-403d-dc0c-e24582701427"
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        " \n",
        "# Create the model\n",
        "model = models.Sequential()\n",
        " \n",
        "# Add the vgg convolutional base model\n",
        "model.add(pre_trained_model)\n",
        " \n",
        "# Add new layers\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(1024, activation='relu'))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n",
        " \n",
        "# Show a summary of the model. Check the number of trainable parameters\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "vgg16 (Model)                (None, 7, 7, 512)         14714688  \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 25088)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1024)              25691136  \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 1025      \n",
            "=================================================================\n",
            "Total params: 40,406,849\n",
            "Trainable params: 40,406,849\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1usVCS6mlzM3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "8c6834be-07f8-4617-a3e8-4cf795019afe"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wtPEZEhEtEDV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "a178add4-eb0c-4543-8bff-99dd53082d4d"
      },
      "source": [
        "\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import os\n",
        "import zipfile\n",
        "\n",
        "local_zip = '/content/drive/My Drive/Cocoon Dataset.zip'\n",
        "\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "\n",
        "zip_ref.extractall('/content')\n",
        "zip_ref.close()\n",
        "\n",
        "# Define our example directories and files\n",
        "base_dir = '/content/Cocoon Dataset'\n",
        "\n",
        "train_dir = os.path.join( base_dir, 'Train' )\n",
        "validation_dir = os.path.join( base_dir, 'Test')\n",
        "\n",
        "\n",
        "train_Male_dir = os.path.join(train_dir, 'Male') # Directory with our training cat pictures\n",
        "train_Female_dir = os.path.join(train_dir, 'Female') # Directory with our training dog pictures\n",
        "validation_Male_dir = os.path.join(validation_dir, 'Male') # Directory with our validation cat pictures\n",
        "validation_Female_dir = os.path.join(validation_dir, 'Female')# Directory with our validation dog pictures\n",
        "\n",
        "train_Male_fnames = os.listdir(train_Male_dir)\n",
        "train_Female_fnames = os.listdir(train_Female_dir)\n",
        "validation_Male_fnames = os.listdir(validation_Male_dir)\n",
        "validation_Female_fnames = os.listdir(validation_Female_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 = 21,\n",
        "                                                    class_mode = 'binary', \n",
        "                                                    target_size = (224, 224))     \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  = 10,\n",
        "                                                          class_mode  = 'binary', \n",
        "                                                          target_size = (224, 224))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 357 images belonging to 2 classes.\n",
            "Found 90 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5eN2wkrtHPr"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llDiaK5rtHf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8efcf803-c6c1-4299-f549-89b1dccba2f6"
      },
      "source": [
        "# Compile the model\n",
        "\n",
        "model.compile(optimizer = optimizers.adam(lr=0.0001), \n",
        "              loss = 'binary_crossentropy', \n",
        "              metrics = ['acc'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit_generator(\n",
        "            train_generator,\n",
        "            steps_per_epoch = train_generator.samples/train_generator.batch_size ,\n",
        "            epochs = 30,\n",
        "            validation_data = validation_generator,\n",
        "            validation_steps = validation_generator.samples/validation_generator.batch_size,\n",
        "            verbose = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Please use tf.math.log instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Epoch 1/30\n",
            "17/17 [==============================] - 109s 6s/step - loss: 0.7832 - acc: 0.4594 - val_loss: 0.6874 - val_acc: 0.5556\n",
            "Epoch 2/30\n",
            "17/17 [==============================] - 101s 6s/step - loss: 0.6884 - acc: 0.5686 - val_loss: 0.6869 - val_acc: 0.5556\n",
            "Epoch 3/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6895 - acc: 0.5714 - val_loss: 0.6928 - val_acc: 0.5556\n",
            "Epoch 4/30\n",
            "17/17 [==============================] - 101s 6s/step - loss: 0.6978 - acc: 0.5462 - val_loss: 0.6880 - val_acc: 0.5556\n",
            "Epoch 5/30\n",
            "17/17 [==============================] - 101s 6s/step - loss: 0.6910 - acc: 0.5602 - val_loss: 0.6880 - val_acc: 0.5556\n",
            "Epoch 6/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6892 - acc: 0.5686 - val_loss: 0.6876 - val_acc: 0.5556\n",
            "Epoch 7/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6867 - acc: 0.5714 - val_loss: 0.6882 - val_acc: 0.5556\n",
            "Epoch 8/30\n",
            "17/17 [==============================] - 101s 6s/step - loss: 0.6866 - acc: 0.5714 - val_loss: 0.6871 - val_acc: 0.5556\n",
            "Epoch 9/30\n",
            "17/17 [==============================] - 101s 6s/step - loss: 0.6862 - acc: 0.5630 - val_loss: 0.6870 - val_acc: 0.5556\n",
            "Epoch 10/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6837 - acc: 0.5686 - val_loss: 0.6891 - val_acc: 0.5556\n",
            "Epoch 11/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6868 - acc: 0.5686 - val_loss: 0.6870 - val_acc: 0.5556\n",
            "Epoch 12/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6858 - acc: 0.5686 - val_loss: 0.6883 - val_acc: 0.5556\n",
            "Epoch 13/30\n",
            "17/17 [==============================] - 103s 6s/step - loss: 0.6885 - acc: 0.5686 - val_loss: 0.6873 - val_acc: 0.5556\n",
            "Epoch 14/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6846 - acc: 0.5658 - val_loss: 0.6870 - val_acc: 0.5556\n",
            "Epoch 15/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6849 - acc: 0.5686 - val_loss: 0.6869 - val_acc: 0.5556\n",
