ImageCaptioning_Using_Inception_Resnet_v2.ipynb

{
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  "metadata": {
    "colab": {
      "name": "ImageCaptioning Using Inception Resnet v2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/simran029/ImageVista/blob/master/ImageCaptioning_Using_Inception_Resnet_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arPDI-7pddf3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 97
        },
        "outputId": "c8648b7b-e4f1-4fc5-d217-631f3c781467"
      },
      "source": [
        "# Load libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import numpy as np\n",
        "import os\n",
        "from keras.applications.inception_resnet_v2 import InceptionResNetV2\n",
        "from keras.optimizers import Adam\n",
        "from keras.layers import Dense, Flatten,Input, Convolution2D, Dropout, LSTM, TimeDistributed, Embedding, Bidirectional, Activation, RepeatVector,Concatenate\n",
        "from keras.models import Sequential, Model\n",
        "from keras.utils import np_utils\n",
        "import random\n",
        "from keras.preprocessing import image, sequence\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Grosttoid8vT",
        "colab_type": "code",
        "outputId": "a1342a1b-4f47-45c3-d2b2-6bfb26f639c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLlwyU7-ljCG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load data\n",
        "#images_dir = os.listdir(\"/My Drive/Colab Notebooks\")\n",
        "images_dir = os.listdir(\"/content/gdrive/My Drive/Colab Notebooks/MAJOR/flickr8k/Flickr_Data/Flickr_Data/\")\n",
        "\n",
        "images_path = '/content/gdrive/My Drive/Colab Notebooks/MAJOR/flickr8k/Flickr_Data/Flickr_Data/Images/'\n",
        "captions_path = '/content/gdrive/My Drive/Colab Notebooks/MAJOR/flickr8k/Flickr_Data/Flickr_Data/Flickr_TextData/Flickr8k.token.txt'\n",
        "train_path = '/content/gdrive/My Drive/Colab Notebooks/MAJOR/flickr8k/Flickr_Data/Flickr_Data/Flickr_TextData/Flickr_8k.trainImages.txt'\n",
        "val_path = '/content/gdrive/My Drive/Colab Notebooks/MAJOR/flickr8k/Flickr_Data/Flickr_Data/Flickr_TextData/Flickr_8k.devImages.txt'\n",
        "test_path = '/content/gdrive/My Drive/Colab Notebooks/MAJOR/flickr8k/Flickr_Data/Flickr_Data/Flickr_TextData/Flickr_8k.testImages.txt'\n",
        "\n",
        "captions = open(captions_path, 'r').read().split(\"\\n\")\n",
        "x_train = open(train_path, 'r').read().split(\"\\n\")\n",
        "x_val = open(val_path, 'r').read().split(\"\\n\")\n",
        "x_test = open(test_path, 'r').read().split(\"\\n\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7EhyJ7Zw9m4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Loading captions as values and images as key in dictionary\n",
        "tokens = {}\n",
        "\n",
        "for ix in range(len(captions)-1):\n",
        "    temp = captions[ix].split(\"#\")\n",
        "    if temp[0] in tokens:\n",
        "        tokens[temp[0]].append(temp[1][2:])\n",
        "    else:\n",
        "        tokens[temp[0]] = [temp[1][2:]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lvhtWOoOxHWS",
        "colab_type": "code",
        "outputId": "3266dbdb-10f8-451b-bc90-4c51b92c8b9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "source": [
        "# displaying an image and captions given to it\n",
        "temp = captions[10].split(\"#\")\n",
        "from IPython.display import Image, display\n",
        "z = Image(filename=images_path+temp[0])\n",
        "display(z)\n",
        "\n",
        "for ix in range(len(tokens[temp[0]])):\n",
        "    print(tokens[temp[0]][ix])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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4xt3G0ifb1/i9OGj2h2Q30pKXz941uk5Pa9/+8fKUgnTtuZgu/wD2OMdXafhY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\nfwfS+M3xB4LRGbN+H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YoDLK23uqjyh6+si/fjq/2N8DU5xEcSDRPdoWQrkkRkEdFKUojT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LrGN992A7HfbjGV2iWPILP+0fmqg3hyTuSPWB8YqsXxnfFU5T+JvXOJ5r6H5I829D4GnhFr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\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2dN4DUOm8VqvTdbQ8+fTSeJx2SWmyNjMdZrG3FOJiqRmMCG3Xi83cwQyFQ4UlQ2JiGI++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\nM8wRSzh7LylOBwJk7TH0r//Z\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "A little girl covered in paint sits in front of a painted rainbow with her hands in a bowl .\n",
            "A little girl is sitting in front of a large painted rainbow .\n",
            "A small girl in the grass plays with fingerpaints in front of a white canvas with a rainbow on it .\n",
            "There is a girl with pigtails sitting in front of a rainbow painting .\n",
            "Young girl with pigtails painting outside in the grass .\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxuUR59VxRq7",
        "colab_type": "code",
        "outputId": "c43455cb-3e99-431e-c459-a346c8d7a82f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Creating train, test and validation dataset files with header as 'image_id' and 'captions'\n",
        "train_dataset = open('flickr_8k_train_dataset.txt','wb')\n",
        "train_dataset.write(b\"image_id\\tcaptions\\n\")\n",
        "\n",
        "val_dataset = open('flickr_8k_val_dataset.txt','wb')\n",
        "val_dataset.write(b\"image_id\\tcaptions\\n\")\n",
        "\n",
        "test_dataset = open('flickr_8k_test_dataset.txt','wb')\n",
        "test_dataset.write(b\"image_id\\tcaptions\\n\")\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5b3fDf1xZdo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Populating the above created files for train, test and validation dataset with image ids and captions for each of these images\n",
        "for img in x_train:\n",
        "    if img == '':\n",
        "        continue\n",
        "    for capt in tokens[img]:\n",
        "        caption = \"<start> \"+ capt + \" <end>\"\n",
        "        train_dataset.write((img+\"\\t\"+caption+\"\\n\").encode())\n",
        "        train_dataset.flush()\n",
        "train_dataset.close()\n",
        "\n",
        "for img in x_test:\n",
        "    if img == '':\n",
        "        continue\n",
        "    for capt in tokens[img]:\n",
        "        caption = \"<start> \"+ capt + \" <end>\"\n",
        "        test_dataset.write((img+\"\\t\"+caption+\"\\n\").encode())\n",
        "        test_dataset.flush()\n",
        "test_dataset.close()\n",
        "\n",
        "for img in x_val:\n",
        "    if img == '':\n",
        "        continue\n",
        "    for capt in tokens[img]:\n",
        "        caption = \"<start> \"+ capt + \" <end>\"\n",
        "        val_dataset.write((img+\"\\t\"+caption+\"\\n\").encode())\n",
        "        val_dataset.flush()\n",
        "val_dataset.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FoSWZ9MKxgIG",
        "colab_type": "code",
        "outputId": "2f70fb34-847c-4079-dd7c-f6f2742f4772",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#model = ResNet50(include_top=False,weights='imagenet',input_shape=(224,224,3),pooling='avg')\n",
        "model = InceptionResNetV2(include_top=False, weights='imagenet',input_shape=(224,224,3), pooling='avg')\n",
        "model.summary()\n",
        "# Note: For more details on ResNet50 architecture you can click on hyperlink given below"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4271: The name tf.nn.avg_pool is deprecated. Please use tf.nn.avg_pool2d instead.\n",
            "\n",
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.7/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "219062272/219055592 [==============================] - 3s 0us/step\n",
            "Model: \"inception_resnet_v2\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 111, 111, 32) 864         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 111, 111, 32) 96          conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 111, 111, 32) 0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 109, 109, 32) 9216        activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 109, 109, 32) 96          conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 109, 109, 32) 0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 109, 109, 64) 18432       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 109, 109, 64) 192         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 109, 109, 64) 0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 54, 54, 64)   0           activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 54, 54, 80)   5120        max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 54, 54, 80)   240         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 54, 54, 80)   0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 52, 52, 192)  138240      activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 52, 52, 192)  576         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 52, 52, 192)  0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 25, 25, 192)  0           activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 25, 25, 64)   12288       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 25, 25, 64)   192         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 25, 25, 64)   0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 25, 25, 48)   9216        max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 25, 25, 96)   55296       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 25, 25, 48)   144         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 25, 25, 96)   288         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 25, 25, 48)   0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 25, 25, 96)   0           batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d_1 (AveragePoo (None, 25, 25, 192)  0           max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 25, 25, 96)   18432       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 25, 25, 64)   76800       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 25, 25, 96)   82944       activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 25, 25, 64)   12288       average_pooling2d_1[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 25, 25, 96)   288         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 25, 25, 64)   192         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 25, 25, 96)   288         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 25, 25, 64)   192         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 25, 25, 96)   0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 25, 25, 64)   0           batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 25, 25, 96)   0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 25, 25, 64)   0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "mixed_5b (Concatenate)          (None, 25, 25, 320)  0           activation_6[0][0]               \n",
            "                                                                 activation_8[0][0]               \n",
            "                                                                 activation_11[0][0]              \n",
            "                                                                 activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 25, 25, 32)   10240       mixed_5b[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 25, 25, 32)   96          conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 25, 25, 32)   0           batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 25, 25, 32)   10240       mixed_5b[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 25, 25, 48)   13824       activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 25, 25, 32)   96          conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 25, 25, 48)   144         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 25, 25, 32)   0           batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 25, 25, 48)   0           batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 25, 25, 32)   10240       mixed_5b[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 25, 25, 32)   9216        activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 25, 25, 64)   27648       activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 25, 25, 32)   96          conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 25, 25, 32)   96          conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 25, 25, 64)   192         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 25, 25, 32)   0           batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 25, 25, 32)   0           batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 25, 25, 64)   0           batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block35_1_mixed (Concatenate)   (None, 25, 25, 128)  0           activation_13[0][0]              \n",
            "                                                                 activation_15[0][0]              \n",
            "                                                                 activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block35_1_conv (Conv2D)         (None, 25, 25, 320)  41280       block35_1_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block35_1 (Lambda)              (None, 25, 25, 320)  0           mixed_5b[0][0]                   \n",
            "                                                                 block35_1_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block35_1_ac (Activation)       (None, 25, 25, 320)  0           block35_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 25, 25, 32)   10240       block35_1_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 25, 25, 32)   96          conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_22 (Activation)      (None, 25, 25, 32)   0           batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 25, 25, 32)   10240       block35_1_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 25, 25, 48)   13824       activation_22[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 25, 25, 32)   96          conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 25, 25, 48)   144         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 25, 25, 32)   0           batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_23 (Activation)      (None, 25, 25, 48)   0           batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 25, 25, 32)   10240       block35_1_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 25, 25, 32)   9216        activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 25, 25, 64)   27648       activation_23[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 25, 25, 32)   96          conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 25, 25, 32)   96          conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 25, 25, 64)   192         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 25, 25, 32)   0           batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 25, 25, 32)   0           batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_24 (Activation)      (None, 25, 25, 64)   0           batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block35_2_mixed (Concatenate)   (None, 25, 25, 128)  0           activation_19[0][0]              \n",
            "                                                                 activation_21[0][0]              \n",
            "                                                                 activation_24[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block35_2_conv (Conv2D)         (None, 25, 25, 320)  41280       block35_2_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block35_2 (Lambda)              (None, 25, 25, 320)  0           block35_1_ac[0][0]               \n",
            "                                                                 block35_2_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block35_2_ac (Activation)       (None, 25, 25, 320)  0           block35_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_28 (Conv2D)              (None, 25, 25, 32)   10240       block35_2_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_28 (BatchNo (None, 25, 25, 32)   96          conv2d_28[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_28 (Activation)      (None, 25, 25, 32)   0           batch_normalization_28[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 25, 25, 32)   10240       block35_2_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_29 (Conv2D)              (None, 25, 25, 48)   13824       activation_28[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_26 (BatchNo (None, 25, 25, 32)   96          conv2d_26[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_29 (BatchNo (None, 25, 25, 48)   144         conv2d_29[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_26 (Activation)      (None, 25, 25, 32)   0           batch_normalization_26[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_29 (Activation)      (None, 25, 25, 48)   0           batch_normalization_29[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 25, 25, 32)   10240       block35_2_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_27 (Conv2D)              (None, 25, 25, 32)   9216        activation_26[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_30 (Conv2D)              (None, 25, 25, 64)   27648       activation_29[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 25, 25, 32)   96          conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_27 (BatchNo (None, 25, 25, 32)   96          conv2d_27[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_30 (BatchNo (None, 25, 25, 64)   192         conv2d_30[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_25 (Activation)      (None, 25, 25, 32)   0           batch_normalization_25[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_27 (Activation)      (None, 25, 25, 32)   0           batch_normalization_27[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_30 (Activation)      (None, 25, 25, 64)   0           batch_normalization_30[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block35_3_mixed (Concatenate)   (None, 25, 25, 128)  0           activation_25[0][0]              \n",
            "                                                                 activation_27[0][0]              \n",
