GradCAM_Vgg_16_FER.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6W7o0CGriCf",
        "outputId": "64dcb872-2660-4c7f-bbc2-674c00b0b936"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WcX3F9zYaiAG",
        "outputId": "c798a9c8-8e9a-4924-ff72-6d8ac40d92ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting scikit-plot\n",
            "  Downloading scikit_plot-0.3.7-py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from scikit-plot) (3.7.1)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.10/dist-packages (from scikit-plot) (1.2.2)\n",
            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.10/dist-packages (from scikit-plot) (1.11.4)\n",
            "Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.10/dist-packages (from scikit-plot) (1.3.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (4.49.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.4.5)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (23.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18->scikit-plot) (3.3.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=1.4.0->scikit-plot) (1.16.0)\n",
            "Installing collected packages: scikit-plot\n",
            "Successfully installed scikit-plot-0.3.7\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.25.2)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (4.8.0.76)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.10/dist-packages (9.4.0)\n",
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
            "Requirement already satisfied: imutils in /usr/local/lib/python3.10/dist-packages (0.5.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.2.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.5.26)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.5.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.9.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (16.0.6)\n",
            "Requirement already satisfied: ml-dtypes~=0.2.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (67.7.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.10.0)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.36.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.62.0)\n",
            "Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.2)\n",
            "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.15.0)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.3.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.49.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.5)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.42.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (2.27.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (1.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.5.2)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (2.31.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow) (3.0.1)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (5.3.3)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.3.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (1.3.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow) (2024.2.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow) (0.5.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow) (3.2.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install scikit-plot\n",
        "!pip install numpy opencv-python pillow tensorflow keras imutils scikit-learn matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XIj1ZDTyauEY",
        "outputId": "f0f85bcc-6736-4406-fd29-d1baf0951139"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.15.0)\n",
            "Collecting tensorflow\n",
            "  Downloading tensorflow-2.16.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (589.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m589.8/589.8 MB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (0.19.3)\n",
            "Collecting scikit-image\n",
            "  Downloading scikit_image-0.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (14.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.7/14.7 MB\u001b[0m \u001b[31m24.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.5.26)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.5.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.2.0)\n",
            "Collecting h5py>=3.10.0 (from tensorflow)\n",
            "  Downloading h5py-3.10.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.8/4.8 MB\u001b[0m \u001b[31m105.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (16.0.6)\n",
            "Collecting ml-dtypes~=0.3.1 (from tensorflow)\n",
            "  Downloading ml_dtypes-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m99.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow) (23.2)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.31.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow) (67.7.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (4.10.0)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.14.1)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.62.0)\n",
            "Collecting tensorboard<2.17,>=2.16 (from tensorflow)\n",
            "  Downloading tensorboard-2.16.2-py3-none-any.whl (5.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m104.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting keras>=3.0.0 (from tensorflow)\n",
            "  Downloading keras-3.0.5-py3-none-any.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m73.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (0.36.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow) (1.25.2)\n",
            "Requirement already satisfied: scipy>=1.8 in /usr/local/lib/python3.10/dist-packages (from scikit-image) (1.11.4)\n",
            "Requirement already satisfied: networkx>=2.8 in /usr/local/lib/python3.10/dist-packages (from scikit-image) (3.2.1)\n",
