usingInception.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "venka.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkrCxlMHDm21",
        "colab_type": "code",
        "outputId": "42020053-4223-41f8-da44-845ad33bf303",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive/')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive/; to attempt to forcibly remount, call drive.mount(\"/content/gdrive/\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sngxIk9OEKYy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "os.chdir('/content/gdrive/My Drive/')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekpS4UDxDX2J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.applications.inception_v3 import InceptionV3,preprocess_input,decode_predictions\n",
        "from keras.preprocessing import image\n",
        "import numpy as np\n",
        "from keras.layers import Dense, GlobalAveragePooling2D,Dropout,Input\n",
        "# from keras.layers.advanced_activations import LeakyReLU, ELU\n",
        "from keras.models import Sequential,Model\n",
        "from keras import backend as K\n",
        "from IPython.display import display"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZb2kUKZF_aU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_gen_args = dict(preprocessing_function=preprocess_input, #Define the dictionary for Image data Generator\n",
        "    rotation_range=30,\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",
        "    vertical_flip = True)\n",
        "\n",
        "train_datagen = image.ImageDataGenerator(**data_gen_args)\n",
        "test_datagen = image.ImageDataGenerator(**data_gen_args)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAzthRuSDigx",
        "colab_type": "code",
        "outputId": "fcc06b59-1839-409c-b52b-a2fc0518764f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "base_model  = InceptionV3(weights = 'imagenet', include_top=False)\n",
        "print('loaded model')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loaded model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F0Wc2iAOEUan",
        "colab_type": "code",
        "outputId": "83737c20-44fa-423f-9928-724412839cdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "train_generator = train_datagen.flow_from_directory(\"/content/gdrive/My Drive/MDataset/train\",\n",
        "                                                    target_size=(299,299),batch_size=32)\n",
        "\n",
        "valid_generator = test_datagen.flow_from_directory(\"/content/gdrive/My Drive/MDataset/test\",\n",
        "                                                     target_size=(299,299),batch_size=32)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1695 images belonging to 2 classes.\n",
            "Found 607 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DflbNzo-Nppn",
        "colab_type": "code",
        "outputId": "62e40467-e84a-4022-945d-c02beb6be60e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        }
      },
      "source": [
        "from keras.layers import Conv2D,MaxPooling2D,Flatten\n",
        "\n",
        "benchmark = Sequential()\n",
        "benchmark.add(Conv2D(filters = 16, kernel_size = 2, padding = 'same', activation = 'relu', input_shape = (299,299,3)))\n",
        "benchmark.add(MaxPooling2D(pool_size=2,padding='same'))\n",
        "benchmark.add(Conv2D(filters = 32, kernel_size = 2, padding = 'same', activation = 'relu'))\n",
        "benchmark.add(MaxPooling2D(pool_size=2,padding='same'))\n",
        "benchmark.add(Conv2D(filters = 64, kernel_size = 2, padding = 'same', activation = 'relu'))\n",
        "benchmark.add(MaxPooling2D(pool_size=2,padding='same'))\n",
        "benchmark.add(Dropout(0.3))\n",
        "benchmark.add(Flatten())\n",
        "benchmark.add(Dense(512, activation='relu'))\n",
        "benchmark.add(Dense(2, activation='softmax'))\n",
        "\n",
        "benchmark.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_192 (Conv2D)          (None, 299, 299, 16)      208       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 150, 150, 16)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_193 (Conv2D)          (None, 150, 150, 32)      2080      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_13 (MaxPooling (None, 75, 75, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_194 (Conv2D)          (None, 75, 75, 64)        8256      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_14 (MaxPooling (None, 38, 38, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 38, 38, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 92416)             0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 512)               47317504  \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 2)                 1026      \n",
            "=================================================================\n",
            "Total params: 47,329,074\n",
            "Trainable params: 47,329,074\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53G2hLEAx3RO",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okAn0hPTNuNX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "benchmark.compile(loss = 'categorical_crossentropy',optimizer='rmsprop', metrics = ['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0xl5UbANybV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.callbacks import ModelCheckpoint,EarlyStopping\n",
        "\n",
        "checkpoint_path = \"benchmark.hdf5\"\n",
        "checkpoint_dir = os.path.dirname(checkpoint_path)\n",
        "\n",
        "# Save the model with best weights\n",
        "checkpointer = ModelCheckpoint(checkpoint_path, verbose=1,save_best_only=True)\n",
        "# Stop the training if the model shows no improvement \n",
        "stopper = EarlyStopping(monitor='val_loss',min_delta=0.1,patience=0,verbose=1,mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGiQHvACN0Yi",
        "colab_type": "code",
        "outputId": "00e773e8-cb4b-4d6c-8c54-a8174bfb4136",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "history = benchmark.fit_generator(train_generator, steps_per_epoch = 13,validation_data=valid_generator,validation_steps=3, epochs=5,verbose=1,callbacks=[checkpointer])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "13/13 [==============================] - 260s 20s/step - loss: 8.3852 - acc: 0.4399 - val_loss: 8.6529 - val_acc: 0.4632\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 8.65287, saving model to benchmark.hdf5\n",
            "Epoch 2/5\n",
            "13/13 [==============================] - 188s 14s/step - loss: 8.9321 - acc: 0.4457 - val_loss: 3.7819 - val_acc: 0.5521\n",
            "\n",
            "Epoch 00002: val_loss improved from 8.65287 to 3.78189, saving model to benchmark.hdf5\n",
            "Epoch 3/5\n",
            "13/13 [==============================] - 87s 7s/step - loss: 6.9819 - acc: 0.5481 - val_loss: 6.8838 - val_acc: 0.5729\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 3.78189\n",
            "Epoch 4/5\n",
            "13/13 [==============================] - 80s 6s/step - loss: 7.0129 - acc: 0.5649 - val_loss: 6.0443 - val_acc: 0.6250\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 3.78189\n",
            "Epoch 5/5\n",
            "13/13 [==============================] - 76s 6s/step - loss: 6.4317 - acc: 0.6010 - val_loss: 7.7233 - val_acc: 0.5208\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 3.78189\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AV4JCB09cD7S",
        "colab_type": "text"
      },
      "source": [
        "Transfer Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nV7YEtfSN79j",
        "colab_type": "code",
        "outputId": "648aa0cf-580d-4db3-9a51-113e8da2e1c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 11495
        }
      },
      "source": [
        "# Define the output layers for Inceptionv3\n",
        "last = base_model.output\n",
        "pool = GlobalAveragePooling2D()(last)\n",
        "x = Dense(512, activation='relu')(pool)\n",
        "x = Dropout(0.5)(x)\n",
        "preds = Dense(2,activation='softmax')(x)\n",
        "\n",
