TRAIN_ON_PEID.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TRAIN_ON_PEID.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fc1ulpkjyKOU",
        "outputId": "133e01f3-c655-4811-f1c0-7398912bdc12"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbJa_28-yDiB"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "from skimage import io\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras import backend as K \n",
        "from keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.layers import GlobalAveragePooling2D, Dense, Activation, BatchNormalization, Dropout, Input\n",
        "from keras.models import Model, Sequential\n",
        "from sklearn.model_selection import train_test_split\n",
        "import cv2\n",
        "from keras.applications.vgg16 import  VGG16, preprocess_input\n",
        "from skimage.util import view_as_windows \n",
        "from scipy.io import loadmat\n",
        "from scipy.stats import pearsonr, spearmanr"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCcv3VKwv-sw"
      },
      "source": [
        "#Data loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "agJlL982f-Cm",
        "outputId": "fa570661-ef62-4638-8579-ec3247533ec6"
      },
      "source": [
        "!unzip Path-to-PEID-Dataset-downloaded/PEID.zip"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/drive/MyDrive/Projet-5-em/Project-kadid10k/PEID.zip\n",
            "   creating: PEID/\n",
            "  inflating: PEID/data.mat           \n",
            "   creating: PEID/encimg/\n",
            "  inflating: PEID/encimg/airport_01_1.j2k  \n",
            "  inflating: PEID/encimg/airport_01_2.j2k  \n",
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            "  inflating: PEID/encimg/airport_02_1.jpg  \n",
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            "  inflating: PEID/encimg/airport_02_4.jpg  \n",
            "  inflating: PEID/encimg/airport_02_5.jpg  \n",
            "  inflating: PEID/encimg/airport_02_6.jpg  \n",
            "  inflating: PEID/encimg/airport_03_1.j2k  \n",
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            "  inflating: PEID/encimg/airport_03_3.j2k  \n",
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            "  inflating: PEID/encimg/blackboard_01_1.j2k  \n",
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            "  inflating: PEID/encimg/fresco_01_1.j2k  \n",
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            "  inflating: PEID/encimg/house_01_1.j2k  \n",
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            "  inflating: PEID/encimg/lion_01_1.j2k  \n",
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            "  inflating: PEID/encimg/road_01_4.j2k  \n",
            "  inflating: PEID/encimg/road_01_5.j2k  \n",
            "  inflating: PEID/encimg/road_02_1.jpg  \n",
            "  inflating: PEID/encimg/road_02_2.jpg  \n",
            "  inflating: PEID/encimg/road_02_3.jpg  \n",
            "  inflating: PEID/encimg/road_02_4.jpg  \n",
            "  inflating: PEID/encimg/road_02_5.jpg  \n",
            "  inflating: PEID/encimg/road_02_6.jpg  \n",
            "  inflating: PEID/encimg/road_03_1.j2k  \n",
            "  inflating: PEID/encimg/road_03_2.j2k  \n",
            "  inflating: PEID/encimg/road_03_3.j2k  \n",
            "  inflating: PEID/encimg/road_03_4.j2k  \n",
            "  inflating: PEID/encimg/road_03_5.j2k  \n",
            "  inflating: PEID/encimg/road_03_6.j2k  \n",
            "  inflating: PEID/encimg/road_04_1.j2k  \n",
            "  inflating: PEID/encimg/road_04_2.j2k  \n",
            "  inflating: PEID/encimg/road_04_3.j2k  \n",
            "  inflating: PEID/encimg/road_04_4.j2k  \n",
            "  inflating: PEID/encimg/road_04_5.j2k  \n",
            "  inflating: PEID/encimg/road_05_1.j2k  \n",
            "  inflating: PEID/encimg/road_05_2.j2k  \n",
            "  inflating: PEID/encimg/road_05_3.j2k  \n",
            "  inflating: PEID/encimg/road_05_4.j2k  \n",
            "  inflating: PEID/encimg/road_05_5.j2k  \n",
            "  inflating: PEID/encimg/road_06_1.j2k  \n",
            "  inflating: PEID/encimg/road_06_2.j2k  \n",
            "  inflating: PEID/encimg/road_06_3.j2k  \n",
