sentiment_analysis.ipynb

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      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aaolcay/Convolutional-Neural-Network-Examples/blob/main/sentiment_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "D0bxQMAaQab2"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from PIL import Image"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Download Kaggle Dataset** "
      ],
      "metadata": {
        "id": "9MqWkxseJr4L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install kaggle python library\n",
        "!pip install kaggle"
      ],
      "metadata": {
        "id": "grS6ApxUHSZ4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "%cd /gdrive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uE9ydGFHYtT",
        "outputId": "a8824a63-b28d-498d-f721-c6adae16fa74"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n",
            "/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Note that you need to create `New API Token` from your `Kaggle` account and upload it into a folder created for the data. In this example, I put downloaded `json` file into a folder called `Data`. "
      ],
      "metadata": {
        "id": "cRSj1j2DcQa6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ['KAGGLE_CONFIG_DIR'] = '/gdrive/MyDrive/Colab Notebooks/Sentiment Analysis/Data'"
      ],
      "metadata": {
        "id": "ppBto7c1ItHS"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# changing the working directory\n",
        "%cd '/gdrive/MyDrive/Colab Notebooks/Sentiment Analysis/Data'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bmjQKf-bI5se",
        "outputId": "bdc747a3-6d3e-4c76-9982-bb9929189ce7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/gdrive/MyDrive/Colab Notebooks/Sentiment Analysis/Data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the current working directory\n",
        "!pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PKjBWdnTJdhw",
        "outputId": "6b6dce23-6d5e-441f-f540-a3b920547bb5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/gdrive/MyDrive/Colab Notebooks/Sentiment Analysis/Data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Open Kaggle link in which the dataset was located, `Copy API command` and paste the cell below after `!`"
      ],
      "metadata": {
        "id": "OQ_Q072nc3YO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d deadskull7/fer2013"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBGFNLnFJC8Y",
        "outputId": "99f43903-c202-4f5f-a511-bc4ff2e78d11"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading fer2013.zip to /gdrive/MyDrive/Colab Notebooks/Sentiment Analysis/Data\n",
            " 90% 87.0M/96.6M [00:00<00:00, 164MB/s]\n",
            "100% 96.6M/96.6M [00:00<00:00, 168MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip \\*.zip && rm *.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MGz9jD-VJHrN",
        "outputId": "4e4e9109-e0ed-4edf-d43b-8d9ec60d9580"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  fer2013.zip\n",
            "  inflating: fer2013.csv             \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Prepare Data**"
      ],
      "metadata": {
        "id": "qXxvxMOGJ6CS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AcpepgPoKaZk",
        "outputId": "8e28bf38-6ec8-47c3-acaa-44e1aef90cf2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fer2013.csv  kaggle.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('fer2013.csv')"
      ],
      "metadata": {
        "id": "AFrbLHXFJnaI"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(10)"
      ],
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          "height": 363
        },
        "id": "GEBEwUJcKdIc",
        "outputId": "be121425-5fa7-4b09-92ab-74eacbbdb026"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   emotion                                             pixels     Usage\n",
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    {
      "cell_type": "code",
      "source": [
        "data['Usage'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGkSW2RqKwjU",
        "outputId": "8bec93db-8798-4d59-c71f-9db45025717a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Training       28709\n",
              "PublicTest      3589\n",
              "PrivateTest     3589\n",
              "Name: Usage, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Split Data into Train and Test**"
      ],
      "metadata": {
        "id": "DUXq9BVALAbd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = data[data.Usage=='Training']"
      ],
      "metadata": {
        "id": "KuBAAiSzKtUS"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_pixels = train_data.pixels"
      ],
      "metadata": {
        "id": "ORPnY3beLOZx"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_pixels = train_data_pixels.str.split(\" \").tolist()"
      ],
      "metadata": {
        "id": "YP-v-8lNLvzB"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_pixels = pd.DataFrame(train_data_pixels, dtype=int)"
      ],
      "metadata": {
        "id": "gd2QXqWqMOOi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_image = train_data_pixels.values"
      ],
      "metadata": {
        "id": "Zg4LNNZ2Mz85"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_image.astype(np.float)"
      ],
      "metadata": {
        "id": "1Xkqs1-9NtD4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = data[data.Usage=='PublicTest']\n",
        "test_data_pixels = test_data.pixels\n",
        "test_data_pixels = test_data_pixels.str.split(\" \").tolist()\n",
        "test_data_pixels = pd.DataFrame(test_data_pixels, dtype=int)\n",
        "test_data_image = test_data_pixels.values\n",
        "test_data_image.astype(np.float)"
      ],
      "metadata": {
        "id": "xOpx_QUwL9F8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Targets**\n",
        "For training data"
      ],
      "metadata": {
        "id": "ic6q--YfdlrL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find number of targets/labels/class\n",
        "num_class = len(np.unique(data.emotion.values))"
      ],
      "metadata": {
        "id": "IkpObW77ehFx"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels = train_data[\"emotion\"]"
      ],
      "metadata": {
        "id": "IF5v6ZTlPebi"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels = train_labels.values"
      ],
      "metadata": {
        "id": "fqB1wXotd4Dt"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We apply one-hot coding to use softmax in the model to do prediction for each class."