            "                                                                 activation_30[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block35_3_conv (Conv2D)         (None, 25, 25, 320)  41280       block35_3_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block35_3 (Lambda)              (None, 25, 25, 320)  0           block35_2_ac[0][0]               \n",
            "                                                                 block35_3_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block35_3_ac (Activation)       (None, 25, 25, 320)  0           block35_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_34 (Conv2D)              (None, 25, 25, 32)   10240       block35_3_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_34 (BatchNo (None, 25, 25, 32)   96          conv2d_34[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_34 (Activation)      (None, 25, 25, 32)   0           batch_normalization_34[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_32 (Conv2D)              (None, 25, 25, 32)   10240       block35_3_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_35 (Conv2D)              (None, 25, 25, 48)   13824       activation_34[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_32 (BatchNo (None, 25, 25, 32)   96          conv2d_32[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_35 (BatchNo (None, 25, 25, 48)   144         conv2d_35[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_32 (Activation)      (None, 25, 25, 32)   0           batch_normalization_32[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_35 (Activation)      (None, 25, 25, 48)   0           batch_normalization_35[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_31 (Conv2D)              (None, 25, 25, 32)   10240       block35_3_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_33 (Conv2D)              (None, 25, 25, 32)   9216        activation_32[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_36 (Conv2D)              (None, 25, 25, 64)   27648       activation_35[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_31 (BatchNo (None, 25, 25, 32)   96          conv2d_31[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_33 (BatchNo (None, 25, 25, 32)   96          conv2d_33[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_36 (BatchNo (None, 25, 25, 64)   192         conv2d_36[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_31 (Activation)      (None, 25, 25, 32)   0           batch_normalization_31[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_33 (Activation)      (None, 25, 25, 32)   0           batch_normalization_33[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_36 (Activation)      (None, 25, 25, 64)   0           batch_normalization_36[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block35_4_mixed (Concatenate)   (None, 25, 25, 128)  0           activation_31[0][0]              \n",
            "                                                                 activation_33[0][0]              \n",
            "                                                                 activation_36[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block35_4_conv (Conv2D)         (None, 25, 25, 320)  41280       block35_4_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block35_4 (Lambda)              (None, 25, 25, 320)  0           block35_3_ac[0][0]               \n",
            "                                                                 block35_4_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block35_4_ac (Activation)       (None, 25, 25, 320)  0           block35_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_40 (Conv2D)              (None, 25, 25, 32)   10240       block35_4_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_40 (BatchNo (None, 25, 25, 32)   96          conv2d_40[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_40 (Activation)      (None, 25, 25, 32)   0           batch_normalization_40[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_38 (Conv2D)              (None, 25, 25, 32)   10240       block35_4_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_41 (Conv2D)              (None, 25, 25, 48)   13824       activation_40[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_38 (BatchNo (None, 25, 25, 32)   96          conv2d_38[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_41 (BatchNo (None, 25, 25, 48)   144         conv2d_41[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_38 (Activation)      (None, 25, 25, 32)   0           batch_normalization_38[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_41 (Activation)      (None, 25, 25, 48)   0           batch_normalization_41[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_37 (Conv2D)              (None, 25, 25, 32)   10240       block35_4_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_39 (Conv2D)              (None, 25, 25, 32)   9216        activation_38[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 25, 25, 64)   27648       activation_41[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_37 (BatchNo (None, 25, 25, 32)   96          conv2d_37[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_39 (BatchNo (None, 25, 25, 32)   96          conv2d_39[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_42 (BatchNo (None, 25, 25, 64)   192         conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_37 (Activation)      (None, 25, 25, 32)   0           batch_normalization_37[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_39 (Activation)      (None, 25, 25, 32)   0           batch_normalization_39[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 25, 25, 64)   0           batch_normalization_42[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block35_5_mixed (Concatenate)   (None, 25, 25, 128)  0           activation_37[0][0]              \n",
            "                                                                 activation_39[0][0]              \n",
            "                                                                 activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block35_5_conv (Conv2D)         (None, 25, 25, 320)  41280       block35_5_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block35_5 (Lambda)              (None, 25, 25, 320)  0           block35_4_ac[0][0]               \n",
            "                                                                 block35_5_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block35_5_ac (Activation)       (None, 25, 25, 320)  0           block35_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_46 (Conv2D)              (None, 25, 25, 32)   10240       block35_5_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_46 (BatchNo (None, 25, 25, 32)   96          conv2d_46[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_46 (Activation)      (None, 25, 25, 32)   0           batch_normalization_46[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 25, 25, 32)   10240       block35_5_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_47 (Conv2D)              (None, 25, 25, 48)   13824       activation_46[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_44 (BatchNo (None, 25, 25, 32)   96          conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_47 (BatchNo (None, 25, 25, 48)   144         conv2d_47[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 25, 25, 32)   0           batch_normalization_44[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_47 (Activation)      (None, 25, 25, 48)   0           batch_normalization_47[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 25, 25, 32)   10240       block35_5_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 25, 25, 32)   9216        activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_48 (Conv2D)              (None, 25, 25, 64)   27648       activation_47[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_43 (BatchNo (None, 25, 25, 32)   96          conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_45 (BatchNo (None, 25, 25, 32)   96          conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_48 (BatchNo (None, 25, 25, 64)   192         conv2d_48[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 25, 25, 32)   0           batch_normalization_43[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 25, 25, 32)   0           batch_normalization_45[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_48 (Activation)      (None, 25, 25, 64)   0           batch_normalization_48[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block35_6_mixed (Concatenate)   (None, 25, 25, 128)  0           activation_43[0][0]              \n",
            "                                                                 activation_45[0][0]              \n",
            "                                                                 activation_48[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block35_6_conv (Conv2D)         (None, 25, 25, 320)  41280       block35_6_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block35_6 (Lambda)              (None, 25, 25, 320)  0           block35_5_ac[0][0]               \n",
            "                                                                 block35_6_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block35_6_ac (Activation)       (None, 25, 25, 320)  0           block35_6[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_52 (Conv2D)              (None, 25, 25, 32)   10240       block35_6_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_52 (BatchNo (None, 25, 25, 32)   96          conv2d_52[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_52 (Activation)      (None, 25, 25, 32)   0           batch_normalization_52[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_50 (Conv2D)              (None, 25, 25, 32)   10240       block35_6_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_53 (Conv2D)              (None, 25, 25, 48)   13824       activation_52[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_50 (BatchNo (None, 25, 25, 32)   96          conv2d_50[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_53 (BatchNo (None, 25, 25, 48)   144         conv2d_53[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_50 (Activation)      (None, 25, 25, 32)   0           batch_normalization_50[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_53 (Activation)      (None, 25, 25, 48)   0           batch_normalization_53[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_49 (Conv2D)              (None, 25, 25, 32)   10240       block35_6_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_51 (Conv2D)              (None, 25, 25, 32)   9216        activation_50[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_54 (Conv2D)              (None, 25, 25, 64)   27648       activation_53[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_49 (BatchNo (None, 25, 25, 32)   96          conv2d_49[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_51 (BatchNo (None, 25, 25, 32)   96          conv2d_51[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_54 (BatchNo (None, 25, 25, 64)   192         conv2d_54[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_49 (Activation)      (None, 25, 25, 32)   0           batch_normalization_49[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_51 (Activation)      (None, 25, 25, 32)   0           batch_normalization_51[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_54 (Activation)      (None, 25, 25, 64)   0           batch_normalization_54[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block35_7_mixed (Concatenate)   (None, 25, 25, 128)  0           activation_49[0][0]              \n",
            "                                                                 activation_51[0][0]              \n",
            "                                                                 activation_54[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block35_7_conv (Conv2D)         (None, 25, 25, 320)  41280       block35_7_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block35_7 (Lambda)              (None, 25, 25, 320)  0           block35_6_ac[0][0]               \n",
            "                                                                 block35_7_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block35_7_ac (Activation)       (None, 25, 25, 320)  0           block35_7[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_58 (Conv2D)              (None, 25, 25, 32)   10240       block35_7_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_58 (BatchNo (None, 25, 25, 32)   96          conv2d_58[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_58 (Activation)      (None, 25, 25, 32)   0           batch_normalization_58[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_56 (Conv2D)              (None, 25, 25, 32)   10240       block35_7_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_59 (Conv2D)              (None, 25, 25, 48)   13824       activation_58[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_56 (BatchNo (None, 25, 25, 32)   96          conv2d_56[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_59 (BatchNo (None, 25, 25, 48)   144         conv2d_59[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_56 (Activation)      (None, 25, 25, 32)   0           batch_normalization_56[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_59 (Activation)      (None, 25, 25, 48)   0           batch_normalization_59[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_55 (Conv2D)              (None, 25, 25, 32)   10240       block35_7_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_57 (Conv2D)              (None, 25, 25, 32)   9216        activation_56[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_60 (Conv2D)              (None, 25, 25, 64)   27648       activation_59[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_55 (BatchNo (None, 25, 25, 32)   96          conv2d_55[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_57 (BatchNo (None, 25, 25, 32)   96          conv2d_57[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_60 (BatchNo (None, 25, 25, 64)   192         conv2d_60[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_55 (Activation)      (None, 25, 25, 32)   0           batch_normalization_55[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_57 (Activation)      (None, 25, 25, 32)   0           batch_normalization_57[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_60 (Activation)      (None, 25, 25, 64)   0           batch_normalization_60[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block35_8_mixed (Concatenate)   (None, 25, 25, 128)  0           activation_55[0][0]              \n",
            "                                                                 activation_57[0][0]              \n",
            "                                                                 activation_60[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block35_8_conv (Conv2D)         (None, 25, 25, 320)  41280       block35_8_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block35_8 (Lambda)              (None, 25, 25, 320)  0           block35_7_ac[0][0]               \n",
            "                                                                 block35_8_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block35_8_ac (Activation)       (None, 25, 25, 320)  0           block35_8[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_64 (Conv2D)              (None, 25, 25, 32)   10240       block35_8_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_64 (BatchNo (None, 25, 25, 32)   96          conv2d_64[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_64 (Activation)      (None, 25, 25, 32)   0           batch_normalization_64[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_62 (Conv2D)              (None, 25, 25, 32)   10240       block35_8_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_65 (Conv2D)              (None, 25, 25, 48)   13824       activation_64[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_62 (BatchNo (None, 25, 25, 32)   96          conv2d_62[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_65 (BatchNo (None, 25, 25, 48)   144         conv2d_65[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_62 (Activation)      (None, 25, 25, 32)   0           batch_normalization_62[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_65 (Activation)      (None, 25, 25, 48)   0           batch_normalization_65[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_61 (Conv2D)              (None, 25, 25, 32)   10240       block35_8_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_63 (Conv2D)              (None, 25, 25, 32)   9216        activation_62[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_66 (Conv2D)              (None, 25, 25, 64)   27648       activation_65[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_61 (BatchNo (None, 25, 25, 32)   96          conv2d_61[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_63 (BatchNo (None, 25, 25, 32)   96          conv2d_63[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_66 (BatchNo (None, 25, 25, 64)   192         conv2d_66[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_61 (Activation)      (None, 25, 25, 32)   0           batch_normalization_61[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_63 (Activation)      (None, 25, 25, 32)   0           batch_normalization_63[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_66 (Activation)      (None, 25, 25, 64)   0           batch_normalization_66[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block35_9_mixed (Concatenate)   (None, 25, 25, 128)  0           activation_61[0][0]              \n",
            "                                                                 activation_63[0][0]              \n",
            "                                                                 activation_66[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block35_9_conv (Conv2D)         (None, 25, 25, 320)  41280       block35_9_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block35_9 (Lambda)              (None, 25, 25, 320)  0           block35_8_ac[0][0]               \n",