            "Epoch 16/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6869 - acc: 0.5686 - val_loss: 0.6874 - val_acc: 0.5556\n",
            "Epoch 17/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6842 - acc: 0.5686 - val_loss: 0.6891 - val_acc: 0.5556\n",
            "Epoch 18/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6869 - acc: 0.5686 - val_loss: 0.6871 - val_acc: 0.5556\n",
            "Epoch 19/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6826 - acc: 0.5686 - val_loss: 0.6874 - val_acc: 0.5556\n",
            "Epoch 20/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6845 - acc: 0.5686 - val_loss: 0.6882 - val_acc: 0.5556\n",
            "Epoch 21/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6816 - acc: 0.5686 - val_loss: 0.6883 - val_acc: 0.5556\n",
            "Epoch 22/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6791 - acc: 0.5686 - val_loss: 0.6909 - val_acc: 0.5556\n",
            "Epoch 23/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.7002 - acc: 0.5602 - val_loss: 0.6875 - val_acc: 0.5556\n",
            "Epoch 24/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6897 - acc: 0.5686 - val_loss: 0.6872 - val_acc: 0.5556\n",
            "Epoch 25/30\n",
            "17/17 [==============================] - 101s 6s/step - loss: 0.6926 - acc: 0.5658 - val_loss: 0.6907 - val_acc: 0.5556\n",
            "Epoch 26/30\n",
            "17/17 [==============================] - 102s 6s/step - loss: 0.6883 - acc: 0.5686 - val_loss: 0.6872 - val_acc: 0.5556\n",
            "Epoch 27/30\n",
            "17/17 [==============================] - 101s 6s/step - loss: 0.6874 - acc: 0.5686 - val_loss: 0.6870 - val_acc: 0.5556\n",
            "Epoch 28/30\n",
            "17/17 [==============================] - 101s 6s/step - loss: 0.6859 - acc: 0.5686 - val_loss: 0.6874 - val_acc: 0.5556\n",
            "Epoch 29/30\n",
            "17/17 [==============================] - 101s 6s/step - loss: 0.6870 - acc: 0.5686 - val_loss: 0.6871 - val_acc: 0.5556\n",
            "Epoch 30/30\n",
            "17/17 [==============================] - 101s 6s/step - loss: 0.6865 - acc: 0.5686 - val_loss: 0.6873 - val_acc: 0.5556\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f0HrEb4itKxY"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atWMdc4QtK7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "b8f8451a-1ccf-4796-d7d0-341b1b528073"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "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",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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APcDhQGvgN2Z2dCHLGGFm2WaWnZOTU76AlPRFRIoUT9JfAxwRM9wyGpenPtAB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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CzeW2NZEd7MO"
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense\n",
        "import numpy as np\n",
        "import cv2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSAEkd3qA_m3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "c5dbbbff-fe72-4f02-a79f-c6f13e5f4082"
      },
      "source": [
        "validation_male_list = []\n",
        "for name in validation_Male_fnames:\n",
        "  base_dir = '/content/Cocoon Dataset/Test/Male/' + name\n",
        "  img = cv2.imread(base_dir)\n",
        "  img = cv2.resize(img,(224,224))\n",
        "  img = np.reshape(img,[1,224,224,3])\n",
        "  matrix_test = img\n",
        "  layer_name = 'dense_1'\n",
        "  intermediate_layer_model = Model(input = model.input, output = model.get_layer(layer_name).output)\n",
        "  intermediate_output = intermediate_layer_model.predict(matrix_test)\n",
        "  intermediate_output_list = [name] + intermediate_output[0].tolist()\n",
        "  validation_male_list.append(intermediate_output_list)\n",
        "  \n",
        "validation_male_list = np.array(validation_male_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"vg..., outputs=Tensor(\"de...)`\n",
            "  if __name__ == '__main__':\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJPPvsvpLdFp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "a52ac649-ae97-4dd3-8484-c29d9c706dab"
      },
      "source": [
        "validation_female_list = []\n",
        "for name in validation_Female_fnames:\n",
        "  base_dir = '/content/Cocoon Dataset/Test/Female/' + name\n",
        "  img = cv2.imread(base_dir)\n",
        "  img = cv2.resize(img,(224,224))\n",
        "  img = np.reshape(img,[1,224,224,3])\n",
        "  matrix_test = img\n",
        "  layer_name = 'dense_1'\n",
        "  intermediate_layer_model = Model(input = model.input, output = model.get_layer(layer_name).output)\n",
        "  intermediate_output = intermediate_layer_model.predict(matrix_test)\n",
        "  intermediate_output_list = [name] + intermediate_output[0].tolist()\n",
        "  validation_female_list.append(intermediate_output_list)\n",
        "  \n",
        "validation_female_list = np.array(validation_female_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"vg..., outputs=Tensor(\"de...)`\n",
            "  if __name__ == '__main__':\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFstj1gWL6QO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "7367160c-24c1-41d6-c07f-c3cb95aa1627"
      },
      "source": [
        "train_male_list = []\n",
        "for name in train_Male_fnames:\n",
        "  base_dir = '/content/Cocoon Dataset/Train/Male/' + name\n",
        "  img = cv2.imread(base_dir)\n",
        "  img = cv2.resize(img,(224,224))\n",
        "  img = np.reshape(img,[1,224,224,3])\n",
        "  matrix_test = img\n",
        "  layer_name = 'dense_1'\n",
        "  intermediate_layer_model = Model(input = model.input, output = model.get_layer(layer_name).output)\n",
        "  intermediate_output = intermediate_layer_model.predict(matrix_test)\n",
        "  intermediate_output_list = [name] + intermediate_output[0].tolist()\n",
        "  train_male_list.append(intermediate_output_list)\n",
        "  \n",
        "train_male_list = np.array(train_male_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"vg..., outputs=Tensor(\"de...)`\n",
            "  if __name__ == '__main__':\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UoTR8WjMKJh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "0acc8ab6-33c7-41a6-dd7e-c04a13bf1596"
      },
      "source": [
        "train_female_list = []\n",
        "for name in train_Female_fnames:\n",
        "  base_dir = '/content/Cocoon Dataset/Train/Female/' + name\n",