            "Requirement already satisfied: pillow>=9.0.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image) (9.4.0)\n",
            "Requirement already satisfied: imageio>=2.27 in /usr/local/lib/python3.10/dist-packages (from scikit-image) (2.31.6)\n",
            "Requirement already satisfied: tifffile>=2022.8.12 in /usr/local/lib/python3.10/dist-packages (from scikit-image) (2024.2.12)\n",
            "Requirement already satisfied: lazy_loader>=0.3 in /usr/local/lib/python3.10/dist-packages (from scikit-image) (0.3)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow) (0.42.0)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.0.0->tensorflow) (13.7.1)\n",
            "Collecting namex (from keras>=3.0.0->tensorflow)\n",
            "  Downloading namex-0.0.7-py3-none-any.whl (5.8 kB)\n",
            "Requirement already satisfied: dm-tree in /usr/local/lib/python3.10/dist-packages (from keras>=3.0.0->tensorflow) (0.1.8)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow) (2024.2.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.5.2)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.17,>=2.16->tensorflow) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.17,>=2.16->tensorflow) (3.0.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.0.0->tensorflow) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.0.0->tensorflow) (2.16.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow) (0.1.2)\n",
            "Installing collected packages: namex, ml-dtypes, h5py, tensorboard, scikit-image, keras, tensorflow\n",
            "  Attempting uninstall: ml-dtypes\n",
            "    Found existing installation: ml-dtypes 0.2.0\n",
            "    Uninstalling ml-dtypes-0.2.0:\n",
            "      Successfully uninstalled ml-dtypes-0.2.0\n",
            "  Attempting uninstall: h5py\n",
            "    Found existing installation: h5py 3.9.0\n",
            "    Uninstalling h5py-3.9.0:\n",
            "      Successfully uninstalled h5py-3.9.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.15.2\n",
            "    Uninstalling tensorboard-2.15.2:\n",
            "      Successfully uninstalled tensorboard-2.15.2\n",
            "  Attempting uninstall: scikit-image\n",
            "    Found existing installation: scikit-image 0.19.3\n",
            "    Uninstalling scikit-image-0.19.3:\n",
            "      Successfully uninstalled scikit-image-0.19.3\n",
            "  Attempting uninstall: keras\n",
            "    Found existing installation: keras 2.15.0\n",
            "    Uninstalling keras-2.15.0:\n",
            "      Successfully uninstalled keras-2.15.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.15.0\n",
            "    Uninstalling tensorflow-2.15.0:\n",
            "      Successfully uninstalled tensorflow-2.15.0\n",
            "Successfully installed h5py-3.10.0 keras-3.0.5 ml-dtypes-0.3.2 namex-0.0.7 scikit-image-0.22.0 tensorboard-2.16.2 tensorflow-2.16.1\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade tensorflow scikit-image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cdxuLbqFSwqy"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Layer, Flatten\n",
        "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense\n",
        "from tensorflow.keras.applications import VGG16\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwpcR7CGadmA",
        "outputId": "abc06d5a-52e7-4912-b3df-ede9189aeb81"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Anger', 'Contempt', 'Disgust', 'Fear', 'Happy', 'Sadness', 'Surprise']\n"
          ]
        }
      ],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import os,cv2\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "from pylab import rcParams\n",
        "rcParams['figure.figsize'] = 20, 10\n",
        "import scikitplot\n",
        "\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from matplotlib import pyplot\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense , Activation , Dropout ,Flatten\n",
        "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Flatten, Dense\n",
        "from keras.metrics import categorical_accuracy\n",
        "from keras.models import model_from_json\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import *\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "import os\n",
        "print(os.listdir(\"/content/drive/MyDrive/Autoencoder/Autoencoder-FER-CA/CK+48\"))\n",
        "\n",
        "\n",
        "# Any results you write to the current directory are saved as output\n",
        "\n",
        "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
        "from sklearn.datasets import make_classification\n",
        "from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
        "\n",
        "\n",
        "import scikitplot\n",
        "\n",
        "import keras\n",
        "from keras.models import Model, load_model\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense , Activation , Dropout ,Flatten\n",
        "from keras.metrics import categorical_accuracy\n",
        "from keras.models import model_from_json\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.optimizers import*\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from keras.layers import UpSampling2D\n",
        "from tqdm import tqdm\n",
        "from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D, Dense,Reshape\n",
        "from keras.models import Model\n",
        "from matplotlib import pyplot\n",
        "from matplotlib.offsetbox import OffsetImage, AnnotationBbox, TextArea\n",
        "\n",
        "import random\n",
        "import scipy\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.utils import to_categorical"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qR_VoApULmG",