      ],
      "metadata": {
        "id": "21gmR4U5eAf6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow.keras as keras\n",
        "train_labels = keras.utils.to_categorical(train_labels, num_class)"
      ],
      "metadata": {
        "id": "WTUnBTgJd48u"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFXRJ50Ae2fs",
        "outputId": "4b75919b-83e7-429c-ad3f-b2d2e0d8bf74"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28709, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For test data"
      ],
      "metadata": {
        "id": "6vbuzH6SfQef"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels = test_data[\"emotion\"]\n",
        "test_labels = test_labels.values\n",
        "test_labels = keras.utils.to_categorical(test_labels, num_class)"
      ],
      "metadata": {
        "id": "h9tstqzPe5RG"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qXpbENLlffRs",
        "outputId": "47864c33-c303-4c91-e64b-b969eb7fe9e4"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3589, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary of dataset**\n",
        "\n",
        "`train_data_image`: train data (28709, 2304)\n",
        "\n",
        "`test_data_image`: test data (3589, 2304)\n",
        "\n",
        "`train_labels`: train labels (28709, 7)\n",
        "\n",
        "`test_labels`: test labels (3589, 7)"
      ],
      "metadata": {
        "id": "CAP3I0n0f32T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RtXjib1zhgiK",
        "outputId": "0dc2ba33-2c96-4cef-d9b4-0d2d191e8814"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28709, 2304)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape train data to make (Number of samples, 48, 48)\n",
        "train_image = np.zeros((train_data_image.shape[0],48,48))\n",
        "for i in range(train_data_image.shape[0]):\n",
        "  train_image[i,:,:] = train_data_image[i].reshape(48,48)"
      ],
      "metadata": {
        "id": "YejhDuYTflNg"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k2Z_IAEQgFFV",
        "outputId": "b873b154-4573-4bb6-b796-8fba9d0865a4"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28709, 48, 48)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AiBazqQoht8r",
        "outputId": "ba05ec82-14c9-4fa7-ba67-9c94621f587f"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3589, 2304)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reshape test data to make (Number of samples, 48, 48)\n",
        "test_image = np.zeros((test_data_image.shape[0],48,48))\n",
        "for i in range(test_data_image.shape[0]):\n",
        "  test_image[i,:,:] = test_data_image[i].reshape(48,48)"
      ],
      "metadata": {
        "id": "WzIySttKgyjc"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_image.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jj7eNRaJhoX6",
        "outputId": "d13379ed-4a20-4163-e635-908e0d2c6f2b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3589, 48, 48)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uR6hPI-WhqO-",
        "outputId": "9e7ff286-ae0f-4fde-a0b9-67226096f25e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28709, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhPVuUMoh1Ig",
        "outputId": "8efa73b6-9d04-4214-865d-d8d8ba54e100"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3589, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Summary of dataset after reshaping both train and test data**\n",
        "\n",
        "`train_image`: train data (28709, 48, 48)\n",
        "\n",
        "`test_image`: test data (3589, 48, 48)\n",
        "\n",
        "`train_labels`: train labels (28709, 7)\n",
        "\n",
        "`test_labels`: test labels (3589, 7)"
      ],
      "metadata": {
        "id": "XFmoO1Wwh9TL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(5,5))\n",
        "labels_class = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']\n",
        "for k in range(20):\n",
        "  fig.add_subplot(4,5,k+1)\n",
        "  plt.imshow(train_image[k,:,:], cmap='gray')\n",
        "  label = np.where(train_labels[k]==1)\n",
        "  num_label = label[0][0]\n",
        "  plt.title(f'{labels_class[num_label]}')\n",
        "  fig.tight_layout(pad=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "n6BaavKvh2rP",
        "outputId": "99b070dd-1e79-48b7-fa2c-45fa9d6345ae"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "See number of images in training and test sets"
      ],
      "metadata": {
        "id": "iV_-9mU_kejf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "emotions = train_data.emotion.values\n",
        "num_emotions = len(np.unique(emotions))\n",
        "number_class = np.zeros(num_emotions)\n",
        "for i in range(num_emotions):\n",
        "  number_class[i] = len(np.where(emotions==i)[0])\n",
        "plt.figure()\n",
        "plt.bar(labels_class,number_class)\n",
        "plt.ylabel('Number of Samples')\n",
        "plt.title('Data Distribution in Train Set')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "a7M5aYsvezS6",
        "outputId": "0d671b9c-e215-444a-fd7d-a7d5300f8ce4"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emotions_test = test_data.emotion.values\n",
        "num_emotions_test = len(np.unique(emotions_test))\n",
        "number_class_test = np.zeros(num_emotions_test)\n",
        "for i in range(num_emotions_test):\n",
        "  number_class_test[i] = len(np.where(emotions_test==i)[0])\n",
        "plt.figure()\n",
        "plt.bar(labels_class,number_class_test)\n",
        "plt.ylabel('Number of Samples')\n",
        "plt.title('Data Distribution in Test Set')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "D0R9kUPLmbSr",
        "outputId": "2f43affe-e6fc-4e1a-be4c-ab85bb375bfd"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As seen, the images of disgust emotion is very less. Therefore, we fit the number of other classes to 3000 in training dataset and to 300 in the test dataset."