        "  img = cv2.imread(base_dir)\n",
        "  img = cv2.resize(img,(224,224))\n",
        "  img = np.reshape(img,[1,224,224,3])\n",
        "  matrix_test = img\n",
        "  layer_name = 'dense_1'\n",
        "  intermediate_layer_model = Model(input = model.input, output = model.get_layer(layer_name).output)\n",
        "  intermediate_output = intermediate_layer_model.predict(matrix_test)\n",
        "  intermediate_output_list = [name] + intermediate_output[0].tolist()\n",
        "  train_female_list.append(intermediate_output_list)\n",
        "  \n",
        "train_female_list = np.array(train_female_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"vg..., outputs=Tensor(\"de...)`\n",
            "  if __name__ == '__main__':\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "graDVrcLj68c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "c9a7dd35-37e6-45bf-965f-daa6969b339d"
      },
      "source": [
        "intermediate_output[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.04516411, 0.18483284, 0.16722398, ..., 0.05645609, 0.25139302,\n",
              "       0.11341704], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXva6MtWc-Gb"
      },
      "source": [
        "data = np.concatenate((validation_male_list,validation_female_list,train_male_list,train_female_list), axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lXBxBAbUO3yk"
      },
      "source": [
        "import pandas as pd\n",
        "df=pd.DataFrame(data=data[0: ,0:],\n",
        "            index=[i for i in range(data.shape[0])],\n",
        "            columns=['f'+str(i) for i in range(data.shape[1])])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5F2mX7-Sad_3"
      },
      "source": [
        "df = df.rename(columns={'f0':'File Name'})\n",
        "df = df.set_index('File Name')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IpDrOOcFdojO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "722eac94-44d0-4772-c588-17794c4d234d"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 447 entries, SK777A.jpg to 46B.jpg\n",
            "Columns: 1024 entries, f1 to f1024\n",
            "dtypes: object(1024)\n",
            "memory usage: 3.5+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4nptduMRN8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "796c5efc-09b9-4bad-af6f-eeea874f4c6a"
      },
      "source": [
        "drop_column = []\n",
        "for i in range(1,1025):\n",
        "  count = 0\n",
        "  for each in df['f' + str(i)]:\n",
        "    if float(each)<= 0.0:\n",
        "      count +=1\n",
        "  if count == len(df):\n",
        "    drop_column.append('f' + str(i))\n",
        "\n",
        "len(drop_column)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "531"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdX69H-eTjDe"
      },
      "source": [
        "df = df.drop(columns = drop_column)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PBuua2IyO3up",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "67acd85d-0302-476d-b5e4-2410249e653b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "</table>\n",
              "<p>5 rows × 493 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                              f1  ...                f1024\n",
              "File Name                         ...                     \n",
              "SK777A.jpg  0.054964736104011536  ...  0.13619039952754974\n",
              "74Aa.jpg     0.06117307394742966  ...  0.15061648190021515\n",
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              "SK772A.jpg  0.056103043258190155  ...  0.13883543014526367\n",
              "\n",
              "[5 rows x 493 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qFEGnDV4U-P-"
      },
      "source": [
        "column_name = []\n",
        "for i in range(1,494):\n",
        "  column_name.append('f' + str(i))\n",
        "\n",
        "df.columns = column_name"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OtvJHSApVdBL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "e2f7bb1d-1c6e-4006-c65e-37461d1bee00"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "      <td>0.2519139349460602</td>\n",
              "      <td>0.08080592006444931</td>\n",
              "      <td>0.38582292199134827</td>\n",
              "      <td>0.16858088970184326</td>\n",
              "      <td>0.237325057387352</td>\n",
              "      <td>0.06887291371822357</td>\n",
              "      <td>0.3075893521308899</td>\n",
              "      <td>0.13883543014526367</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 493 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                              f1  ...                 f493\n",
              "File Name                         ...                     \n",
              "SK777A.jpg  0.054964736104011536  ...  0.13619039952754974\n",
              "74Aa.jpg     0.06117307394742966  ...  0.15061648190021515\n",
              "SK780B.jpg   0.06054367125034332  ...   0.1491539478302002\n",
              "SK751A.jpg   0.05900680646300316  ...  0.14558279514312744\n",
              "SK772A.jpg  0.056103043258190155  ...  0.13883543014526367\n",
              "\n",
              "[5 rows x 493 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uyRJWzqbO3rU"
      },
      "source": [
        " df.to_excel('output.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2gNgHRB61RiO"
      },
      "source": [
        "## For inception"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RO4pgY8CelTU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "4aa7e95e-69a2-4fa4-8875-a275a6d94835"
      },
      "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 = (300, 300, 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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-10-07 10:46:51--  https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.204.128, 2404:6800:4008:c04::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.204.128|: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",
            "\r          /tmp/ince   0%[                    ]       0  --.-KB/s               \r         /tmp/incep   4%[                    ]   4.01M  14.4MB/s               \r        /tmp/incept   9%[>                   ]   8.01M  16.4MB/s               \r       /tmp/incepti  47%[========>           ]  40.01M  40.1MB/s               \r      /tmp/inceptio  85%[================>   ]  72.01M  59.3MB/s               \r     /tmp/inception  95%[==================> ]  80.01M  55.2MB/s               \r/tmp/inception_v3_w 100%[===================>]  83.84M  57.3MB/s    in 1.5s    \n",