        "model = Model(input=base_model.input,output=preds)\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, None, None, 3 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_98 (Conv2D)              (None, None, None, 3 864         input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_95 (BatchNo (None, None, None, 3 96          conv2d_98[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_95 (Activation)      (None, None, None, 3 0           batch_normalization_95[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_99 (Conv2D)              (None, None, None, 3 9216        activation_95[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_96 (BatchNo (None, None, None, 3 96          conv2d_99[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_96 (Activation)      (None, None, None, 3 0           batch_normalization_96[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_100 (Conv2D)             (None, None, None, 6 18432       activation_96[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_97 (BatchNo (None, None, None, 6 192         conv2d_100[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_97 (Activation)      (None, None, None, 6 0           batch_normalization_97[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2D)  (None, None, None, 6 0           activation_97[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_101 (Conv2D)             (None, None, None, 8 5120        max_pooling2d_8[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_98 (BatchNo (None, None, None, 8 240         conv2d_101[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_98 (Activation)      (None, None, None, 8 0           batch_normalization_98[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_102 (Conv2D)             (None, None, None, 1 138240      activation_98[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_99 (BatchNo (None, None, None, 1 576         conv2d_102[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_99 (Activation)      (None, None, None, 1 0           batch_normalization_99[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2D)  (None, None, None, 1 0           activation_99[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_106 (Conv2D)             (None, None, None, 6 12288       max_pooling2d_9[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_103 (BatchN (None, None, None, 6 192         conv2d_106[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_103 (Activation)     (None, None, None, 6 0           batch_normalization_103[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_104 (Conv2D)             (None, None, None, 4 9216        max_pooling2d_9[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_107 (Conv2D)             (None, None, None, 9 55296       activation_103[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_101 (BatchN (None, None, None, 4 144         conv2d_104[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_104 (BatchN (None, None, None, 9 288         conv2d_107[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_101 (Activation)     (None, None, None, 4 0           batch_normalization_101[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_104 (Activation)     (None, None, None, 9 0           batch_normalization_104[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_10 (AveragePo (None, None, None, 1 0           max_pooling2d_9[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_103 (Conv2D)             (None, None, None, 6 12288       max_pooling2d_9[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_105 (Conv2D)             (None, None, None, 6 76800       activation_101[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_108 (Conv2D)             (None, None, None, 9 82944       activation_104[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_109 (Conv2D)             (None, None, None, 3 6144        average_pooling2d_10[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_100 (BatchN (None, None, None, 6 192         conv2d_103[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_102 (BatchN (None, None, None, 6 192         conv2d_105[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_105 (BatchN (None, None, None, 9 288         conv2d_108[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_106 (BatchN (None, None, None, 3 96          conv2d_109[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_100 (Activation)     (None, None, None, 6 0           batch_normalization_100[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_102 (Activation)     (None, None, None, 6 0           batch_normalization_102[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_105 (Activation)     (None, None, None, 9 0           batch_normalization_105[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_106 (Activation)     (None, None, None, 3 0           batch_normalization_106[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed0 (Concatenate)            (None, None, None, 2 0           activation_100[0][0]             \n",
            "                                                                 activation_102[0][0]             \n",
            "                                                                 activation_105[0][0]             \n",
            "                                                                 activation_106[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_113 (Conv2D)             (None, None, None, 6 16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_110 (BatchN (None, None, None, 6 192         conv2d_113[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_110 (Activation)     (None, None, None, 6 0           batch_normalization_110[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_111 (Conv2D)             (None, None, None, 4 12288       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_114 (Conv2D)             (None, None, None, 9 55296       activation_110[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_108 (BatchN (None, None, None, 4 144         conv2d_111[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_111 (BatchN (None, None, None, 9 288         conv2d_114[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_108 (Activation)     (None, None, None, 4 0           batch_normalization_108[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_111 (Activation)     (None, None, None, 9 0           batch_normalization_111[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_11 (AveragePo (None, None, None, 2 0           mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_110 (Conv2D)             (None, None, None, 6 16384       mixed0[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_112 (Conv2D)             (None, None, None, 6 76800       activation_108[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_115 (Conv2D)             (None, None, None, 9 82944       activation_111[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_116 (Conv2D)             (None, None, None, 6 16384       average_pooling2d_11[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_107 (BatchN (None, None, None, 6 192         conv2d_110[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_109 (BatchN (None, None, None, 6 192         conv2d_112[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_112 (BatchN (None, None, None, 9 288         conv2d_115[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_113 (BatchN (None, None, None, 6 192         conv2d_116[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_107 (Activation)     (None, None, None, 6 0           batch_normalization_107[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_109 (Activation)     (None, None, None, 6 0           batch_normalization_109[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_112 (Activation)     (None, None, None, 9 0           batch_normalization_112[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_113 (Activation)     (None, None, None, 6 0           batch_normalization_113[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed1 (Concatenate)            (None, None, None, 2 0           activation_107[0][0]             \n",
            "                                                                 activation_109[0][0]             \n",
            "                                                                 activation_112[0][0]             \n",