      ],
      "metadata": {
        "id": "rVPOnjRdzL0j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMDGTvcwBVZg",
        "outputId": "03bec65f-c85d-4ad1-e7fc-0c1afc6898bb"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28709, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "labels = np.array([[1,0,0,0,0,0,0],\n",
        "                   [0,1,0,0,0,0,0],\n",
        "                   [0,0,1,0,0,0,0],\n",
        "                   [0,0,0,1,0,0,0],\n",
        "                   [0,0,0,0,1,0,0],\n",
        "                   [0,0,0,0,0,1,0],\n",
        "                   [0,0,0,0,0,0,1]], dtype=\"float32\")\n",
        "train = np.zeros((3000*6 + len(np.where(emotions==1)[0]), 48, 48))\n",
        "train_label = np.zeros((3000*6 + len(np.where(emotions==1)[0]), 7))\n",
        "count = 0\n",
        "for i in range(7):\n",
        "  start = count\n",
        "  classes = np.where(np.all(train_labels == labels[i], axis=1))[0]\n",
        "  if i == 1:\n",
        "    end = start + len(classes)\n",
        "    print(f\"start:{start}, end:{end}\")\n",
        "    train[start:end,:,:] = train_image[classes,:,:]\n",
        "    train_label[start:end,:] = train_labels[classes,:]\n",
        "  else:\n",
        "    end = start + 3000\n",
        "    print(f\"start:{start}, end:{end}\")\n",
        "    train[start:end,:,:] = train_image[classes[0:3000],:,:]\n",
        "    train_label[start:end,:] = train_labels[classes[0:3000],:]\n",
        "  count = end"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUXqr6vEzkc-",
        "outputId": "7b9e8a90-2650-47ec-a241-dae9336d1cb7"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "start:0, end:3000\n",
            "start:3000, end:3436\n",
            "start:3436, end:6436\n",
            "start:6436, end:9436\n",
            "start:9436, end:12436\n",
            "start:12436, end:15436\n",
            "start:15436, end:18436\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_image = train\n",
        "train_labels = train_label"
      ],
      "metadata": {
        "id": "ehBBUQDuETc-"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "number_class = np.zeros(7)\n",
        "for i in range(7):\n",
        "  number_class[i] = len(np.where(np.all(train_labels == labels[i], axis=1))[0])\n",
        "\n",
        "plt.figure()\n",
        "plt.bar(labels_class,number_class)\n",
        "plt.ylabel('Number of Samples')\n",
        "plt.title('Data Distribution in Train Set')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "aSsWFSGk87U1",
        "outputId": "aa2adfd2-b49f-4053-d63c-aa01ef00247e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test = np.zeros((400*6 + len(np.where(np.all(test_labels == labels[1], axis=1))[0]), 48, 48))\n",
        "test_label = np.zeros((400*6 + len(np.where(np.all(test_labels == labels[1], axis=1))[0]), 7))\n",
        "count = 0\n",
        "for i in range(7):\n",
        "  start = count\n",
        "  classes = np.where(np.all(test_labels == labels[i], axis=1))[0]\n",
        "  if i == 1:\n",
        "    end = start + len(classes)\n",
        "    print(f\"start:{start}, end:{end}\")\n",
        "    test[start:end,:,:] = test_image[classes,:,:]\n",
        "    test_label[start:end,:] = test_labels[classes,:]\n",
        "  else:\n",
        "    end = start + 400\n",
        "    print(f\"start:{start}, end:{end}\")\n",
        "    test[start:end,:,:] = test_image[classes[0:400],:,:]\n",
        "    test_label[start:end,:] = test_labels[classes[0:400],:]\n",
        "  count = end"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDzU2oZTFEC0",
        "outputId": "8a656040-d916-45f7-ddb8-bc2b43fad083"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "start:0, end:400\n",
            "start:400, end:456\n",
            "start:456, end:856\n",
            "start:856, end:1256\n",
            "start:1256, end:1656\n",
            "start:1656, end:2056\n",
            "start:2056, end:2456\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_image = test\n",
        "test_labels = test_label"
      ],
      "metadata": {
        "id": "CET3ntIWGBMJ"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "number_class = np.zeros(7)\n",
        "for i in range(7):\n",
        "  number_class[i] = len(np.where(np.all(test_labels == labels[i], axis=1))[0])\n",
        "\n",
        "plt.figure()\n",
        "plt.bar(labels_class,number_class)\n",
        "plt.ylabel('Number of Samples')\n",
        "plt.title('Data Distribution in Test Set')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "gj0rwxM3F65M",
        "outputId": "6f7b8c34-5d41-4b14-c043-85d4e771069d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_image = train_image[...,np.newaxis]\n",
        "test_image = test_image[...,np.newaxis]"
      ],
      "metadata": {
        "id": "jHfFgAxMwXJ_"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Rescale Image Pixels\n",
        "train_image = (train_image-np.mean(train_image))/np.std(train_image)\n",
        "test_image = (test_image-np.mean(test_image))/np.std(test_image)"
      ],
      "metadata": {
        "id": "wL0HKrk66pJT"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_shape = (train_image.shape[1],train_image.shape[2],train_image.shape[3])"
      ],
      "metadata": {
        "id": "VpycHpY8wj4N"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "input_shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h4c8SsI4wuim",
        "outputId": "8e507833-4362-4ff1-e0a1-93c0259ed923"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48, 48, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Convolutional Neural Network**"
      ],
      "metadata": {
        "id": "wTAahF4bTzYl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**Keras API Model**\n",
        "\n",
        "We implemented the same model of [this competitor](https://www.kaggle.com/code/anantgupt/facial-emotion-recogination-91-accuracy-train-set), as he/she has gotten a quite high accuracy."