            "\n",
            "2019-10-07 10:46:53 (57.3 MB/s) - ‘/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5’ saved [87910968/87910968]\n",
            "\n",
            "last layer output shape:  (None, 17, 17, 768)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LuGRUCkLs7Zh"
      },
      "source": [
        "\n",
        "\n",
        "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 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 = ['acc'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5Tph4ycZQ3m"
      },
      "source": [
        "\n",
        "\n",
        "# Train the model\n",
        "history = model.fit_generator(\n",
        "            train_generator,\n",
        "            steps_per_epoch = train_generator.samples/train_generator.batch_size ,\n",
        "            epochs = 30,\n",
        "            validation_data = validation_generator,\n",
        "            validation_steps = validation_generator.samples/validation_generator.batch_size,\n",
        "            verbose = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-oE65P--kAA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "145dc4bf-d933-4adf-8de4-cf50e9c88804"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, 300, 300, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_94 (Conv2D)              (None, 149, 149, 32) 864         input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_94 (BatchNo (None, 149, 149, 32) 96          conv2d_94[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_94 (Activation)      (None, 149, 149, 32) 0           batch_normalization_94[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_95 (Conv2D)              (None, 147, 147, 32) 9216        activation_94[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_95 (BatchNo (None, 147, 147, 32) 96          conv2d_95[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_95 (Activation)      (None, 147, 147, 32) 0           batch_normalization_95[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_96 (Conv2D)              (None, 147, 147, 64) 18432       activation_95[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_96 (BatchNo (None, 147, 147, 64) 192         conv2d_96[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_96 (Activation)      (None, 147, 147, 64) 0           batch_normalization_96[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 73, 73, 64)   0           activation_96[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_97 (Conv2D)              (None, 73, 73, 80)   5120        max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_97 (BatchNo (None, 73, 73, 80)   240         conv2d_97[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_97 (Activation)      (None, 73, 73, 80)   0           batch_normalization_97[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_98 (Conv2D)              (None, 71, 71, 192)  138240      activation_97[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_98 (BatchNo (None, 71, 71, 192)  576         conv2d_98[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_98 (Activation)      (None, 71, 71, 192)  0           batch_normalization_98[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2D)  (None, 35, 35, 192)  0           activation_98[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_102 (Conv2D)             (None, 35, 35, 64)   12288       max_pooling2d_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_102 (BatchN (None, 35, 35, 64)   192         conv2d_102[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_102 (Activation)     (None, 35, 35, 64)   0           batch_normalization_102[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_100 (Conv2D)             (None, 35, 35, 48)   9216        max_pooling2d_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_103 (Conv2D)             (None, 35, 35, 96)   55296       activation_102[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_100 (BatchN (None, 35, 35, 48)   144         conv2d_100[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_103 (BatchN (None, 35, 35, 96)   288         conv2d_103[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_100 (Activation)     (None, 35, 35, 48)   0           batch_normalization_100[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_103 (Activation)     (None, 35, 35, 96)   0           batch_normalization_103[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_9 (AveragePoo (None, 35, 35, 192)  0           max_pooling2d_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_99 (Conv2D)              (None, 35, 35, 64)   12288       max_pooling2d_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_101 (Conv2D)             (None, 35, 35, 64)   76800       activation_100[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_104 (Conv2D)             (None, 35, 35, 96)   82944       activation_103[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_105 (Conv2D)             (None, 35, 35, 32)   6144        average_pooling2d_9[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_99 (BatchNo (None, 35, 35, 64)   192         conv2d_99[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_101 (BatchN (None, 35, 35, 64)   192         conv2d_101[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_104 (BatchN (None, 35, 35, 96)   288         conv2d_104[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_105 (BatchN (None, 35, 35, 32)   96          conv2d_105[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_99 (Activation)      (None, 35, 35, 64)   0           batch_normalization_99[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_101 (Activation)     (None, 35, 35, 64)   0           batch_normalization_101[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_104 (Activation)     (None, 35, 35, 96)   0           batch_normalization_104[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_105 (Activation)     (None, 35, 35, 32)   0           batch_normalization_105[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, 35, 35, 256)  0           activation_99[0][0]              \n",
            "                                                                 activation_101[0][0]             \n",
            "                                                                 activation_104[0][0]             \n",