            "                                                                 activation_113[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_120 (Conv2D)             (None, None, None, 6 18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_117 (BatchN (None, None, None, 6 192         conv2d_120[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_117 (Activation)     (None, None, None, 6 0           batch_normalization_117[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_118 (Conv2D)             (None, None, None, 4 13824       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_121 (Conv2D)             (None, None, None, 9 55296       activation_117[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_115 (BatchN (None, None, None, 4 144         conv2d_118[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_118 (BatchN (None, None, None, 9 288         conv2d_121[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_115 (Activation)     (None, None, None, 4 0           batch_normalization_115[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_118 (Activation)     (None, None, None, 9 0           batch_normalization_118[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_12 (AveragePo (None, None, None, 2 0           mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_117 (Conv2D)             (None, None, None, 6 18432       mixed1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_119 (Conv2D)             (None, None, None, 6 76800       activation_115[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_122 (Conv2D)             (None, None, None, 9 82944       activation_118[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_123 (Conv2D)             (None, None, None, 6 18432       average_pooling2d_12[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_114 (BatchN (None, None, None, 6 192         conv2d_117[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_116 (BatchN (None, None, None, 6 192         conv2d_119[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_119 (BatchN (None, None, None, 9 288         conv2d_122[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_120 (BatchN (None, None, None, 6 192         conv2d_123[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_114 (Activation)     (None, None, None, 6 0           batch_normalization_114[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_116 (Activation)     (None, None, None, 6 0           batch_normalization_116[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_119 (Activation)     (None, None, None, 9 0           batch_normalization_119[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_120 (Activation)     (None, None, None, 6 0           batch_normalization_120[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed2 (Concatenate)            (None, None, None, 2 0           activation_114[0][0]             \n",
            "                                                                 activation_116[0][0]             \n",
            "                                                                 activation_119[0][0]             \n",
            "                                                                 activation_120[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_125 (Conv2D)             (None, None, None, 6 18432       mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_122 (BatchN (None, None, None, 6 192         conv2d_125[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_122 (Activation)     (None, None, None, 6 0           batch_normalization_122[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_126 (Conv2D)             (None, None, None, 9 55296       activation_122[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_123 (BatchN (None, None, None, 9 288         conv2d_126[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_123 (Activation)     (None, None, None, 9 0           batch_normalization_123[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_124 (Conv2D)             (None, None, None, 3 995328      mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_127 (Conv2D)             (None, None, None, 9 82944       activation_123[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_121 (BatchN (None, None, None, 3 1152        conv2d_124[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_124 (BatchN (None, None, None, 9 288         conv2d_127[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_121 (Activation)     (None, None, None, 3 0           batch_normalization_121[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_124 (Activation)     (None, None, None, 9 0           batch_normalization_124[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling2D) (None, None, None, 2 0           mixed2[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed3 (Concatenate)            (None, None, None, 7 0           activation_121[0][0]             \n",
            "                                                                 activation_124[0][0]             \n",
            "                                                                 max_pooling2d_10[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_132 (Conv2D)             (None, None, None, 1 98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_129 (BatchN (None, None, None, 1 384         conv2d_132[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_129 (Activation)     (None, None, None, 1 0           batch_normalization_129[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_133 (Conv2D)             (None, None, None, 1 114688      activation_129[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_130 (BatchN (None, None, None, 1 384         conv2d_133[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_130 (Activation)     (None, None, None, 1 0           batch_normalization_130[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_129 (Conv2D)             (None, None, None, 1 98304       mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_134 (Conv2D)             (None, None, None, 1 114688      activation_130[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_126 (BatchN (None, None, None, 1 384         conv2d_129[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_131 (BatchN (None, None, None, 1 384         conv2d_134[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_126 (Activation)     (None, None, None, 1 0           batch_normalization_126[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_131 (Activation)     (None, None, None, 1 0           batch_normalization_131[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_130 (Conv2D)             (None, None, None, 1 114688      activation_126[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_135 (Conv2D)             (None, None, None, 1 114688      activation_131[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_127 (BatchN (None, None, None, 1 384         conv2d_130[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_132 (BatchN (None, None, None, 1 384         conv2d_135[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_127 (Activation)     (None, None, None, 1 0           batch_normalization_127[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_132 (Activation)     (None, None, None, 1 0           batch_normalization_132[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_13 (AveragePo (None, None, None, 7 0           mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_128 (Conv2D)             (None, None, None, 1 147456      mixed3[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_131 (Conv2D)             (None, None, None, 1 172032      activation_127[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_136 (Conv2D)             (None, None, None, 1 172032      activation_132[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_137 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_13[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_125 (BatchN (None, None, None, 1 576         conv2d_128[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_128 (BatchN (None, None, None, 1 576         conv2d_131[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_133 (BatchN (None, None, None, 1 576         conv2d_136[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_134 (BatchN (None, None, None, 1 576         conv2d_137[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_125 (Activation)     (None, None, None, 1 0           batch_normalization_125[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_128 (Activation)     (None, None, None, 1 0           batch_normalization_128[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_133 (Activation)     (None, None, None, 1 0           batch_normalization_133[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_134 (Activation)     (None, None, None, 1 0           batch_normalization_134[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed4 (Concatenate)            (None, None, None, 7 0           activation_125[0][0]             \n",