      ],
      "metadata": {
        "id": "9YEWhMODnY4P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow.keras as keras\n",
        "import keras.regularizers as regularizers"
      ],
      "metadata": {
        "id": "cRQJhl1pyTr-"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_input = keras.Input(shape=input_shape, name='model_input')\n",
        "\n",
        "model_conv_1 = keras.layers.Conv2D(32,\n",
        "                                  kernel_size = (3,3),\n",
        "                                  strides = (1,1),\n",
        "                                  activation='relu',\n",
        "                                  padding='same',\n",
        "                                  name = 'conv_layer_1'   \n",
        "                                  )(model_input)\n",
        "\n",
        "model_conv_2 = keras.layers.Conv2D(64,\n",
        "                                  kernel_size = (3,3),\n",
        "                                  strides = (1,1),\n",
        "                                  activation='relu',\n",
        "                                  padding='same',\n",
        "                                  name = 'conv_layer_2'   \n",
        "                                  )(model_conv_1)\n",
        "\n",
        "batch_1 = keras.layers.BatchNormalization(name='encoder_bn_1')(model_conv_2)\n",
        "\n",
        "model_max_pool = keras.layers.MaxPool2D((2,2), # grid size\n",
        "                                     name='max_pool_1')(batch_1)\n",
        "\n",
        "dropout1 = keras.layers.Dropout(0.25, name='dropout1')(model_max_pool)\n",
        "\n",
        "model_conv_3 = keras.layers.Conv2D(128,\n",
        "                                  kernel_size = (3,3),\n",
        "                                  strides = (1,1),\n",
        "                                  activation='relu',\n",
        "                                  padding='same',\n",
        "                                  kernel_regularizer=regularizers.l2(0.01),\n",
        "                                  name = 'conv_layer_3'   \n",
        "                                  )(dropout1)\n",
        "\n",
        "model_conv_4 = keras.layers.Conv2D(256,\n",
        "                                  kernel_size = (3,3),\n",
        "                                  strides = (1,1),\n",
        "                                  activation='relu',\n",
        "                                  kernel_regularizer=regularizers.l2(0.01),\n",
        "                                  name = 'conv_layer_4'   \n",
        "                                  )(model_conv_3)\n",
        "\n",
        "batch_2 = keras.layers.BatchNormalization(name='encoder_bn_2')(model_conv_4)\n",
        "\n",
        "model_max_pool2 = keras.layers.MaxPool2D((2,2), # grid size\n",
        "                                     name='max_pool_2')(batch_2)\n",
        "\n",
        "dropout2 = keras.layers.Dropout(0.25, name='dropout2')(model_max_pool2)\n",
        "\n",
        "flatten1 = keras.layers.Flatten(name='flatten_1')(dropout2)\n",
        "\n",
        "dense1 = keras.layers.Dense(1024, \n",
        "                            activation='relu', \n",
        "                            name='Dense_1')(flatten1)\n",
        "\n",
        "dropout3 = keras.layers.Dropout(0.5, name='dropout3')(dense1)\n",
        "\n",
        "output = keras.layers.Dense(num_emotions_test, \n",
        "                            activation='softmax', \n",
        "                            name='model_output')(dropout3)\n",
        "\n",
        "# Make this network a model\n",
        "model_API = keras.Model(model_input, output, name='conv_model_API_fer')    "
      ],
      "metadata": {
        "id": "8lR8OtjNmx7M"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_API.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ucFH4EDZ0g46",
        "outputId": "a6a22311-03f9-4dda-b307-0f704361d297"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"conv_model_API_fer\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " model_input (InputLayer)    [(None, 48, 48, 1)]       0         \n",
            "                                                                 \n",
            " conv_layer_1 (Conv2D)       (None, 48, 48, 32)        320       \n",
            "                                                                 \n",
            " conv_layer_2 (Conv2D)       (None, 48, 48, 64)        18496     \n",
            "                                                                 \n",
            " encoder_bn_1 (BatchNormaliz  (None, 48, 48, 64)       256       \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " max_pool_1 (MaxPooling2D)   (None, 24, 24, 64)        0         \n",
            "                                                                 \n",
            " dropout1 (Dropout)          (None, 24, 24, 64)        0         \n",
            "                                                                 \n",
            " conv_layer_3 (Conv2D)       (None, 24, 24, 128)       73856     \n",
            "                                                                 \n",
            " conv_layer_4 (Conv2D)       (None, 22, 22, 256)       295168    \n",
            "                                                                 \n",