            "  inflating: PEID/encimg/road_06_4.j2k  \n",
            "  inflating: PEID/encimg/road_06_5.j2k  \n",
            "  inflating: PEID/encimg/road_07_1.bmp  \n",
            "  inflating: PEID/encimg/road_07_2.bmp  \n",
            "  inflating: PEID/encimg/road_07_3.bmp  \n",
            "  inflating: PEID/encimg/road_07_4.bmp  \n",
            "  inflating: PEID/encimg/road_07_5.bmp  \n",
            "  inflating: PEID/encimg/road_07_6.bmp  \n",
            "  inflating: PEID/encimg/road_08_1.bmp  \n",
            "  inflating: PEID/encimg/road_08_2.bmp  \n",
            "  inflating: PEID/encimg/road_08_3.bmp  \n",
            "  inflating: PEID/encimg/road_08_4.bmp  \n",
            "  inflating: PEID/encimg/road_09_1.bmp  \n",
            "  inflating: PEID/encimg/road_09_2.bmp  \n",
            "  inflating: PEID/encimg/road_09_3.bmp  \n",
            "  inflating: PEID/encimg/road_09_4.bmp  \n",
            "  inflating: PEID/encimg/road_09_5.bmp  \n",
            "  inflating: PEID/encimg/road_09_6.bmp  \n",
            "  inflating: PEID/encimg/road_10_1.bmp  \n",
            "  inflating: PEID/encimg/road_10_2.bmp  \n",
            "  inflating: PEID/encimg/road_10_3.bmp  \n",
            "  inflating: PEID/encimg/road_10_4.bmp  \n",
            "  inflating: PEID/encimg/road_10_5.bmp  \n",
            "  inflating: PEID/encimg/road_10_6.bmp  \n",
            "  inflating: PEID/encimg/stone_01_1.j2k  \n",
            "  inflating: PEID/encimg/stone_01_2.j2k  \n",
            "  inflating: PEID/encimg/stone_01_3.j2k  \n",
            "  inflating: PEID/encimg/stone_01_4.j2k  \n",
            "  inflating: PEID/encimg/stone_01_5.j2k  \n",
            "  inflating: PEID/encimg/stone_02_1.jpg  \n",
            "  inflating: PEID/encimg/stone_02_2.jpg  \n",
            "  inflating: PEID/encimg/stone_02_3.jpg  \n",
            "  inflating: PEID/encimg/stone_02_4.jpg  \n",
            "  inflating: PEID/encimg/stone_02_5.jpg  \n",
            "  inflating: PEID/encimg/stone_02_6.jpg  \n",
            "  inflating: PEID/encimg/stone_03_1.j2k  \n",
            "  inflating: PEID/encimg/stone_03_2.j2k  \n",
            "  inflating: PEID/encimg/stone_03_3.j2k  \n",
            "  inflating: PEID/encimg/stone_03_4.j2k  \n",
            "  inflating: PEID/encimg/stone_03_5.j2k  \n",
            "  inflating: PEID/encimg/stone_03_6.j2k  \n",
            "  inflating: PEID/encimg/stone_04_1.j2k  \n",
            "  inflating: PEID/encimg/stone_04_2.j2k  \n",
            "  inflating: PEID/encimg/stone_04_3.j2k  \n",
            "  inflating: PEID/encimg/stone_04_4.j2k  \n",
            "  inflating: PEID/encimg/stone_04_5.j2k  \n",
            "  inflating: PEID/encimg/stone_05_1.j2k  \n",
            "  inflating: PEID/encimg/stone_05_2.j2k  \n",
            "  inflating: PEID/encimg/stone_05_3.j2k  \n",
            "  inflating: PEID/encimg/stone_05_4.j2k  \n",
            "  inflating: PEID/encimg/stone_05_5.j2k  \n",
            "  inflating: PEID/encimg/stone_06_1.j2k  \n",
            "  inflating: PEID/encimg/stone_06_2.j2k  \n",
            "  inflating: PEID/encimg/stone_06_3.j2k  \n",
            "  inflating: PEID/encimg/stone_06_4.j2k  \n",
            "  inflating: PEID/encimg/stone_06_5.j2k  \n",
            "  inflating: PEID/encimg/stone_07_1.bmp  \n",
            "  inflating: PEID/encimg/stone_07_2.bmp  \n",
            "  inflating: PEID/encimg/stone_07_3.bmp  \n",
            "  inflating: PEID/encimg/stone_07_4.bmp  \n",
            "  inflating: PEID/encimg/stone_07_5.bmp  \n",
            "  inflating: PEID/encimg/stone_07_6.bmp  \n",
            "  inflating: PEID/encimg/stone_08_1.bmp  \n",
            "  inflating: PEID/encimg/stone_08_2.bmp  \n",
            "  inflating: PEID/encimg/stone_08_3.bmp  \n",
            "  inflating: PEID/encimg/stone_08_4.bmp  \n",
            "  inflating: PEID/encimg/stone_09_1.bmp  \n",
            "  inflating: PEID/encimg/stone_09_2.bmp  \n",
            "  inflating: PEID/encimg/stone_09_3.bmp  \n",
            "  inflating: PEID/encimg/stone_09_4.bmp  \n",
            "  inflating: PEID/encimg/stone_09_5.bmp  \n",
            "  inflating: PEID/encimg/stone_09_6.bmp  \n",