            "                                                                 activation_128[0][0]             \n",
            "                                                                 activation_133[0][0]             \n",
            "                                                                 activation_134[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_142 (Conv2D)             (None, None, None, 1 122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_139 (BatchN (None, None, None, 1 480         conv2d_142[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_139 (Activation)     (None, None, None, 1 0           batch_normalization_139[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_143 (Conv2D)             (None, None, None, 1 179200      activation_139[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_140 (BatchN (None, None, None, 1 480         conv2d_143[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_140 (Activation)     (None, None, None, 1 0           batch_normalization_140[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_139 (Conv2D)             (None, None, None, 1 122880      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_144 (Conv2D)             (None, None, None, 1 179200      activation_140[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_136 (BatchN (None, None, None, 1 480         conv2d_139[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_141 (BatchN (None, None, None, 1 480         conv2d_144[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_136 (Activation)     (None, None, None, 1 0           batch_normalization_136[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_141 (Activation)     (None, None, None, 1 0           batch_normalization_141[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_140 (Conv2D)             (None, None, None, 1 179200      activation_136[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_145 (Conv2D)             (None, None, None, 1 179200      activation_141[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_137 (BatchN (None, None, None, 1 480         conv2d_140[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_142 (BatchN (None, None, None, 1 480         conv2d_145[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_137 (Activation)     (None, None, None, 1 0           batch_normalization_137[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_142 (Activation)     (None, None, None, 1 0           batch_normalization_142[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_14 (AveragePo (None, None, None, 7 0           mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_138 (Conv2D)             (None, None, None, 1 147456      mixed4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_141 (Conv2D)             (None, None, None, 1 215040      activation_137[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_146 (Conv2D)             (None, None, None, 1 215040      activation_142[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_147 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_14[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_135 (BatchN (None, None, None, 1 576         conv2d_138[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_138 (BatchN (None, None, None, 1 576         conv2d_141[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_143 (BatchN (None, None, None, 1 576         conv2d_146[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_144 (BatchN (None, None, None, 1 576         conv2d_147[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_135 (Activation)     (None, None, None, 1 0           batch_normalization_135[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_138 (Activation)     (None, None, None, 1 0           batch_normalization_138[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_143 (Activation)     (None, None, None, 1 0           batch_normalization_143[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_144 (Activation)     (None, None, None, 1 0           batch_normalization_144[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed5 (Concatenate)            (None, None, None, 7 0           activation_135[0][0]             \n",
            "                                                                 activation_138[0][0]             \n",
            "                                                                 activation_143[0][0]             \n",
            "                                                                 activation_144[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_152 (Conv2D)             (None, None, None, 1 122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_149 (BatchN (None, None, None, 1 480         conv2d_152[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_149 (Activation)     (None, None, None, 1 0           batch_normalization_149[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_153 (Conv2D)             (None, None, None, 1 179200      activation_149[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_150 (BatchN (None, None, None, 1 480         conv2d_153[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_150 (Activation)     (None, None, None, 1 0           batch_normalization_150[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_149 (Conv2D)             (None, None, None, 1 122880      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_154 (Conv2D)             (None, None, None, 1 179200      activation_150[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_146 (BatchN (None, None, None, 1 480         conv2d_149[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_151 (BatchN (None, None, None, 1 480         conv2d_154[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_146 (Activation)     (None, None, None, 1 0           batch_normalization_146[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_151 (Activation)     (None, None, None, 1 0           batch_normalization_151[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_150 (Conv2D)             (None, None, None, 1 179200      activation_146[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_155 (Conv2D)             (None, None, None, 1 179200      activation_151[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_147 (BatchN (None, None, None, 1 480         conv2d_150[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_152 (BatchN (None, None, None, 1 480         conv2d_155[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_147 (Activation)     (None, None, None, 1 0           batch_normalization_147[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_152 (Activation)     (None, None, None, 1 0           batch_normalization_152[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_15 (AveragePo (None, None, None, 7 0           mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_148 (Conv2D)             (None, None, None, 1 147456      mixed5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_151 (Conv2D)             (None, None, None, 1 215040      activation_147[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_156 (Conv2D)             (None, None, None, 1 215040      activation_152[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_157 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_15[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_145 (BatchN (None, None, None, 1 576         conv2d_148[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_148 (BatchN (None, None, None, 1 576         conv2d_151[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_153 (BatchN (None, None, None, 1 576         conv2d_156[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_154 (BatchN (None, None, None, 1 576         conv2d_157[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_145 (Activation)     (None, None, None, 1 0           batch_normalization_145[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_148 (Activation)     (None, None, None, 1 0           batch_normalization_148[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_153 (Activation)     (None, None, None, 1 0           batch_normalization_153[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_154 (Activation)     (None, None, None, 1 0           batch_normalization_154[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed6 (Concatenate)            (None, None, None, 7 0           activation_145[0][0]             \n",