            " encoder_bn_2 (BatchNormaliz  (None, 22, 22, 256)      1024      \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " max_pool_2 (MaxPooling2D)   (None, 11, 11, 256)       0         \n",
            "                                                                 \n",
            " dropout2 (Dropout)          (None, 11, 11, 256)       0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 30976)             0         \n",
            "                                                                 \n",
            " Dense_1 (Dense)             (None, 1024)              31720448  \n",
            "                                                                 \n",
            " dropout3 (Dropout)          (None, 1024)              0         \n",
            "                                                                 \n",
            " model_output (Dense)        (None, 7)                 7175      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 32,116,743\n",
            "Trainable params: 32,116,103\n",
            "Non-trainable params: 640\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Compiling the model\n",
        "model_API.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), \n",
        "                  loss='categorical_crossentropy', \n",
        "                  metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "o9_xvaAz0TvA"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ModelCheckpoint\n",
        "checkpointer = ModelCheckpoint(filepath= 'data/face_model.h5', verbose=1, save_best_only=True)\n",
        "\n",
        "# Training\n",
        "history = model_API.fit(train_image, \n",
        "                        train_labels, \n",
        "                        validation_data=(test_image, test_labels), \n",
        "                        batch_size=100, epochs=50, \n",
        "                        shuffle=True, callbacks=[checkpointer], \n",
        "                        verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7uY7pyWKHHz",
        "outputId": "cb9603d4-f31b-456e-c007-c022c74b8a3f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\n",
            "Epoch 1: val_loss improved from inf to 29.48911, saving model to data/face_model.h5\n",
            "185/185 - 28s - loss: 6.9673 - accuracy: 0.2496 - val_loss: 29.4891 - val_accuracy: 0.1629 - 28s/epoch - 151ms/step\n",
            "Epoch 2/50\n",
            "\n",
            "Epoch 2: val_loss improved from 29.48911 to 22.18777, saving model to data/face_model.h5\n",
            "185/185 - 15s - loss: 2.8649 - accuracy: 0.3190 - val_loss: 22.1878 - val_accuracy: 0.1800 - 15s/epoch - 79ms/step\n",
            "Epoch 3/50\n",
            "\n",
            "Epoch 3: val_loss improved from 22.18777 to 6.73187, saving model to data/face_model.h5\n",
            "185/185 - 13s - loss: 2.1962 - accuracy: 0.3528 - val_loss: 6.7319 - val_accuracy: 0.2866 - 13s/epoch - 70ms/step\n",
            "Epoch 4/50\n",
            "\n",
            "Epoch 4: val_loss improved from 6.73187 to 1.84573, saving model to data/face_model.h5\n",
            "185/185 - 13s - loss: 1.8549 - accuracy: 0.3772 - val_loss: 1.8457 - val_accuracy: 0.3852 - 13s/epoch - 69ms/step\n",
            "Epoch 5/50\n",
            "\n",
            "Epoch 5: val_loss did not improve from 1.84573\n",
            "185/185 - 11s - loss: 1.7013 - accuracy: 0.4041 - val_loss: 2.2452 - val_accuracy: 0.3819 - 11s/epoch - 60ms/step\n",
            "Epoch 6/50\n",
            "\n",
            "Epoch 6: val_loss improved from 1.84573 to 1.64393, saving model to data/face_model.h5\n",
            "185/185 - 15s - loss: 1.6173 - accuracy: 0.4229 - val_loss: 1.6439 - val_accuracy: 0.4275 - 15s/epoch - 80ms/step\n",
            "Epoch 7/50\n",
            "\n",
            "Epoch 7: val_loss improved from 1.64393 to 1.54614, saving model to data/face_model.h5\n",
            "185/185 - 13s - loss: 1.5515 - accuracy: 0.4433 - val_loss: 1.5461 - val_accuracy: 0.4601 - 13s/epoch - 71ms/step\n",
            "Epoch 8/50\n",
            "\n",
            "Epoch 8: val_loss did not improve from 1.54614\n",
            "185/185 - 11s - loss: 1.5049 - accuracy: 0.4633 - val_loss: 1.5819 - val_accuracy: 0.4251 - 11s/epoch - 59ms/step\n",
            "Epoch 9/50\n",
            "\n",
            "Epoch 9: val_loss improved from 1.54614 to 1.54320, saving model to data/face_model.h5\n",
            "185/185 - 15s - loss: 1.4471 - accuracy: 0.4864 - val_loss: 1.5432 - val_accuracy: 0.4446 - 15s/epoch - 80ms/step\n",
            "Epoch 10/50\n",
            "\n",
            "Epoch 10: val_loss improved from 1.54320 to 1.51651, saving model to data/face_model.h5\n",
            "185/185 - 13s - loss: 1.4154 - accuracy: 0.4990 - val_loss: 1.5165 - val_accuracy: 0.4723 - 13s/epoch - 73ms/step\n",
            "Epoch 11/50\n",
            "\n",
            "Epoch 11: val_loss improved from 1.51651 to 1.42543, saving model to data/face_model.h5\n",
            "185/185 - 14s - loss: 1.3820 - accuracy: 0.5163 - val_loss: 1.4254 - val_accuracy: 0.5057 - 14s/epoch - 73ms/step\n",
            "Epoch 12/50\n",
            "\n",
            "Epoch 12: val_loss did not improve from 1.42543\n",
            "185/185 - 11s - loss: 1.3174 - accuracy: 0.5441 - val_loss: 1.4822 - val_accuracy: 0.4906 - 11s/epoch - 60ms/step\n",