            "  inflating: PEID/encimg/stone_10_1.bmp  \n",
            "  inflating: PEID/encimg/stone_10_2.bmp  \n",
            "  inflating: PEID/encimg/stone_10_3.bmp  \n",
            "  inflating: PEID/encimg/stone_10_4.bmp  \n",
            "  inflating: PEID/encimg/stone_10_5.bmp  \n",
            "  inflating: PEID/encimg/stone_10_6.bmp  \n",
            "  inflating: PEID/encimg/tower_01_1.j2k  \n",
            "  inflating: PEID/encimg/tower_01_2.j2k  \n",
            "  inflating: PEID/encimg/tower_01_3.j2k  \n",
            "  inflating: PEID/encimg/tower_01_4.j2k  \n",
            "  inflating: PEID/encimg/tower_01_5.j2k  \n",
            "  inflating: PEID/encimg/tower_02_1.jpg  \n",
            "  inflating: PEID/encimg/tower_02_2.jpg  \n",
            "  inflating: PEID/encimg/tower_02_3.jpg  \n",
            "  inflating: PEID/encimg/tower_02_4.jpg  \n",
            "  inflating: PEID/encimg/tower_02_5.jpg  \n",
            "  inflating: PEID/encimg/tower_02_6.jpg  \n",
            "  inflating: PEID/encimg/tower_03_1.j2k  \n",
            "  inflating: PEID/encimg/tower_03_2.j2k  \n",
            "  inflating: PEID/encimg/tower_03_3.j2k  \n",
            "  inflating: PEID/encimg/tower_03_4.j2k  \n",
            "  inflating: PEID/encimg/tower_03_5.j2k  \n",
            "  inflating: PEID/encimg/tower_03_6.j2k  \n",
            "  inflating: PEID/encimg/tower_04_1.j2k  \n",
            "  inflating: PEID/encimg/tower_04_2.j2k  \n",
            "  inflating: PEID/encimg/tower_04_3.j2k  \n",
            "  inflating: PEID/encimg/tower_04_4.j2k  \n",
            "  inflating: PEID/encimg/tower_04_5.j2k  \n",
            "  inflating: PEID/encimg/tower_05_1.j2k  \n",
            "  inflating: PEID/encimg/tower_05_2.j2k  \n",
            "  inflating: PEID/encimg/tower_05_3.j2k  \n",
            "  inflating: PEID/encimg/tower_05_4.j2k  \n",
            "  inflating: PEID/encimg/tower_05_5.j2k  \n",
            "  inflating: PEID/encimg/tower_06_1.j2k  \n",
            "  inflating: PEID/encimg/tower_06_2.j2k  \n",
            "  inflating: PEID/encimg/tower_06_3.j2k  \n",
            "  inflating: PEID/encimg/tower_06_4.j2k  \n",
            "  inflating: PEID/encimg/tower_06_5.j2k  \n",
            "  inflating: PEID/encimg/tower_07_1.bmp  \n",
            "  inflating: PEID/encimg/tower_07_2.bmp  \n",
            "  inflating: PEID/encimg/tower_07_3.bmp  \n",
            "  inflating: PEID/encimg/tower_07_4.bmp  \n",
            "  inflating: PEID/encimg/tower_07_5.bmp  \n",
            "  inflating: PEID/encimg/tower_07_6.bmp  \n",
            "  inflating: PEID/encimg/tower_08_1.bmp  \n",
            "  inflating: PEID/encimg/tower_08_2.bmp  \n",
            "  inflating: PEID/encimg/tower_08_3.bmp  \n",
            "  inflating: PEID/encimg/tower_08_4.bmp  \n",
            "  inflating: PEID/encimg/tower_09_1.bmp  \n",
            "  inflating: PEID/encimg/tower_09_2.bmp  \n",
            "  inflating: PEID/encimg/tower_09_3.bmp  \n",
            "  inflating: PEID/encimg/tower_09_4.bmp  \n",
            "  inflating: PEID/encimg/tower_09_5.bmp  \n",
            "  inflating: PEID/encimg/tower_09_6.bmp  \n",
            "  inflating: PEID/encimg/tower_10_1.bmp  \n",
            "  inflating: PEID/encimg/tower_10_2.bmp  \n",
            "  inflating: PEID/encimg/tower_10_3.bmp  \n",
            "  inflating: PEID/encimg/tower_10_4.bmp  \n",
            "  inflating: PEID/encimg/tower_10_5.bmp  \n",
            "  inflating: PEID/encimg/tower_10_6.bmp  \n",
            "  inflating: PEID/encimg/water_01_1.j2k  \n",
            "  inflating: PEID/encimg/water_01_2.j2k  \n",
            "  inflating: PEID/encimg/water_01_3.j2k  \n",
            "  inflating: PEID/encimg/water_01_4.j2k  \n",
            "  inflating: PEID/encimg/water_01_5.j2k  \n",
            "  inflating: PEID/encimg/water_02_1.jpg  \n",
            "  inflating: PEID/encimg/water_02_2.jpg  \n",
            "  inflating: PEID/encimg/water_02_3.jpg  \n",
            "  inflating: PEID/encimg/water_02_4.jpg  \n",
            "  inflating: PEID/encimg/water_02_5.jpg  \n",
            "  inflating: PEID/encimg/water_02_6.jpg  \n",