        "outputId": "806d89d3-f318-4dfa-8a8c-3706e0ff0fde"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 836 images belonging to 7 classes.\n",
            "Found 145 images belonging to 7 classes.\n"
          ]
        }
      ],
      "source": [
        "batch_size = 32\n",
        "img_size = 224\n",
        "\n",
        "datagen = ImageDataGenerator(rescale=1/255.,\n",
        "                             rotation_range=10,\n",
        "                             width_shift_range=0.2,\n",
        "                             height_shift_range=0.2,\n",
        "                             shear_range=0.2,\n",
        "                             zoom_range=0.2,\n",
        "                             horizontal_flip=True,\n",
        "                             fill_mode='nearest',\n",
        "                             validation_split=0.15)\n",
        "\n",
        "train_generator = datagen.flow_from_directory('/content/drive/MyDrive/Autoencoder/Autoencoder-FER-CA/CK+48',\n",
        "                                                target_size=(img_size, img_size),\n",
        "                                                batch_size=batch_size,\n",
        "                                                shuffle=True,\n",
        "                                                class_mode='categorical',\n",
        "                                                subset='training')\n",
        "\n",
        "validation_generator = datagen.flow_from_directory('/content/drive/MyDrive/Autoencoder/Autoencoder-FER-CA/CK+48',\n",
        "                                                    target_size=(img_size, img_size),\n",
        "                                                    batch_size=batch_size,\n",
        "                                                    shuffle=False,\n",
        "                                                    class_mode='categorical',\n",
        "                                                    subset='validation')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names=['Anger', 'Contempt', 'Disgust', 'Fear', 'Happy', 'Sadness', 'Surprise']"
      ],
      "metadata": {
        "id": "6BEPslb4mBNU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "chf3-3eLXjhN",
        "outputId": "a48d7dc5-9a71-4b89-de63-b3000f7cc257"
      },
      "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",
            "\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 0us/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m3\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │           \u001b[38;5;34m1,792\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m224\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │          \u001b[38;5;34m36,928\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m64\u001b[0m)        │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │          \u001b[38;5;34m73,856\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m112\u001b[0m, \u001b[38;5;34m128\u001b[0m)       │         \u001b[38;5;34m147,584\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m128\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m295,168\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m56\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │         \u001b[38;5;34m590,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m256\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m1,180,160\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_pool (\u001b[38;5;33mMaxPooling2D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv1 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv2 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv3 (\u001b[38;5;33mConv2D\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m512\u001b[0m)         │       \u001b[38;5;34m2,359,808\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ global_average_pooling2d             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)             │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │         \u001b[38;5;34m262,656\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m)                   │           \u001b[38;5;34m3,591\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,792</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │          <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block1_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)        │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │          <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">112</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)       │         <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block2_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">56</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block3_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block4_pool (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ block5_conv3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)         │       <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ global_average_pooling2d             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>)             │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">262,656</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">3,591</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m14,980,935\u001b[0m (57.15 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">14,980,935</span> (57.15 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m14,980,935\u001b[0m (57.15 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">14,980,935</span> (57.15 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "base_model = VGG16(include_top = False,\n",