            "                                                                 block35_9_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block35_9_ac (Activation)       (None, 25, 25, 320)  0           block35_9[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_70 (Conv2D)              (None, 25, 25, 32)   10240       block35_9_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_70 (BatchNo (None, 25, 25, 32)   96          conv2d_70[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_70 (Activation)      (None, 25, 25, 32)   0           batch_normalization_70[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_68 (Conv2D)              (None, 25, 25, 32)   10240       block35_9_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_71 (Conv2D)              (None, 25, 25, 48)   13824       activation_70[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_68 (BatchNo (None, 25, 25, 32)   96          conv2d_68[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_71 (BatchNo (None, 25, 25, 48)   144         conv2d_71[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_68 (Activation)      (None, 25, 25, 32)   0           batch_normalization_68[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_71 (Activation)      (None, 25, 25, 48)   0           batch_normalization_71[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_67 (Conv2D)              (None, 25, 25, 32)   10240       block35_9_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_69 (Conv2D)              (None, 25, 25, 32)   9216        activation_68[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_72 (Conv2D)              (None, 25, 25, 64)   27648       activation_71[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_67 (BatchNo (None, 25, 25, 32)   96          conv2d_67[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_69 (BatchNo (None, 25, 25, 32)   96          conv2d_69[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_72 (BatchNo (None, 25, 25, 64)   192         conv2d_72[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_67 (Activation)      (None, 25, 25, 32)   0           batch_normalization_67[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_69 (Activation)      (None, 25, 25, 32)   0           batch_normalization_69[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_72 (Activation)      (None, 25, 25, 64)   0           batch_normalization_72[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block35_10_mixed (Concatenate)  (None, 25, 25, 128)  0           activation_67[0][0]              \n",
            "                                                                 activation_69[0][0]              \n",
            "                                                                 activation_72[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block35_10_conv (Conv2D)        (None, 25, 25, 320)  41280       block35_10_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block35_10 (Lambda)             (None, 25, 25, 320)  0           block35_9_ac[0][0]               \n",
            "                                                                 block35_10_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block35_10_ac (Activation)      (None, 25, 25, 320)  0           block35_10[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_74 (Conv2D)              (None, 25, 25, 256)  81920       block35_10_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_74 (BatchNo (None, 25, 25, 256)  768         conv2d_74[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_74 (Activation)      (None, 25, 25, 256)  0           batch_normalization_74[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_75 (Conv2D)              (None, 25, 25, 256)  589824      activation_74[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_75 (BatchNo (None, 25, 25, 256)  768         conv2d_75[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_75 (Activation)      (None, 25, 25, 256)  0           batch_normalization_75[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_73 (Conv2D)              (None, 12, 12, 384)  1105920     block35_10_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_76 (Conv2D)              (None, 12, 12, 384)  884736      activation_75[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_73 (BatchNo (None, 12, 12, 384)  1152        conv2d_73[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_76 (BatchNo (None, 12, 12, 384)  1152        conv2d_76[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_73 (Activation)      (None, 12, 12, 384)  0           batch_normalization_73[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_76 (Activation)      (None, 12, 12, 384)  0           batch_normalization_76[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 12, 12, 320)  0           block35_10_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "mixed_6a (Concatenate)          (None, 12, 12, 1088) 0           activation_73[0][0]              \n",
            "                                                                 activation_76[0][0]              \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_78 (Conv2D)              (None, 12, 12, 128)  139264      mixed_6a[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_78 (BatchNo (None, 12, 12, 128)  384         conv2d_78[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_78 (Activation)      (None, 12, 12, 128)  0           batch_normalization_78[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_79 (Conv2D)              (None, 12, 12, 160)  143360      activation_78[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_79 (BatchNo (None, 12, 12, 160)  480         conv2d_79[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_79 (Activation)      (None, 12, 12, 160)  0           batch_normalization_79[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_77 (Conv2D)              (None, 12, 12, 192)  208896      mixed_6a[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_80 (Conv2D)              (None, 12, 12, 192)  215040      activation_79[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_77 (BatchNo (None, 12, 12, 192)  576         conv2d_77[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_80 (BatchNo (None, 12, 12, 192)  576         conv2d_80[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_77 (Activation)      (None, 12, 12, 192)  0           batch_normalization_77[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_80 (Activation)      (None, 12, 12, 192)  0           batch_normalization_80[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block17_1_mixed (Concatenate)   (None, 12, 12, 384)  0           activation_77[0][0]              \n",
            "                                                                 activation_80[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block17_1_conv (Conv2D)         (None, 12, 12, 1088) 418880      block17_1_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_1 (Lambda)              (None, 12, 12, 1088) 0           mixed_6a[0][0]                   \n",
            "                                                                 block17_1_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_1_ac (Activation)       (None, 12, 12, 1088) 0           block17_1[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_82 (Conv2D)              (None, 12, 12, 128)  139264      block17_1_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_82 (BatchNo (None, 12, 12, 128)  384         conv2d_82[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_82 (Activation)      (None, 12, 12, 128)  0           batch_normalization_82[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_83 (Conv2D)              (None, 12, 12, 160)  143360      activation_82[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_83 (BatchNo (None, 12, 12, 160)  480         conv2d_83[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_83 (Activation)      (None, 12, 12, 160)  0           batch_normalization_83[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_81 (Conv2D)              (None, 12, 12, 192)  208896      block17_1_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_84 (Conv2D)              (None, 12, 12, 192)  215040      activation_83[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_81 (BatchNo (None, 12, 12, 192)  576         conv2d_81[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_84 (BatchNo (None, 12, 12, 192)  576         conv2d_84[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_81 (Activation)      (None, 12, 12, 192)  0           batch_normalization_81[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_84 (Activation)      (None, 12, 12, 192)  0           batch_normalization_84[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block17_2_mixed (Concatenate)   (None, 12, 12, 384)  0           activation_81[0][0]              \n",
            "                                                                 activation_84[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block17_2_conv (Conv2D)         (None, 12, 12, 1088) 418880      block17_2_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_2 (Lambda)              (None, 12, 12, 1088) 0           block17_1_ac[0][0]               \n",
            "                                                                 block17_2_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_2_ac (Activation)       (None, 12, 12, 1088) 0           block17_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_86 (Conv2D)              (None, 12, 12, 128)  139264      block17_2_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_86 (BatchNo (None, 12, 12, 128)  384         conv2d_86[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_86 (Activation)      (None, 12, 12, 128)  0           batch_normalization_86[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_87 (Conv2D)              (None, 12, 12, 160)  143360      activation_86[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_87 (BatchNo (None, 12, 12, 160)  480         conv2d_87[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_87 (Activation)      (None, 12, 12, 160)  0           batch_normalization_87[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_85 (Conv2D)              (None, 12, 12, 192)  208896      block17_2_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_88 (Conv2D)              (None, 12, 12, 192)  215040      activation_87[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_85 (BatchNo (None, 12, 12, 192)  576         conv2d_85[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_88 (BatchNo (None, 12, 12, 192)  576         conv2d_88[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_85 (Activation)      (None, 12, 12, 192)  0           batch_normalization_85[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_88 (Activation)      (None, 12, 12, 192)  0           batch_normalization_88[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block17_3_mixed (Concatenate)   (None, 12, 12, 384)  0           activation_85[0][0]              \n",
            "                                                                 activation_88[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block17_3_conv (Conv2D)         (None, 12, 12, 1088) 418880      block17_3_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_3 (Lambda)              (None, 12, 12, 1088) 0           block17_2_ac[0][0]               \n",
            "                                                                 block17_3_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_3_ac (Activation)       (None, 12, 12, 1088) 0           block17_3[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_90 (Conv2D)              (None, 12, 12, 128)  139264      block17_3_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_90 (BatchNo (None, 12, 12, 128)  384         conv2d_90[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_90 (Activation)      (None, 12, 12, 128)  0           batch_normalization_90[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_91 (Conv2D)              (None, 12, 12, 160)  143360      activation_90[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_91 (BatchNo (None, 12, 12, 160)  480         conv2d_91[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_91 (Activation)      (None, 12, 12, 160)  0           batch_normalization_91[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_89 (Conv2D)              (None, 12, 12, 192)  208896      block17_3_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_92 (Conv2D)              (None, 12, 12, 192)  215040      activation_91[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_89 (BatchNo (None, 12, 12, 192)  576         conv2d_89[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_92 (BatchNo (None, 12, 12, 192)  576         conv2d_92[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_89 (Activation)      (None, 12, 12, 192)  0           batch_normalization_89[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_92 (Activation)      (None, 12, 12, 192)  0           batch_normalization_92[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block17_4_mixed (Concatenate)   (None, 12, 12, 384)  0           activation_89[0][0]              \n",
            "                                                                 activation_92[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block17_4_conv (Conv2D)         (None, 12, 12, 1088) 418880      block17_4_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_4 (Lambda)              (None, 12, 12, 1088) 0           block17_3_ac[0][0]               \n",
            "                                                                 block17_4_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_4_ac (Activation)       (None, 12, 12, 1088) 0           block17_4[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_94 (Conv2D)              (None, 12, 12, 128)  139264      block17_4_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_94 (BatchNo (None, 12, 12, 128)  384         conv2d_94[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_94 (Activation)      (None, 12, 12, 128)  0           batch_normalization_94[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_95 (Conv2D)              (None, 12, 12, 160)  143360      activation_94[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_95 (BatchNo (None, 12, 12, 160)  480         conv2d_95[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_95 (Activation)      (None, 12, 12, 160)  0           batch_normalization_95[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_93 (Conv2D)              (None, 12, 12, 192)  208896      block17_4_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_96 (Conv2D)              (None, 12, 12, 192)  215040      activation_95[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_93 (BatchNo (None, 12, 12, 192)  576         conv2d_93[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_96 (BatchNo (None, 12, 12, 192)  576         conv2d_96[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_93 (Activation)      (None, 12, 12, 192)  0           batch_normalization_93[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_96 (Activation)      (None, 12, 12, 192)  0           batch_normalization_96[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block17_5_mixed (Concatenate)   (None, 12, 12, 384)  0           activation_93[0][0]              \n",
            "                                                                 activation_96[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block17_5_conv (Conv2D)         (None, 12, 12, 1088) 418880      block17_5_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_5 (Lambda)              (None, 12, 12, 1088) 0           block17_4_ac[0][0]               \n",
            "                                                                 block17_5_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_5_ac (Activation)       (None, 12, 12, 1088) 0           block17_5[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_98 (Conv2D)              (None, 12, 12, 128)  139264      block17_5_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_98 (BatchNo (None, 12, 12, 128)  384         conv2d_98[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_98 (Activation)      (None, 12, 12, 128)  0           batch_normalization_98[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_99 (Conv2D)              (None, 12, 12, 160)  143360      activation_98[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_99 (BatchNo (None, 12, 12, 160)  480         conv2d_99[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_99 (Activation)      (None, 12, 12, 160)  0           batch_normalization_99[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_97 (Conv2D)              (None, 12, 12, 192)  208896      block17_5_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_100 (Conv2D)             (None, 12, 12, 192)  215040      activation_99[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_97 (BatchNo (None, 12, 12, 192)  576         conv2d_97[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_100 (BatchN (None, 12, 12, 192)  576         conv2d_100[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_97 (Activation)      (None, 12, 12, 192)  0           batch_normalization_97[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "activation_100 (Activation)     (None, 12, 12, 192)  0           batch_normalization_100[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_6_mixed (Concatenate)   (None, 12, 12, 384)  0           activation_97[0][0]              \n",
            "                                                                 activation_100[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_6_conv (Conv2D)         (None, 12, 12, 1088) 418880      block17_6_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_6 (Lambda)              (None, 12, 12, 1088) 0           block17_5_ac[0][0]               \n",
            "                                                                 block17_6_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_6_ac (Activation)       (None, 12, 12, 1088) 0           block17_6[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_102 (Conv2D)             (None, 12, 12, 128)  139264      block17_6_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_102 (BatchN (None, 12, 12, 128)  384         conv2d_102[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_102 (Activation)     (None, 12, 12, 128)  0           batch_normalization_102[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_103 (Conv2D)             (None, 12, 12, 160)  143360      activation_102[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_103 (BatchN (None, 12, 12, 160)  480         conv2d_103[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_103 (Activation)     (None, 12, 12, 160)  0           batch_normalization_103[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_101 (Conv2D)             (None, 12, 12, 192)  208896      block17_6_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_104 (Conv2D)             (None, 12, 12, 192)  215040      activation_103[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_101 (BatchN (None, 12, 12, 192)  576         conv2d_101[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_104 (BatchN (None, 12, 12, 192)  576         conv2d_104[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_101 (Activation)     (None, 12, 12, 192)  0           batch_normalization_101[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_104 (Activation)     (None, 12, 12, 192)  0           batch_normalization_104[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_7_mixed (Concatenate)   (None, 12, 12, 384)  0           activation_101[0][0]             \n",
