Inception.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Inception.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/utkarsh-crypto/Gender_classification_VGG-16/blob/main/Inception.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TqbdTLtqyY12"
      },
      "outputs": [],
      "source": [
        "#Importing Important Modules\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.layers import Input, Lambda, Dense, Flatten , Dropout, GlobalAveragePooling2D, BatchNormalization\n",
        "from keras.models import Model\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import pandas as pd\n",
        "from tensorflow.python.keras.applications.inception_v3 import InceptionV3\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking whether GPU available\n",
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "metadata": {
        "id": "ICxrI8yyymS8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92d35176-735a-4d08-c567-5ae061463797"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Mounting Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "RFRYR6qpyu9e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e1e68e0-b82d-41e0-a6c5-974c35454fa0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Extracting zipped dataset\n",
        "drive_path = \"/content/drive/MyDrive/cnn/UTKFace.zip\"\n",
        "local_path = '/content'\n",
        "!cp '{drive_path}' .\n",
        "os.chdir(local_path)\n",
        "!unzip -q 'UTKFace.zip'"
      ],
      "metadata": {
        "id": "KptitUR1yxnm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Give dataset path\n",
        "train_path = '/content/UTKFace/train'\n",
        "test_path = '/content/UTKFace/test'"
      ],
      "metadata": {
        "id": "EhopWlHMy8yW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Have a look at how the dataset looks\n",
        "from PIL import Image \n",
        "import os \n",
        "from IPython.display import display\n",
        "from IPython.display import Image as _Imgdis\n",
        "# creating a object  \n",
        "\n",
        "  \n",
        "folder = train_path+'/male'\n",
        "\n",
        "\n",
        "onlymalefiles = [f for f in os.listdir(folder) if os.path.isfile(os.path.join(folder, f))]\n",
        "print(\"Working with {0} images\".format(len(onlymalefiles)))\n",
        "print(\"Image examples: \")\n",
        "\n",
        "\n",
        "for i in range(10):\n",
        "    print(onlymalefiles[i])\n",
        "    display(_Imgdis(filename=folder + \"/\" + onlymalefiles[i], width=299, height=299))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "C8BzKTauzDCb",
        "outputId": "d011fbd0-9238-4ea5-8074-f6f42ac617cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Working with 9205 images\n",
            "Image examples: \n",
            "25_0_0_20170117203843296.jpg.chip.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 299,
              "height": 299
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18_0_1_20170117140107728.jpg.chip.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 299,
              "height": 299
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "90_0_1_20170113184758168.jpg.chip.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 299,
              "height": 299
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "43_0_0_20170104210240958.jpg.chip.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 299,
              "height": 299
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "78_0_1_20170117193425610.jpg.chip.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 299,
              "height": 299
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "73_0_3_20170119212043986.jpg.chip.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 299,
              "height": 299
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34_0_0_20170117182718307.jpg.chip.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 299,
              "height": 299
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26_0_1_20170116235022816.jpg.chip.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 299,
              "height": 299
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "32_0_0_20170117134727342.jpg.chip.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 299,
              "height": 299
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "45_0_3_20170119204725919.jpg.chip.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 299,
              "height": 299
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pre-Processing\n",
        "data_generator = ImageDataGenerator(preprocessing_function=tf.keras.applications.inception_v3.preprocess_input)\n",
        "\n",
        "\n",
        "train_generator = data_generator.flow_from_directory(\n",
        "        '/content/UTKFace/train',\n",
        "        target_size=(299, 299),\n",
        "        batch_size=12,\n",
        "        class_mode='categorical')\n",
        "\n",
        "\n",
        "validation_generator = data_generator.flow_from_directory(\n",
        "        '/content/UTKFace/val',\n",
        "        target_size=(299, 299),\n",
        "        batch_size=12,\n",
        "        class_mode='categorical')"
      ],
      "metadata": {
        "id": "1nAS5irdzLl3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c83ff568-ca11-41fd-f09d-d37b0b8689df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 17287 images belonging to 2 classes.\n",
            "Found 4616 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_classes=2"
      ],
      "metadata": {
        "id": "Ytn54_nPPvRV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "incept = tf.keras.applications.InceptionV3(\n",
        "    include_top=True,\n",
        "    weights=\"imagenet\",\n",
        "    pooling=None,\n",
        ")\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "for layer in incept.layers:\n",
        " layer.trainable = False\n",
        "\n",
        "#Add a layer where input is the output of the  second last layer \n",
        "x = Dense(2, activation='softmax', name='predictions')(incept.layers[-2].output)\n",
        "\n",
        "#Then create the corresponding model \n",
        "my_model = Model(inputs = incept.input,outputs = x)\n",
        "my_model.summary()"
      ],
      "metadata": {
        "id": "O5wOCMK9QxgV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2812d7b-09f8-4944-ff44-018e38607085"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5\n",
            "96116736/96112376 [==============================] - 1s 0us/step\n",
            "96124928/96112376 [==============================] - 1s 0us/step\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 299, 299, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " conv2d (Conv2D)                (None, 149, 149, 32  864         ['input_1[0][0]']                \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization (BatchNorm  (None, 149, 149, 32  96         ['conv2d[0][0]']                 \n",
            " alization)                     )                                                                 \n",
            "                                                                                                  \n",
            " activation (Activation)        (None, 149, 149, 32  0           ['batch_normalization[0][0]']    \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_1 (Conv2D)              (None, 147, 147, 32  9216        ['activation[0][0]']             \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_1 (BatchNo  (None, 147, 147, 32  96         ['conv2d_1[0][0]']               \n",
            " rmalization)                   )                                                                 \n",
            "                                                                                                  \n",