            "  inflating: PEID/encimg/water_03_1.j2k  \n",
            "  inflating: PEID/encimg/water_03_2.j2k  \n",
            "  inflating: PEID/encimg/water_03_3.j2k  \n",
            "  inflating: PEID/encimg/water_03_4.j2k  \n",
            "  inflating: PEID/encimg/water_03_5.j2k  \n",
            "  inflating: PEID/encimg/water_03_6.j2k  \n",
            "  inflating: PEID/encimg/water_04_1.j2k  \n",
            "  inflating: PEID/encimg/water_04_2.j2k  \n",
            "  inflating: PEID/encimg/water_04_3.j2k  \n",
            "  inflating: PEID/encimg/water_04_4.j2k  \n",
            "  inflating: PEID/encimg/water_04_5.j2k  \n",
            "  inflating: PEID/encimg/water_05_1.j2k  \n",
            "  inflating: PEID/encimg/water_05_2.j2k  \n",
            "  inflating: PEID/encimg/water_05_3.j2k  \n",
            "  inflating: PEID/encimg/water_05_4.j2k  \n",
            "  inflating: PEID/encimg/water_05_5.j2k  \n",
            "  inflating: PEID/encimg/water_06_1.j2k  \n",
            "  inflating: PEID/encimg/water_06_2.j2k  \n",
            "  inflating: PEID/encimg/water_06_3.j2k  \n",
            "  inflating: PEID/encimg/water_06_4.j2k  \n",
            "  inflating: PEID/encimg/water_06_5.j2k  \n",
            "  inflating: PEID/encimg/water_07_1.bmp  \n",
            "  inflating: PEID/encimg/water_07_2.bmp  \n",
            "  inflating: PEID/encimg/water_07_3.bmp  \n",
            "  inflating: PEID/encimg/water_07_4.bmp  \n",
            "  inflating: PEID/encimg/water_07_5.bmp  \n",
            "  inflating: PEID/encimg/water_07_6.bmp  \n",
            "  inflating: PEID/encimg/water_08_1.bmp  \n",
            "  inflating: PEID/encimg/water_08_2.bmp  \n",
            "  inflating: PEID/encimg/water_08_3.bmp  \n",
            "  inflating: PEID/encimg/water_08_4.bmp  \n",
            "  inflating: PEID/encimg/water_09_1.bmp  \n",
            "  inflating: PEID/encimg/water_09_2.bmp  \n",
            "  inflating: PEID/encimg/water_09_3.bmp  \n",
            "  inflating: PEID/encimg/water_09_4.bmp  \n",
            "  inflating: PEID/encimg/water_09_5.bmp  \n",
            "  inflating: PEID/encimg/water_09_6.bmp  \n",
            "  inflating: PEID/encimg/water_10_1.bmp  \n",
            "  inflating: PEID/encimg/water_10_2.bmp  \n",
            "  inflating: PEID/encimg/water_10_3.bmp  \n",
            "  inflating: PEID/encimg/water_10_4.bmp  \n",
            "  inflating: PEID/encimg/water_10_5.bmp  \n",
            "  inflating: PEID/encimg/water_10_6.bmp  \n",
            "  inflating: PEID/README.txt         \n",
            "   creating: PEID/refimg/\n",
            "  inflating: PEID/refimg/airport.bmp  \n",
            "  inflating: PEID/refimg/animal.bmp  \n",
            "  inflating: PEID/refimg/blackboard.bmp  \n",
            "  inflating: PEID/refimg/book.bmp    \n",
            "  inflating: PEID/refimg/bridge.bmp  \n",
            "  inflating: PEID/refimg/bus.bmp     \n",
            "  inflating: PEID/refimg/child.bmp   \n",
            "  inflating: PEID/refimg/crowd.bmp   \n",
            "  inflating: PEID/refimg/fresco.bmp  \n",
            "  inflating: PEID/refimg/house.bmp   \n",
            "  inflating: PEID/refimg/lion.bmp    \n",
            "  inflating: PEID/refimg/lotus.bmp   \n",
            "  inflating: PEID/refimg/meeting.bmp  \n",
            "  inflating: PEID/refimg/monument.bmp  \n",
            "  inflating: PEID/refimg/mountain.bmp  \n",
            "  inflating: PEID/refimg/people.bmp  \n",
            "  inflating: PEID/refimg/road.bmp    \n",
            "  inflating: PEID/refimg/stone.bmp   \n",
            "  inflating: PEID/refimg/tower.bmp   \n",
            "  inflating: PEID/refimg/water.bmp   \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlF-9LqxjSBD",
        "outputId": "104f9229-82c5-4c91-ecf7-5d9967c36cb9"
      },
      "source": [
        "# Put the reference and degraded images together in the same folder\n",
        "\n",
        "!cp /content/PEID/refimg/* /content/PEID/encimg/\n",
        "len(os.listdir('/content/PEID/encimg/'))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1100"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "morPjuaMyTYj",