        "                   weights = 'imagenet',\n",
        "                   input_shape = (img_size,img_size,3))\n",
        "\n",
        "last = base_model.layers[-2].output\n",
        "x = GlobalAveragePooling2D()(last)\n",
        "x = Dense(512,'relu')(x)\n",
        "x = Dense(7, activation='softmax')(x)\n",
        "model = Model(inputs=base_model.input, outputs=x)\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=Adam(learning_rate=0.0001),\n",
        "              metrics=['accuracy'])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Yk2kzKvgXmy7"
      },
      "outputs": [],
      "source": [
        "model_name = \"model.keras\"\n",
        "checkpoint = ModelCheckpoint(model_name,\n",
        "                            monitor=\"val_loss\",\n",
        "                            mode=\"min\",\n",
        "                            save_best_only = True,\n",
        "                            verbose=1)\n",
        "\n",
        "earlystopping = EarlyStopping(monitor='val_loss',min_delta = 0, patience = 5, verbose = 1, restore_best_weights=True)\n",
        "\n",
        "learning_rate_reduction = ReduceLROnPlateau(monitor='val_loss',\n",
        "                                            patience=3,\n",
        "                                            verbose=1,\n",
        "                                            factor=0.2,\n",
        "                                            min_lr=0.00000001)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAt8dCkwXpbH",
        "outputId": "46adf585-1c89-4b48-a1b1-fb67cdbadb14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14s/step - accuracy: 0.2458 - loss: 1.9536 \n",
            "Epoch 1: val_loss improved from inf to 1.73718, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m604s\u001b[0m 17s/step - accuracy: 0.2458 - loss: 1.9517 - val_accuracy: 0.2552 - val_loss: 1.7372 - learning_rate: 1.0000e-04\n",
            "Epoch 2/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 546ms/step - accuracy: 0.3420 - loss: 1.7162\n",
            "Epoch 2: val_loss improved from 1.73718 to 1.24835, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 662ms/step - accuracy: 0.3437 - loss: 1.7127 - val_accuracy: 0.4276 - val_loss: 1.2484 - learning_rate: 1.0000e-04\n",
            "Epoch 3/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 536ms/step - accuracy: 0.4750 - loss: 1.3544\n",
            "Epoch 3: val_loss did not improve from 1.24835\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 733ms/step - accuracy: 0.4757 - loss: 1.3523 - val_accuracy: 0.5241 - val_loss: 1.2996 - learning_rate: 1.0000e-04\n",
            "Epoch 4/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 531ms/step - accuracy: 0.5852 - loss: 1.0607\n",
            "Epoch 4: val_loss improved from 1.24835 to 1.11094, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 651ms/step - accuracy: 0.5870 - loss: 1.0575 - val_accuracy: 0.5793 - val_loss: 1.1109 - learning_rate: 1.0000e-04\n",
            "Epoch 5/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 551ms/step - accuracy: 0.6812 - loss: 0.8130\n",
            "Epoch 5: val_loss improved from 1.11094 to 0.59312, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 666ms/step - accuracy: 0.6823 - loss: 0.8100 - val_accuracy: 0.7517 - val_loss: 0.5931 - learning_rate: 1.0000e-04\n",
            "Epoch 6/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 537ms/step - accuracy: 0.7239 - loss: 0.7722\n",
            "Epoch 6: val_loss did not improve from 0.59312\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 635ms/step - accuracy: 0.7224 - loss: 0.7763 - val_accuracy: 0.7103 - val_loss: 0.8190 - learning_rate: 1.0000e-04\n",
            "Epoch 7/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 560ms/step - accuracy: 0.7351 - loss: 0.6754\n",
            "Epoch 7: val_loss improved from 0.59312 to 0.52230, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m24s\u001b[0m 789ms/step - accuracy: 0.7356 - loss: 0.6736 - val_accuracy: 0.8207 - val_loss: 0.5223 - learning_rate: 1.0000e-04\n",
            "Epoch 8/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 530ms/step - accuracy: 0.8167 - loss: 0.4753\n",
            "Epoch 8: val_loss improved from 0.52230 to 0.43058, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m38s\u001b[0m 646ms/step - accuracy: 0.8177 - loss: 0.4734 - val_accuracy: 0.8138 - val_loss: 0.4306 - learning_rate: 1.0000e-04\n",
            "Epoch 9/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 543ms/step - accuracy: 0.8594 - loss: 0.3856\n",
            "Epoch 9: val_loss improved from 0.43058 to 0.42178, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 663ms/step - accuracy: 0.8595 - loss: 0.3848 - val_accuracy: 0.8621 - val_loss: 0.4218 - learning_rate: 1.0000e-04\n",
            "Epoch 10/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 526ms/step - accuracy: 0.8532 - loss: 0.3570\n",
            "Epoch 10: val_loss did not improve from 0.42178\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 621ms/step - accuracy: 0.8531 - loss: 0.3578 - val_accuracy: 0.8000 - val_loss: 0.7394 - learning_rate: 1.0000e-04\n",
            "Epoch 11/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 576ms/step - accuracy: 0.8337 - loss: 0.4546\n",
            "Epoch 11: val_loss improved from 0.42178 to 0.37210, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 691ms/step - accuracy: 0.8344 - loss: 0.4534 - val_accuracy: 0.8690 - val_loss: 0.3721 - learning_rate: 1.0000e-04\n",
            "Epoch 12/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 551ms/step - accuracy: 0.8917 - loss: 0.2848\n",