            " activation_1 (Activation)      (None, 147, 147, 32  0           ['batch_normalization_1[0][0]']  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " conv2d_2 (Conv2D)              (None, 147, 147, 64  18432       ['activation_1[0][0]']           \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " batch_normalization_2 (BatchNo  (None, 147, 147, 64  192        ['conv2d_2[0][0]']               \n",
            " rmalization)                   )                                                                 \n",
            "                                                                                                  \n",
            " activation_2 (Activation)      (None, 147, 147, 64  0           ['batch_normalization_2[0][0]']  \n",
            "                                )                                                                 \n",
            "                                                                                                  \n",
            " max_pooling2d (MaxPooling2D)   (None, 73, 73, 64)   0           ['activation_2[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_3 (Conv2D)              (None, 73, 73, 80)   5120        ['max_pooling2d[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_3 (BatchNo  (None, 73, 73, 80)  240         ['conv2d_3[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_3 (Activation)      (None, 73, 73, 80)   0           ['batch_normalization_3[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_4 (Conv2D)              (None, 71, 71, 192)  138240      ['activation_3[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_4 (BatchNo  (None, 71, 71, 192)  576        ['conv2d_4[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_4 (Activation)      (None, 71, 71, 192)  0           ['batch_normalization_4[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling2d_1 (MaxPooling2D)  (None, 35, 35, 192)  0          ['activation_4[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_8 (Conv2D)              (None, 35, 35, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
            "                                                                                                  \n",
            " batch_normalization_8 (BatchNo  (None, 35, 35, 64)  192         ['conv2d_8[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_8 (Activation)      (None, 35, 35, 64)   0           ['batch_normalization_8[0][0]']  \n",
            "                                                                                                  \n",
            " conv2d_6 (Conv2D)              (None, 35, 35, 48)   9216        ['max_pooling2d_1[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_9 (Conv2D)              (None, 35, 35, 96)   55296       ['activation_8[0][0]']           \n",
            "                                                                                                  \n",
            " batch_normalization_6 (BatchNo  (None, 35, 35, 48)  144         ['conv2d_6[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_9 (BatchNo  (None, 35, 35, 96)  288         ['conv2d_9[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " activation_6 (Activation)      (None, 35, 35, 48)   0           ['batch_normalization_6[0][0]']  \n",
            "                                                                                                  \n",
            " activation_9 (Activation)      (None, 35, 35, 96)   0           ['batch_normalization_9[0][0]']  \n",
            "                                                                                                  \n",
            " average_pooling2d (AveragePool  (None, 35, 35, 192)  0          ['max_pooling2d_1[0][0]']        \n",
            " ing2D)                                                                                           \n",
            "                                                                                                  \n",
            " conv2d_5 (Conv2D)              (None, 35, 35, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_7 (Conv2D)              (None, 35, 35, 64)   76800       ['activation_6[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_10 (Conv2D)             (None, 35, 35, 96)   82944       ['activation_9[0][0]']           \n",
            "                                                                                                  \n",
            " conv2d_11 (Conv2D)             (None, 35, 35, 32)   6144        ['average_pooling2d[0][0]']      \n",
            "                                                                                                  \n",
            " batch_normalization_5 (BatchNo  (None, 35, 35, 64)  192         ['conv2d_5[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_7 (BatchNo  (None, 35, 35, 64)  192         ['conv2d_7[0][0]']               \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " batch_normalization_10 (BatchN  (None, 35, 35, 96)  288         ['conv2d_10[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_11 (BatchN  (None, 35, 35, 32)  96          ['conv2d_11[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_5 (Activation)      (None, 35, 35, 64)   0           ['batch_normalization_5[0][0]']  \n",
            "                                                                                                  \n",
            " activation_7 (Activation)      (None, 35, 35, 64)   0           ['batch_normalization_7[0][0]']  \n",
            "                                                                                                  \n",
            " activation_10 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_10[0][0]'] \n",
            "                                                                                                  \n",
            " activation_11 (Activation)     (None, 35, 35, 32)   0           ['batch_normalization_11[0][0]'] \n",
            "                                                                                                  \n",
            " mixed0 (Concatenate)           (None, 35, 35, 256)  0           ['activation_5[0][0]',           \n",
            "                                                                  'activation_7[0][0]',           \n",
            "                                                                  'activation_10[0][0]',          \n",
            "                                                                  'activation_11[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_15 (Conv2D)             (None, 35, 35, 64)   16384       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_15 (BatchN  (None, 35, 35, 64)  192         ['conv2d_15[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_15 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_15[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_13 (Conv2D)             (None, 35, 35, 48)   12288       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_16 (Conv2D)             (None, 35, 35, 96)   55296       ['activation_15[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_13 (BatchN  (None, 35, 35, 48)  144         ['conv2d_13[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_16 (BatchN  (None, 35, 35, 96)  288         ['conv2d_16[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_13 (Activation)     (None, 35, 35, 48)   0           ['batch_normalization_13[0][0]'] \n",
            "                                                                                                  \n",
