Face_Emotion_Detection.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "print(f\"TensorFlow version: {tf.__version__}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwsT52iKTFMV",
        "outputId": "f5ec73b3-373f-4f97-e69e-1c284b3b01dc"
      },
      "id": "TwsT52iKTFMV",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow version: 2.13.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import to_categorical\n",
        "from keras.preprocessing.image import load_img\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "--b70YerT6CE"
      },
      "id": "--b70YerT6CE",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get helper_functions.py script from course GitHub\n",
        "!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
        "\n",
        "# Import helper functions we're going to use\n",
        "from helper_functions import create_tensorboard_callback, plot_loss_curves, unzip_data, walk_through_dir"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e-BcUsnVTJCo",
        "outputId": "f790f393-92ba-413c-ad77-c53aaf020a5d"
      },
      "id": "e-BcUsnVTJCo",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-09-30 18:33:37--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10246 (10K) [text/plain]\n",
            "Saving to: ‘helper_functions.py’\n",
            "\n",
            "\rhelper_functions.py   0%[                    ]       0  --.-KB/s               \rhelper_functions.py 100%[===================>]  10.01K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-09-30 18:33:37 (83.9 MB/s) - ‘helper_functions.py’ saved [10246/10246]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Downloading the Dataset"
      ],
      "metadata": {
        "id": "Q4Y3Er9qT_3m"
      },
      "id": "Q4Y3Er9qT_3m"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xO2qPEhMTOYo",
        "outputId": "7b079cc0-c6f5-412b-ece5-60b4bc813b2c"
      },
      "id": "xO2qPEhMTOYo",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.4)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.0.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "# Upload the kaggle.json file\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "zvuw8iTnTPyF",
        "outputId": "bd7cd84d-896a-4053-ca86-bb24acc0a7bb"
      },
      "id": "zvuw8iTnTPyF",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-092fc054-2643-4876-8d92-5e0e8c04e5d3\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-092fc054-2643-4876-8d92-5e0e8c04e5d3\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Move the kaggle.json file to the appropriate directory\n",
        "!mkdir -p ~/.kaggle\n",
        "!mv kaggle.json ~/.kaggle/\n",
        "\n",
        "# Set permissions for the kaggle.json file\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n"
      ],
      "metadata": {
        "id": "V8HNbqzBTm8J"
      },
      "id": "V8HNbqzBTm8J",
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d jonathanoheix/face-expression-recognition-dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QF2acFgYTqBB",
        "outputId": "e4da84fe-ac92-494c-ad84-5373668289c8"
      },
      "id": "QF2acFgYTqBB",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading face-expression-recognition-dataset.zip to /content\n",
            " 94% 113M/121M [00:01<00:00, 91.6MB/s] \n",
            "100% 121M/121M [00:01<00:00, 85.2MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unzip_data(\"/content/face-expression-recognition-dataset.zip\")"
      ],
      "metadata": {
        "id": "9BoJ5MFATxx3"
      },
      "id": "9BoJ5MFATxx3",
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Preprocessing"
      ],
      "metadata": {
        "id": "hkuBNVo2UIwE"
      },
      "id": "hkuBNVo2UIwE"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "02e9c221",
      "metadata": {
        "id": "02e9c221"
      },
      "outputs": [],
      "source": [
        "TRAIN_DIR = '/content/images/train'\n",
        "TEST_DIR = '/content/images/validation'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View an image\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import random\n",
        "\n",
        "def view_random_image(target_dir, target_class):\n",
        "  # Setup target directory (we'll view images from here)\n",
        "  target_folder = target_dir+\"/\"+target_class\n",
        "\n",
        "  # Get a random image path\n",
        "  random_image = random.sample(os.listdir(target_folder),1)\n",
        "\n",
        "  # Read in the image and plot it using matplotlib\n",
        "  img = mpimg.imread(target_folder + \"/\" + random_image[0])\n",
        "  plt.imshow(img)\n",
        "  plt.title(target_class)\n",
        "  plt.axis(\"off\");\n",
        "\n",