        "outputId": "25925847-0fa1-4390-e3c2-f9fed0109f11"
      },
      "source": [
        "train_visual_security_file = pd.read_csv('Path to visual security data file/PEID_train_file_visual.csv')\n",
        "train_visual_security_file.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>encrypted_image</th>\n",
              "      <th>reference_image</th>\n",
              "      <th>visual_quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>airport_01_1.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.023810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>airport_01_2.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.031746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>airport_01_3.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.119048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>airport_01_5.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.547619</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    encrypted_image reference_image  visual_quality\n",
              "0       airport.bmp     airport.bmp        1.000000\n",
              "1  airport_01_1.j2k     airport.bmp        0.023810\n",
              "2  airport_01_2.j2k     airport.bmp        0.031746\n",
              "3  airport_01_3.j2k     airport.bmp        0.119048\n",
              "4  airport_01_5.j2k     airport.bmp        0.547619"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4-bYTDLWkJsQ",
        "outputId": "f624ae3b-3994-493e-c95d-905a792b49e9"
      },
      "source": [
        "train_image_quality_file = pd.read_csv('Path to visual quality data file/PEID_train_file.csv')\n",
        "train_image_quality_file.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>encrypted_image</th>\n",
              "      <th>reference_image</th>\n",
              "      <th>ground_truth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>airport_01_1.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>airport_01_2.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>airport_01_3.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>airport_01_5.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    encrypted_image reference_image  ground_truth\n",
              "0       airport.bmp     airport.bmp          1.00\n",
              "1  airport_01_1.j2k     airport.bmp          0.20\n",
              "2  airport_01_2.j2k     airport.bmp          0.20\n",
              "3  airport_01_3.j2k     airport.bmp          0.20\n",
              "4  airport_01_5.j2k     airport.bmp          0.31"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "_sKwkwguyX9N",
        "outputId": "bd808e00-faf1-4cfc-8337-78079cd3ef35"
      },
      "source": [
        "train = train_visual_security_file\n",
        "train.insert(3, 'ground_truth',train_image_quality_file['ground_truth'].values)\n",
        "train"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>encrypted_image</th>\n",
              "      <th>reference_image</th>\n",
              "      <th>visual_quality</th>\n",
              "      <th>ground_truth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>airport_01_1.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.023810</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>airport_01_2.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.031746</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>airport_01_3.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.119048</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>airport_01_5.j2k</td>\n",
              "      <td>airport.bmp</td>\n",
              "      <td>0.547619</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>875</th>\n",
              "      <td>water_09_4.bmp</td>\n",
              "      <td>water.bmp</td>\n",
              "      <td>0.388889</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>876</th>\n",
              "      <td>water_09_6.bmp</td>\n",
              "      <td>water.bmp</td>\n",
              "      <td>0.753968</td>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>877</th>\n",
              "      <td>water_10_4.bmp</td>\n",
              "      <td>water.bmp</td>\n",
              "      <td>0.357143</td>\n",