            "                                                                 activation_105[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_109 (Conv2D)             (None, 35, 35, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_109 (BatchN (None, 35, 35, 64)   192         conv2d_109[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_109 (Activation)     (None, 35, 35, 64)   0           batch_normalization_109[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_107 (Conv2D)             (None, 35, 35, 48)   12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_110 (Conv2D)             (None, 35, 35, 96)   55296       activation_109[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_107 (BatchN (None, 35, 35, 48)   144         conv2d_107[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_110 (BatchN (None, 35, 35, 96)   288         conv2d_110[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_107 (Activation)     (None, 35, 35, 48)   0           batch_normalization_107[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_110 (Activation)     (None, 35, 35, 96)   0           batch_normalization_110[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_10 (AveragePo (None, 35, 35, 256)  0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_106 (Conv2D)             (None, 35, 35, 64)   16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_108 (Conv2D)             (None, 35, 35, 64)   76800       activation_107[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_111 (Conv2D)             (None, 35, 35, 96)   82944       activation_110[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_112 (Conv2D)             (None, 35, 35, 64)   16384       average_pooling2d_10[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_106 (BatchN (None, 35, 35, 64)   192         conv2d_106[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_108 (BatchN (None, 35, 35, 64)   192         conv2d_108[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_111 (BatchN (None, 35, 35, 96)   288         conv2d_111[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_112 (BatchN (None, 35, 35, 64)   192         conv2d_112[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_106 (Activation)     (None, 35, 35, 64)   0           batch_normalization_106[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_108 (Activation)     (None, 35, 35, 64)   0           batch_normalization_108[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_111 (Activation)     (None, 35, 35, 96)   0           batch_normalization_111[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_112 (Activation)     (None, 35, 35, 64)   0           batch_normalization_112[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, 35, 35, 288)  0           activation_106[0][0]             \n",
            "                                                                 activation_108[0][0]             \n",
            "                                                                 activation_111[0][0]             \n",
            "                                                                 activation_112[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_116 (Conv2D)             (None, 35, 35, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_116 (BatchN (None, 35, 35, 64)   192         conv2d_116[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_116 (Activation)     (None, 35, 35, 64)   0           batch_normalization_116[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_114 (Conv2D)             (None, 35, 35, 48)   13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_117 (Conv2D)             (None, 35, 35, 96)   55296       activation_116[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_114 (BatchN (None, 35, 35, 48)   144         conv2d_114[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_117 (BatchN (None, 35, 35, 96)   288         conv2d_117[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_114 (Activation)     (None, 35, 35, 48)   0           batch_normalization_114[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_117 (Activation)     (None, 35, 35, 96)   0           batch_normalization_117[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_11 (AveragePo (None, 35, 35, 288)  0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_113 (Conv2D)             (None, 35, 35, 64)   18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_115 (Conv2D)             (None, 35, 35, 64)   76800       activation_114[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_118 (Conv2D)             (None, 35, 35, 96)   82944       activation_117[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_119 (Conv2D)             (None, 35, 35, 64)   18432       average_pooling2d_11[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_113 (BatchN (None, 35, 35, 64)   192         conv2d_113[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_115 (BatchN (None, 35, 35, 64)   192         conv2d_115[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_118 (BatchN (None, 35, 35, 96)   288         conv2d_118[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_119 (BatchN (None, 35, 35, 64)   192         conv2d_119[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_113 (Activation)     (None, 35, 35, 64)   0           batch_normalization_113[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_115 (Activation)     (None, 35, 35, 64)   0           batch_normalization_115[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_118 (Activation)     (None, 35, 35, 96)   0           batch_normalization_118[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_119 (Activation)     (None, 35, 35, 64)   0           batch_normalization_119[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, 35, 35, 288)  0           activation_113[0][0]             \n",
            "                                                                 activation_115[0][0]             \n",
            "                                                                 activation_118[0][0]             \n",
