project1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "project1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNmjCYEGhi3sBj/jsVXgh7M",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/athmaja-n/AI-PROJECT/blob/master/project1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EefYGRX-5uEf"
      },
      "source": [
        "%tensorflow_version 1.x \n",
        "import os \n",
        "import shutil\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt \n",
        "import keras\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UX5FPzqm_usP",
        "outputId": "438116da-ccb1-41a8-9bb3-084e87411a10"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 45,
      "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": "cM-1fOilA48i"
      },
      "source": [
        "train_img_gen = ImageDataGenerator(\n",
        "    rescale = 1/255.0,\n",
        "    rotation_range = 60,\n",
        "    width_shift_range = 0.2,\n",
        "    height_shift_range = 0.2,\n",
        "    shear_range = 0.3,\n",
        "    zoom_range = 0.3,\n",
        "    horizontal_flip = True,\n",
        ")\n",
        "val_img_gen = ImageDataGenerator(\n",
        "    rescale = 1/255.0,\n",
        ")"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x4wDo_cAD7cn",
        "outputId": "c8d5cae9-9729-41ec-c488-78966b012dcf"
      },
      "source": [
        "train_gen = train_img_gen.flow_from_directory(\n",
        "    '/content/drive/MyDrive/flower_102/train',\n",
        "    batch_size = 8,\n",
        "    \n",
        "\n",
        ")\n",
        "val_gen = val_img_gen.flow_from_directory(\n",
        "    '/content/drive/MyDrive/flower_102/valid',\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 6577 images belonging to 102 classes.\n",
            "Found 818 images belonging to 102 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKfvmjSSELl7",
        "outputId": "5c4c8d94-d376-4f2a-ca47-39354d791bcd"
      },
      "source": [
        "for (x, y) in train_gen:\n",
        "  print(x.shape, y.shape)\n",
        "  break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8, 256, 256, 3) (8, 102)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0vteZNNBBItb"
      },
      "source": [
        "cat_to_name={'1': 'pink primrose',\n",
        " '10': 'globe thistle',\n",
        " '100': 'blanket flower',\n",
        " '101': 'trumpet creeper',\n",
        " '102': 'blackberry lily',\n",
        " '11': 'snapdragon',\n",
        " '12': \"colt's foot\",\n",
        " '13': 'king protea',\n",
        " '14': 'spear thistle',\n",
        " '15': 'yellow iris',\n",
        " '16': 'globe-flower',\n",
        " '17': 'purple coneflower',\n",
        " '18': 'peruvian lily',\n",
        " '19': 'balloon flower',\n",
        " '2': 'hard-leaved pocket orchid',\n",
        " '20': 'giant white arum lily',\n",
        " '21': 'fire lily',\n",
        " '22': 'pincushion flower',\n",
        " '23': 'fritillary',\n",
        " '24': 'red ginger',\n",
        " '25': 'grape hyacinth',\n",
        " '26': 'corn poppy',\n",
        " '27': 'prince of wales feathers',\n",
        " '28': 'stemless gentian',\n",
        " '29': 'artichoke',\n",
        " '3': 'canterbury bells',\n",
        " '30': 'sweet william',\n",
        " '31': 'carnation',\n",
        " '32': 'garden phlox',\n",
        " '33': 'love in the mist',\n",
        " '34': 'mexican aster',\n",
        " '35': 'alpine sea holly',\n",
        " '36': 'ruby-lipped cattleya',\n",
        " '37': 'cape flower',\n",
        " '38': 'great masterwort',\n",
        " '39': 'siam tulip',\n",
        " '4': 'sweet pea',\n",
        " '40': 'lenten rose',\n",
        " '41': 'barbeton daisy',\n",
        " '42': 'daffodil',\n",
        " '43': 'sword lily',\n",
        " '44': 'poinsettia',\n",
        " '45': 'bolero deep blue',\n",
        " '46': 'wallflower',\n",
        " '47': 'marigold',\n",
        " '48': 'buttercup',\n",
        " '49': 'oxeye daisy',\n",
        " '5': 'english marigold',\n",
        " '50': 'common dandelion',\n",
        " '51': 'petunia',\n",
        " '52': 'wild pansy',\n",
        " '53': 'primula',\n",
        " '54': 'sunflower',\n",
        " '55': 'pelargonium',\n",
        " '56': 'bishop of llandaff',\n",
        " '57': 'gaura',\n",
        " '58': 'geranium',\n",
        " '59': 'orange dahlia',\n",
        " '6': 'tiger lily',\n",
        " '60': 'pink-yellow dahlia',\n",
        " '61': 'cautleya spicata',\n",
        " '62': 'japanese anemone',\n",
        " '63': 'black-eyed susan',\n",
        " '64': 'silverbush',\n",
        " '65': 'californian poppy',\n",
        " '66': 'osteospermum',\n",
        " '67': 'spring crocus',\n",
        " '68': 'bearded iris',\n",
        " '69': 'windflower',\n",
        " '7': 'moon orchid',\n",
        " '70': 'tree poppy',\n",
        " '71': 'gazania',\n",
        " '72': 'azalea',\n",
        " '73': 'water lily',\n",
        " '74': 'rose',\n",
        " '75': 'thorn apple',\n",
        " '76': 'morning glory',\n",
        " '77': 'passion flower',\n",
        " '78': 'lotus lotus',\n",
        " '79': 'toad lily',\n",
        " '8': 'bird of paradise',\n",
        " '80': 'anthurium',\n",
        " '81': 'frangipani',\n",
        " '82': 'clematis',\n",
        " '83': 'hibiscus',\n",
        " '84': 'columbine',\n",
        " '85': 'desert-rose',\n",
        " '86': 'tree mallow',\n",
        " '87': 'magnolia',\n",
        " '88': 'cyclamen',\n",
        " '89': 'watercress',\n",
        " '9': 'monkshood',\n",
        " '90': 'canna lily',\n",
        " '91': 'hippeastrum',\n",
        " '92': 'bee balm',\n",
        " '93': 'ball moss',\n",
        " '94': 'foxglove',\n",
        " '95': 'bougainvillea',\n",
        " '96': 'camellia',\n",
        " '97': 'mallow',\n",
        " '98': 'mexican petunia',\n",
        " '99': 'bromelia'}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDF3KRInEfba"
      },
      "source": [
        "import cv2\n",
        "img=cv2.imread('/content/drive/MyDrive/flower_102/train/100/image_07893.jpg')\n",
        "true_label = cat_to_name[\"100\"]\n",
        "img_det = cv2.resize(img,(256,256))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "vDGvW-n-Eid4",
        "outputId": "6cbcfa2f-9bf8-4c36-b9cf-d6bdc51367f3"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.title(true_label)\n",
        "plt.imshow(img_det)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f47895b1310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2s3Qn5BLEwHI"
      },
      "source": [