            "                                                                 activation_104[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_7_conv (Conv2D)         (None, 12, 12, 1088) 418880      block17_7_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_7 (Lambda)              (None, 12, 12, 1088) 0           block17_6_ac[0][0]               \n",
            "                                                                 block17_7_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_7_ac (Activation)       (None, 12, 12, 1088) 0           block17_7[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_106 (Conv2D)             (None, 12, 12, 128)  139264      block17_7_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_106 (BatchN (None, 12, 12, 128)  384         conv2d_106[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_106 (Activation)     (None, 12, 12, 128)  0           batch_normalization_106[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_107 (Conv2D)             (None, 12, 12, 160)  143360      activation_106[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_107 (BatchN (None, 12, 12, 160)  480         conv2d_107[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_107 (Activation)     (None, 12, 12, 160)  0           batch_normalization_107[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_105 (Conv2D)             (None, 12, 12, 192)  208896      block17_7_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_108 (Conv2D)             (None, 12, 12, 192)  215040      activation_107[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_105 (BatchN (None, 12, 12, 192)  576         conv2d_105[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_108 (BatchN (None, 12, 12, 192)  576         conv2d_108[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_105 (Activation)     (None, 12, 12, 192)  0           batch_normalization_105[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_108 (Activation)     (None, 12, 12, 192)  0           batch_normalization_108[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_8_mixed (Concatenate)   (None, 12, 12, 384)  0           activation_105[0][0]             \n",
            "                                                                 activation_108[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_8_conv (Conv2D)         (None, 12, 12, 1088) 418880      block17_8_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_8 (Lambda)              (None, 12, 12, 1088) 0           block17_7_ac[0][0]               \n",
            "                                                                 block17_8_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_8_ac (Activation)       (None, 12, 12, 1088) 0           block17_8[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_110 (Conv2D)             (None, 12, 12, 128)  139264      block17_8_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_110 (BatchN (None, 12, 12, 128)  384         conv2d_110[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_110 (Activation)     (None, 12, 12, 128)  0           batch_normalization_110[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_111 (Conv2D)             (None, 12, 12, 160)  143360      activation_110[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_111 (BatchN (None, 12, 12, 160)  480         conv2d_111[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_111 (Activation)     (None, 12, 12, 160)  0           batch_normalization_111[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_109 (Conv2D)             (None, 12, 12, 192)  208896      block17_8_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_112 (Conv2D)             (None, 12, 12, 192)  215040      activation_111[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_109 (BatchN (None, 12, 12, 192)  576         conv2d_109[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_112 (BatchN (None, 12, 12, 192)  576         conv2d_112[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_109 (Activation)     (None, 12, 12, 192)  0           batch_normalization_109[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_112 (Activation)     (None, 12, 12, 192)  0           batch_normalization_112[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_9_mixed (Concatenate)   (None, 12, 12, 384)  0           activation_109[0][0]             \n",
            "                                                                 activation_112[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_9_conv (Conv2D)         (None, 12, 12, 1088) 418880      block17_9_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_9 (Lambda)              (None, 12, 12, 1088) 0           block17_8_ac[0][0]               \n",
            "                                                                 block17_9_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_9_ac (Activation)       (None, 12, 12, 1088) 0           block17_9[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_114 (Conv2D)             (None, 12, 12, 128)  139264      block17_9_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_114 (BatchN (None, 12, 12, 128)  384         conv2d_114[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_114 (Activation)     (None, 12, 12, 128)  0           batch_normalization_114[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_115 (Conv2D)             (None, 12, 12, 160)  143360      activation_114[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_115 (BatchN (None, 12, 12, 160)  480         conv2d_115[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_115 (Activation)     (None, 12, 12, 160)  0           batch_normalization_115[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_113 (Conv2D)             (None, 12, 12, 192)  208896      block17_9_ac[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_116 (Conv2D)             (None, 12, 12, 192)  215040      activation_115[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_113 (BatchN (None, 12, 12, 192)  576         conv2d_113[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_116 (BatchN (None, 12, 12, 192)  576         conv2d_116[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_113 (Activation)     (None, 12, 12, 192)  0           batch_normalization_113[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_116 (Activation)     (None, 12, 12, 192)  0           batch_normalization_116[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_10_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_113[0][0]             \n",
            "                                                                 activation_116[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_10_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_10_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_10 (Lambda)             (None, 12, 12, 1088) 0           block17_9_ac[0][0]               \n",
            "                                                                 block17_10_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_10_ac (Activation)      (None, 12, 12, 1088) 0           block17_10[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_118 (Conv2D)             (None, 12, 12, 128)  139264      block17_10_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_118 (BatchN (None, 12, 12, 128)  384         conv2d_118[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_118 (Activation)     (None, 12, 12, 128)  0           batch_normalization_118[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_119 (Conv2D)             (None, 12, 12, 160)  143360      activation_118[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_119 (BatchN (None, 12, 12, 160)  480         conv2d_119[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_119 (Activation)     (None, 12, 12, 160)  0           batch_normalization_119[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_117 (Conv2D)             (None, 12, 12, 192)  208896      block17_10_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_120 (Conv2D)             (None, 12, 12, 192)  215040      activation_119[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_117 (BatchN (None, 12, 12, 192)  576         conv2d_117[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_120 (BatchN (None, 12, 12, 192)  576         conv2d_120[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_117 (Activation)     (None, 12, 12, 192)  0           batch_normalization_117[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_120 (Activation)     (None, 12, 12, 192)  0           batch_normalization_120[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_11_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_117[0][0]             \n",
            "                                                                 activation_120[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_11_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_11_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_11 (Lambda)             (None, 12, 12, 1088) 0           block17_10_ac[0][0]              \n",
            "                                                                 block17_11_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_11_ac (Activation)      (None, 12, 12, 1088) 0           block17_11[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_122 (Conv2D)             (None, 12, 12, 128)  139264      block17_11_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_122 (BatchN (None, 12, 12, 128)  384         conv2d_122[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_122 (Activation)     (None, 12, 12, 128)  0           batch_normalization_122[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_123 (Conv2D)             (None, 12, 12, 160)  143360      activation_122[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_123 (BatchN (None, 12, 12, 160)  480         conv2d_123[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_123 (Activation)     (None, 12, 12, 160)  0           batch_normalization_123[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_121 (Conv2D)             (None, 12, 12, 192)  208896      block17_11_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_124 (Conv2D)             (None, 12, 12, 192)  215040      activation_123[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_121 (BatchN (None, 12, 12, 192)  576         conv2d_121[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_124 (BatchN (None, 12, 12, 192)  576         conv2d_124[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_121 (Activation)     (None, 12, 12, 192)  0           batch_normalization_121[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_124 (Activation)     (None, 12, 12, 192)  0           batch_normalization_124[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_12_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_121[0][0]             \n",
            "                                                                 activation_124[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_12_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_12_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_12 (Lambda)             (None, 12, 12, 1088) 0           block17_11_ac[0][0]              \n",
            "                                                                 block17_12_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_12_ac (Activation)      (None, 12, 12, 1088) 0           block17_12[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_126 (Conv2D)             (None, 12, 12, 128)  139264      block17_12_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_126 (BatchN (None, 12, 12, 128)  384         conv2d_126[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_126 (Activation)     (None, 12, 12, 128)  0           batch_normalization_126[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_127 (Conv2D)             (None, 12, 12, 160)  143360      activation_126[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_127 (BatchN (None, 12, 12, 160)  480         conv2d_127[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_127 (Activation)     (None, 12, 12, 160)  0           batch_normalization_127[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_125 (Conv2D)             (None, 12, 12, 192)  208896      block17_12_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_128 (Conv2D)             (None, 12, 12, 192)  215040      activation_127[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_125 (BatchN (None, 12, 12, 192)  576         conv2d_125[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_128 (BatchN (None, 12, 12, 192)  576         conv2d_128[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_125 (Activation)     (None, 12, 12, 192)  0           batch_normalization_125[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_128 (Activation)     (None, 12, 12, 192)  0           batch_normalization_128[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_13_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_125[0][0]             \n",
            "                                                                 activation_128[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_13_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_13_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_13 (Lambda)             (None, 12, 12, 1088) 0           block17_12_ac[0][0]              \n",
            "                                                                 block17_13_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_13_ac (Activation)      (None, 12, 12, 1088) 0           block17_13[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_130 (Conv2D)             (None, 12, 12, 128)  139264      block17_13_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_130 (BatchN (None, 12, 12, 128)  384         conv2d_130[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_130 (Activation)     (None, 12, 12, 128)  0           batch_normalization_130[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_131 (Conv2D)             (None, 12, 12, 160)  143360      activation_130[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_131 (BatchN (None, 12, 12, 160)  480         conv2d_131[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_131 (Activation)     (None, 12, 12, 160)  0           batch_normalization_131[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_129 (Conv2D)             (None, 12, 12, 192)  208896      block17_13_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_132 (Conv2D)             (None, 12, 12, 192)  215040      activation_131[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_129 (BatchN (None, 12, 12, 192)  576         conv2d_129[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_132 (BatchN (None, 12, 12, 192)  576         conv2d_132[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_129 (Activation)     (None, 12, 12, 192)  0           batch_normalization_129[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_132 (Activation)     (None, 12, 12, 192)  0           batch_normalization_132[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_14_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_129[0][0]             \n",
            "                                                                 activation_132[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_14_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_14_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_14 (Lambda)             (None, 12, 12, 1088) 0           block17_13_ac[0][0]              \n",
            "                                                                 block17_14_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_14_ac (Activation)      (None, 12, 12, 1088) 0           block17_14[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_134 (Conv2D)             (None, 12, 12, 128)  139264      block17_14_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_134 (BatchN (None, 12, 12, 128)  384         conv2d_134[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_134 (Activation)     (None, 12, 12, 128)  0           batch_normalization_134[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_135 (Conv2D)             (None, 12, 12, 160)  143360      activation_134[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_135 (BatchN (None, 12, 12, 160)  480         conv2d_135[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_135 (Activation)     (None, 12, 12, 160)  0           batch_normalization_135[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_133 (Conv2D)             (None, 12, 12, 192)  208896      block17_14_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_136 (Conv2D)             (None, 12, 12, 192)  215040      activation_135[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_133 (BatchN (None, 12, 12, 192)  576         conv2d_133[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_136 (BatchN (None, 12, 12, 192)  576         conv2d_136[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_133 (Activation)     (None, 12, 12, 192)  0           batch_normalization_133[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_136 (Activation)     (None, 12, 12, 192)  0           batch_normalization_136[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_15_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_133[0][0]             \n",
            "                                                                 activation_136[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_15_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_15_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_15 (Lambda)             (None, 12, 12, 1088) 0           block17_14_ac[0][0]              \n",