            " activation_16 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_16[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_1 (AveragePo  (None, 35, 35, 256)  0          ['mixed0[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_12 (Conv2D)             (None, 35, 35, 64)   16384       ['mixed0[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_14 (Conv2D)             (None, 35, 35, 64)   76800       ['activation_13[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_17 (Conv2D)             (None, 35, 35, 96)   82944       ['activation_16[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_18 (Conv2D)             (None, 35, 35, 64)   16384       ['average_pooling2d_1[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_12 (BatchN  (None, 35, 35, 64)  192         ['conv2d_12[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_14 (BatchN  (None, 35, 35, 64)  192         ['conv2d_14[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_17 (BatchN  (None, 35, 35, 96)  288         ['conv2d_17[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_18 (BatchN  (None, 35, 35, 64)  192         ['conv2d_18[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_12 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_12[0][0]'] \n",
            "                                                                                                  \n",
            " activation_14 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_14[0][0]'] \n",
            "                                                                                                  \n",
            " activation_17 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_17[0][0]'] \n",
            "                                                                                                  \n",
            " activation_18 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_18[0][0]'] \n",
            "                                                                                                  \n",
            " mixed1 (Concatenate)           (None, 35, 35, 288)  0           ['activation_12[0][0]',          \n",
            "                                                                  'activation_14[0][0]',          \n",
            "                                                                  'activation_17[0][0]',          \n",
            "                                                                  'activation_18[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_22 (Conv2D)             (None, 35, 35, 64)   18432       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_22 (BatchN  (None, 35, 35, 64)  192         ['conv2d_22[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_22 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_22[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_20 (Conv2D)             (None, 35, 35, 48)   13824       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_23 (Conv2D)             (None, 35, 35, 96)   55296       ['activation_22[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_20 (BatchN  (None, 35, 35, 48)  144         ['conv2d_20[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_23 (BatchN  (None, 35, 35, 96)  288         ['conv2d_23[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_20 (Activation)     (None, 35, 35, 48)   0           ['batch_normalization_20[0][0]'] \n",
            "                                                                                                  \n",
            " activation_23 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_23[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_2 (AveragePo  (None, 35, 35, 288)  0          ['mixed1[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_19 (Conv2D)             (None, 35, 35, 64)   18432       ['mixed1[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_21 (Conv2D)             (None, 35, 35, 64)   76800       ['activation_20[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_24 (Conv2D)             (None, 35, 35, 96)   82944       ['activation_23[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_25 (Conv2D)             (None, 35, 35, 64)   18432       ['average_pooling2d_2[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_19 (BatchN  (None, 35, 35, 64)  192         ['conv2d_19[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_21 (BatchN  (None, 35, 35, 64)  192         ['conv2d_21[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_24 (BatchN  (None, 35, 35, 96)  288         ['conv2d_24[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_25 (BatchN  (None, 35, 35, 64)  192         ['conv2d_25[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_19 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_19[0][0]'] \n",
            "                                                                                                  \n",
            " activation_21 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_21[0][0]'] \n",
            "                                                                                                  \n",
            " activation_24 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_24[0][0]'] \n",
            "                                                                                                  \n",
            " activation_25 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_25[0][0]'] \n",
            "                                                                                                  \n",
            " mixed2 (Concatenate)           (None, 35, 35, 288)  0           ['activation_19[0][0]',          \n",
            "                                                                  'activation_21[0][0]',          \n",
            "                                                                  'activation_24[0][0]',          \n",
            "                                                                  'activation_25[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_27 (Conv2D)             (None, 35, 35, 64)   18432       ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_27 (BatchN  (None, 35, 35, 64)  192         ['conv2d_27[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_27 (Activation)     (None, 35, 35, 64)   0           ['batch_normalization_27[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_28 (Conv2D)             (None, 35, 35, 96)   55296       ['activation_27[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_28 (BatchN  (None, 35, 35, 96)  288         ['conv2d_28[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_28 (Activation)     (None, 35, 35, 96)   0           ['batch_normalization_28[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_26 (Conv2D)             (None, 17, 17, 384)  995328      ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_29 (Conv2D)             (None, 17, 17, 96)   82944       ['activation_28[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_26 (BatchN  (None, 17, 17, 384)  1152       ['conv2d_26[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_29 (BatchN  (None, 17, 17, 96)  288         ['conv2d_29[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_26 (Activation)     (None, 17, 17, 384)  0           ['batch_normalization_26[0][0]'] \n",
            "                                                                                                  \n",
            " activation_29 (Activation)     (None, 17, 17, 96)   0           ['batch_normalization_29[0][0]'] \n",
            "                                                                                                  \n",
            " max_pooling2d_2 (MaxPooling2D)  (None, 17, 17, 288)  0          ['mixed2[0][0]']                 \n",
            "                                                                                                  \n",
            " mixed3 (Concatenate)           (None, 17, 17, 768)  0           ['activation_26[0][0]',          \n",
            "                                                                  'activation_29[0][0]',          \n",