        "  print(f\"Image shape: {img.shape}\") # show the shape of the image\n",
        "\n",
        "  return img\n"
      ],
      "metadata": {
        "id": "zhqZqJTzUda4"
      },
      "id": "zhqZqJTzUda4",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "view_random_image(TRAIN_DIR,\"sad\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "B7qBxLuQUhnm",
        "outputId": "8de9f51e-8cf7-496f-c964-1568668e9152"
      },
      "id": "B7qBxLuQUhnm",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image shape: (48, 48)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 35,  40,  54, ...,  61,  83, 117],\n",
              "       [ 37,  52,  61, ...,  63, 118, 187],\n",
              "       [ 41,  53,  56, ...,  70, 158, 184],\n",
              "       ...,\n",
              "       [155, 121, 103, ...,  67,  72,  75],\n",
              "       [ 90,  89,  82, ...,  70,  70,  71],\n",
              "       [ 92,  95,  83, ...,  73,  73,  71]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "ede9c1f1",
      "metadata": {
        "id": "ede9c1f1"
      },
      "outputs": [],
      "source": [
        "def createdataframe(dir):\n",
        "    image_paths = []\n",
        "    labels = []\n",
        "    for label in os.listdir(dir):\n",
        "        for imagename in os.listdir(os.path.join(dir,label)):\n",
        "            image_paths.append(os.path.join(dir,label,imagename))\n",
        "            labels.append(label)\n",
        "        print(label, \"completed\")\n",
        "    return image_paths,labels\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "a3a8f1a9",
      "metadata": {
        "id": "a3a8f1a9",
        "outputId": "f0f3d978-d3e2-4126-8344-a0ad0dab5872",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "happy completed\n",
            "neutral completed\n",
            "disgust completed\n",
            "sad completed\n",
            "surprise completed\n",
            "fear completed\n",
            "angry completed\n"
          ]
        }
      ],
      "source": [
        "train = pd.DataFrame()\n",
        "train['image'], train['label'] = createdataframe(TRAIN_DIR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "c8a0cd29",
      "metadata": {
        "id": "c8a0cd29",
        "outputId": "de64fa46-1acd-433c-a9c2-0da6e3e11586",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                       image  label\n",
            "0      /content/images/train/happy/21301.jpg  happy\n",
            "1      /content/images/train/happy/20059.jpg  happy\n",
            "2       /content/images/train/happy/5171.jpg  happy\n",
            "3      /content/images/train/happy/25766.jpg  happy\n",
            "4       /content/images/train/happy/6843.jpg  happy\n",
            "...                                      ...    ...\n",
            "28816   /content/images/train/angry/2381.jpg  angry\n",
            "28817  /content/images/train/angry/20798.jpg  angry\n",
            "28818  /content/images/train/angry/18360.jpg  angry\n",
            "28819   /content/images/train/angry/1694.jpg  angry\n",
            "28820  /content/images/train/angry/10907.jpg  angry\n",
            "\n",
            "[28821 rows x 2 columns]\n"
          ]
        }
      ],
      "source": [
        "print(train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "7f9688fc",
      "metadata": {
        "id": "7f9688fc",
        "outputId": "aaed2841-fc3c-4924-9de4-b6ffb8c3a625",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "happy completed\n",
            "neutral completed\n",
            "disgust completed\n",
            "sad completed\n",
            "surprise completed\n",
            "fear completed\n",
            "angry completed\n"
          ]
        }
      ],
      "source": [
        "test = pd.DataFrame()\n",
        "test['image'], test['label'] = createdataframe(TEST_DIR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "7f504f5d",
      "metadata": {
        "id": "7f504f5d",
        "outputId": "cbff9a51-3749-4a80-f1ac-4b4e869ba00b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                           image  label\n",
            "0      /content/images/validation/happy/8082.jpg  happy\n",
            "1     /content/images/validation/happy/34102.jpg  happy\n",
            "2     /content/images/validation/happy/18835.jpg  happy\n",
            "3      /content/images/validation/happy/3278.jpg  happy\n",
            "4        /content/images/validation/happy/80.jpg  happy\n",
            "...                                          ...    ...\n",
            "7061  /content/images/validation/angry/20287.jpg  angry\n",
            "7062   /content/images/validation/angry/3802.jpg  angry\n",
            "7063  /content/images/validation/angry/29980.jpg  angry\n",