            "                                                                 block17_15_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_15_ac (Activation)      (None, 12, 12, 1088) 0           block17_15[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_138 (Conv2D)             (None, 12, 12, 128)  139264      block17_15_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_138 (BatchN (None, 12, 12, 128)  384         conv2d_138[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_138 (Activation)     (None, 12, 12, 128)  0           batch_normalization_138[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_139 (Conv2D)             (None, 12, 12, 160)  143360      activation_138[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_139 (BatchN (None, 12, 12, 160)  480         conv2d_139[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_139 (Activation)     (None, 12, 12, 160)  0           batch_normalization_139[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_137 (Conv2D)             (None, 12, 12, 192)  208896      block17_15_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_140 (Conv2D)             (None, 12, 12, 192)  215040      activation_139[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_137 (BatchN (None, 12, 12, 192)  576         conv2d_137[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_140 (BatchN (None, 12, 12, 192)  576         conv2d_140[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_137 (Activation)     (None, 12, 12, 192)  0           batch_normalization_137[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_140 (Activation)     (None, 12, 12, 192)  0           batch_normalization_140[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_16_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_137[0][0]             \n",
            "                                                                 activation_140[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_16_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_16_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_16 (Lambda)             (None, 12, 12, 1088) 0           block17_15_ac[0][0]              \n",
            "                                                                 block17_16_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_16_ac (Activation)      (None, 12, 12, 1088) 0           block17_16[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_142 (Conv2D)             (None, 12, 12, 128)  139264      block17_16_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_142 (BatchN (None, 12, 12, 128)  384         conv2d_142[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_142 (Activation)     (None, 12, 12, 128)  0           batch_normalization_142[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_143 (Conv2D)             (None, 12, 12, 160)  143360      activation_142[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_143 (BatchN (None, 12, 12, 160)  480         conv2d_143[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_143 (Activation)     (None, 12, 12, 160)  0           batch_normalization_143[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_141 (Conv2D)             (None, 12, 12, 192)  208896      block17_16_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_144 (Conv2D)             (None, 12, 12, 192)  215040      activation_143[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_141 (BatchN (None, 12, 12, 192)  576         conv2d_141[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_144 (BatchN (None, 12, 12, 192)  576         conv2d_144[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_141 (Activation)     (None, 12, 12, 192)  0           batch_normalization_141[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_144 (Activation)     (None, 12, 12, 192)  0           batch_normalization_144[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_17_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_141[0][0]             \n",
            "                                                                 activation_144[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_17_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_17_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_17 (Lambda)             (None, 12, 12, 1088) 0           block17_16_ac[0][0]              \n",
            "                                                                 block17_17_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_17_ac (Activation)      (None, 12, 12, 1088) 0           block17_17[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_146 (Conv2D)             (None, 12, 12, 128)  139264      block17_17_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_146 (BatchN (None, 12, 12, 128)  384         conv2d_146[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_146 (Activation)     (None, 12, 12, 128)  0           batch_normalization_146[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_147 (Conv2D)             (None, 12, 12, 160)  143360      activation_146[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_147 (BatchN (None, 12, 12, 160)  480         conv2d_147[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_147 (Activation)     (None, 12, 12, 160)  0           batch_normalization_147[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_145 (Conv2D)             (None, 12, 12, 192)  208896      block17_17_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_148 (Conv2D)             (None, 12, 12, 192)  215040      activation_147[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_145 (BatchN (None, 12, 12, 192)  576         conv2d_145[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_148 (BatchN (None, 12, 12, 192)  576         conv2d_148[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_145 (Activation)     (None, 12, 12, 192)  0           batch_normalization_145[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_148 (Activation)     (None, 12, 12, 192)  0           batch_normalization_148[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_18_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_145[0][0]             \n",
            "                                                                 activation_148[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_18_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_18_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_18 (Lambda)             (None, 12, 12, 1088) 0           block17_17_ac[0][0]              \n",
            "                                                                 block17_18_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_18_ac (Activation)      (None, 12, 12, 1088) 0           block17_18[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_150 (Conv2D)             (None, 12, 12, 128)  139264      block17_18_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_150 (BatchN (None, 12, 12, 128)  384         conv2d_150[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_150 (Activation)     (None, 12, 12, 128)  0           batch_normalization_150[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_151 (Conv2D)             (None, 12, 12, 160)  143360      activation_150[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_151 (BatchN (None, 12, 12, 160)  480         conv2d_151[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_151 (Activation)     (None, 12, 12, 160)  0           batch_normalization_151[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_149 (Conv2D)             (None, 12, 12, 192)  208896      block17_18_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_152 (Conv2D)             (None, 12, 12, 192)  215040      activation_151[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_149 (BatchN (None, 12, 12, 192)  576         conv2d_149[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_152 (BatchN (None, 12, 12, 192)  576         conv2d_152[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_149 (Activation)     (None, 12, 12, 192)  0           batch_normalization_149[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_152 (Activation)     (None, 12, 12, 192)  0           batch_normalization_152[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_19_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_149[0][0]             \n",
            "                                                                 activation_152[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_19_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_19_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_19 (Lambda)             (None, 12, 12, 1088) 0           block17_18_ac[0][0]              \n",
            "                                                                 block17_19_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_19_ac (Activation)      (None, 12, 12, 1088) 0           block17_19[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_154 (Conv2D)             (None, 12, 12, 128)  139264      block17_19_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_154 (BatchN (None, 12, 12, 128)  384         conv2d_154[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_154 (Activation)     (None, 12, 12, 128)  0           batch_normalization_154[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_155 (Conv2D)             (None, 12, 12, 160)  143360      activation_154[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_155 (BatchN (None, 12, 12, 160)  480         conv2d_155[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_155 (Activation)     (None, 12, 12, 160)  0           batch_normalization_155[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_153 (Conv2D)             (None, 12, 12, 192)  208896      block17_19_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_156 (Conv2D)             (None, 12, 12, 192)  215040      activation_155[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_153 (BatchN (None, 12, 12, 192)  576         conv2d_153[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_156 (BatchN (None, 12, 12, 192)  576         conv2d_156[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_153 (Activation)     (None, 12, 12, 192)  0           batch_normalization_153[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_156 (Activation)     (None, 12, 12, 192)  0           batch_normalization_156[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block17_20_mixed (Concatenate)  (None, 12, 12, 384)  0           activation_153[0][0]             \n",
            "                                                                 activation_156[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block17_20_conv (Conv2D)        (None, 12, 12, 1088) 418880      block17_20_mixed[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block17_20 (Lambda)             (None, 12, 12, 1088) 0           block17_19_ac[0][0]              \n",
            "                                                                 block17_20_conv[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block17_20_ac (Activation)      (None, 12, 12, 1088) 0           block17_20[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_161 (Conv2D)             (None, 12, 12, 256)  278528      block17_20_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_161 (BatchN (None, 12, 12, 256)  768         conv2d_161[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_161 (Activation)     (None, 12, 12, 256)  0           batch_normalization_161[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_157 (Conv2D)             (None, 12, 12, 256)  278528      block17_20_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_159 (Conv2D)             (None, 12, 12, 256)  278528      block17_20_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_162 (Conv2D)             (None, 12, 12, 288)  663552      activation_161[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_157 (BatchN (None, 12, 12, 256)  768         conv2d_157[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_159 (BatchN (None, 12, 12, 256)  768         conv2d_159[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_162 (BatchN (None, 12, 12, 288)  864         conv2d_162[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_157 (Activation)     (None, 12, 12, 256)  0           batch_normalization_157[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_159 (Activation)     (None, 12, 12, 256)  0           batch_normalization_159[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_162 (Activation)     (None, 12, 12, 288)  0           batch_normalization_162[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_158 (Conv2D)             (None, 5, 5, 384)    884736      activation_157[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_160 (Conv2D)             (None, 5, 5, 288)    663552      activation_159[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_163 (Conv2D)             (None, 5, 5, 320)    829440      activation_162[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_158 (BatchN (None, 5, 5, 384)    1152        conv2d_158[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_160 (BatchN (None, 5, 5, 288)    864         conv2d_160[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_163 (BatchN (None, 5, 5, 320)    960         conv2d_163[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_158 (Activation)     (None, 5, 5, 384)    0           batch_normalization_158[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_160 (Activation)     (None, 5, 5, 288)    0           batch_normalization_160[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_163 (Activation)     (None, 5, 5, 320)    0           batch_normalization_163[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 5, 5, 1088)   0           block17_20_ac[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "mixed_7a (Concatenate)          (None, 5, 5, 2080)   0           activation_158[0][0]             \n",
            "                                                                 activation_160[0][0]             \n",
            "                                                                 activation_163[0][0]             \n",
            "                                                                 max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_165 (Conv2D)             (None, 5, 5, 192)    399360      mixed_7a[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_165 (BatchN (None, 5, 5, 192)    576         conv2d_165[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_165 (Activation)     (None, 5, 5, 192)    0           batch_normalization_165[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_166 (Conv2D)             (None, 5, 5, 224)    129024      activation_165[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_166 (BatchN (None, 5, 5, 224)    672         conv2d_166[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_166 (Activation)     (None, 5, 5, 224)    0           batch_normalization_166[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_164 (Conv2D)             (None, 5, 5, 192)    399360      mixed_7a[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_167 (Conv2D)             (None, 5, 5, 256)    172032      activation_166[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_164 (BatchN (None, 5, 5, 192)    576         conv2d_164[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_167 (BatchN (None, 5, 5, 256)    768         conv2d_167[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_164 (Activation)     (None, 5, 5, 192)    0           batch_normalization_164[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_167 (Activation)     (None, 5, 5, 256)    0           batch_normalization_167[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block8_1_mixed (Concatenate)    (None, 5, 5, 448)    0           activation_164[0][0]             \n",
            "                                                                 activation_167[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_1_conv (Conv2D)          (None, 5, 5, 2080)   933920      block8_1_mixed[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_1 (Lambda)               (None, 5, 5, 2080)   0           mixed_7a[0][0]                   \n",
            "                                                                 block8_1_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block8_1_ac (Activation)        (None, 5, 5, 2080)   0           block8_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_169 (Conv2D)             (None, 5, 5, 192)    399360      block8_1_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_169 (BatchN (None, 5, 5, 192)    576         conv2d_169[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_169 (Activation)     (None, 5, 5, 192)    0           batch_normalization_169[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_170 (Conv2D)             (None, 5, 5, 224)    129024      activation_169[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_170 (BatchN (None, 5, 5, 224)    672         conv2d_170[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_170 (Activation)     (None, 5, 5, 224)    0           batch_normalization_170[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_168 (Conv2D)             (None, 5, 5, 192)    399360      block8_1_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_171 (Conv2D)             (None, 5, 5, 256)    172032      activation_170[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_168 (BatchN (None, 5, 5, 192)    576         conv2d_168[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_171 (BatchN (None, 5, 5, 256)    768         conv2d_171[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_168 (Activation)     (None, 5, 5, 192)    0           batch_normalization_168[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_171 (Activation)     (None, 5, 5, 256)    0           batch_normalization_171[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block8_2_mixed (Concatenate)    (None, 5, 5, 448)    0           activation_168[0][0]             \n",
            "                                                                 activation_171[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_2_conv (Conv2D)          (None, 5, 5, 2080)   933920      block8_2_mixed[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_2 (Lambda)               (None, 5, 5, 2080)   0           block8_1_ac[0][0]                \n",