            "                                                                  'max_pooling2d_2[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_34 (Conv2D)             (None, 17, 17, 128)  98304       ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_34 (BatchN  (None, 17, 17, 128)  384        ['conv2d_34[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_34 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_34[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_35 (Conv2D)             (None, 17, 17, 128)  114688      ['activation_34[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_35 (BatchN  (None, 17, 17, 128)  384        ['conv2d_35[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_35 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_35[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_31 (Conv2D)             (None, 17, 17, 128)  98304       ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_36 (Conv2D)             (None, 17, 17, 128)  114688      ['activation_35[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_31 (BatchN  (None, 17, 17, 128)  384        ['conv2d_31[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_36 (BatchN  (None, 17, 17, 128)  384        ['conv2d_36[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_31 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_31[0][0]'] \n",
            "                                                                                                  \n",
            " activation_36 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_36[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_32 (Conv2D)             (None, 17, 17, 128)  114688      ['activation_31[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_37 (Conv2D)             (None, 17, 17, 128)  114688      ['activation_36[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_32 (BatchN  (None, 17, 17, 128)  384        ['conv2d_32[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_37 (BatchN  (None, 17, 17, 128)  384        ['conv2d_37[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_32 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_32[0][0]'] \n",
            "                                                                                                  \n",
            " activation_37 (Activation)     (None, 17, 17, 128)  0           ['batch_normalization_37[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_3 (AveragePo  (None, 17, 17, 768)  0          ['mixed3[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_30 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed3[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_33 (Conv2D)             (None, 17, 17, 192)  172032      ['activation_32[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_38 (Conv2D)             (None, 17, 17, 192)  172032      ['activation_37[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_39 (Conv2D)             (None, 17, 17, 192)  147456      ['average_pooling2d_3[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_30 (BatchN  (None, 17, 17, 192)  576        ['conv2d_30[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_33 (BatchN  (None, 17, 17, 192)  576        ['conv2d_33[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_38 (BatchN  (None, 17, 17, 192)  576        ['conv2d_38[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_39 (BatchN  (None, 17, 17, 192)  576        ['conv2d_39[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_30 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_30[0][0]'] \n",
            "                                                                                                  \n",
            " activation_33 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_33[0][0]'] \n",
            "                                                                                                  \n",
            " activation_38 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_38[0][0]'] \n",
            "                                                                                                  \n",
            " activation_39 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_39[0][0]'] \n",
            "                                                                                                  \n",
            " mixed4 (Concatenate)           (None, 17, 17, 768)  0           ['activation_30[0][0]',          \n",
            "                                                                  'activation_33[0][0]',          \n",
            "                                                                  'activation_38[0][0]',          \n",
            "                                                                  'activation_39[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_44 (Conv2D)             (None, 17, 17, 160)  122880      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_44 (BatchN  (None, 17, 17, 160)  480        ['conv2d_44[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_44 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_44[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_45 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_44[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_45 (BatchN  (None, 17, 17, 160)  480        ['conv2d_45[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_45 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_45[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_41 (Conv2D)             (None, 17, 17, 160)  122880      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_46 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_45[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_41 (BatchN  (None, 17, 17, 160)  480        ['conv2d_41[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_46 (BatchN  (None, 17, 17, 160)  480        ['conv2d_46[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_41 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_41[0][0]'] \n",
            "                                                                                                  \n",
            " activation_46 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_46[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_42 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_41[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_47 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_46[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_42 (BatchN  (None, 17, 17, 160)  480        ['conv2d_42[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_47 (BatchN  (None, 17, 17, 160)  480        ['conv2d_47[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_42 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_42[0][0]'] \n",
            "                                                                                                  \n",
            " activation_47 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_47[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_4 (AveragePo  (None, 17, 17, 768)  0          ['mixed4[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_40 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed4[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_43 (Conv2D)             (None, 17, 17, 192)  215040      ['activation_42[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_48 (Conv2D)             (None, 17, 17, 192)  215040      ['activation_47[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_49 (Conv2D)             (None, 17, 17, 192)  147456      ['average_pooling2d_4[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_40 (BatchN  (None, 17, 17, 192)  576        ['conv2d_40[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_43 (BatchN  (None, 17, 17, 192)  576        ['conv2d_43[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_48 (BatchN  (None, 17, 17, 192)  576        ['conv2d_48[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_49 (BatchN  (None, 17, 17, 192)  576        ['conv2d_49[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_40 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_40[0][0]'] \n",
            "                                                                                                  \n",
            " activation_43 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_43[0][0]'] \n",