              "      <td>0.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>878</th>\n",
              "      <td>water_10_5.bmp</td>\n",
              "      <td>water.bmp</td>\n",
              "      <td>0.349206</td>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>879</th>\n",
              "      <td>water_10_6.bmp</td>\n",
              "      <td>water.bmp</td>\n",
              "      <td>0.539683</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>880 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      encrypted_image reference_image  visual_quality  ground_truth\n",
              "0         airport.bmp     airport.bmp        1.000000          1.00\n",
              "1    airport_01_1.j2k     airport.bmp        0.023810          0.20\n",
              "2    airport_01_2.j2k     airport.bmp        0.031746          0.20\n",
              "3    airport_01_3.j2k     airport.bmp        0.119048          0.20\n",
              "4    airport_01_5.j2k     airport.bmp        0.547619          0.31\n",
              "..                ...             ...             ...           ...\n",
              "875    water_09_4.bmp       water.bmp        0.388889          0.22\n",
              "876    water_09_6.bmp       water.bmp        0.753968          0.44\n",
              "877    water_10_4.bmp       water.bmp        0.357143          0.26\n",
              "878    water_10_5.bmp       water.bmp        0.349206          0.27\n",
              "879    water_10_6.bmp       water.bmp        0.539683          0.35\n",
              "\n",
              "[880 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Edh-5ihypD8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2a93d0e-467c-4c91-ed29-de9ef7cf6584"
      },
      "source": [
        "trainedModel = VGG16(weights='imagenet', include_top=False, input_shape=(224,224,3))\n",
        "\n",
        "x = GlobalAveragePooling2D()(trainedModel.output)\n",
        "\n",
        "layer = Dense(512,kernel_initializer = 'normal')(x)\n",
        "layer = BatchNormalization()(layer)\n",
        "layer = Activation('relu')(layer)\n",
        "layer = Dropout(0.2)(layer)\n",
        "\n",
        "layer = Dense(512, kernel_initializer = 'normal')(layer)\n",
        "layer = BatchNormalization()(layer)\n",
        "layer = Activation('relu')(layer)\n",
        "layer = Dropout(0.2)(layer)\n",
        "\n",
        "outputs = Dense(2)(layer)\n",
        "model = Model(inputs = trainedModel.input, outputs =outputs)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 1s 0us/step\n",
            "58900480/58889256 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KgQZ-AQyToux",
        "outputId": "0453d0aa-7e4d-4a2e-80bc-26dedcb9d27f"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 512)               2048      \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 512)               2048      \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 2)                 1026      \n",
            "=================================================================\n",
            "Total params: 15,245,122\n",
            "Trainable params: 15,243,074\n",
            "Non-trainable params: 2,048\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AiRjJa8AqLxr"
      },
      "source": [
        "# Patches extraction and saving to speed up the training process"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20ijbSH5pZED"
      },
      "source": [
        "# os.chdir('/content/PEID/encimg/')\n",
        "# x, yimage, yvisual = [], [], []\n",
        "# for encimg, labelVisual, labelImage in zip(train['encrypted_image'], train['visual_quality'], train['ground_truth']):\n",
        "#   i = 1\n",
        "#   imageR = image.load_img(encimg)\n",
        "#   imageR = np.expand_dims(imageR, axis=0)\n",
        "\n",
        "#   patches = tf.image.extract_patches(images=imageR, sizes=[1, 256, 256, 1], strides=[1, 256,256,1], rates=[1, 1, 1, 1], padding='VALID')\n",
        "#   patches = tf.reshape(patches,[-1,256,256,3])\n",
        "#   for patch in patches:\n",
        "#     reshape_patch = cv2.resize(patch, (224,224), interpolation = cv2.INTER_AREA)\n",
        "#     test = image.img_to_array(reshape_patch).astype('uint8')\n",