        "from keras.models import * \n",
        "from keras.layers import *\n",
        "from keras.optimizers import *"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRAcnY0cBzXP"
      },
      "source": [
        "adam = Adam(lr=0.00003)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LtdswcKXCFxF"
      },
      "source": [
        "from keras.callbacks import Callback,  EarlyStopping\n",
        "es = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0.05, patience=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHuE2iw4CITk"
      },
      "source": [
        "es = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0.05, patience=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1gpHXuWCTJ8"
      },
      "source": [
        "from keras_applications.resnext import ResNeXt50\n",
        "resnext = ResNeXt50(weights='imagenet', input_shape = (256, 256, 3), classes=102, include_top=False, backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SqTpPTbQCfX9"
      },
      "source": [
        "res_avg = GlobalAveragePooling2D()(resnext.output)\n",
        "res_fc_1 = Dense(256, activation='relu')(res_avg)\n",
        "res_fc_2 = Dense(128, activation='relu')(res_fc_1)\n",
        "res_d = Dropout(0.25)(res_fc_2)\n",
        "res_fc_3 = Dense(102, activation='softmax')(res_d)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQe_YUGuCpDj"
      },
      "source": [
        "resnext = Model(resnext.input, outputs  = res_fc_3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T3KFWDfHCxx4",
        "outputId": "315fd743-4105-4e45-f2c5-454853ac0ed5"
      },
      "source": [
        "resnext.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"resnext50\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, 256, 256, 3)  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 262, 262, 3)  0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, 128, 128, 64) 9408        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, 128, 128, 64) 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, 128, 128, 64) 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 130, 130, 64) 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, 64, 64, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, 64, 64, 128)  8192        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, 64, 64, 128)  512         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, 64, 64, 128)  0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_pad (ZeroPadding (None, 66, 66, 128)  0           conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (DepthwiseC (None, 64, 64, 512)  4608        conv2_block1_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_33 (Reshape)            (None, 64, 64, 32, 4 0           conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_reduce (Lambda)  (None, 64, 64, 32, 4 0           reshape_33[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_34 (Reshape)            (None, 64, 64, 128)  0           conv2_block1_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, 64, 64, 128)  512         reshape_34[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, 64, 64, 128)  0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, 64, 64, 256)  16384       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, 64, 64, 256)  32768       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, 64, 64, 256)  1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, 64, 64, 256)  1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, 64, 64, 256)  0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, 64, 64, 256)  0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, 64, 64, 128)  32768       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, 64, 64, 128)  512         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, 64, 64, 128)  0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_pad (ZeroPadding (None, 66, 66, 128)  0           conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (DepthwiseC (None, 64, 64, 512)  4608        conv2_block2_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_35 (Reshape)            (None, 64, 64, 32, 4 0           conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_reduce (Lambda)  (None, 64, 64, 32, 4 0           reshape_35[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_36 (Reshape)            (None, 64, 64, 128)  0           conv2_block2_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, 64, 64, 128)  512         reshape_36[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, 64, 64, 128)  0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, 64, 64, 256)  32768       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, 64, 64, 256)  1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, 64, 64, 256)  0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, 64, 64, 256)  0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, 64, 64, 128)  32768       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, 64, 64, 128)  512         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, 64, 64, 128)  0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_pad (ZeroPadding (None, 66, 66, 128)  0           conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (DepthwiseC (None, 64, 64, 512)  4608        conv2_block3_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_37 (Reshape)            (None, 64, 64, 32, 4 0           conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_reduce (Lambda)  (None, 64, 64, 32, 4 0           reshape_37[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_38 (Reshape)            (None, 64, 64, 128)  0           conv2_block3_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, 64, 64, 128)  512         reshape_38[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, 64, 64, 128)  0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, 64, 64, 256)  32768       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, 64, 64, 256)  1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, 64, 64, 256)  0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, 64, 64, 256)  0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, 64, 64, 256)  65536       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, 64, 64, 256)  1024        conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, 64, 64, 256)  0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_pad (ZeroPadding (None, 66, 66, 256)  0           conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (DepthwiseC (None, 32, 32, 2048) 18432       conv3_block1_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_39 (Reshape)            (None, 32, 32, 32, 8 0           conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_reduce (Lambda)  (None, 32, 32, 32, 8 0           reshape_39[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_40 (Reshape)            (None, 32, 32, 256)  0           conv3_block1_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, 32, 32, 256)  1024        reshape_40[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, 32, 32, 256)  0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, 32, 32, 512)  131072      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, 32, 32, 512)  131072      conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, 32, 32, 512)  2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, 32, 32, 512)  2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, 32, 32, 512)  0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, 32, 32, 512)  0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, 32, 32, 256)  131072      conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, 32, 32, 256)  1024        conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, 32, 32, 256)  0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_pad (ZeroPadding (None, 34, 34, 256)  0           conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (DepthwiseC (None, 32, 32, 2048) 18432       conv3_block2_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_41 (Reshape)            (None, 32, 32, 32, 8 0           conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_reduce (Lambda)  (None, 32, 32, 32, 8 0           reshape_41[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_42 (Reshape)            (None, 32, 32, 256)  0           conv3_block2_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, 32, 32, 256)  1024        reshape_42[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, 32, 32, 256)  0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, 32, 32, 512)  131072      conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, 32, 32, 512)  2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, 32, 32, 512)  0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, 32, 32, 512)  0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, 32, 32, 256)  131072      conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, 32, 32, 256)  1024        conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, 32, 32, 256)  0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_pad (ZeroPadding (None, 34, 34, 256)  0           conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (DepthwiseC (None, 32, 32, 2048) 18432       conv3_block3_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_43 (Reshape)            (None, 32, 32, 32, 8 0           conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_reduce (Lambda)  (None, 32, 32, 32, 8 0           reshape_43[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_44 (Reshape)            (None, 32, 32, 256)  0           conv3_block3_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, 32, 32, 256)  1024        reshape_44[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, 32, 32, 256)  0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, 32, 32, 512)  131072      conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, 32, 32, 512)  2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, 32, 32, 512)  0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, 32, 32, 512)  0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, 32, 32, 256)  131072      conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, 32, 32, 256)  1024        conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, 32, 32, 256)  0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_pad (ZeroPadding (None, 34, 34, 256)  0           conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (DepthwiseC (None, 32, 32, 2048) 18432       conv3_block4_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_45 (Reshape)            (None, 32, 32, 32, 8 0           conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_reduce (Lambda)  (None, 32, 32, 32, 8 0           reshape_45[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_46 (Reshape)            (None, 32, 32, 256)  0           conv3_block4_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, 32, 32, 256)  1024        reshape_46[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, 32, 32, 256)  0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, 32, 32, 512)  131072      conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, 32, 32, 512)  2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, 32, 32, 512)  0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, 32, 32, 512)  0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, 32, 32, 512)  262144      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, 32, 32, 512)  2048        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, 32, 32, 512)  0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_pad (ZeroPadding (None, 34, 34, 512)  0           conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (DepthwiseC (None, 16, 16, 8192) 73728       conv4_block1_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_47 (Reshape)            (None, 16, 16, 32, 1 0           conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_reduce (Lambda)  (None, 16, 16, 32, 1 0           reshape_47[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_48 (Reshape)            (None, 16, 16, 512)  0           conv4_block1_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, 16, 16, 512)  2048        reshape_48[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, 16, 16, 512)  0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, 16, 16, 1024) 524288      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, 16, 16, 1024) 524288      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, 16, 16, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, 16, 16, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, 16, 16, 1024) 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, 16, 16, 1024) 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, 16, 16, 512)  524288      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, 16, 16, 512)  2048        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, 16, 16, 512)  0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_pad (ZeroPadding (None, 18, 18, 512)  0           conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (DepthwiseC (None, 16, 16, 8192) 73728       conv4_block2_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_49 (Reshape)            (None, 16, 16, 32, 1 0           conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_reduce (Lambda)  (None, 16, 16, 32, 1 0           reshape_49[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_50 (Reshape)            (None, 16, 16, 512)  0           conv4_block2_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, 16, 16, 512)  2048        reshape_50[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, 16, 16, 512)  0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, 16, 16, 1024) 524288      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, 16, 16, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, 16, 16, 1024) 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, 16, 16, 1024) 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, 16, 16, 512)  524288      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, 16, 16, 512)  2048        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, 16, 16, 512)  0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_pad (ZeroPadding (None, 18, 18, 512)  0           conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (DepthwiseC (None, 16, 16, 8192) 73728       conv4_block3_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_51 (Reshape)            (None, 16, 16, 32, 1 0           conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_reduce (Lambda)  (None, 16, 16, 32, 1 0           reshape_51[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_52 (Reshape)            (None, 16, 16, 512)  0           conv4_block3_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, 16, 16, 512)  2048        reshape_52[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, 16, 16, 512)  0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, 16, 16, 1024) 524288      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, 16, 16, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, 16, 16, 1024) 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, 16, 16, 1024) 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, 16, 16, 512)  524288      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, 16, 16, 512)  2048        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, 16, 16, 512)  0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_pad (ZeroPadding (None, 18, 18, 512)  0           conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (DepthwiseC (None, 16, 16, 8192) 73728       conv4_block4_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_53 (Reshape)            (None, 16, 16, 32, 1 0           conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_reduce (Lambda)  (None, 16, 16, 32, 1 0           reshape_53[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_54 (Reshape)            (None, 16, 16, 512)  0           conv4_block4_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, 16, 16, 512)  2048        reshape_54[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, 16, 16, 512)  0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, 16, 16, 1024) 524288      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, 16, 16, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, 16, 16, 1024) 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, 16, 16, 1024) 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, 16, 16, 512)  524288      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, 16, 16, 512)  2048        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, 16, 16, 512)  0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_pad (ZeroPadding (None, 18, 18, 512)  0           conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (DepthwiseC (None, 16, 16, 8192) 73728       conv4_block5_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_55 (Reshape)            (None, 16, 16, 32, 1 0           conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_reduce (Lambda)  (None, 16, 16, 32, 1 0           reshape_55[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_56 (Reshape)            (None, 16, 16, 512)  0           conv4_block5_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, 16, 16, 512)  2048        reshape_56[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, 16, 16, 512)  0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, 16, 16, 1024) 524288      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, 16, 16, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, 16, 16, 1024) 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, 16, 16, 1024) 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, 16, 16, 512)  524288      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, 16, 16, 512)  2048        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, 16, 16, 512)  0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_pad (ZeroPadding (None, 18, 18, 512)  0           conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (DepthwiseC (None, 16, 16, 8192) 73728       conv4_block6_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_57 (Reshape)            (None, 16, 16, 32, 1 0           conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_reduce (Lambda)  (None, 16, 16, 32, 1 0           reshape_57[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_58 (Reshape)            (None, 16, 16, 512)  0           conv4_block6_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, 16, 16, 512)  2048        reshape_58[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, 16, 16, 512)  0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, 16, 16, 1024) 524288      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, 16, 16, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, 16, 16, 1024) 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, 16, 16, 1024) 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, 16, 16, 1024) 1048576     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, 16, 16, 1024) 4096        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, 16, 16, 1024) 0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_pad (ZeroPadding (None, 18, 18, 1024) 0           conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (DepthwiseC (None, 8, 8, 32768)  294912      conv5_block1_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_59 (Reshape)            (None, 8, 8, 32, 32, 0           conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_reduce (Lambda)  (None, 8, 8, 32, 32) 0           reshape_59[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_60 (Reshape)            (None, 8, 8, 1024)   0           conv5_block1_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, 8, 8, 1024)   