            "                                                                                                  \n",
            " activation_48 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_48[0][0]'] \n",
            "                                                                                                  \n",
            " activation_49 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_49[0][0]'] \n",
            "                                                                                                  \n",
            " mixed5 (Concatenate)           (None, 17, 17, 768)  0           ['activation_40[0][0]',          \n",
            "                                                                  'activation_43[0][0]',          \n",
            "                                                                  'activation_48[0][0]',          \n",
            "                                                                  'activation_49[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_54 (Conv2D)             (None, 17, 17, 160)  122880      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_54 (BatchN  (None, 17, 17, 160)  480        ['conv2d_54[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_54 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_54[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_55 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_54[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_55 (BatchN  (None, 17, 17, 160)  480        ['conv2d_55[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_55 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_55[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_51 (Conv2D)             (None, 17, 17, 160)  122880      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_56 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_55[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_51 (BatchN  (None, 17, 17, 160)  480        ['conv2d_51[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_56 (BatchN  (None, 17, 17, 160)  480        ['conv2d_56[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_51 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_51[0][0]'] \n",
            "                                                                                                  \n",
            " activation_56 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_56[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_52 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_51[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_57 (Conv2D)             (None, 17, 17, 160)  179200      ['activation_56[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_52 (BatchN  (None, 17, 17, 160)  480        ['conv2d_52[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_57 (BatchN  (None, 17, 17, 160)  480        ['conv2d_57[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_52 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_52[0][0]'] \n",
            "                                                                                                  \n",
            " activation_57 (Activation)     (None, 17, 17, 160)  0           ['batch_normalization_57[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_5 (AveragePo  (None, 17, 17, 768)  0          ['mixed5[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_50 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed5[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_53 (Conv2D)             (None, 17, 17, 192)  215040      ['activation_52[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_58 (Conv2D)             (None, 17, 17, 192)  215040      ['activation_57[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_59 (Conv2D)             (None, 17, 17, 192)  147456      ['average_pooling2d_5[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_50 (BatchN  (None, 17, 17, 192)  576        ['conv2d_50[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_53 (BatchN  (None, 17, 17, 192)  576        ['conv2d_53[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_58 (BatchN  (None, 17, 17, 192)  576        ['conv2d_58[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_59 (BatchN  (None, 17, 17, 192)  576        ['conv2d_59[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_50 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_50[0][0]'] \n",
            "                                                                                                  \n",
            " activation_53 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_53[0][0]'] \n",
            "                                                                                                  \n",
            " activation_58 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_58[0][0]'] \n",
            "                                                                                                  \n",
            " activation_59 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_59[0][0]'] \n",
            "                                                                                                  \n",
            " mixed6 (Concatenate)           (None, 17, 17, 768)  0           ['activation_50[0][0]',          \n",
            "                                                                  'activation_53[0][0]',          \n",
            "                                                                  'activation_58[0][0]',          \n",
            "                                                                  'activation_59[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_64 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_64 (BatchN  (None, 17, 17, 192)  576        ['conv2d_64[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_64 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_64[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_65 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_64[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_65 (BatchN  (None, 17, 17, 192)  576        ['conv2d_65[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_65 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_65[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_61 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_66 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_65[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_61 (BatchN  (None, 17, 17, 192)  576        ['conv2d_61[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_66 (BatchN  (None, 17, 17, 192)  576        ['conv2d_66[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_61 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_61[0][0]'] \n",
            "                                                                                                  \n",
            " activation_66 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_66[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_62 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_61[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_67 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_66[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_62 (BatchN  (None, 17, 17, 192)  576        ['conv2d_62[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_67 (BatchN  (None, 17, 17, 192)  576        ['conv2d_67[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_62 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_62[0][0]'] \n",
            "                                                                                                  \n",
            " activation_67 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_67[0][0]'] \n",
            "                                                                                                  \n",