        "#     filename = str(i)+'_'+encimg.split('.')[0]+'.png'\n",
        "#     x.append(filename)\n",
        "#     yimage.append(labelImage)\n",
        "#     yvisual.append(labelVisual)\n",
        "#     cv2.imwrite('/content/patches/'+filename, cv2.cvtColor(test, cv2.COLOR_RGB2BGR))\n",
        "#     i = i + 1"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUV8pCP6p3PN",
        "outputId": "14b4ea46-1caf-4a12-8aac-980277453093"
      },
      "source": [
        "# len(os.listdir('/content/patches/'))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3520"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "KxAHtZVQp5Uu",
        "outputId": "fb16fa68-bb85-4678-f5bb-3fa4be038aba"
      },
      "source": [
        "# tempdat = {\n",
        "#     'images': x,\n",
        "#     'image_score': yimage,\n",
        "#     'visual_score':yvisual\n",
        "# }\n",
        "# dataset = pd.DataFrame(tempdat, columns=['images','image_score','visual_score'])\n",
        "# dataset"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>images</th>\n",
              "      <th>image_score</th>\n",
              "      <th>visual_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1_airport.png</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2_airport.png</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3_airport.png</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4_airport.png</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1_airport_01_1.png</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.023810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3515</th>\n",
              "      <td>4_water_10_5.png</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.349206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3516</th>\n",
              "      <td>1_water_10_6.png</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.539683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3517</th>\n",
              "      <td>2_water_10_6.png</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.539683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3518</th>\n",
              "      <td>3_water_10_6.png</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.539683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3519</th>\n",
              "      <td>4_water_10_6.png</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.539683</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3520 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  images  image_score  visual_score\n",
              "0          1_airport.png         1.00      1.000000\n",
              "1          2_airport.png         1.00      1.000000\n",
              "2          3_airport.png         1.00      1.000000\n",
              "3          4_airport.png         1.00      1.000000\n",
              "4     1_airport_01_1.png         0.20      0.023810\n",
              "...                  ...          ...           ...\n",
              "3515    4_water_10_5.png         0.27      0.349206\n",
              "3516    1_water_10_6.png         0.35      0.539683\n",
              "3517    2_water_10_6.png         0.35      0.539683\n",
              "3518    3_water_10_6.png         0.35      0.539683\n",
              "3519    4_water_10_6.png         0.35      0.539683\n",
              "\n",
              "[3520 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qYCy9KH9ty9E"
      },
      "source": [
        "# def train_generator(batch_size):\n",
        "\n",
        "#   while True:\n",
        "#     for pos in range(0, len(dataset), batch_size):\n",
        "#       temp = dataset.iloc[pos:pos + batch_size] \n",
        "      \n",
        "#       encrypted_batch, ytrain = [], []\n",
        "\n",
        "#       for encimg, y_image, y_visual in zip(temp.images, temp.image_score, temp.visual_score):  \n",
        "#         encrypted_image = image.load_img('/content/patches/'+encimg)\n",
        "#         encrypted_image = image.img_to_array(encrypted_image)\n",
        "   \n",
        "#         normalise_patch = encrypted_image / 255.0\n",
        "\n",
        "#         encrypted_batch.append(normalise_patch)\n",