4096        reshape_60[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, 8, 8, 1024)   0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, 8, 8, 2048)   2097152     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, 8, 8, 2048)   2097152     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, 8, 8, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, 8, 8, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, 8, 8, 2048)   0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, 8, 8, 2048)   0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, 8, 8, 1024)   2097152     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, 8, 8, 1024)   4096        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, 8, 8, 1024)   0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_pad (ZeroPadding (None, 10, 10, 1024) 0           conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (DepthwiseC (None, 8, 8, 32768)  294912      conv5_block2_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_61 (Reshape)            (None, 8, 8, 32, 32, 0           conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_reduce (Lambda)  (None, 8, 8, 32, 32) 0           reshape_61[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_62 (Reshape)            (None, 8, 8, 1024)   0           conv5_block2_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, 8, 8, 1024)   4096        reshape_62[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, 8, 8, 1024)   0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, 8, 8, 2048)   2097152     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, 8, 8, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, 8, 8, 2048)   0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, 8, 8, 2048)   0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, 8, 8, 1024)   2097152     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, 8, 8, 1024)   4096        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, 8, 8, 1024)   0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_pad (ZeroPadding (None, 10, 10, 1024) 0           conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (DepthwiseC (None, 8, 8, 32768)  294912      conv5_block3_2_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "reshape_63 (Reshape)            (None, 8, 8, 32, 32, 0           conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_reduce (Lambda)  (None, 8, 8, 32, 32) 0           reshape_63[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "reshape_64 (Reshape)            (None, 8, 8, 1024)   0           conv5_block3_2_reduce[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, 8, 8, 1024)   4096        reshape_64[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, 8, 8, 1024)   0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, 8, 8, 2048)   2097152     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, 8, 8, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, 8, 8, 2048)   0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, 8, 8, 2048)   0           conv5_block3_add[0][0]           \n",
            "==================================================================================================\n",
            "Total params: 23,048,128\n",
            "Trainable params: 22,979,904\n",
            "Non-trainable params: 68,224\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqOdTEinC4ZB"
      },
      "source": [
        "resnext.compile(loss='categorical_crossentropy', optimizer=adam, metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOWEHRo1DCD5"
      },
      "source": [
        "cp_resnext = keras.callbacks.ModelCheckpoint('/content/drive/MyDrive/model/model-{epoch:03d}-{accuracy:03f}-{val_accuracy:03f}.h5', monitor='val_loss', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', period = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "ASOsJ8msDJzt",
        "outputId": "a1e13329-e706-4f6a-cf59-8e6d4cd1c3b4"
      },
      "source": [
        "resnext_hist = resnext.fit_generator(train_gen, steps_per_epoch=6552//8, validation_data=val_gen, validation_steps=818//8, epochs=10, callbacks=[es, cp_resnext])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "  4/819 [..............................] - ETA: 5:53:32 - loss: 4.8486 - accuracy: 0.0000e+00"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-43-2993cfedcd71>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresnext_hist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresnext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6552\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m818\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcp_resnext\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1730\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1731\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1732\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1733\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1734\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    218\u001b[0m                                             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m                                             \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m                                             reset_metrics=False)\n\u001b[0m\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight, reset_metrics)\u001b[0m\n\u001b[1;32m   1512\u001b[0m             \u001b[0mins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1513\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1514\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1516\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreset_metrics\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3474\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3475\u001b[0m     fetched = self._callable_fn(*array_vals,\n\u001b[0;32m-> 3476\u001b[0;31m                                 run_metadata=self.run_metadata)\n\u001b[0m\u001b[1;32m   3477\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3478\u001b[0m     output_structure = nest.pack_sequence_as(\n",
            "\u001b[0;32m/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1470\u001b[0m         ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[1;32m   1471\u001b[0m                                                \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1472\u001b[0;31m                                                run_metadata_ptr)\n\u001b[0m\u001b[1;32m   1473\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1474\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    }
  ]
}