            "Epoch 12: val_loss improved from 0.37210 to 0.29407, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 704ms/step - accuracy: 0.8921 - loss: 0.2843 - val_accuracy: 0.8966 - val_loss: 0.2941 - learning_rate: 1.0000e-04\n",
            "Epoch 13/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 545ms/step - accuracy: 0.9364 - loss: 0.1812\n",
            "Epoch 13: val_loss improved from 0.29407 to 0.21077, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 659ms/step - accuracy: 0.9364 - loss: 0.1812 - val_accuracy: 0.9241 - val_loss: 0.2108 - learning_rate: 1.0000e-04\n",
            "Epoch 14/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 524ms/step - accuracy: 0.9475 - loss: 0.1654\n",
            "Epoch 14: val_loss did not improve from 0.21077\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 608ms/step - accuracy: 0.9477 - loss: 0.1651 - val_accuracy: 0.8966 - val_loss: 0.3073 - learning_rate: 1.0000e-04\n",
            "Epoch 15/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 547ms/step - accuracy: 0.9536 - loss: 0.1354\n",
            "Epoch 15: val_loss did not improve from 0.21077\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 630ms/step - accuracy: 0.9536 - loss: 0.1353 - val_accuracy: 0.9241 - val_loss: 0.3649 - learning_rate: 1.0000e-04\n",
            "Epoch 16/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 539ms/step - accuracy: 0.9457 - loss: 0.1339\n",
            "Epoch 16: val_loss improved from 0.21077 to 0.12538, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 675ms/step - accuracy: 0.9461 - loss: 0.1331 - val_accuracy: 0.9724 - val_loss: 0.1254 - learning_rate: 1.0000e-04\n",
            "Epoch 17/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 562ms/step - accuracy: 0.9290 - loss: 0.2858\n",
            "Epoch 17: val_loss did not improve from 0.12538\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 645ms/step - accuracy: 0.9286 - loss: 0.2854 - val_accuracy: 0.9172 - val_loss: 0.2315 - learning_rate: 1.0000e-04\n",
            "Epoch 18/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 573ms/step - accuracy: 0.9444 - loss: 0.1664\n",
            "Epoch 18: val_loss did not improve from 0.12538\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 658ms/step - accuracy: 0.9448 - loss: 0.1654 - val_accuracy: 0.9379 - val_loss: 0.2415 - learning_rate: 1.0000e-04\n",
            "Epoch 19/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 547ms/step - accuracy: 0.9616 - loss: 0.0968\n",
            "Epoch 19: val_loss improved from 0.12538 to 0.09660, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 661ms/step - accuracy: 0.9613 - loss: 0.0972 - val_accuracy: 0.9862 - val_loss: 0.0966 - learning_rate: 1.0000e-04\n",
            "Epoch 20/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 551ms/step - accuracy: 0.9403 - loss: 0.1979\n",
            "Epoch 20: val_loss improved from 0.09660 to 0.07208, saving model to model.keras\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 670ms/step - accuracy: 0.9406 - loss: 0.1971 - val_accuracy: 0.9724 - val_loss: 0.0721 - learning_rate: 1.0000e-04\n",
            "Epoch 21/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 576ms/step - accuracy: 0.9839 - loss: 0.0593\n",
            "Epoch 21: val_loss did not improve from 0.07208\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 662ms/step - accuracy: 0.9838 - loss: 0.0596 - val_accuracy: 0.9586 - val_loss: 0.1213 - learning_rate: 1.0000e-04\n",
            "Epoch 22/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 533ms/step - accuracy: 0.9799 - loss: 0.0490\n",
            "Epoch 22: val_loss did not improve from 0.07208\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 617ms/step - accuracy: 0.9803 - loss: 0.0485 - val_accuracy: 0.9517 - val_loss: 0.2377 - learning_rate: 1.0000e-04\n",
            "Epoch 23/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 544ms/step - accuracy: 0.9688 - loss: 0.0907\n",
            "Epoch 23: val_loss did not improve from 0.07208\n",
            "\n",
            "Epoch 23: ReduceLROnPlateau reducing learning rate to 1.9999999494757503e-05.\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 629ms/step - accuracy: 0.9676 - loss: 0.0939 - val_accuracy: 0.8897 - val_loss: 0.3150 - learning_rate: 1.0000e-04\n",
            "Epoch 24/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 509ms/step - accuracy: 0.9543 - loss: 0.1105\n",
            "Epoch 24: val_loss did not improve from 0.07208\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 600ms/step - accuracy: 0.9548 - loss: 0.1099 - val_accuracy: 0.9655 - val_loss: 0.1161 - learning_rate: 2.0000e-05\n",
            "Epoch 25/50\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 567ms/step - accuracy: 0.9914 - loss: 0.0377\n",
            "Epoch 25: val_loss did not improve from 0.07208\n",
            "\u001b[1m27/27\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 650ms/step - accuracy: 0.9914 - loss: 0.0377 - val_accuracy: 0.9793 - val_loss: 0.1006 - learning_rate: 2.0000e-05\n",
            "Epoch 25: early stopping\n",
            "Restoring model weights from the end of the best epoch: 20.\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(train_generator,\n",
        "                    epochs=50,\n",
        "                    validation_data=validation_generator,\n",
        "                    callbacks=[checkpoint,earlystopping,learning_rate_reduction])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "NTgQHyljeNDg",
        "outputId": "d34d9224-9f21-4437-9393-f56e378ab0ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "rubioQg4eXat",