            "                                                                 activation_119[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_121 (Conv2D)             (None, 35, 35, 64)   18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_121 (BatchN (None, 35, 35, 64)   192         conv2d_121[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_121 (Activation)     (None, 35, 35, 64)   0           batch_normalization_121[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_122 (Conv2D)             (None, 35, 35, 96)   55296       activation_121[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_122 (BatchN (None, 35, 35, 96)   288         conv2d_122[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_122 (Activation)     (None, 35, 35, 96)   0           batch_normalization_122[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_120 (Conv2D)             (None, 17, 17, 384)  995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_123 (Conv2D)             (None, 17, 17, 96)   82944       activation_122[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_120 (BatchN (None, 17, 17, 384)  1152        conv2d_120[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_123 (BatchN (None, 17, 17, 96)   288         conv2d_123[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_120 (Activation)     (None, 17, 17, 384)  0           batch_normalization_120[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_123 (Activation)     (None, 17, 17, 96)   0           batch_normalization_123[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2D)  (None, 17, 17, 288)  0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, 17, 17, 768)  0           activation_120[0][0]             \n",
            "                                                                 activation_123[0][0]             \n",
            "                                                                 max_pooling2d_6[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_128 (Conv2D)             (None, 17, 17, 128)  98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_128 (BatchN (None, 17, 17, 128)  384         conv2d_128[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_128 (Activation)     (None, 17, 17, 128)  0           batch_normalization_128[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_129 (Conv2D)             (None, 17, 17, 128)  114688      activation_128[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_129 (BatchN (None, 17, 17, 128)  384         conv2d_129[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_129 (Activation)     (None, 17, 17, 128)  0           batch_normalization_129[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_125 (Conv2D)             (None, 17, 17, 128)  98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_130 (Conv2D)             (None, 17, 17, 128)  114688      activation_129[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_125 (BatchN (None, 17, 17, 128)  384         conv2d_125[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_130 (BatchN (None, 17, 17, 128)  384         conv2d_130[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_125 (Activation)     (None, 17, 17, 128)  0           batch_normalization_125[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_130 (Activation)     (None, 17, 17, 128)  0           batch_normalization_130[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_126 (Conv2D)             (None, 17, 17, 128)  114688      activation_125[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_131 (Conv2D)             (None, 17, 17, 128)  114688      activation_130[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_126 (BatchN (None, 17, 17, 128)  384         conv2d_126[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_131 (BatchN (None, 17, 17, 128)  384         conv2d_131[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_126 (Activation)     (None, 17, 17, 128)  0           batch_normalization_126[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_131 (Activation)     (None, 17, 17, 128)  0           batch_normalization_131[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_12 (AveragePo (None, 17, 17, 768)  0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_124 (Conv2D)             (None, 17, 17, 192)  147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_127 (Conv2D)             (None, 17, 17, 192)  172032      activation_126[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_132 (Conv2D)             (None, 17, 17, 192)  172032      activation_131[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_133 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_12[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_124 (BatchN (None, 17, 17, 192)  576         conv2d_124[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_127 (BatchN (None, 17, 17, 192)  576         conv2d_127[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_132 (BatchN (None, 17, 17, 192)  576         conv2d_132[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_133 (BatchN (None, 17, 17, 192)  576         conv2d_133[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_124 (Activation)     (None, 17, 17, 192)  0           batch_normalization_124[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_127 (Activation)     (None, 17, 17, 192)  0           batch_normalization_127[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_132 (Activation)     (None, 17, 17, 192)  0           batch_normalization_132[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_133 (Activation)     (None, 17, 17, 192)  0           batch_normalization_133[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, 17, 17, 768)  0           activation_124[0][0]             \n",
            "                                                                 activation_127[0][0]             \n",
            "                                                                 activation_132[0][0]             \n",
            "                                                                 activation_133[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_138 (Conv2D)             (None, 17, 17, 160)  122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_138 (BatchN (None, 17, 17, 160)  480         conv2d_138[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_138 (Activation)     (None, 17, 17, 160)  0           batch_normalization_138[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_139 (Conv2D)             (None, 17, 17, 160)  179200      activation_138[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_139 (BatchN (None, 17, 17, 160)  480         conv2d_139[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_139 (Activation)     (None, 17, 17, 160)  0           batch_normalization_139[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_135 (Conv2D)             (None, 17, 17, 160)  122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_140 (Conv2D)             (None, 17, 17, 160)  179200      activation_139[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_135 (BatchN (None, 17, 17, 160)  480         conv2d_135[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_140 (BatchN (None, 17, 17, 