            "Epoch 13/50\n",
            "\n",
            "Epoch 13: val_loss did not improve from 1.42543\n",
            "185/185 - 11s - loss: 1.2671 - accuracy: 0.5632 - val_loss: 1.4898 - val_accuracy: 0.4971 - 11s/epoch - 60ms/step\n",
            "Epoch 14/50\n",
            "\n",
            "Epoch 14: val_loss did not improve from 1.42543\n",
            "185/185 - 11s - loss: 1.2159 - accuracy: 0.5877 - val_loss: 1.4551 - val_accuracy: 0.5183 - 11s/epoch - 61ms/step\n",
            "Epoch 15/50\n",
            "\n",
            "Epoch 15: val_loss improved from 1.42543 to 1.41838, saving model to data/face_model.h5\n",
            "185/185 - 15s - loss: 1.1677 - accuracy: 0.6061 - val_loss: 1.4184 - val_accuracy: 0.5163 - 15s/epoch - 79ms/step\n",
            "Epoch 16/50\n",
            "\n",
            "Epoch 16: val_loss did not improve from 1.41838\n",
            "185/185 - 11s - loss: 1.1219 - accuracy: 0.6279 - val_loss: 1.4490 - val_accuracy: 0.5257 - 11s/epoch - 59ms/step\n",
            "Epoch 17/50\n",
            "\n",
            "Epoch 17: val_loss did not improve from 1.41838\n",
            "185/185 - 11s - loss: 1.0529 - accuracy: 0.6510 - val_loss: 1.4229 - val_accuracy: 0.5257 - 11s/epoch - 59ms/step\n",
            "Epoch 18/50\n",
            "\n",
            "Epoch 18: val_loss did not improve from 1.41838\n",
            "185/185 - 11s - loss: 1.0238 - accuracy: 0.6638 - val_loss: 1.4650 - val_accuracy: 0.5318 - 11s/epoch - 60ms/step\n",
            "Epoch 19/50\n",
            "\n",
            "Epoch 19: val_loss did not improve from 1.41838\n",
            "185/185 - 11s - loss: 0.9660 - accuracy: 0.6891 - val_loss: 1.4216 - val_accuracy: 0.5411 - 11s/epoch - 60ms/step\n",
            "Epoch 20/50\n",
            "\n",
            "Epoch 20: val_loss did not improve from 1.41838\n",
            "185/185 - 11s - loss: 0.9180 - accuracy: 0.7018 - val_loss: 1.4716 - val_accuracy: 0.5228 - 11s/epoch - 61ms/step\n",
            "Epoch 21/50\n",
            "\n",
            "Epoch 21: val_loss did not improve from 1.41838\n",
            "185/185 - 11s - loss: 0.8814 - accuracy: 0.7153 - val_loss: 1.4620 - val_accuracy: 0.5289 - 11s/epoch - 61ms/step\n",
            "Epoch 22/50\n",
            "\n",
            "Epoch 22: val_loss improved from 1.41838 to 1.40814, saving model to data/face_model.h5\n",
            "185/185 - 15s - loss: 0.8500 - accuracy: 0.7236 - val_loss: 1.4081 - val_accuracy: 0.5464 - 15s/epoch - 80ms/step\n",
            "Epoch 23/50\n",
            "\n",
            "Epoch 23: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.8098 - accuracy: 0.7434 - val_loss: 1.5152 - val_accuracy: 0.5489 - 11s/epoch - 59ms/step\n",
            "Epoch 24/50\n",
            "\n",
            "Epoch 24: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.7690 - accuracy: 0.7534 - val_loss: 1.4508 - val_accuracy: 0.5350 - 11s/epoch - 59ms/step\n",
            "Epoch 25/50\n",
            "\n",
            "Epoch 25: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.7562 - accuracy: 0.7629 - val_loss: 1.4575 - val_accuracy: 0.5456 - 11s/epoch - 59ms/step\n",
            "Epoch 26/50\n",
            "\n",
            "Epoch 26: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.7249 - accuracy: 0.7729 - val_loss: 1.4910 - val_accuracy: 0.5423 - 11s/epoch - 60ms/step\n",
            "Epoch 27/50\n",
            "\n",
            "Epoch 27: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.7058 - accuracy: 0.7791 - val_loss: 1.4265 - val_accuracy: 0.5550 - 11s/epoch - 59ms/step\n",
            "Epoch 28/50\n",
            "\n",
            "Epoch 28: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.6778 - accuracy: 0.7888 - val_loss: 1.5039 - val_accuracy: 0.5513 - 11s/epoch - 59ms/step\n",
            "Epoch 29/50\n",
            "\n",
            "Epoch 29: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.6660 - accuracy: 0.7938 - val_loss: 1.4968 - val_accuracy: 0.5493 - 11s/epoch - 59ms/step\n",
            "Epoch 30/50\n",
            "\n",
            "Epoch 30: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.6327 - accuracy: 0.8065 - val_loss: 1.4907 - val_accuracy: 0.5464 - 11s/epoch - 59ms/step\n",
            "Epoch 31/50\n",
            "\n",
            "Epoch 31: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.6407 - accuracy: 0.8052 - val_loss: 1.6061 - val_accuracy: 0.5501 - 11s/epoch - 60ms/step\n",
            "Epoch 32/50\n",
            "\n",
            "Epoch 32: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.6062 - accuracy: 0.8180 - val_loss: 1.6523 - val_accuracy: 0.5366 - 11s/epoch - 60ms/step\n",
            "Epoch 33/50\n",
            "\n",
            "Epoch 33: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5891 - accuracy: 0.8222 - val_loss: 1.4859 - val_accuracy: 0.5452 - 11s/epoch - 60ms/step\n",
            "Epoch 34/50\n",
            "\n",
            "Epoch 34: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5742 - accuracy: 0.8285 - val_loss: 1.6038 - val_accuracy: 0.5460 - 11s/epoch - 58ms/step\n",
            "Epoch 35/50\n",
            "\n",
            "Epoch 35: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5969 - accuracy: 0.8260 - val_loss: 1.7724 - val_accuracy: 0.5387 - 11s/epoch - 58ms/step\n",
            "Epoch 36/50\n",
            "\n",