            "                                                                 block8_2_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block8_2_ac (Activation)        (None, 5, 5, 2080)   0           block8_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_173 (Conv2D)             (None, 5, 5, 192)    399360      block8_2_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_173 (BatchN (None, 5, 5, 192)    576         conv2d_173[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_173 (Activation)     (None, 5, 5, 192)    0           batch_normalization_173[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_174 (Conv2D)             (None, 5, 5, 224)    129024      activation_173[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_174 (BatchN (None, 5, 5, 224)    672         conv2d_174[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_174 (Activation)     (None, 5, 5, 224)    0           batch_normalization_174[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_172 (Conv2D)             (None, 5, 5, 192)    399360      block8_2_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_175 (Conv2D)             (None, 5, 5, 256)    172032      activation_174[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_172 (BatchN (None, 5, 5, 192)    576         conv2d_172[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_175 (BatchN (None, 5, 5, 256)    768         conv2d_175[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_172 (Activation)     (None, 5, 5, 192)    0           batch_normalization_172[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_175 (Activation)     (None, 5, 5, 256)    0           batch_normalization_175[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block8_3_mixed (Concatenate)    (None, 5, 5, 448)    0           activation_172[0][0]             \n",
            "                                                                 activation_175[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_3_conv (Conv2D)          (None, 5, 5, 2080)   933920      block8_3_mixed[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_3 (Lambda)               (None, 5, 5, 2080)   0           block8_2_ac[0][0]                \n",
            "                                                                 block8_3_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block8_3_ac (Activation)        (None, 5, 5, 2080)   0           block8_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_177 (Conv2D)             (None, 5, 5, 192)    399360      block8_3_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_177 (BatchN (None, 5, 5, 192)    576         conv2d_177[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_177 (Activation)     (None, 5, 5, 192)    0           batch_normalization_177[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_178 (Conv2D)             (None, 5, 5, 224)    129024      activation_177[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_178 (BatchN (None, 5, 5, 224)    672         conv2d_178[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_178 (Activation)     (None, 5, 5, 224)    0           batch_normalization_178[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_176 (Conv2D)             (None, 5, 5, 192)    399360      block8_3_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_179 (Conv2D)             (None, 5, 5, 256)    172032      activation_178[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_176 (BatchN (None, 5, 5, 192)    576         conv2d_176[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_179 (BatchN (None, 5, 5, 256)    768         conv2d_179[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_176 (Activation)     (None, 5, 5, 192)    0           batch_normalization_176[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_179 (Activation)     (None, 5, 5, 256)    0           batch_normalization_179[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block8_4_mixed (Concatenate)    (None, 5, 5, 448)    0           activation_176[0][0]             \n",
            "                                                                 activation_179[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_4_conv (Conv2D)          (None, 5, 5, 2080)   933920      block8_4_mixed[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_4 (Lambda)               (None, 5, 5, 2080)   0           block8_3_ac[0][0]                \n",
            "                                                                 block8_4_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block8_4_ac (Activation)        (None, 5, 5, 2080)   0           block8_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_181 (Conv2D)             (None, 5, 5, 192)    399360      block8_4_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_181 (BatchN (None, 5, 5, 192)    576         conv2d_181[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_181 (Activation)     (None, 5, 5, 192)    0           batch_normalization_181[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_182 (Conv2D)             (None, 5, 5, 224)    129024      activation_181[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_182 (BatchN (None, 5, 5, 224)    672         conv2d_182[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_182 (Activation)     (None, 5, 5, 224)    0           batch_normalization_182[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_180 (Conv2D)             (None, 5, 5, 192)    399360      block8_4_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_183 (Conv2D)             (None, 5, 5, 256)    172032      activation_182[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_180 (BatchN (None, 5, 5, 192)    576         conv2d_180[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_183 (BatchN (None, 5, 5, 256)    768         conv2d_183[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_180 (Activation)     (None, 5, 5, 192)    0           batch_normalization_180[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_183 (Activation)     (None, 5, 5, 256)    0           batch_normalization_183[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block8_5_mixed (Concatenate)    (None, 5, 5, 448)    0           activation_180[0][0]             \n",
            "                                                                 activation_183[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_5_conv (Conv2D)          (None, 5, 5, 2080)   933920      block8_5_mixed[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_5 (Lambda)               (None, 5, 5, 2080)   0           block8_4_ac[0][0]                \n",
            "                                                                 block8_5_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block8_5_ac (Activation)        (None, 5, 5, 2080)   0           block8_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_185 (Conv2D)             (None, 5, 5, 192)    399360      block8_5_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_185 (BatchN (None, 5, 5, 192)    576         conv2d_185[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_185 (Activation)     (None, 5, 5, 192)    0           batch_normalization_185[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_186 (Conv2D)             (None, 5, 5, 224)    129024      activation_185[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_186 (BatchN (None, 5, 5, 224)    672         conv2d_186[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_186 (Activation)     (None, 5, 5, 224)    0           batch_normalization_186[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_184 (Conv2D)             (None, 5, 5, 192)    399360      block8_5_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_187 (Conv2D)             (None, 5, 5, 256)    172032      activation_186[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_184 (BatchN (None, 5, 5, 192)    576         conv2d_184[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_187 (BatchN (None, 5, 5, 256)    768         conv2d_187[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_184 (Activation)     (None, 5, 5, 192)    0           batch_normalization_184[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_187 (Activation)     (None, 5, 5, 256)    0           batch_normalization_187[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block8_6_mixed (Concatenate)    (None, 5, 5, 448)    0           activation_184[0][0]             \n",
            "                                                                 activation_187[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_6_conv (Conv2D)          (None, 5, 5, 2080)   933920      block8_6_mixed[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_6 (Lambda)               (None, 5, 5, 2080)   0           block8_5_ac[0][0]                \n",
            "                                                                 block8_6_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block8_6_ac (Activation)        (None, 5, 5, 2080)   0           block8_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_189 (Conv2D)             (None, 5, 5, 192)    399360      block8_6_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_189 (BatchN (None, 5, 5, 192)    576         conv2d_189[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_189 (Activation)     (None, 5, 5, 192)    0           batch_normalization_189[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_190 (Conv2D)             (None, 5, 5, 224)    129024      activation_189[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_190 (BatchN (None, 5, 5, 224)    672         conv2d_190[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_190 (Activation)     (None, 5, 5, 224)    0           batch_normalization_190[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_188 (Conv2D)             (None, 5, 5, 192)    399360      block8_6_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_191 (Conv2D)             (None, 5, 5, 256)    172032      activation_190[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_188 (BatchN (None, 5, 5, 192)    576         conv2d_188[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_191 (BatchN (None, 5, 5, 256)    768         conv2d_191[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_188 (Activation)     (None, 5, 5, 192)    0           batch_normalization_188[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_191 (Activation)     (None, 5, 5, 256)    0           batch_normalization_191[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block8_7_mixed (Concatenate)    (None, 5, 5, 448)    0           activation_188[0][0]             \n",
            "                                                                 activation_191[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_7_conv (Conv2D)          (None, 5, 5, 2080)   933920      block8_7_mixed[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_7 (Lambda)               (None, 5, 5, 2080)   0           block8_6_ac[0][0]                \n",
            "                                                                 block8_7_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block8_7_ac (Activation)        (None, 5, 5, 2080)   0           block8_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_193 (Conv2D)             (None, 5, 5, 192)    399360      block8_7_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_193 (BatchN (None, 5, 5, 192)    576         conv2d_193[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_193 (Activation)     (None, 5, 5, 192)    0           batch_normalization_193[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_194 (Conv2D)             (None, 5, 5, 224)    129024      activation_193[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_194 (BatchN (None, 5, 5, 224)    672         conv2d_194[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_194 (Activation)     (None, 5, 5, 224)    0           batch_normalization_194[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_192 (Conv2D)             (None, 5, 5, 192)    399360      block8_7_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_195 (Conv2D)             (None, 5, 5, 256)    172032      activation_194[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_192 (BatchN (None, 5, 5, 192)    576         conv2d_192[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_195 (BatchN (None, 5, 5, 256)    768         conv2d_195[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_192 (Activation)     (None, 5, 5, 192)    0           batch_normalization_192[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_195 (Activation)     (None, 5, 5, 256)    0           batch_normalization_195[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block8_8_mixed (Concatenate)    (None, 5, 5, 448)    0           activation_192[0][0]             \n",
            "                                                                 activation_195[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_8_conv (Conv2D)          (None, 5, 5, 2080)   933920      block8_8_mixed[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_8 (Lambda)               (None, 5, 5, 2080)   0           block8_7_ac[0][0]                \n",
            "                                                                 block8_8_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block8_8_ac (Activation)        (None, 5, 5, 2080)   0           block8_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_197 (Conv2D)             (None, 5, 5, 192)    399360      block8_8_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_197 (BatchN (None, 5, 5, 192)    576         conv2d_197[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_197 (Activation)     (None, 5, 5, 192)    0           batch_normalization_197[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_198 (Conv2D)             (None, 5, 5, 224)    129024      activation_197[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_198 (BatchN (None, 5, 5, 224)    672         conv2d_198[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_198 (Activation)     (None, 5, 5, 224)    0           batch_normalization_198[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_196 (Conv2D)             (None, 5, 5, 192)    399360      block8_8_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_199 (Conv2D)             (None, 5, 5, 256)    172032      activation_198[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_196 (BatchN (None, 5, 5, 192)    576         conv2d_196[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_199 (BatchN (None, 5, 5, 256)    768         conv2d_199[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_196 (Activation)     (None, 5, 5, 192)    0           batch_normalization_196[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_199 (Activation)     (None, 5, 5, 256)    0           batch_normalization_199[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block8_9_mixed (Concatenate)    (None, 5, 5, 448)    0           activation_196[0][0]             \n",
            "                                                                 activation_199[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_9_conv (Conv2D)          (None, 5, 5, 2080)   933920      block8_9_mixed[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_9 (Lambda)               (None, 5, 5, 2080)   0           block8_8_ac[0][0]                \n",
            "                                                                 block8_9_conv[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "block8_9_ac (Activation)        (None, 5, 5, 2080)   0           block8_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_201 (Conv2D)             (None, 5, 5, 192)    399360      block8_9_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_201 (BatchN (None, 5, 5, 192)    576         conv2d_201[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_201 (Activation)     (None, 5, 5, 192)    0           batch_normalization_201[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_202 (Conv2D)             (None, 5, 5, 224)    129024      activation_201[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_202 (BatchN (None, 5, 5, 224)    672         conv2d_202[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_202 (Activation)     (None, 5, 5, 224)    0           batch_normalization_202[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_200 (Conv2D)             (None, 5, 5, 192)    399360      block8_9_ac[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_203 (Conv2D)             (None, 5, 5, 256)    172032      activation_202[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_200 (BatchN (None, 5, 5, 192)    576         conv2d_200[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_203 (BatchN (None, 5, 5, 256)    768         conv2d_203[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "activation_200 (Activation)     (None, 5, 5, 192)    0           batch_normalization_200[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "activation_203 (Activation)     (None, 5, 5, 256)    0           batch_normalization_203[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block8_10_mixed (Concatenate)   (None, 5, 5, 448)    0           activation_200[0][0]             \n",
            "                                                                 activation_203[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block8_10_conv (Conv2D)         (None, 5, 5, 2080)   933920      block8_10_mixed[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block8_10 (Lambda)              (None, 5, 5, 2080)   0           block8_9_ac[0][0]                \n",
            "                                                                 block8_10_conv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "conv_7b (Conv2D)                (None, 5, 5, 1536)   3194880     block8_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv_7b_bn (BatchNormalization) (None, 5, 5, 1536)   4608        conv_7b[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv_7b_ac (Activation)         (None, 5, 5, 1536)   0           conv_7b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 1536)         0           conv_7b_ac[0][0]                 \n",
            "==================================================================================================\n",
            "Total params: 54,336,736\n",
            "Trainable params: 54,276,192\n",
            "Non-trainable params: 60,544\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d0JxJSWexo8z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Helper function to process images\n",
        "def preprocessing(img_path):\n",
        "    im = image.load_img(img_path, target_size=(224,224,3))\n",
        "    im = image.img_to_array(im)\n",
        "    im = np.expand_dims(im, axis=0)\n",