160)  480         conv2d_140[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_135 (Activation)     (None, 17, 17, 160)  0           batch_normalization_135[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_140 (Activation)     (None, 17, 17, 160)  0           batch_normalization_140[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_136 (Conv2D)             (None, 17, 17, 160)  179200      activation_135[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_141 (Conv2D)             (None, 17, 17, 160)  179200      activation_140[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_136 (BatchN (None, 17, 17, 160)  480         conv2d_136[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_141 (BatchN (None, 17, 17, 160)  480         conv2d_141[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_136 (Activation)     (None, 17, 17, 160)  0           batch_normalization_136[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_141 (Activation)     (None, 17, 17, 160)  0           batch_normalization_141[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_13 (AveragePo (None, 17, 17, 768)  0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_134 (Conv2D)             (None, 17, 17, 192)  147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_137 (Conv2D)             (None, 17, 17, 192)  215040      activation_136[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_142 (Conv2D)             (None, 17, 17, 192)  215040      activation_141[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_143 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_13[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_134 (BatchN (None, 17, 17, 192)  576         conv2d_134[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_137 (BatchN (None, 17, 17, 192)  576         conv2d_137[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_142 (BatchN (None, 17, 17, 192)  576         conv2d_142[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_143 (BatchN (None, 17, 17, 192)  576         conv2d_143[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_134 (Activation)     (None, 17, 17, 192)  0           batch_normalization_134[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_137 (Activation)     (None, 17, 17, 192)  0           batch_normalization_137[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_142 (Activation)     (None, 17, 17, 192)  0           batch_normalization_142[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_143 (Activation)     (None, 17, 17, 192)  0           batch_normalization_143[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, 17, 17, 768)  0           activation_134[0][0]             \n",
            "                                                                 activation_137[0][0]             \n",
            "                                                                 activation_142[0][0]             \n",
            "                                                                 activation_143[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_148 (Conv2D)             (None, 17, 17, 160)  122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_148 (BatchN (None, 17, 17, 160)  480         conv2d_148[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_148 (Activation)     (None, 17, 17, 160)  0           batch_normalization_148[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_149 (Conv2D)             (None, 17, 17, 160)  179200      activation_148[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_149 (BatchN (None, 17, 17, 160)  480         conv2d_149[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_149 (Activation)     (None, 17, 17, 160)  0           batch_normalization_149[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_145 (Conv2D)             (None, 17, 17, 160)  122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_150 (Conv2D)             (None, 17, 17, 160)  179200      activation_149[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_145 (BatchN (None, 17, 17, 160)  480         conv2d_145[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_150 (BatchN (None, 17, 17, 160)  480         conv2d_150[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_145 (Activation)     (None, 17, 17, 160)  0           batch_normalization_145[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_150 (Activation)     (None, 17, 17, 160)  0           batch_normalization_150[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_146 (Conv2D)             (None, 17, 17, 160)  179200      activation_145[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_151 (Conv2D)             (None, 17, 17, 160)  179200      activation_150[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_146 (BatchN (None, 17, 17, 160)  480         conv2d_146[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_151 (BatchN (None, 17, 17, 160)  480         conv2d_151[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_146 (Activation)     (None, 17, 17, 160)  0           batch_normalization_146[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_151 (Activation)     (None, 17, 17, 160)  0           batch_normalization_151[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_14 (AveragePo (None, 17, 17, 768)  0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_144 (Conv2D)             (None, 17, 17, 192)  147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_147 (Conv2D)             (None, 17, 17, 192)  215040      activation_146[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_152 (Conv2D)             (None, 17, 17, 192)  215040      activation_151[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_153 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_14[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_144 (BatchN (None, 17, 17, 192)  576         conv2d_144[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_147 (BatchN (None, 17, 17, 192)  576         conv2d_147[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_152 (BatchN (None, 17, 17, 192)  576         conv2d_152[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_153 (BatchN (None, 17, 17, 192)  576         conv2d_153[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_144 (Activation)     (None, 17, 17, 192)  0           batch_normalization_144[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_147 (Activation)     (None, 17, 17, 192)  0           batch_normalization_147[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_152 (Activation)     (None, 17, 17, 192)  0           batch_normalization_152[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_153 (Activation)     (None, 17, 17, 192)  0           batch_normalization_153[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, 17, 17, 768)  0           activation_144[0][0]             \n",