            " average_pooling2d_6 (AveragePo  (None, 17, 17, 768)  0          ['mixed6[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_60 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed6[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_63 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_62[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_68 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_67[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_69 (Conv2D)             (None, 17, 17, 192)  147456      ['average_pooling2d_6[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_60 (BatchN  (None, 17, 17, 192)  576        ['conv2d_60[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_63 (BatchN  (None, 17, 17, 192)  576        ['conv2d_63[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_68 (BatchN  (None, 17, 17, 192)  576        ['conv2d_68[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_69 (BatchN  (None, 17, 17, 192)  576        ['conv2d_69[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_60 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_60[0][0]'] \n",
            "                                                                                                  \n",
            " activation_63 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_63[0][0]'] \n",
            "                                                                                                  \n",
            " activation_68 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_68[0][0]'] \n",
            "                                                                                                  \n",
            " activation_69 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_69[0][0]'] \n",
            "                                                                                                  \n",
            " mixed7 (Concatenate)           (None, 17, 17, 768)  0           ['activation_60[0][0]',          \n",
            "                                                                  'activation_63[0][0]',          \n",
            "                                                                  'activation_68[0][0]',          \n",
            "                                                                  'activation_69[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_72 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_72 (BatchN  (None, 17, 17, 192)  576        ['conv2d_72[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_72 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_72[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_73 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_72[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_73 (BatchN  (None, 17, 17, 192)  576        ['conv2d_73[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_73 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_73[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_70 (Conv2D)             (None, 17, 17, 192)  147456      ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_74 (Conv2D)             (None, 17, 17, 192)  258048      ['activation_73[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_70 (BatchN  (None, 17, 17, 192)  576        ['conv2d_70[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_74 (BatchN  (None, 17, 17, 192)  576        ['conv2d_74[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_70 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_70[0][0]'] \n",
            "                                                                                                  \n",
            " activation_74 (Activation)     (None, 17, 17, 192)  0           ['batch_normalization_74[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_71 (Conv2D)             (None, 8, 8, 320)    552960      ['activation_70[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_75 (Conv2D)             (None, 8, 8, 192)    331776      ['activation_74[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_71 (BatchN  (None, 8, 8, 320)   960         ['conv2d_71[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_75 (BatchN  (None, 8, 8, 192)   576         ['conv2d_75[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_71 (Activation)     (None, 8, 8, 320)    0           ['batch_normalization_71[0][0]'] \n",
            "                                                                                                  \n",
            " activation_75 (Activation)     (None, 8, 8, 192)    0           ['batch_normalization_75[0][0]'] \n",
            "                                                                                                  \n",
            " max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 768)   0           ['mixed7[0][0]']                 \n",
            "                                                                                                  \n",
            " mixed8 (Concatenate)           (None, 8, 8, 1280)   0           ['activation_71[0][0]',          \n",
            "                                                                  'activation_75[0][0]',          \n",
            "                                                                  'max_pooling2d_3[0][0]']        \n",
            "                                                                                                  \n",
            " conv2d_80 (Conv2D)             (None, 8, 8, 448)    573440      ['mixed8[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_80 (BatchN  (None, 8, 8, 448)   1344        ['conv2d_80[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_80 (Activation)     (None, 8, 8, 448)    0           ['batch_normalization_80[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_77 (Conv2D)             (None, 8, 8, 384)    491520      ['mixed8[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_81 (Conv2D)             (None, 8, 8, 384)    1548288     ['activation_80[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_77 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_77[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_81 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_81[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_77 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_77[0][0]'] \n",
            "                                                                                                  \n",
            " activation_81 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_81[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_78 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_77[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_79 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_77[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_82 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_81[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_83 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_81[0][0]']          \n",
            "                                                                                                  \n",
            " average_pooling2d_7 (AveragePo  (None, 8, 8, 1280)  0           ['mixed8[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_76 (Conv2D)             (None, 8, 8, 320)    409600      ['mixed8[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_78 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_78[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_79 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_79[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_82 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_82[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_83 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_83[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_84 (Conv2D)             (None, 8, 8, 192)    245760      ['average_pooling2d_7[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_76 (BatchN  (None, 8, 8, 320)   960         ['conv2d_76[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_78 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_78[0][0]'] \n",