        "    return im"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98_42k6Dxzcw",
        "colab_type": "code",
        "outputId": "4eecd723-6c0f-4aa3-fa1f-a6922b83ee4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "train_data = {}\n",
        "ctr=0\n",
        "for ix in x_train:\n",
        "    if ix == \"\":\n",
        "        continue\n",
        "    if ctr >= 3000:\n",
        "        break\n",
        "    ctr+=1\n",
        "    if ctr%1000==0:\n",
        "        print(ctr)\n",
        "    path = images_path + ix\n",
        "    img = preprocessing(path)\n",
        "    pred = model.predict(img).reshape(1536)\n",
        "    train_data[ix] = pred"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1000\n",
            "2000\n",
            "3000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTzlJH-_y-sR",
        "colab_type": "code",
        "outputId": "85a1825f-2c38-42bd-faf0-38b024291823",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_data['2513260012_03d33305cf.jpg'].shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1536,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACLW9ux65ckw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# opening train_encoded_images.p file and dumping it's content\n",
        "with open( \"train_encoded_images.p\", \"wb\" ) as pickle_f:\n",
        "    pickle.dump(train_data, pickle_f )  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdECPGRe5fTR",
        "colab_type": "code",
        "outputId": "bc86210a-2159-4689-e83b-d9e1963ffced",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Loading image and its corresponding caption into a dataframe and then storing values from dataframe into 'ds'\n",
        "pd_dataset = pd.read_csv(\"flickr_8k_train_dataset.txt\", delimiter='\\t')\n",
        "ds = pd_dataset.values\n",
        "print(ds.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(30000, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCkdOhCN5lIQ",
        "colab_type": "code",
        "outputId": "b1ebc045-e3a5-4873-d5b3-77f0ede313ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "pd_dataset.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>captions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2513260012_03d33305cf.jpg</td>\n",
              "      <td>&lt;start&gt; A black dog is running after a white d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2513260012_03d33305cf.jpg</td>\n",
              "      <td>&lt;start&gt; Black dog chasing brown dog through sn...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2513260012_03d33305cf.jpg</td>\n",
              "      <td>&lt;start&gt; Two dogs chase each other across the s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2513260012_03d33305cf.jpg</td>\n",
              "      <td>&lt;start&gt; Two dogs play together in the snow . &lt;...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2513260012_03d33305cf.jpg</td>\n",
              "      <td>&lt;start&gt; Two dogs running through a low lying b...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    image_id                                           captions\n",
              "0  2513260012_03d33305cf.jpg  <start> A black dog is running after a white d...\n",
              "1  2513260012_03d33305cf.jpg  <start> Black dog chasing brown dog through sn...\n",
              "2  2513260012_03d33305cf.jpg  <start> Two dogs chase each other across the s...\n",
              "3  2513260012_03d33305cf.jpg  <start> Two dogs play together in the snow . <...\n",
              "4  2513260012_03d33305cf.jpg  <start> Two dogs running through a low lying b..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0wuZtK45pMI",
        "colab_type": "code",
        "outputId": "1f49ba58-1429-4c7b-d5f9-c3c64c9c2cdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Storing all the captions from ds into a list\n",
        "sentences = []\n",
        "for ix in range(ds.shape[0]):\n",
        "    sentences.append(ds[ix, 1])\n",
        "    \n",
        "print(len(sentences))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqTs9AbX5s2G",
        "colab_type": "code",
        "outputId": "953dcdf9-79ac-4760-8494-f080de8031ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "# First 5 captions stored in sentences\n",
        "sentences[:5]"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<start> A black dog is running after a white dog in the snow . <end>',\n",
              " '<start> Black dog chasing brown dog through snow <end>',\n",
              " '<start> Two dogs chase each other across the snowy ground . <end>',\n",
              " '<start> Two dogs play together in the snow . <end>',\n",
              " '<start> Two dogs running through a low lying body of water . <end>']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6ork37V5wVM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Splitting each captions stored in 'sentences' and storing them in 'words' as list of list\n",
        "words = [i.split() for i in sentences]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ETPLbXi75zfa",
        "colab_type": "code",
        "outputId": "819d84b5-15a3-45e1-c40c-673be5e2a5e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Creating a list of all unique words\n",
        "unique = []\n",
        "for i in words:\n",
        "    unique.extend(i)\n",
        "unique = list(set(unique))\n",
        "\n",
        "print(len(unique))\n",
        "\n",
        "vocab_size = len(unique)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8253\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O86M4kvo57PO",
        "colab_type": "code",
        "outputId": "c7a0ce57-3dcb-43ef-b5d2-6c9c19ef9ed2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# Vectorization\n",
        "word_2_indices = {val:index for index, val in enumerate(unique)}\n",
        "indices_2_word = {index:val for index, val in enumerate(unique)}\n",
        "word_2_indices['UNK'] = 0\n",
        "word_2_indices['raining'] = 8253\n",
        "indices_2_word[0] = 'UNK'\n",
        "indices_2_word[8253] = 'raining'\n",
        "print(word_2_indices['<start>'])\n",
        "print(indices_2_word[4011])\n",
        "print(word_2_indices['<end>'])\n",
        "print(indices_2_word[8051])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6362\n",
            "pad\n",
            "3470\n",
            "stroll\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4oSyNr5Z6L7j",
        "colab_type": "code",
        "outputId": "4dd1cf71-9b4c-4388-d09e-78961503fe0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "vocab_size = len(word_2_indices.keys())\n",
        "print(vocab_size)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8254\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDJEHLZp6Pgj",
        "colab_type": "code",
        "outputId": "f46bcb16-d8b6-4326-9182-12c3db7c9022",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "max_len = 0\n",
        "\n",
        "for i in sentences:\n",
        "    i = i.split()\n",
        "    if len(i) > max_len:\n",
        "        max_len = len(i)\n",
        "\n",
        "print(max_len)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "40\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PdaUzOdl6Tqw",
        "colab_type": "code",
        "outputId": "999fea0e-209b-4b60-d40a-ed5c453eac54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "padded_sequences, subsequent_words = [], []\n",
        "\n",
        "for ix in range(ds.shape[0]):\n",
        "    partial_seqs = []\n",
        "    next_words = []\n",
        "    text = ds[ix, 1].split()\n",
        "    text = [word_2_indices[i] for i in text]\n",
        "    for i in range(1, len(text)):\n",
        "        partial_seqs.append(text[:i])\n",
        "        next_words.append(text[i])\n",
        "    padded_partial_seqs = sequence.pad_sequences(partial_seqs, max_len, padding='post')\n",
        "\n",
        "    next_words_1hot = np.zeros([len(next_words), vocab_size], dtype=np.bool)\n",
        "    \n",
        "    #Vectorization\n",
        "    for i,next_word in enumerate(next_words):\n",
        "        next_words_1hot[i, next_word] = 1\n",
        "        \n",
        "    padded_sequences.append(padded_partial_seqs)\n",
        "    subsequent_words.append(next_words_1hot)\n",
        "    \n",
        "padded_sequences = np.asarray(padded_sequences)\n",
        "subsequent_words = np.asarray(subsequent_words)\n",
        "\n",
        "print(padded_sequences.shape)\n",
        "print(subsequent_words.shape)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(30000,)\n",
            "(30000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-Uy-Rqa6bsx",
        "colab_type": "code",
        "outputId": "574cff75-6127-46e7-9d88-9ece1eba1275",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        }
      },
      "source": [
        "print(padded_sequences[0])"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6362    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057 2681    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057 2681 3753    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057 2681 3753 1492    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057 2681 3753 1492 1988    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057 2681 3753 1492 1988 1584    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057 2681 3753 1492 1988 1584 7057    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057 2681 3753 1492 1988 1584 7057 3070    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057 2681 3753 1492 1988 1584 7057 3070 1499    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057 2681 3753 1492 1988 1584 7057 3070 1499 4665    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]\n",
            " [6362 2445 5966 7057 2681 3753 1492 1988 1584 7057 3070 1499 4665 2314\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0    0    0\n",
            "     0    0    0    0    0    0    0    0    0    0    0    0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sg8OQXDU6et8",
        "colab_type": "code",
        "outputId": "57c46a9a-c1c1-445c-95a7-182c08f169bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for ix in range(len(padded_sequences[0])):\n",
        "    for iy in range(max_len):\n",
        "        print(indices_2_word[padded_sequences[0][ix][iy]],)\n",
        "    print(\"\\n\")\n",
        "\n",
        "print(len(padded_sequences[0]))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<start>\n",
            "UNK\n",
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            "\n",
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            "<start>\n",
            "A\n",
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            "\n",
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            "<start>\n",
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            "\n",
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            "<start>\n",
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            "\n",
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            "<start>\n",
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            "\n",
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            "\n",
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            "\n",
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            "<start>\n",
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            "black\n",
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            "after\n",
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            "\n",
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            "<start>\n",
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            "after\n",
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            "\n",
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            "<start>\n",
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            "a\n",
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            "\n",
            "\n",
            "<start>\n",
            "A\n",
            "black\n",
            "dog\n",
            "is\n",
            "running\n",
            "after\n",
            "a\n",
            "white\n",
            "dog\n",
            "in\n",
            "UNK\n",
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            "UNK\n",
            "UNK\n",
            "\n",
            "\n",
            "<start>\n",
            "A\n",
            "black\n",
            "dog\n",
            "is\n",
            "running\n",
            "after\n",
            "a\n",
            "white\n",
            "dog\n",
            "in\n",
            "the\n",
            "UNK\n",
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            "UNK\n",
            "UNK\n",
            "UNK\n",
            "\n",
            "\n",
            "<start>\n",
            "A\n",
            "black\n",
            "dog\n",
            "is\n",
            "running\n",
            "after\n",
            "a\n",
            "white\n",
            "dog\n",
            "in\n",
            "the\n",
            "snow\n",
            "UNK\n",
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            "UNK\n",
            "UNK\n",
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            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "\n",
            "\n",
            "<start>\n",
            "A\n",
            "black\n",
            "dog\n",
            "is\n",
            "running\n",
            "after\n",
            "a\n",
            "white\n",
            "dog\n",
            "in\n",
            "the\n",
            "snow\n",
            ".\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
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            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "UNK\n",
            "\n",
            "\n",
            "14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TEb8ihs06lkl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_of_images = 2000\n",
        "captions = np.zeros([0, max_len])\n",
        "next_words = np.zeros([0, vocab_size])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0IiT2iW6ssu",
        "colab_type": "code",
        "outputId": "0eef5d70-aaea-4110-d483-eab5c57fcf05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "for ix in range(num_of_images):#img_to_padded_seqs.shape[0]):\n",
        "    captions = np.concatenate([captions, padded_sequences[ix]])\n",
        "    next_words = np.concatenate([next_words, subsequent_words[ix]])\n",
        "\n",
        "np.save(\"captions.npy\", captions)\n",
        "np.save(\"next_words.npy\", next_words)\n",
        "\n",
        "print(captions.shape)\n",
        "print(next_words.shape)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(25493, 40)\n",
            "(25493, 8254)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6r2dSaBb7ttk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "f= open('/content/gdrive/My Drive/Colab Notebooks/MAJOR/flickr8k/train_encoded_images.p', 'rb')\n",
        "encoded_images = pickle.load(f, encoding=\"bytes\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRlty6GE9peS",
        "colab_type": "code",
        "outputId": "a2900d55-7ee0-4882-b4c7-2403bf92762e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "imgs = []\n",
        "\n",
        "for ix in range(ds.shape[0]):\n",
        "    if ds[ix, 0].encode() in encoded_images.keys():\n",
        "#         print(ix, encoded_images[ds[ix, 0].encode()])\n",
        "        imgs.append(list(encoded_images[ds[ix, 0].encode()]))\n",
        "\n",
        "imgs = np.asarray(imgs)\n",
        "print(imgs.shape)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(15000, 2048)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNYOGq539xxv",
        "colab_type": "code",
        "outputId": "b4cd2dc3-3d0f-4f40-becc-2fc16477ff44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "images = []\n",
        "\n",
        "for ix in range(num_of_images):\n",
        "    for iy in range(padded_sequences[ix].shape[0]):\n",
        "        images.append(imgs[ix])\n",
        "        \n",
        "images = np.asarray(images)\n",
        "\n",
        "np.save(\"images.npy\", images)\n",
        "\n",
        "print(images.shape)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(25493, 2048)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tc2XbNP91n4",
        "colab_type": "code",
        "outputId": "ede5c3ee-5f44-40a5-a72a-ea54d8b6288c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "image_names = []\n",
        "\n",
        "for ix in range(num_of_images):\n",
        "    for iy in range(padded_sequences[ix].shape[0]):\n",
        "        image_names.append(ds[ix, 0])\n",
        "        \n",
        "image_names = np.asarray(image_names)\n",
        "\n",
        "np.save(\"image_names.npy\", image_names)\n",
        "\n",
        "print(len(image_names))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25493\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1apBClpg979j",
        "colab_type": "code",
        "outputId": "9044c938-e096-464e-8f71-043992072776",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "captions = np.load(\"captions.npy\")\n",
        "next_words = np.load(\"next_words.npy\")\n",
        "\n",
        "print(captions.shape)\n",
        "print(next_words.shape)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(25493, 40)\n",
            "(25493, 8254)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ym_6pYn6-BCd",
        "colab_type": "code",
        "outputId": "8019550b-58d3-4a17-bafc-1c2e7f916754",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "images = np.load(\"images.npy\")\n",
        "\n",
        "print(images.shape)\n",
        "(25493, 2048)\n",
        "imag = np.load(\"image_names.npy\")\n",
        "        \n",
        "print(imag.shape)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(25493, 2048)\n",
            "(25493,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJ3gGLHt-GjG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embedding_size = 128\n",
        "max_len = 40"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGFaX_wN-HmX",
        "colab_type": "code",
        "outputId": "0f6a6f2f-a9a6-4eaa-a6f8-7fe6b0643294",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "image_model = Sequential()\n",
        "\n",