        "outputId": "87e33b44-c094-4f86-e714-73d23da60d1b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(20,8))\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(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()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3k3NZcgHeZTi"
      },
      "source": [
        "**Model Evaluation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fXyR7f-uec2N"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDQ4PB3yefJy",
        "outputId": "65b8d947-193b-4feb-c5ca-dd892041b132"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 570ms/step\n"
          ]
        }
      ],
      "source": [
        "y_val = validation_generator.classes\n",
        "y_pred = model.predict(validation_generator)\n",
        "y_pred = np.argmax(y_pred,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tT-2pAnBehoG",
        "outputId": "d8ae8cec-8b76-4ddf-eb18-71354e4afe62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.90      0.95        20\n",
            "           1       1.00      1.00      1.00         8\n",
            "           2       0.93      1.00      0.96        26\n",
            "           3       1.00      1.00      1.00        11\n",
            "           4       1.00      1.00      1.00        31\n",
            "           5       0.92      1.00      0.96        12\n",
            "           6       1.00      0.97      0.99        37\n",
            "\n",
            "    accuracy                           0.98       145\n",
            "   macro avg       0.98      0.98      0.98       145\n",
            "weighted avg       0.98      0.98      0.98       145\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_val,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "50fSAatFekWL"
      },
      "outputs": [],
      "source": [
        "class_indices = validation_generator.class_indices\n",
        "indices = {v:k for k,v in class_indices.items()}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kBZPE77xemaC"
      },
      "outputs": [],
      "source": [
        "filenames = validation_generator.filenames"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "Vrd8zRdIeoIS",
        "outputId": "5a038496-a3b8-4388-aac1-4338ccf1be91"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      filename actual predicted   Same\n",
              "0  Anger/S010_004_00000017.png  Anger     Anger   True\n",
              "1  Anger/S010_004_00000018.png  Anger     Anger   True\n",
              "2  Anger/S010_004_00000019.png  Anger     Anger   True\n",
              "3  Anger/S011_004_00000019.png  Anger     Anger   True\n",
              "4  Anger/S011_004_00000020.png  Anger     Anger   True\n",
              "5  Anger/S011_004_00000021.png  Anger     Anger   True\n",
              "6  Anger/S014_003_00000028.png  Anger   Disgust  False\n",
              "7  Anger/S014_003_00000029.png  Anger     Anger   True\n",
              "8  Anger/S014_003_00000030.png  Anger   Disgust  False\n",
              "9  Anger/S022_005_00000030.png  Anger     Anger   True"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-df98cec3-83eb-440b-82d4-6a07a83f1107\" class=\"colab-df-container\">\n",
              "    <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>filename</th>\n",
              "      <th>actual</th>\n",
              "      <th>predicted</th>\n",
              "      <th>Same</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Anger/S010_004_00000017.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Anger</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Anger/S010_004_00000018.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Anger</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Anger/S010_004_00000019.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Anger</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Anger/S011_004_00000019.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Anger</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Anger/S011_004_00000020.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Anger</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Anger/S011_004_00000021.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Anger</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Anger/S014_003_00000028.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Disgust</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Anger/S014_003_00000029.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Anger</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Anger/S014_003_00000030.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Disgust</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Anger/S022_005_00000030.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Anger</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df98cec3-83eb-440b-82d4-6a07a83f1107')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-df98cec3-83eb-440b-82d4-6a07a83f1107 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-df98cec3-83eb-440b-82d4-6a07a83f1107');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-035bf79a-b5cd-4a39-ab6e-8eede6dc7ae7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-035bf79a-b5cd-4a39-ab6e-8eede6dc7ae7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-035bf79a-b5cd-4a39-ab6e-8eede6dc7ae7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "val_df",
              "summary": "{\n  \"name\": \"val_df\",\n  \"rows\": 145,\n  \"fields\": [\n    {\n      \"column\": \"filename\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 145,\n        \"samples\": [\n          \"Happy/S011_006_00000012.png\",\n          \"Surprise/S044_001_00000024.png\",\n          \"Contempt/S147_002_00000012.png\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actual\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Anger\",\n          \"Contempt\",\n          \"Sadness\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Anger\",\n          \"Disgust\",\n          \"Sadness\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Same\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "val_df = pd.DataFrame()\n",