            "                                                                 activation_148[0][0]             \n",
            "                                                                 activation_153[0][0]             \n",
            "                                                                 activation_154[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_162 (Conv2D)             (None, None, None, 1 147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_159 (BatchN (None, None, None, 1 576         conv2d_162[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_159 (Activation)     (None, None, None, 1 0           batch_normalization_159[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_163 (Conv2D)             (None, None, None, 1 258048      activation_159[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_160 (BatchN (None, None, None, 1 576         conv2d_163[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_160 (Activation)     (None, None, None, 1 0           batch_normalization_160[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_159 (Conv2D)             (None, None, None, 1 147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_164 (Conv2D)             (None, None, None, 1 258048      activation_160[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_156 (BatchN (None, None, None, 1 576         conv2d_159[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_161 (BatchN (None, None, None, 1 576         conv2d_164[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_156 (Activation)     (None, None, None, 1 0           batch_normalization_156[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_161 (Activation)     (None, None, None, 1 0           batch_normalization_161[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_160 (Conv2D)             (None, None, None, 1 258048      activation_156[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_165 (Conv2D)             (None, None, None, 1 258048      activation_161[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_157 (BatchN (None, None, None, 1 576         conv2d_160[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_162 (BatchN (None, None, None, 1 576         conv2d_165[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_157 (Activation)     (None, None, None, 1 0           batch_normalization_157[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_162 (Activation)     (None, None, None, 1 0           batch_normalization_162[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_16 (AveragePo (None, None, None, 7 0           mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_158 (Conv2D)             (None, None, None, 1 147456      mixed6[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_161 (Conv2D)             (None, None, None, 1 258048      activation_157[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_166 (Conv2D)             (None, None, None, 1 258048      activation_162[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_167 (Conv2D)             (None, None, None, 1 147456      average_pooling2d_16[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_155 (BatchN (None, None, None, 1 576         conv2d_158[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_158 (BatchN (None, None, None, 1 576         conv2d_161[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_163 (BatchN (None, None, None, 1 576         conv2d_166[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_164 (BatchN (None, None, None, 1 576         conv2d_167[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_155 (Activation)     (None, None, None, 1 0           batch_normalization_155[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_158 (Activation)     (None, None, None, 1 0           batch_normalization_158[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_163 (Activation)     (None, None, None, 1 0           batch_normalization_163[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_164 (Activation)     (None, None, None, 1 0           batch_normalization_164[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed7 (Concatenate)            (None, None, None, 7 0           activation_155[0][0]             \n",
            "                                                                 activation_158[0][0]             \n",
            "                                                                 activation_163[0][0]             \n",
            "                                                                 activation_164[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_170 (Conv2D)             (None, None, None, 1 147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_167 (BatchN (None, None, None, 1 576         conv2d_170[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_167 (Activation)     (None, None, None, 1 0           batch_normalization_167[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_171 (Conv2D)             (None, None, None, 1 258048      activation_167[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_168 (BatchN (None, None, None, 1 576         conv2d_171[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_168 (Activation)     (None, None, None, 1 0           batch_normalization_168[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_168 (Conv2D)             (None, None, None, 1 147456      mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_172 (Conv2D)             (None, None, None, 1 258048      activation_168[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_165 (BatchN (None, None, None, 1 576         conv2d_168[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_169 (BatchN (None, None, None, 1 576         conv2d_172[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_165 (Activation)     (None, None, None, 1 0           batch_normalization_165[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_169 (Activation)     (None, None, None, 1 0           batch_normalization_169[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_169 (Conv2D)             (None, None, None, 3 552960      activation_165[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_173 (Conv2D)             (None, None, None, 1 331776      activation_169[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_166 (BatchN (None, None, None, 3 960         conv2d_169[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_170 (BatchN (None, None, None, 1 576         conv2d_173[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_166 (Activation)     (None, None, None, 3 0           batch_normalization_166[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_170 (Activation)     (None, None, None, 1 0           batch_normalization_170[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling2D) (None, None, None, 7 0           mixed7[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "mixed8 (Concatenate)            (None, None, None, 1 0           activation_166[0][0]             \n",
            "                                                                 activation_170[0][0]             \n",