            "Epoch 36: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5669 - accuracy: 0.8323 - val_loss: 1.6803 - val_accuracy: 0.5517 - 11s/epoch - 60ms/step\n",
            "Epoch 37/50\n",
            "\n",
            "Epoch 37: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5446 - accuracy: 0.8383 - val_loss: 1.9341 - val_accuracy: 0.5489 - 11s/epoch - 60ms/step\n",
            "Epoch 38/50\n",
            "\n",
            "Epoch 38: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5558 - accuracy: 0.8414 - val_loss: 1.6648 - val_accuracy: 0.5318 - 11s/epoch - 60ms/step\n",
            "Epoch 39/50\n",
            "\n",
            "Epoch 39: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5465 - accuracy: 0.8398 - val_loss: 1.6957 - val_accuracy: 0.5432 - 11s/epoch - 59ms/step\n",
            "Epoch 40/50\n",
            "\n",
            "Epoch 40: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5200 - accuracy: 0.8477 - val_loss: 1.6679 - val_accuracy: 0.5452 - 11s/epoch - 59ms/step\n",
            "Epoch 41/50\n",
            "\n",
            "Epoch 41: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5184 - accuracy: 0.8534 - val_loss: 1.8065 - val_accuracy: 0.5334 - 11s/epoch - 60ms/step\n",
            "Epoch 42/50\n",
            "\n",
            "Epoch 42: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5003 - accuracy: 0.8539 - val_loss: 1.7906 - val_accuracy: 0.5301 - 11s/epoch - 60ms/step\n",
            "Epoch 43/50\n",
            "\n",
            "Epoch 43: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.5017 - accuracy: 0.8540 - val_loss: 1.7397 - val_accuracy: 0.5533 - 11s/epoch - 58ms/step\n",
            "Epoch 44/50\n",
            "\n",
            "Epoch 44: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.4978 - accuracy: 0.8604 - val_loss: 1.5545 - val_accuracy: 0.5476 - 11s/epoch - 58ms/step\n",
            "Epoch 45/50\n",
            "\n",
            "Epoch 45: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.4752 - accuracy: 0.8654 - val_loss: 1.6789 - val_accuracy: 0.5468 - 11s/epoch - 60ms/step\n",
            "Epoch 46/50\n",
            "\n",
            "Epoch 46: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.4620 - accuracy: 0.8666 - val_loss: 1.7834 - val_accuracy: 0.5521 - 11s/epoch - 59ms/step\n",
            "Epoch 47/50\n",
            "\n",
            "Epoch 47: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.4615 - accuracy: 0.8712 - val_loss: 1.6200 - val_accuracy: 0.5387 - 11s/epoch - 58ms/step\n",
            "Epoch 48/50\n",
            "\n",
            "Epoch 48: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.4671 - accuracy: 0.8668 - val_loss: 1.7549 - val_accuracy: 0.5358 - 11s/epoch - 60ms/step\n",
            "Epoch 49/50\n",
            "\n",
            "Epoch 49: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.4428 - accuracy: 0.8726 - val_loss: 1.7596 - val_accuracy: 0.5277 - 11s/epoch - 61ms/step\n",
            "Epoch 50/50\n",
            "\n",
            "Epoch 50: val_loss did not improve from 1.40814\n",
            "185/185 - 11s - loss: 0.4397 - accuracy: 0.8750 - val_loss: 1.8005 - val_accuracy: 0.5456 - 11s/epoch - 58ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save model to json\n",
        "model_json = model_API.to_json()\n",
        "with open('/gdrive/MyDrive/Colab Notebooks/Sentiment Analysis/Data/data/face_model.json', 'w') as json_file:\n",
        "  json_file.write(model_json)"
      ],
      "metadata": {
        "id": "CoVOkXQ0KqMD"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,3))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.ylabel('Loss')\n",
        "plt.plot(history.history['loss'], color ='r', label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], color ='b', label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.ylabel('Accuracy')\n",
        "plt.plot(history.history['accuracy'], color ='g', label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], color ='m', label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "SLvetd8gbrEB",
        "outputId": "188ad4dc-ee84-40ed-fa57-31c08577eb76"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_url = \"https://yt3.googleusercontent.com/ytc/AL5GRJUWzuK6FB01l_NrmtdbWhgq8j4zMnb6SJyeR8ID=s900-c-k-c0x00ffffff-no-rj\""
      ],
      "metadata": {
        "id": "Al46lOsjcG-U"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from io import BytesIO\n",
        "image_url_resp = requests.get(image_url)\n",
        "image = Image.open(BytesIO(image_url_resp.content)).convert('L') # convert to grayscale\n",
        "image = image.resize((48,48))\n",
        "image = np.array(image, dtype=\"float32\") / 255.0 # normalize pixel values to [0, 1]\n",
        "image = np.expand_dims(image, axis=-1) # add an extra dimension for the single channel\n",
        "\n",
        "print(image.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bmvgrUgVc38B",
        "outputId": "53e8d4c6-8c77-478f-da8e-7c663cf2109c"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(48, 48, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "plt.imshow(image, cmap=\"gray\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "etsJB_3fc-pA",
        "outputId": "5903d221-06d6-4da1-dc62-7d3a3cbc1c80"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image = image[np.newaxis,...]"