        "val_df['filename'] = filenames\n",
        "val_df['actual'] = y_val\n",
        "val_df['predicted'] = y_pred\n",
        "val_df['actual'] = val_df['actual'].apply(lambda x: indices[x])\n",
        "val_df['predicted'] = val_df['predicted'].apply(lambda x: indices[x])\n",
        "val_df.loc[val_df['actual']==val_df['predicted'],'Same'] = True\n",
        "val_df.loc[val_df['actual']!=val_df['predicted'],'Same'] = False\n",
        "val_df.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tzy080uoeqIp"
      },
      "outputs": [],
      "source": [
        "val_df = val_df.sample(frac=1).reset_index(drop=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fOZ3G1WEes_6"
      },
      "source": [
        "Image Visulazation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GJP0-BQsevul"
      },
      "outputs": [],
      "source": [
        "def readImage(path):\n",
        "    img = load_img(path,color_mode='rgb',target_size=(img_size,img_size))\n",
        "    img = img_to_array(img)\n",
        "    img = img/255.\n",
        "\n",
        "    return img\n",
        "\n",
        "def display_images(temp_df):\n",
        "    temp_df = temp_df.reset_index(drop=True)\n",
        "    plt.figure(figsize = (20 , 20))\n",
        "    n = 0\n",
        "    for i in range(10):\n",
        "        n+=1\n",
        "        plt.subplot(5 , 5, n)\n",
        "        plt.subplots_adjust(hspace = 0.2 , wspace = 0.1)\n",
        "        image = readImage(f\"/content/drive/MyDrive/Autoencoder/Autoencoder-FER-CA/CK+48/{temp_df.filename[i]}\")\n",
        "        plt.imshow(image)\n",
        "        plt.title(f'A: {temp_df.actual[i]} P: {temp_df.predicted[i]}')\n",
        "        plt.axis('off')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "oNX7HkN-e2X_",
        "outputId": "490f7301-d315-40b9-c8f2-3de1c535a1e9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "display_images(val_df[val_df['Same']==True])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T4h2D875fARI"
      },
      "outputs": [],
      "source": [
        "def make_gradcam_heatmap(img_array, model, pred_index=None):\n",
        "\n",
        "    grad_model = Model(inputs=model.inputs, outputs=[model.layers[-4].output, model.output])\n",
        "\n",
        "    with tf.GradientTape() as tape:\n",
        "        last_conv_layer_output, preds = grad_model(img_array)\n",
        "        if pred_index is None:\n",
        "            pred_index = tf.argmax(preds[0])\n",
        "        class_channel = preds[:, pred_index]\n",
        "\n",
        "    grads = tape.gradient(class_channel, last_conv_layer_output)\n",
        "\n",
        "    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n",
        "\n",
        "    last_conv_layer_output = last_conv_layer_output[0]\n",
        "    heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]\n",
        "    heatmap = tf.squeeze(heatmap)\n",
        "\n",
        "    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n",
        "    return heatmap.numpy(), preds"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "meL1SBZUfCER"
      },
      "outputs": [],
      "source": [
        "import matplotlib.cm as cm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "5R19sznzfDrE",
        "outputId": "1c064620-a979-4d45-b70e-b537b9a31f97"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           filename    actual predicted   Same\n",
              "0     Disgust/S022_006_00000015.png   Disgust   Disgust   True\n",
              "1    Surprise/S014_001_00000028.png  Surprise  Surprise   True\n",
              "2       Anger/S011_004_00000021.png     Anger     Anger   True\n",
              "3       Anger/S014_003_00000028.png     Anger   Disgust  False\n",
              "4    Surprise/S044_001_00000023.png  Surprise  Surprise   True\n",
              "..                              ...       ...       ...    ...\n",
              "140  Contempt/S139_002_00000011.png  Contempt  Contempt   True\n",
              "141   Sadness/S042_002_00000015.png   Sadness   Sadness   True\n",
              "142   Disgust/S044_006_00000017.png   Disgust   Disgust   True\n",
              "143     Anger/S014_003_00000029.png     Anger     Anger   True\n",
              "144     Happy/S042_006_00000016.png     Happy     Happy   True\n",
              "\n",
              "[145 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-04d56c88-b411-4e6e-bef7-29150957106a\" class=\"colab-df-container\">\n",
              "    <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>filename</th>\n",
              "      <th>actual</th>\n",
              "      <th>predicted</th>\n",
              "      <th>Same</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Disgust/S022_006_00000015.png</td>\n",
              "      <td>Disgust</td>\n",
              "      <td>Disgust</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Surprise/S014_001_00000028.png</td>\n",
              "      <td>Surprise</td>\n",
              "      <td>Surprise</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Anger/S011_004_00000021.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Anger</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Anger/S014_003_00000028.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Disgust</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Surprise/S044_001_00000023.png</td>\n",
              "      <td>Surprise</td>\n",