            "                                                                 max_pooling2d_11[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_178 (Conv2D)             (None, None, None, 4 573440      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_175 (BatchN (None, None, None, 4 1344        conv2d_178[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_175 (Activation)     (None, None, None, 4 0           batch_normalization_175[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_175 (Conv2D)             (None, None, None, 3 491520      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_179 (Conv2D)             (None, None, None, 3 1548288     activation_175[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_172 (BatchN (None, None, None, 3 1152        conv2d_175[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_176 (BatchN (None, None, None, 3 1152        conv2d_179[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_172 (Activation)     (None, None, None, 3 0           batch_normalization_172[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_176 (Activation)     (None, None, None, 3 0           batch_normalization_176[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_176 (Conv2D)             (None, None, None, 3 442368      activation_172[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_177 (Conv2D)             (None, None, None, 3 442368      activation_172[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_180 (Conv2D)             (None, None, None, 3 442368      activation_176[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_181 (Conv2D)             (None, None, None, 3 442368      activation_176[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_17 (AveragePo (None, None, None, 1 0           mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_174 (Conv2D)             (None, None, None, 3 409600      mixed8[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_173 (BatchN (None, None, None, 3 1152        conv2d_176[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_174 (BatchN (None, None, None, 3 1152        conv2d_177[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_177 (BatchN (None, None, None, 3 1152        conv2d_180[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_178 (BatchN (None, None, None, 3 1152        conv2d_181[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_182 (Conv2D)             (None, None, None, 1 245760      average_pooling2d_17[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_171 (BatchN (None, None, None, 3 960         conv2d_174[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_173 (Activation)     (None, None, None, 3 0           batch_normalization_173[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_174 (Activation)     (None, None, None, 3 0           batch_normalization_174[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_177 (Activation)     (None, None, None, 3 0           batch_normalization_177[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_178 (Activation)     (None, None, None, 3 0           batch_normalization_178[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_179 (BatchN (None, None, None, 1 576         conv2d_182[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_171 (Activation)     (None, None, None, 3 0           batch_normalization_171[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_173[0][0]             \n",
            "                                                                 activation_174[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, None, None, 7 0           activation_177[0][0]             \n",
            "                                                                 activation_178[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_179 (Activation)     (None, None, None, 1 0           batch_normalization_179[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed9 (Concatenate)            (None, None, None, 2 0           activation_171[0][0]             \n",
            "                                                                 mixed9_0[0][0]                   \n",
            "                                                                 concatenate_3[0][0]              \n",
            "                                                                 activation_179[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_187 (Conv2D)             (None, None, None, 4 917504      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_184 (BatchN (None, None, None, 4 1344        conv2d_187[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_184 (Activation)     (None, None, None, 4 0           batch_normalization_184[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_184 (Conv2D)             (None, None, None, 3 786432      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_188 (Conv2D)             (None, None, None, 3 1548288     activation_184[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_181 (BatchN (None, None, None, 3 1152        conv2d_184[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_185 (BatchN (None, None, None, 3 1152        conv2d_188[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_181 (Activation)     (None, None, None, 3 0           batch_normalization_181[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_185 (Activation)     (None, None, None, 3 0           batch_normalization_185[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_185 (Conv2D)             (None, None, None, 3 442368      activation_181[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_186 (Conv2D)             (None, None, None, 3 442368      activation_181[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_189 (Conv2D)             (None, None, None, 3 442368      activation_185[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_190 (Conv2D)             (None, None, None, 3 442368      activation_185[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_18 (AveragePo (None, None, None, 2 0           mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_183 (Conv2D)             (None, None, None, 3 655360      mixed9[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_182 (BatchN (None, None, None, 3 1152        conv2d_185[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_183 (BatchN (None, None, None, 3 1152        conv2d_186[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_186 (BatchN (None, None, None, 3 1152        conv2d_189[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_187 (BatchN (None, None, None, 3 1152        conv2d_190[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_191 (Conv2D)             (None, None, None, 1 393216      average_pooling2d_18[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_180 (BatchN (None, None, None, 3 960         conv2d_183[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_182 (Activation)     (None, None, None, 3 0           batch_normalization_182[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_183 (Activation)     (None, None, None, 3 0           batch_normalization_183[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_186 (Activation)     (None, None, None, 3 0           batch_normalization_186[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_187 (Activation)     (None, None, None, 3 0           batch_normalization_187[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_188 (BatchN (None, None, None, 1 576         conv2d_191[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_180 (Activation)     (None, None, None, 3 0           batch_normalization_180[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_182[0][0]             \n",
            "                                                                 activation_183[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_4 (Concatenate)     (None, None, None, 7 0           activation_186[0][0]             \n",
            "                                                                 activation_187[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_188 (Activation)     (None, None, None, 1 0           batch_normalization_188[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "mixed10 (Concatenate)           (None, None, None, 2 0           activation_180[0][0]             \n",
            "                                                                 mixed9_1[0][0]                   \n",
            "                                                                 concatenate_4[0][0]              \n",
            "                                                                 activation_188[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_2 (Glo (None, 2048)         0           mixed10[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_7 (Dense)                 (None, 512)          1049088     global_average_pooling2d_2[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "dropout_4 (Dropout)             (None, 512)          0           dense_7[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_8 (Dense)                 (None, 2)            1026        dropout_4[0][0]                  \n",
            "==================================================================================================\n",
            "Total params: 22,852,898\n",
            "Trainable params: 22,818,466\n",
            "Non-trainable params: 34,432\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