        "image_model.add(Dense(embedding_size, input_shape=(2048,), activation='relu'))\n",
        "image_model.add(RepeatVector(max_len))\n",
        "\n",
        "image_model.summary()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_5 (Dense)              (None, 128)               262272    \n",
            "_________________________________________________________________\n",
            "repeat_vector_3 (RepeatVecto (None, 40, 128)           0         \n",
            "=================================================================\n",
            "Total params: 262,272\n",
            "Trainable params: 262,272\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJQnIuB9-Tp1",
        "colab_type": "code",
        "outputId": "f1fa8967-3b53-477c-bebd-0dc93b3ec64c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "language_model = Sequential()\n",
        "\n",
        "language_model.add(Embedding(input_dim=vocab_size, output_dim=embedding_size, input_length=max_len))\n",
        "language_model.add(LSTM(256, return_sequences=True))\n",
        "language_model.add(TimeDistributed(Dense(embedding_size)))\n",
        "\n",
        "language_model.summary()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 40, 128)           1056512   \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 40, 256)           394240    \n",
            "_________________________________________________________________\n",
            "time_distributed_1 (TimeDist (None, 40, 128)           32896     \n",
            "=================================================================\n",
            "Total params: 1,483,648\n",
            "Trainable params: 1,483,648\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0ZPBjVz-dFg",
        "colab_type": "code",
        "outputId": "eff9ef1e-d306-45cb-a22d-6f0f9797a7b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        }
      },
      "source": [
        "conca = Concatenate()([image_model.output, language_model.output])\n",
        "x = LSTM(128, return_sequences=True)(conca)\n",
        "x = LSTM(512, return_sequences=False)(x)\n",
        "x = Dense(vocab_size)(x)\n",
        "out = Activation('softmax')(x)\n",
        "model = Model(inputs=[image_model.input, language_model.input], outputs = out)\n",
        "\n",
        "# model.load_weights(\"../input/model_weights.h5\")\n",
        "model.compile(loss='categorical_crossentropy', optimizer='RMSprop', metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "embedding_1_input (InputLayer)  (None, 40)           0                                            \n",
            "__________________________________________________________________________________________________\n",
            "dense_5_input (InputLayer)      (None, 2048)         0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding_1 (Embedding)         (None, 40, 128)      1056512     embedding_1_input[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 128)          262272      dense_5_input[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "lstm_1 (LSTM)                   (None, 40, 256)      394240      embedding_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "repeat_vector_3 (RepeatVector)  (None, 40, 128)      0           dense_5[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "time_distributed_1 (TimeDistrib (None, 40, 128)      32896       lstm_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 40, 256)      0           repeat_vector_3[0][0]            \n",
            "                                                                 time_distributed_1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "lstm_4 (LSTM)                   (None, 40, 128)      197120      concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "lstm_5 (LSTM)                   (None, 512)          1312768     lstm_4[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "dense_6 (Dense)                 (None, 8254)         4234302     lstm_5[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "activation_205 (Activation)     (None, 8254)         0           dense_6[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 7,490,110\n",
            "Trainable params: 7,490,110\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2qyhahR-jIq",
        "colab_type": "code",
        "outputId": "d1730923-3548-4d93-95bd-e6f524cf5b17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "hist = model.fit([images, captions], next_words, batch_size=512, epochs=30)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "Epoch 1/30\n",
            "25493/25493 [==============================] - 344s 13ms/step - loss: 5.7014 - acc: 0.0739\n",
            "Epoch 2/30\n",
            "25493/25493 [==============================] - 327s 13ms/step - loss: 5.1906 - acc: 0.1175\n",
            "Epoch 3/30\n",
            "25493/25493 [==============================] - 321s 13ms/step - loss: 5.2456 - acc: 0.0848\n",
            "Epoch 4/30\n",
            "25493/25493 [==============================] - 327s 13ms/step - loss: 5.1373 - acc: 0.0977\n",
            "Epoch 5/30\n",
            "25493/25493 [==============================] - 338s 13ms/step - loss: 5.0886 - acc: 0.0976\n",
            "Epoch 6/30\n",
            "25493/25493 [==============================] - 335s 13ms/step - loss: 5.1246 - acc: 0.0830\n",
            "Epoch 7/30\n",
            "25493/25493 [==============================] - 330s 13ms/step - loss: 4.9320 - acc: 0.1096\n",
            "Epoch 8/30\n",
            "25493/25493 [==============================] - 345s 14ms/step - loss: 4.9823 - acc: 0.0898\n",
            "Epoch 9/30\n",
            "25493/25493 [==============================] - 341s 13ms/step - loss: 4.8145 - acc: 0.1214\n",
            "Epoch 10/30\n",
            "25493/25493 [==============================] - 340s 13ms/step - loss: 4.5930 - acc: 0.1560\n",
            "Epoch 11/30\n",
            "25493/25493 [==============================] - 347s 14ms/step - loss: 4.4032 - acc: 0.1823\n",
            "Epoch 12/30\n",
            "25493/25493 [==============================] - 342s 13ms/step - loss: 4.2905 - acc: 0.1940\n",
            "Epoch 13/30\n",
            "25493/25493 [==============================] - 339s 13ms/step - loss: 4.1473 - acc: 0.2084\n",
            "Epoch 14/30\n",
            "25493/25493 [==============================] - 326s 13ms/step - loss: 4.0505 - acc: 0.2328\n",
            "Epoch 15/30\n",
            "25493/25493 [==============================] - 334s 13ms/step - loss: 3.9073 - acc: 0.2535\n",
            "Epoch 16/30\n",
            "25493/25493 [==============================] - 331s 13ms/step - loss: 3.8116 - acc: 0.2623\n",
            "Epoch 17/30\n",
            "25493/25493 [==============================] - 313s 12ms/step - loss: 3.7092 - acc: 0.2714\n",
            "Epoch 18/30\n",
            "25493/25493 [==============================] - 312s 12ms/step - loss: 3.6470 - acc: 0.2740\n",
            "Epoch 19/30\n",
            "25493/25493 [==============================] - 312s 12ms/step - loss: 3.5812 - acc: 0.2789\n",
            "Epoch 20/30\n",
            "25493/25493 [==============================] - 306s 12ms/step - loss: 3.4969 - acc: 0.2863\n",
            "Epoch 21/30\n",
            "25493/25493 [==============================] - 303s 12ms/step - loss: 3.4424 - acc: 0.2882\n",
            "Epoch 22/30\n",
            "25493/25493 [==============================] - 309s 12ms/step - loss: 3.3733 - acc: 0.2902\n",
            "Epoch 23/30\n",
            "25493/25493 [==============================] - 313s 12ms/step - loss: 3.3212 - acc: 0.2949\n",
            "Epoch 24/30\n",
            "25493/25493 [==============================] - 308s 12ms/step - loss: 3.2514 - acc: 0.3018\n",
            "Epoch 25/30\n",
            "25493/25493 [==============================] - 306s 12ms/step - loss: 3.1997 - acc: 0.3058\n",
            "Epoch 26/30\n",
            "25493/25493 [==============================] - 303s 12ms/step - loss: 3.1295 - acc: 0.3116\n",
            "Epoch 27/30\n",
            "25493/25493 [==============================] - 309s 12ms/step - loss: 3.0862 - acc: 0.3097\n",
            "Epoch 28/30\n",
            "25493/25493 [==============================] - 303s 12ms/step - loss: 3.0173 - acc: 0.3180\n",
            "Epoch 29/30\n",
            "25493/25493 [==============================] - 302s 12ms/step - loss: 2.9850 - acc: 0.3179\n",
            "Epoch 30/30\n",
            "25493/25493 [==============================] - 300s 12ms/step - loss: 2.9192 - acc: 0.3253\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8h4qeo87OUnQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save_weights(\"model_weights.h5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEDuPSCaO9GH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocessing(img_path):\n",
        "    im = image.load_img(img_path, target_size=(224,224,3))\n",
        "    im = image.img_to_array(im)\n",
        "    im = np.expand_dims(im, axis=0)\n",
        "    return im"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mtcu03e1PDP2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_encoding(model, img):\n",
        "    image = preprocessing(img)\n",
        "    pred = model.predict(image).reshape(2048)\n",
        "    return pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpe1esjOPHiP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "6ab8de99-feb5-492d-b301-3e67a44e59ae"
      },
      "source": [
        "from keras.applications.resnet50 import ResNet50\n",
        "resnet = ResNet50(include_top=False,weights='imagenet',input_shape=(224,224,3),pooling='avg')"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4479: The name tf.truncated_normal is deprecated. Please use tf.random.truncated_normal instead.\n",
            "\n",
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94658560/94653016 [==============================] - 2s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FytSRiHiPrPE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img = \"/content/drive/My Drive/Colab Notebooks/MAJOR/flickr8k/Flickr_Data/Flickr_Data/Images/1453366750_6e8cf601bf.jpg\"\n",
        "\n",
        "test_img = get_encoding(resnet, img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LUR-aLdiXs2c",
        "colab_type": "code",
        "outputId": "4e990f72-0195-499e-b0bc-ab761a43acfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E6HGZ-G8PsCN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict_captions(image):\n",
        "    start_word = [\"<start>\"]\n",
        "    while True:\n",
        "        par_caps = [word_2_indices[i] for i in start_word]\n",
        "        par_caps = sequence.pad_sequences([par_caps], maxlen=max_len, padding='post')\n",
        "        preds = model.predict([np.array([image]), np.array(par_caps)])\n",
        "        word_pred = indices_2_word[np.argmax(preds[0])]\n",
        "        start_word.append(word_pred)\n",
        "        \n",
        "        if word_pred == \"<end>\" or len(start_word) > max_len:\n",
        "            break\n",
        "            \n",
        "    return ' '.join(start_word[1:-1])\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IKgPNWEj1J9M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Argmax_Search = predict_captions(test_img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kL3-g_g2YhSn",
        "colab_type": "code",
        "outputId": "0d6a05aa-7962-4040-946f-bc14ecb200be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "z = Image(filename=img)\n",
        "display(z)\n",
        "\n",
        "print(Argmax_Search)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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O1CreJYfJCRYadUstyV/gURcX49Rjqjr9VjtBFyL8/kcBGutfhqAVdO5JuTgI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Q1Yq7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OX65NfbgLcOwOMMK2OOpJ4A3BYueDsPscK96xgIAu7orXhnCklTEyoOjHG19kQ5/ydptlyoTW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\n7VmVebBycSLbzcD9MaZh48N1BTnZDbeW/rfE6zYJuvmDIL//2Q==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Two children are upside upside a a .\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRCyYH0XcF5V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img = \"/content/drive/My Drive/Colab Notebooks/20190622_151928.jpg\"\n",
        "\n",
        "test_img2 = get_encoding(resnet, img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZTBs6XKVBIZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "506471b5-0f17-4547-fdf1-077b55793665"
      },
      "source": [
        "test_captions = list()\n",
        "for i in x_test:\n",
        "    if i in tokens:\n",
        "        test_captions.append(tokens[i])\n",
        "        \n",
        "print(test_captions[:5])"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['The dogs are in the snow in front of a fence .', 'The dogs play on the snow .', 'Two brown dogs playfully fight in the snow .', 'Two brown dogs wrestle in the snow .', 'Two dogs playing in the snow .'], ['a brown and white dog swimming towards some in the pool', 'A dog in a swimming pool swims toward sombody we cannot see .', 'A dog swims in a pool near a person .', 'Small dog is paddling through the water in a pool .', 'The small brown and white dog is in the pool .'], ['A man and a woman in festive costumes dancing .', 'A man and a woman with feathers on her head dance .', 'A man and a woman wearing decorative costumes and dancing in a crowd of onlookers .', 'one performer wearing a feathered headdress dancing with another performer in the streets', 'Two people are dancing with drums on the right and a crowd behind them .'], ['A couple of people sit outdoors at a table with an umbrella and talk .', 'Three people are sitting at an outside picnic bench with an umbrella .', 'Three people sit at an outdoor cafe .', 'Three people sit at an outdoor table in front of a building painted like the Union Jack .', 'Three people sit at a picnic table outside of a building painted like a union jack .'], ['A man is wearing a Sooners red football shirt and helmet .', 'A Oklahoma Sooners football player wearing his jersey number 28 .', 'A Sooners football player weas the number 28 and black armbands .', 'Guy in red and white football uniform', 'The American footballer is wearing a red and white strip .']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ssFjEkh7bnk9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "48e37b8f-1598-49bc-fffe-affb74e33424"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2R4adAQVfkc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "c738eaa3-0e18-44cf-ac99-9410fd42042f"
      },
      "source": [
        "predicted_captions = list()\n",
        "images_path='/content/gdrive/My Drive/Colab Notebooks/MAJOR/flickr8k/Flickr_Data/Flickr_Data/Images/'\n",
        "for i in x_test:\n",
        "    if i is not \"\":\n",
        "        x = images_path + i\n",
        "        test_img = get_encoding(resnet, x)\n",
        "        #print(img_path)\n",
        "        predicted_captions.append(predict_captions(test_img))\n",
        "    \n",
        "predicted_captions[:5]"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Two dogs playing on the the .',\n",
              " 'A man is in a a a the the water .',\n",
              " 'A boy in a a a a a a a a a .',\n",
              " 'A woman in on a a a a .',\n",
              " 'A football player in red red and is a a red red red red red red red red red red red red red red red red red red red red red red red red red red red red red']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k4dqFquJV3pQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from nltk.translate.bleu_score import corpus_bleu, sentence_bleu"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cur5xiOoV7B_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "56935bae-d67a-479b-a712-456711f9def3"
      },
      "source": [
        "# bleu score\n",
        "score1 = corpus_bleu(test_captions, predicted_captions,weights=(1, 0, 0, 0))\n",
        "score2 = sentence_bleu(test_captions, predicted_captions,weights=(1, 0, 0, 0))"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/nltk/translate/bleu_score.py:490: UserWarning: \n",
            "Corpus/Sentence contains 0 counts of 2-gram overlaps.\n",
            "BLEU scores might be undesirable; use SmoothingFunction().\n",
            "  warnings.warn(_msg)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NKotB7GVhuGk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "score1 = corpus_bleu(test_captions, predicted_captions,weights=(1, 0, 0, 0))\n",
        "score2 = corpus_bleu(test_captions, predicted_captions,weights=(0.5,0.5, 0, 0))\n",
        "score3 = corpus_bleu(test_captions, predicted_captions,weights=(0.33, 0.33, 0.33, 0))\n",
        "score4 = corpus_bleu(test_captions, predicted_captions,weights=(0.25, 0.25, 0.25, 0.25))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwSbFewrWA9B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d8ad2cb0-e2dd-4219-a44f-c77d39f80fa6"
      },
      "source": [
        "print(score1, score2,score3,score4)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7246955060898782 0.5749290591505694 0.4522770080500922 0.35782738048841367\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UA2w9A-lk4G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "613ec106-af89-478a-9e39-0eb780f7e23f"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vasXCcO-k7t3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "43d8dc17-04cb-461a-dd63-048cc768d785"
      },
      "source": [
        "#img_name = \"/content/drive/My Drive/Colab Notebooks/MAJOR/flickr8k/Flickr_Data/Flickr_Data/d.jpg\"\n",
        "img_name = \"/content/drive/My Drive/attachments/d.jpg\"\n",
        "test_img = get_encoding(resnet,img_name)\n",
        "caption = predict_captions(test_img)\n",
        "x= plt.imread(img_name)\n",
        "plt.imshow(x)\n",
        "plt.show()\n",
        "print(caption)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Lzs2Qz+fJ54pUq1XCMKTf7xNFAxrNTbKuzjVXX4UbC7ZX11l9+hTD4ZAgCIiiCEXRyWWL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\nZ36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UER2Px2m1WlNb91Ea39HdZTAYBFSkSe6S67oMRjpzC8EC0sIyquzzxBPrSKpIPpdm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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "A skateboarder is grinding a rail a a a skateboard skateboard .\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}