              "      <td>Surprise</td>\n",
              "      <td>True</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>140</th>\n",
              "      <td>Contempt/S139_002_00000011.png</td>\n",
              "      <td>Contempt</td>\n",
              "      <td>Contempt</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>Sadness/S042_002_00000015.png</td>\n",
              "      <td>Sadness</td>\n",
              "      <td>Sadness</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>Disgust/S044_006_00000017.png</td>\n",
              "      <td>Disgust</td>\n",
              "      <td>Disgust</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>Anger/S014_003_00000029.png</td>\n",
              "      <td>Anger</td>\n",
              "      <td>Anger</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>Happy/S042_006_00000016.png</td>\n",
              "      <td>Happy</td>\n",
              "      <td>Happy</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>145 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04d56c88-b411-4e6e-bef7-29150957106a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-04d56c88-b411-4e6e-bef7-29150957106a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-04d56c88-b411-4e6e-bef7-29150957106a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1806133a-d7f6-49cb-a726-7f8b03864606\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1806133a-d7f6-49cb-a726-7f8b03864606')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1806133a-d7f6-49cb-a726-7f8b03864606 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "val_df",
              "summary": "{\n  \"name\": \"val_df\",\n  \"rows\": 145,\n  \"fields\": [\n    {\n      \"column\": \"filename\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 145,\n        \"samples\": [\n          \"Contempt/S138_008_00000007.png\",\n          \"Contempt/S139_002_00000011.png\",\n          \"Anger/S022_005_00000030.png\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actual\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Disgust\",\n          \"Surprise\",\n          \"Happy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Disgust\",\n          \"Surprise\",\n          \"Happy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Same\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "val_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4McRhVgGfFz-"
      },
      "outputs": [],
      "source": [
        "def gradCAMImage(image):\n",
        "    path = f\"/content/drive/MyDrive/Autoencoder/Autoencoder-FER-CA/CK+48/{image}\"\n",
        "    img = readImage(path)\n",
        "    img = np.expand_dims(img,axis=0)\n",
        "    heatmap,preds = make_gradcam_heatmap(img,model)\n",
        "\n",
        "    img = load_img(path)\n",
        "    img = img_to_array(img)\n",
        "    heatmap = np.uint8(255 * heatmap)\n",
        "\n",
        "    # Use jet colormap to colorize heatmap\n",
        "    jet = cm.get_cmap(\"jet\")\n",
        "\n",
        "    # Use RGB values of the colormap\n",
        "    jet_colors = jet(np.arange(256))[:, :3]\n",
        "    jet_heatmap = jet_colors[heatmap]\n",
        "\n",
        "    # Create an image with RGB colorized heatmap\n",
        "    jet_heatmap = tf.keras.preprocessing.image.array_to_img(jet_heatmap)\n",
        "    jet_heatmap = jet_heatmap.resize((img.shape[1], img.shape[0]))\n",
        "    jet_heatmap = tf.keras.preprocessing.image.img_to_array(jet_heatmap)\n",
        "\n",
        "    # Superimpose the heatmap on original image\n",
        "    superimposed_img = jet_heatmap * 0.8 + img\n",
        "    superimposed_img = tf.keras.preprocessing.image.array_to_img(superimposed_img)\n",
        "\n",
        "    return superimposed_img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FbkKLJmufH9p"
      },
      "outputs": [],
      "source": [
        "def gradcam_of_images(correct_class):\n",
        "    grad_images = []\n",
        "    title = []\n",
        "    temp_df = val_df[val_df['Same']==correct_class]\n",
        "    temp_df = temp_df.reset_index(drop=True)\n",
        "    for i in range(10):\n",
        "        image = temp_df.filename[i]\n",
        "        grad_image = gradCAMImage(image)\n",
        "        grad_images.append(grad_image)\n",
        "        title.append(f\"A: {temp_df.actual[i]} P: {temp_df.predicted[i]}\")\n",
        "\n",
        "    return grad_images, title"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MtI2BqRGfJuT",
        "outputId": "c426ed17-0bb0-47d1-e7e0-ae4cec50a155"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-34-d665254e59bf>:12: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
            "  jet = cm.get_cmap(\"jet\")\n"
          ]
        }
      ],
      "source": [
        "correctly_classified, c_titles = gradcam_of_images(correct_class=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0m8LMEOnfLqw"
      },
      "outputs": [],
      "source": [
        "def display_heatmaps(classified_images,titles):\n",
        "    plt.figure(figsize = (20 , 20))\n",
        "    n = 0\n",
        "    for i in range(10):\n",
        "        n+=1\n",
        "        plt.subplot(5 , 5, n)\n",
        "        plt.subplots_adjust(hspace = 0.2 , wspace = 0.1)\n",
        "        plt.imshow(classified_images[i])\n",
        "        plt.title(titles[i])\n",
        "        plt.axis('off')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "sIojbtJxfNas",
        "outputId": "276bf685-a196-483d-d65e-a96683141a3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 10 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "display_heatmaps(correctly_classified,c_titles)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}