            "  import sys\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E4EK0fgR_HDD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Load the weights for the common layers from the benchmark model\n",
        "base_model.load_weights(filepath='benchmark.hdf5',by_name=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8L6WB2Yp_N-s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Freeze the original layers of Inception3\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjQoi3CM_P1c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Compile the model\n",
        "model.compile(optimizer='adam', loss = 'categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5ZcDYqP_SQA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.callbacks import ModelCheckpoint,EarlyStopping\n",
        "\n",
        "# Save the model with best weights\n",
        "checkpointer = ModelCheckpoint('transfer_learning.hdf5', verbose=1,save_best_only=True)\n",
        "# Stop the traning if the model shows no improvement\n",
        "stopper = EarlyStopping(monitor='val_loss',min_delta=0.1,patience=1,verbose=1,mode='auto')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-8nRKTTy_Wou",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Train the model\n",
        "history_transfer = model.fit_generator(train_generator, steps_per_epoch = 13,validation_data=valid_generator,validation_steps=4, epochs=5,verbose=1,callbacks=[checkpointer])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0RdT9Zk_bKK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "display(history_transfer.history)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JO3SL52wx_RO",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9TfVuFnFAHUr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def plot_training(history):\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",
        "    epochs  = range(len(acc))\n",
        "    \n",
        "    plt.plot(epochs,acc,'b')\n",
        "    plt.plot(epochs,val_acc,'r')\n",
        "    plt.title(\"Training and validation accuracy\")\n",
        "    \n",
        "    plt.figure()\n",
        "    plt.plot(epochs,loss,'b')\n",
        "    plt.plot(epochs,val_loss,'r')\n",
        "    plt.title(\"Training and validation loss\")\n",
        "    \n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hN5Dr7bpAFAX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot_training(history_transfer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XOwOAj1AN00",
        "colab_type": "code",
        "outputId": "c335ba78-bdb1-4e60-834d-f1d0fbf9dde2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 5304
        }
      },
      "source": [
        "for i, layer in enumerate(base_model.layers):\n",
        "    print(i, layer.name)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 input_2\n",
            "1 conv2d_98\n",
            "2 batch_normalization_95\n",
            "3 activation_95\n",
            "4 conv2d_99\n",
            "5 batch_normalization_96\n",
            "6 activation_96\n",
            "7 conv2d_100\n",
            "8 batch_normalization_97\n",
            "9 activation_97\n",
            "10 max_pooling2d_8\n",
            "11 conv2d_101\n",
            "12 batch_normalization_98\n",
            "13 activation_98\n",
            "14 conv2d_102\n",
            "15 batch_normalization_99\n",
            "16 activation_99\n",
            "17 max_pooling2d_9\n",
            "18 conv2d_106\n",
            "19 batch_normalization_103\n",
            "20 activation_103\n",
            "21 conv2d_104\n",
            "22 conv2d_107\n",
            "23 batch_normalization_101\n",
            "24 batch_normalization_104\n",
            "25 activation_101\n",
            "26 activation_104\n",
            "27 average_pooling2d_10\n",
            "28 conv2d_103\n",
            "29 conv2d_105\n",
            "30 conv2d_108\n",
            "31 conv2d_109\n",
            "32 batch_normalization_100\n",
            "33 batch_normalization_102\n",
            "34 batch_normalization_105\n",
            "35 batch_normalization_106\n",
            "36 activation_100\n",
            "37 activation_102\n",
            "38 activation_105\n",
            "39 activation_106\n",
            "40 mixed0\n",
            "41 conv2d_113\n",
            "42 batch_normalization_110\n",
            "43 activation_110\n",
            "44 conv2d_111\n",
            "45 conv2d_114\n",
            "46 batch_normalization_108\n",
            "47 batch_normalization_111\n",
            "48 activation_108\n",
            "49 activation_111\n",
            "50 average_pooling2d_11\n",
            "51 conv2d_110\n",
            "52 conv2d_112\n",
            "53 conv2d_115\n",
            "54 conv2d_116\n",
            "55 batch_normalization_107\n",
            "56 batch_normalization_109\n",
            "57 batch_normalization_112\n",
            "58 batch_normalization_113\n",
            "59 activation_107\n",
            "60 activation_109\n",
            "61 activation_112\n",
            "62 activation_113\n",
            "63 mixed1\n",
            "64 conv2d_120\n",
            "65 batch_normalization_117\n",
            "66 activation_117\n",
            "67 conv2d_118\n",
            "68 conv2d_121\n",
            "69 batch_normalization_115\n",
            "70 batch_normalization_118\n",
            "71 activation_115\n",
            "72 activation_118\n",
            "73 average_pooling2d_12\n",
            "74 conv2d_117\n",
            "75 conv2d_119\n",
            "76 conv2d_122\n",
            "77 conv2d_123\n",
            "78 batch_normalization_114\n",
            "79 batch_normalization_116\n",
            "80 batch_normalization_119\n",
            "81 batch_normalization_120\n",
            "82 activation_114\n",
            "83 activation_116\n",
            "84 activation_119\n",
            "85 activation_120\n",
            "86 mixed2\n",
            "87 conv2d_125\n",
            "88 batch_normalization_122\n",
            "89 activation_122\n",
            "90 conv2d_126\n",
            "91 batch_normalization_123\n",
            "92 activation_123\n",
            "93 conv2d_124\n",
            "94 conv2d_127\n",
            "95 batch_normalization_121\n",
            "96 batch_normalization_124\n",
            "97 activation_121\n",
            "98 activation_124\n",
            "99 max_pooling2d_10\n",
            "100 mixed3\n",
            "101 conv2d_132\n",
            "102 batch_normalization_129\n",
            "103 activation_129\n",
            "104 conv2d_133\n",
            "105 batch_normalization_130\n",
            "106 activation_130\n",
            "107 conv2d_129\n",
            "108 conv2d_134\n",
            "109 batch_normalization_126\n",
            "110 batch_normalization_131\n",
            "111 activation_126\n",
            "112 activation_131\n",
            "113 conv2d_130\n",
            "114 conv2d_135\n",
            "115 batch_normalization_127\n",
            "116 batch_normalization_132\n",
            "117 activation_127\n",
            "118 activation_132\n",
            "119 average_pooling2d_13\n",
            "120 conv2d_128\n",
            "121 conv2d_131\n",
            "122 conv2d_136\n",
            "123 conv2d_137\n",
            "124 batch_normalization_125\n",
            "125 batch_normalization_128\n",
            "126 batch_normalization_133\n",
            "127 batch_normalization_134\n",
            "128 activation_125\n",
            "129 activation_128\n",
            "130 activation_133\n",
            "131 activation_134\n",
            "132 mixed4\n",
            "133 conv2d_142\n",
            "134 batch_normalization_139\n",
            "135 activation_139\n",
            "136 conv2d_143\n",
            "137 batch_normalization_140\n",
            "138 activation_140\n",
            "139 conv2d_139\n",
            "140 conv2d_144\n",
            "141 batch_normalization_136\n",
            "142 batch_normalization_141\n",
            "143 activation_136\n",
            "144 activation_141\n",
            "145 conv2d_140\n",
            "146 conv2d_145\n",
            "147 batch_normalization_137\n",
            "148 batch_normalization_142\n",
            "149 activation_137\n",
            "150 activation_142\n",
            "151 average_pooling2d_14\n",
            "152 conv2d_138\n",
            "153 conv2d_141\n",
            "154 conv2d_146\n",
            "155 conv2d_147\n",