            "                                                                                                  \n",
            " activation_79 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_79[0][0]'] \n",
            "                                                                                                  \n",
            " activation_82 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_82[0][0]'] \n",
            "                                                                                                  \n",
            " activation_83 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_83[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_84 (BatchN  (None, 8, 8, 192)   576         ['conv2d_84[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_76 (Activation)     (None, 8, 8, 320)    0           ['batch_normalization_76[0][0]'] \n",
            "                                                                                                  \n",
            " mixed9_0 (Concatenate)         (None, 8, 8, 768)    0           ['activation_78[0][0]',          \n",
            "                                                                  'activation_79[0][0]']          \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 8, 8, 768)    0           ['activation_82[0][0]',          \n",
            "                                                                  'activation_83[0][0]']          \n",
            "                                                                                                  \n",
            " activation_84 (Activation)     (None, 8, 8, 192)    0           ['batch_normalization_84[0][0]'] \n",
            "                                                                                                  \n",
            " mixed9 (Concatenate)           (None, 8, 8, 2048)   0           ['activation_76[0][0]',          \n",
            "                                                                  'mixed9_0[0][0]',               \n",
            "                                                                  'concatenate[0][0]',            \n",
            "                                                                  'activation_84[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_89 (Conv2D)             (None, 8, 8, 448)    917504      ['mixed9[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_89 (BatchN  (None, 8, 8, 448)   1344        ['conv2d_89[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_89 (Activation)     (None, 8, 8, 448)    0           ['batch_normalization_89[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_86 (Conv2D)             (None, 8, 8, 384)    786432      ['mixed9[0][0]']                 \n",
            "                                                                                                  \n",
            " conv2d_90 (Conv2D)             (None, 8, 8, 384)    1548288     ['activation_89[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_86 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_86[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_90 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_90[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_86 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_86[0][0]'] \n",
            "                                                                                                  \n",
            " activation_90 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_90[0][0]'] \n",
            "                                                                                                  \n",
            " conv2d_87 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_86[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_88 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_86[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_91 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_90[0][0]']          \n",
            "                                                                                                  \n",
            " conv2d_92 (Conv2D)             (None, 8, 8, 384)    442368      ['activation_90[0][0]']          \n",
            "                                                                                                  \n",
            " average_pooling2d_8 (AveragePo  (None, 8, 8, 2048)  0           ['mixed9[0][0]']                 \n",
            " oling2D)                                                                                         \n",
            "                                                                                                  \n",
            " conv2d_85 (Conv2D)             (None, 8, 8, 320)    655360      ['mixed9[0][0]']                 \n",
            "                                                                                                  \n",
            " batch_normalization_87 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_87[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_88 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_88[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_91 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_91[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " batch_normalization_92 (BatchN  (None, 8, 8, 384)   1152        ['conv2d_92[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " conv2d_93 (Conv2D)             (None, 8, 8, 192)    393216      ['average_pooling2d_8[0][0]']    \n",
            "                                                                                                  \n",
            " batch_normalization_85 (BatchN  (None, 8, 8, 320)   960         ['conv2d_85[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_87 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_87[0][0]'] \n",
            "                                                                                                  \n",
            " activation_88 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_88[0][0]'] \n",
            "                                                                                                  \n",
            " activation_91 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_91[0][0]'] \n",
            "                                                                                                  \n",
            " activation_92 (Activation)     (None, 8, 8, 384)    0           ['batch_normalization_92[0][0]'] \n",
            "                                                                                                  \n",
            " batch_normalization_93 (BatchN  (None, 8, 8, 192)   576         ['conv2d_93[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " activation_85 (Activation)     (None, 8, 8, 320)    0           ['batch_normalization_85[0][0]'] \n",
            "                                                                                                  \n",
            " mixed9_1 (Concatenate)         (None, 8, 8, 768)    0           ['activation_87[0][0]',          \n",
            "                                                                  'activation_88[0][0]']          \n",
            "                                                                                                  \n",
            " concatenate_1 (Concatenate)    (None, 8, 8, 768)    0           ['activation_91[0][0]',          \n",
            "                                                                  'activation_92[0][0]']          \n",
            "                                                                                                  \n",
            " activation_93 (Activation)     (None, 8, 8, 192)    0           ['batch_normalization_93[0][0]'] \n",
            "                                                                                                  \n",