        "#         ytrain.append([y_image, y_visual])\n",
        "\n",
        "#       encrypted_batch = np.array(encrypted_batch)\n",
        "#       ytrain = np.array(ytrain)\n",
        "\n",
        "#       yield (encrypted_batch, ytrain)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C_39w_euQq3d"
      },
      "source": [
        "#Data pre-processing\n",
        "Some necessary pre-processing work has been done\n",
        "<ul>\n",
        "  <li>Divide all images into Np small patches of size 256 × 256 without overlapping</li>\n",
        "  <li>Downsample the Np patches of each image to have a size of 224 × 224\n",
        "  </li>\n",
        "  <li>Normalize each image patch by dividing it by 255.0</li>\n",
        "</ul>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dB38yZPOz-AY"
      },
      "source": [
        "def train_generator(batch_size):\n",
        "\n",
        "  while True:\n",
        "    for pos in range(0, len(train), batch_size):\n",
        "      temp = train.iloc[pos:pos + batch_size] \n",
        "      \n",
        "      encrypted_batch, ytrain = [], []\n",
        "\n",
        "      for encimg, y_image, y_visual in zip(temp.encrypted_image, temp.ground_truth, temp.visual_quality):  \n",
        "        encrypted_image = image.load_img('/content/PEID/encimg/'+encimg)\n",
        "        encrypted_image = image.img_to_array(encrypted_image)\n",
        "\n",
        "        patches = view_as_windows(encrypted_image,(256, 256, 3),(256, 256, 3)).reshape((-1,256,256,3))\n",
        "   \n",
        "        for patch in patches:\n",
        "          reshape_patch = cv2.resize(patch, (224,224), interpolation = cv2.INTER_AREA)\n",
        "          normalize_patch = reshape_patch / 255.0\n",
        "\n",
        "          encrypted_batch.append(normalize_patch)\n",
        "          ytrain.append([y_image, y_visual])\n",
        "\n",
        "      encrypted_batch = np.array(encrypted_batch)\n",
        "      ytrain = np.array(ytrain)\n",
        "\n",
        "      yield (encrypted_batch, ytrain)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JeFrnY4-vub0"
      },
      "source": [
        "#Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78ip4xl5TMtZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4fb158f-a1d5-4bde-bbfc-10e48f558b2d"
      },
      "source": [
        "batch_size = 16\n",
        "\n",
        "for layer in model.layers[:3]:\n",
        "  layer.trainable = False\n",
        "\n",
        "for layer in model.layers[3:]:\n",
        "  layer.trainable = True\n",
        "\n",
        "model.compile(\n",
        "    loss= ['mae','mae'],\n",
        "    loss_weights = [0.4, 0.6],\n",
        "    optimizer= tf.optimizers.Adam(0.0001)\n",
        ")\n",
        "\n",
        "callback = [tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='loss',\n",
        "    patience= 40,\n",
        "    mode = 'auto',\n",
        "    min_delta=1e-3,\n",
        "    restore_best_weights = True,\n",
        "    verbose =1\n",
        ")]\n",
        "\n",
        "history = model.fit(\n",
        "    train_generator(batch_size),\n",
        "    epochs = 500,\n",
        "    verbose = 1,\n",
        "    batch_size = batch_size,\n",
        "    callbacks = callback,\n",
        "    steps_per_epoch= len(train)//batch_size\n",
        ")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "55/55 [==============================] - 69s 1s/step - loss: 0.1416\n",
            "Epoch 2/5\n",
            "55/55 [==============================] - 67s 1s/step - loss: 0.1389\n",
            "Epoch 3/5\n",
            "55/55 [==============================] - 67s 1s/step - loss: 0.1347\n",
            "Epoch 4/5\n",
            "55/55 [==============================] - 67s 1s/step - loss: 0.1349\n",
            "Epoch 5/5\n",
            "55/55 [==============================] - 67s 1s/step - loss: 0.1463\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5f-qeuksu1uH"
      },
      "source": [
        "model.save('The path of the folder where you want to save your trained model/VGG160406.h5')"
      ],
      "execution_count": 16,
      "outputs": []
    }
  ]
}