            "156 batch_normalization_135\n",
            "157 batch_normalization_138\n",
            "158 batch_normalization_143\n",
            "159 batch_normalization_144\n",
            "160 activation_135\n",
            "161 activation_138\n",
            "162 activation_143\n",
            "163 activation_144\n",
            "164 mixed5\n",
            "165 conv2d_152\n",
            "166 batch_normalization_149\n",
            "167 activation_149\n",
            "168 conv2d_153\n",
            "169 batch_normalization_150\n",
            "170 activation_150\n",
            "171 conv2d_149\n",
            "172 conv2d_154\n",
            "173 batch_normalization_146\n",
            "174 batch_normalization_151\n",
            "175 activation_146\n",
            "176 activation_151\n",
            "177 conv2d_150\n",
            "178 conv2d_155\n",
            "179 batch_normalization_147\n",
            "180 batch_normalization_152\n",
            "181 activation_147\n",
            "182 activation_152\n",
            "183 average_pooling2d_15\n",
            "184 conv2d_148\n",
            "185 conv2d_151\n",
            "186 conv2d_156\n",
            "187 conv2d_157\n",
            "188 batch_normalization_145\n",
            "189 batch_normalization_148\n",
            "190 batch_normalization_153\n",
            "191 batch_normalization_154\n",
            "192 activation_145\n",
            "193 activation_148\n",
            "194 activation_153\n",
            "195 activation_154\n",
            "196 mixed6\n",
            "197 conv2d_162\n",
            "198 batch_normalization_159\n",
            "199 activation_159\n",
            "200 conv2d_163\n",
            "201 batch_normalization_160\n",
            "202 activation_160\n",
            "203 conv2d_159\n",
            "204 conv2d_164\n",
            "205 batch_normalization_156\n",
            "206 batch_normalization_161\n",
            "207 activation_156\n",
            "208 activation_161\n",
            "209 conv2d_160\n",
            "210 conv2d_165\n",
            "211 batch_normalization_157\n",
            "212 batch_normalization_162\n",
            "213 activation_157\n",
            "214 activation_162\n",
            "215 average_pooling2d_16\n",
            "216 conv2d_158\n",
            "217 conv2d_161\n",
            "218 conv2d_166\n",
            "219 conv2d_167\n",
            "220 batch_normalization_155\n",
            "221 batch_normalization_158\n",
            "222 batch_normalization_163\n",
            "223 batch_normalization_164\n",
            "224 activation_155\n",
            "225 activation_158\n",
            "226 activation_163\n",
            "227 activation_164\n",
            "228 mixed7\n",
            "229 conv2d_170\n",
            "230 batch_normalization_167\n",
            "231 activation_167\n",
            "232 conv2d_171\n",
            "233 batch_normalization_168\n",
            "234 activation_168\n",
            "235 conv2d_168\n",
            "236 conv2d_172\n",
            "237 batch_normalization_165\n",
            "238 batch_normalization_169\n",
            "239 activation_165\n",
            "240 activation_169\n",
            "241 conv2d_169\n",
            "242 conv2d_173\n",
            "243 batch_normalization_166\n",
            "244 batch_normalization_170\n",
            "245 activation_166\n",
            "246 activation_170\n",
            "247 max_pooling2d_11\n",
            "248 mixed8\n",
            "249 conv2d_178\n",
            "250 batch_normalization_175\n",
            "251 activation_175\n",
            "252 conv2d_175\n",
            "253 conv2d_179\n",
            "254 batch_normalization_172\n",
            "255 batch_normalization_176\n",
            "256 activation_172\n",
            "257 activation_176\n",
            "258 conv2d_176\n",
            "259 conv2d_177\n",
            "260 conv2d_180\n",
            "261 conv2d_181\n",
            "262 average_pooling2d_17\n",
            "263 conv2d_174\n",
            "264 batch_normalization_173\n",
            "265 batch_normalization_174\n",
            "266 batch_normalization_177\n",
            "267 batch_normalization_178\n",
            "268 conv2d_182\n",
            "269 batch_normalization_171\n",
            "270 activation_173\n",
            "271 activation_174\n",
            "272 activation_177\n",
            "273 activation_178\n",
            "274 batch_normalization_179\n",
            "275 activation_171\n",
            "276 mixed9_0\n",
            "277 concatenate_3\n",
            "278 activation_179\n",
            "279 mixed9\n",
            "280 conv2d_187\n",
            "281 batch_normalization_184\n",
            "282 activation_184\n",
            "283 conv2d_184\n",
            "284 conv2d_188\n",
            "285 batch_normalization_181\n",
            "286 batch_normalization_185\n",
            "287 activation_181\n",
            "288 activation_185\n",
            "289 conv2d_185\n",
            "290 conv2d_186\n",
            "291 conv2d_189\n",
            "292 conv2d_190\n",
            "293 average_pooling2d_18\n",
            "294 conv2d_183\n",
            "295 batch_normalization_182\n",
            "296 batch_normalization_183\n",
            "297 batch_normalization_186\n",
            "298 batch_normalization_187\n",
            "299 conv2d_191\n",
            "300 batch_normalization_180\n",
            "301 activation_182\n",
            "302 activation_183\n",
            "303 activation_186\n",
            "304 activation_187\n",
            "305 batch_normalization_188\n",
            "306 activation_180\n",
            "307 mixed9_1\n",
            "308 concatenate_4\n",
            "309 activation_188\n",
            "310 mixed10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jt80FHloQ1_d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the weights fromt the fine-tuned model\n",
        "model.load_weights('transfer_learning.hdf5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "is3EKSWFRCLS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.preprocessing.image import img_to_array,load_img\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "%matplotlib inline\n",
        "def pred(img_path):    \n",
        "    img = load_img(img_path,target_size = (299,299)) #Load the image and set the target size to the size of input of our model\n",
        "    x = img_to_array(img) #Convert the image to array\n",
        "    x = np.expand_dims(x,axis=0) #Convert the array to the form (1,x,y,z) \n",
        "    x = preprocess_input(x) # Use the preprocess input function o subtract the mean of all the images\n",
        "    p = np.argmax(model.predict(x)) # Store the argmax of the predictions\n",
        "    if p==0:     # P=0 for basal,P=1 for melanoma , P=2 for squamous\n",
        "        print(\"benign\")\n",
        "    elif p==1:\n",
        "        print(\"melanoma\")\n",
        "    else:\n",
        "        print(\"Nothing\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOQQ2MO4AkFx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Unfreeze the last three inception modules\n",
        "for layer in model.layers[:229]:\n",
        "    layer.trainable = False\n",
        "for layer in model.layers[229:]:\n",
        "    layer.trainable = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Omi4UzQRFqw",
        "colab_type": "code",
        "outputId": "abcd5dd7-c4cb-460a-cfd6-3e7b2286a91a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "imgPath = '/content/gdrive/My Drive/MDataset/test/malignant/ISIC_0010241.jpg'\n",
        "pred(imgPath)\n",
        "z = plt.imread(imgPath) \n",
        "plt.imshow(z);         #print the loaded image"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "melanoma\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3Z//+em9EoIWSpyd1LwFX0poq0k93sk+lF1HWGq2O1hqVp5PVECFJCeo531Q0b97cpgIR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sPknE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Bz4ui9fc81Fonq2MurHlCNKvFKgSEG2C9Il3UINxAFcKEX83kBhBGkKQpVqikC9KEmoXRmPeP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\n4kQ7d9wz3VNISDaqr9y7fxmddglrUKZoOQ5oQMUxbTYpFW6W8xbZt8j8/G0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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_vOHbCeRyGfv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}