      ],
      "metadata": {
        "id": "zj-rBPkHiD5S"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = model_API.predict(image)\n",
        "ind = np.argmax(prediction,axis=1)\n",
        "print(f\"Prediction: {labels_class[ind[0]]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ceOJ8u6dLs9",
        "outputId": "eb2886dc-784f-48d6-f9a9-20024fffd8b5"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 428ms/step\n",
            "Prediction: Happy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####**Evaluate the Model with Private Dataset**"
      ],
      "metadata": {
        "id": "zEwQktOdTp1r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test = data[[\"emotion\", \"pixels\"]][data[\"Usage\"] == \"PrivateTest\"]\n",
        "test[\"pixels\"] = test[\"pixels\"].apply(lambda im: np.fromstring(im, sep=' '))\n",
        "\n",
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7xAHXnXkUMca",
        "outputId": "47576b05-4a23-411a-9664-7e5d9ff94a61"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       emotion                                             pixels\n",
              "32298        0  [170.0, 118.0, 101.0, 88.0, 88.0, 75.0, 78.0, ...\n",
              "32299        5  [7.0, 5.0, 8.0, 6.0, 7.0, 3.0, 2.0, 6.0, 5.0, ...\n",
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              "32301        4  [200.0, 197.0, 149.0, 139.0, 156.0, 89.0, 111....\n",
              "32302        2  [40.0, 28.0, 33.0, 56.0, 45.0, 33.0, 31.0, 78...."
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              "\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-8f425bdd-4788-4047-97a9-7cacac8b863c 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-8f425bdd-4788-4047-97a9-7cacac8b863c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_private = np.vstack(test[\"pixels\"].values)\n",
        "y_test_private = np.array(test[\"emotion\"])"
      ],
      "metadata": {
        "id": "RZhL4Mz1Uamk"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_private = x_test_private.reshape(-1,48,48)"
      ],
      "metadata": {
        "id": "sfLdbej5Ulga"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_private = x_test_private[...,np.newaxis] # add the channel axis\n",
        "y_test_private = keras.utils.to_categorical(y_test_private, num_class) # one-hot encoding"
      ],
      "metadata": {
        "id": "FDE60jLcUd_G"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Private test data shape: {x_test_private.shape}\")\n",
        "print(f\"Private target/label data shape: {y_test_private.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ltTTjFPVQMH",
        "outputId": "07105fd3-c9c9-4077-89f0-31924b9f4854"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Private test data shape: (3589, 48, 48, 1)\n",
            "Private target/label data shape: (3589, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_private = (x_test_private-np.mean(x_test_private))/np.std(x_test_private)"
      ],
      "metadata": {
        "id": "Hx9EZSt8WYrv"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_cross_entropy_loss, categorical_accuracy = model_API.evaluate(x_test_private, y_test_private, verbose=0)\n",
        "print(f\"[categorical_cross_entropy_loss:{categorical_cross_entropy_loss}, categorical_accuracy:{categorical_accuracy*100}]\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N7oa11lCVc9J",
        "outputId": "c6f2f86e-e2e2-4272-f7e0-3e30295e4059"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[categorical_cross_entropy_loss:1.6938530206680298, categorical_accuracy:56.47812485694885]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Discussion:** \n",
        "\n",
        "Our functional model achieved a categorical accuracy of 56% on the private dataset. However, this level of accuracy may not be sufficient for real-world applications. To improve accuracy, different pre-trained models can be used, but it is important to ensure that the data shape is appropriate.\n",
        "\n",
        "For example, the VGG-Face model can be used for sentiment analysis, but it requires images of size (224x224), which is different from the (48x48) face images used in our study. VGG-Face is a deep convolutional neural network originally designed for face recognition, but it can also detect facial expressions. The model was trained on a large dataset of face images and can identify seven basic emotions: anger, disgust, fear, happiness, sadness, surprise, and neutral."
      ],
      "metadata": {
        "id": "L9200qCVWgv4"
      }
    }
  ]
}