            " mixed10 (Concatenate)          (None, 8, 8, 2048)   0           ['activation_85[0][0]',          \n",
            "                                                                  'mixed9_1[0][0]',               \n",
            "                                                                  'concatenate_1[0][0]',          \n",
            "                                                                  'activation_93[0][0]']          \n",
            "                                                                                                  \n",
            " avg_pool (GlobalAveragePooling  (None, 2048)        0           ['mixed10[0][0]']                \n",
            " 2D)                                                                                              \n",
            "                                                                                                  \n",
            " predictions (Dense)            (None, 2)            4098        ['avg_pool[0][0]']               \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 21,806,882\n",
            "Trainable params: 4,098\n",
            "Non-trainable params: 21,802,784\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#from keras.optimizers import Adam\n",
        "\n",
        "my_model.compile(optimizer = 'RMSprop',\n",
        "              loss = 'binary_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "6iBSX4OpSrA1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# use early stopping to optimally terminate training through callbacks\n",
        "from tensorflow.python.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "es=EarlyStopping(monitor='val_accuracy', mode='auto', verbose=1, patience=3)\n",
        "\n",
        "# save best model automatically\n",
        "import h5py\n",
        "mc= ModelCheckpoint('/content/optimal.h5', monitor='val_accuracy', mode='auto', verbose=1, save_best_only=True)\n",
        "cb_list=[es,mc]"
      ],
      "metadata": {
        "id": "vR4A5qcmUkEj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_history=my_model.fit(\n",
        "        train_generator,\n",
        "        epochs=10,\n",
        "        validation_data=validation_generator,\n",
        "        callbacks = cb_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SY7AV1r1UvP3",
        "outputId": "f86f6be3-07c6-478c-ffed-ff9b53ed641e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1441/1441 [==============================] - ETA: 0s - loss: 0.3409 - accuracy: 0.8511\n",
            "Epoch 00001: val_accuracy improved from -inf to 0.83492, saving model to /content/optimal.h5\n",
            "1441/1441 [==============================] - 270s 177ms/step - loss: 0.3409 - accuracy: 0.8511 - val_loss: 0.3553 - val_accuracy: 0.8349\n",
            "Epoch 2/10\n",
            "1441/1441 [==============================] - ETA: 0s - loss: 0.3049 - accuracy: 0.8710\n",
            "Epoch 00002: val_accuracy improved from 0.83492 to 0.84315, saving model to /content/optimal.h5\n",
            "1441/1441 [==============================] - 251s 174ms/step - loss: 0.3049 - accuracy: 0.8710 - val_loss: 0.3432 - val_accuracy: 0.8432\n",
            "Epoch 3/10\n",
            "1441/1441 [==============================] - ETA: 0s - loss: 0.2925 - accuracy: 0.8786\n",
            "Epoch 00003: val_accuracy improved from 0.84315 to 0.84944, saving model to /content/optimal.h5\n",
            "1441/1441 [==============================] - 251s 174ms/step - loss: 0.2925 - accuracy: 0.8786 - val_loss: 0.3372 - val_accuracy: 0.8494\n",
            "Epoch 4/10\n",
            "1441/1441 [==============================] - ETA: 0s - loss: 0.2843 - accuracy: 0.8828\n",
            "Epoch 00004: val_accuracy did not improve from 0.84944\n",
            "1441/1441 [==============================] - 250s 174ms/step - loss: 0.2843 - accuracy: 0.8828 - val_loss: 0.3465 - val_accuracy: 0.8445\n",
            "Epoch 5/10\n",
            "1441/1441 [==============================] - ETA: 0s - loss: 0.2792 - accuracy: 0.8842\n",
            "Epoch 00005: val_accuracy did not improve from 0.84944\n",
            "1441/1441 [==============================] - 250s 174ms/step - loss: 0.2792 - accuracy: 0.8842 - val_loss: 0.3310 - val_accuracy: 0.8486\n",
            "Epoch 6/10\n",
            "1441/1441 [==============================] - ETA: 0s - loss: 0.2737 - accuracy: 0.8881\n",
            "Epoch 00006: val_accuracy improved from 0.84944 to 0.85442, saving model to /content/optimal.h5\n",
            "1441/1441 [==============================] - 252s 175ms/step - loss: 0.2737 - accuracy: 0.8881 - val_loss: 0.3295 - val_accuracy: 0.8544\n",
            "Epoch 7/10\n",
            "1441/1441 [==============================] - ETA: 0s - loss: 0.2707 - accuracy: 0.8893\n",
            "Epoch 00007: val_accuracy did not improve from 0.85442\n",
            "1441/1441 [==============================] - 252s 175ms/step - loss: 0.2707 - accuracy: 0.8893 - val_loss: 0.3312 - val_accuracy: 0.8512\n",
            "Epoch 8/10\n",
            "1441/1441 [==============================] - ETA: 0s - loss: 0.2668 - accuracy: 0.8922\n",
            "Epoch 00008: val_accuracy improved from 0.85442 to 0.85637, saving model to /content/optimal.h5\n",
            "1441/1441 [==============================] - 253s 175ms/step - loss: 0.2668 - accuracy: 0.8922 - val_loss: 0.3268 - val_accuracy: 0.8564\n",
            "Epoch 9/10\n",
            "1441/1441 [==============================] - ETA: 0s - loss: 0.2646 - accuracy: 0.8909\n",
            "Epoch 00009: val_accuracy did not improve from 0.85637\n",
            "1441/1441 [==============================] - 251s 174ms/step - loss: 0.2646 - accuracy: 0.8909 - val_loss: 0.3509 - val_accuracy: 0.8451\n",
            "Epoch 10/10\n",
            "1441/1441 [==============================] - ETA: 0s - loss: 0.2614 - accuracy: 0.8941\n",
            "Epoch 00010: val_accuracy did not improve from 0.85637\n",
            "1441/1441 [==============================] - 252s 175ms/step - loss: 0.2614 - accuracy: 0.8941 - val_loss: 0.3910 - val_accuracy: 0.8332\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#load best model\n",
        "from keras.models import load_model\n",
        "saved_model = load_model('/content/optimal.h5')"
      ],
      "metadata": {
        "id": "LMRvK4D1f1Ai"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#testing on tuned model\n",
        "test_generator = data_generator.flow_from_directory(\n",
        "        '/content/UTKFace/test',\n",
        "        target_size=(299, 299),\n",
        "        class_mode='categorical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pS6ub87uf7rp",
        "outputId": "ae254fbb-7639-47ed-9a22-640d32bb26e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1804 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = saved_model.evaluate(test_generator)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DC2v9ds0gRFA",
        "outputId": "c1f06881-7aeb-4d14-d7c8-2eaab8978b0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "57/57 [==============================] - 21s 278ms/step - loss: 0.4027 - accuracy: 0.8298\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.40266528725624084, 0.8298225998878479]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model_history.history['accuracy'])\n",
        "plt.plot(model_history.history['val_accuracy'])\n",
        "plt.title('model accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Xj1Q5qsDHLXq",
        "outputId": "f8c27a0a-727e-4379-b945-6db3a267c0e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(model_history.history['loss'])\n",
        "plt.plot(model_history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "oMHaH2IRIH-M",
        "outputId": "82b72997-189c-411b-cb08-9dd4818a74de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}