            "                                                                 activation_147[0][0]             \n",
            "                                                                 activation_152[0][0]             \n",
            "                                                                 activation_153[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_158 (Conv2D)             (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_158 (BatchN (None, 17, 17, 192)  576         conv2d_158[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_158 (Activation)     (None, 17, 17, 192)  0           batch_normalization_158[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_159 (Conv2D)             (None, 17, 17, 192)  258048      activation_158[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_159 (BatchN (None, 17, 17, 192)  576         conv2d_159[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_159 (Activation)     (None, 17, 17, 192)  0           batch_normalization_159[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_155 (Conv2D)             (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_160 (Conv2D)             (None, 17, 17, 192)  258048      activation_159[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_155 (BatchN (None, 17, 17, 192)  576         conv2d_155[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_160 (BatchN (None, 17, 17, 192)  576         conv2d_160[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_155 (Activation)     (None, 17, 17, 192)  0           batch_normalization_155[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_160 (Activation)     (None, 17, 17, 192)  0           batch_normalization_160[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_156 (Conv2D)             (None, 17, 17, 192)  258048      activation_155[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_161 (Conv2D)             (None, 17, 17, 192)  258048      activation_160[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_156 (BatchN (None, 17, 17, 192)  576         conv2d_156[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_161 (BatchN (None, 17, 17, 192)  576         conv2d_161[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_156 (Activation)     (None, 17, 17, 192)  0           batch_normalization_156[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_161 (Activation)     (None, 17, 17, 192)  0           batch_normalization_161[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_15 (AveragePo (None, 17, 17, 768)  0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_154 (Conv2D)             (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_157 (Conv2D)             (None, 17, 17, 192)  258048      activation_156[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_162 (Conv2D)             (None, 17, 17, 192)  258048      activation_161[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_163 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_15[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_154 (BatchN (None, 17, 17, 192)  576         conv2d_154[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_157 (BatchN (None, 17, 17, 192)  576         conv2d_157[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_162 (BatchN (None, 17, 17, 192)  576         conv2d_162[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_163 (BatchN (None, 17, 17, 192)  576         conv2d_163[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_154 (Activation)     (None, 17, 17, 192)  0           batch_normalization_154[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_157 (Activation)     (None, 17, 17, 192)  0           batch_normalization_157[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_162 (Activation)     (None, 17, 17, 192)  0           batch_normalization_162[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_163 (Activation)     (None, 17, 17, 192)  0           batch_normalization_163[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, 17, 17, 768)  0           activation_154[0][0]             \n",
            "                                                                 activation_157[0][0]             \n",
            "                                                                 activation_162[0][0]             \n",
            "                                                                 activation_163[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "flatten_2 (Flatten)             (None, 221952)       0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 1024)         227279872   flatten_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout_2 (Dropout)             (None, 1024)         0           dense_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 1)            1025        dropout_2[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 236,256,161\n",
            "Trainable params: 227,280,897\n",
            "Non-trainable params: 8,975,264\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRfQZvsPCemd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f26c586b-2472-43a4-a22e-d0b474ffd723"
      },
      "source": [
        "print(model.layers[-2].output)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensor(\"dropout_2/cond/Merge:0\", shape=(?, 1024), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TvFvf1oAmn4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5a4c5cec-dd5f-4a40-90ad-0483d9ae7a82"
      },
      "source": [
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense\n",
        "import tensorflow as tf\n",
        "\n",
        "g = model.get_layer(name='dropout_2', index=None).output\n",
        "tf.Print(g, [tf.shape(g)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'Print_5:0' shape=(?, 1024) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gPPOo-sDZ1Z-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "27a69625-eb71-43fd-eaf6-968fff7b2d78"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "acc = history.history['acc']\n",
        "val_acc = history.history['val_acc']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "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",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PnNdGGsXH3LmudyCS7JYAJgiGUfbk5bkIp7HOSm3Rwg1ZTZQfYfHi6HoHYIIQ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}