            "7064  /content/images/validation/angry/17502.jpg  angry\n",
            "7065  /content/images/validation/angry/26572.jpg  angry\n",
            "\n",
            "[7066 rows x 2 columns]\n",
            "0        /content/images/validation/happy/8082.jpg\n",
            "1       /content/images/validation/happy/34102.jpg\n",
            "2       /content/images/validation/happy/18835.jpg\n",
            "3        /content/images/validation/happy/3278.jpg\n",
            "4          /content/images/validation/happy/80.jpg\n",
            "                           ...                    \n",
            "7061    /content/images/validation/angry/20287.jpg\n",
            "7062     /content/images/validation/angry/3802.jpg\n",
            "7063    /content/images/validation/angry/29980.jpg\n",
            "7064    /content/images/validation/angry/17502.jpg\n",
            "7065    /content/images/validation/angry/26572.jpg\n",
            "Name: image, Length: 7066, dtype: object\n"
          ]
        }
      ],
      "source": [
        "print(test)\n",
        "print(test['image'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "693324b0",
      "metadata": {
        "id": "693324b0"
      },
      "outputs": [],
      "source": [
        "from tqdm.notebook import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "f52ccbbe",
      "metadata": {
        "id": "f52ccbbe"
      },
      "outputs": [],
      "source": [
        "def extract_features(images):\n",
        "    features = []\n",
        "    for image in tqdm(images):\n",
        "        img = load_img(image,grayscale =  True )\n",
        "        img = np.array(img)\n",
        "        features.append(img)\n",
        "    features = np.array(features)\n",
        "    features = features.reshape(len(features),48,48,1)\n",
        "    return features\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "942a9d40",
      "metadata": {
        "id": "942a9d40",
        "outputId": "2704bd84-0197-498e-e90a-b7f32dc1c0e8",
        "colab": {
          "referenced_widgets": [
            "eab7ad6da6444e1090da9e45296d2eff",
            "8ccf8060009a45d7816a21e81836668c",
            "ff0e97bce11247618f3aed19b9f405db",
            "442bf28c25554de2ab164297d938958f",
            "6be91b42efa743c3b2015343d23afad2",
            "eeae63c3f58a458f9687a13276655439",
            "49dfff94eab5487687335e24bc2aab89",
            "38f81315412f467ba50f082c8ba3d4e4",
            "2ea20156059e474d92185fa230db6aad",
            "9832d21b27cf433aa59adbf0b99c4c9e",
            "0122a5155e4d4ef3aea6f4683879b777"
          ],
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/28821 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "eab7ad6da6444e1090da9e45296d2eff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/utils/image_utils.py:409: UserWarning: grayscale is deprecated. Please use color_mode = \"grayscale\"\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "train_features = extract_features(train['image'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "c1b294ed",
      "metadata": {
        "id": "c1b294ed",
        "outputId": "b10f5fe9-23c2-4141-f4fb-6aa015afb407",
        "colab": {
          "referenced_widgets": [
            "e08c576bfeb54d61a138bdc22cd82e64",
            "afdde48db2e447e7b20a1ef765587c03",
            "44855c41c6494c7ab6e15d089e146a9c",
            "54bfe608930a4a42a05a9946e762945d",
            "7e2bba888cb14e34b1a7fd9c38d11af5",
            "1dce0a43fbf64e50a38571f098c7ca77",
            "c30ed40ceac046c6bb86df309c1d9f0c",
            "18c39eb3da2245f1a7f7cf954e663cc6",
            "0cbb367bdfd04b9aa8c08109a64f29be",
            "4d17676f74eb43679c37c7ab9111b78d",
            "ab9bf6486bd041a4a4d8f5250e2a3efa"
          ],
          "base_uri": "https://localhost:8080/",
          "height": 49
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/7066 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e08c576bfeb54d61a138bdc22cd82e64"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "test_features = extract_features(test['image'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "f78b5d34",
      "metadata": {
        "id": "f78b5d34"
      },
      "outputs": [],
      "source": [
        "x_train = train_features/255.0\n",
        "x_test = test_features/255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "c660fca1",
      "metadata": {
        "id": "c660fca1"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "5384a25b",
      "metadata": {
        "id": "5384a25b",
        "outputId": "ee5ea47d-7f3e-4d8d-e1dd-56e61874bdb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LabelEncoder()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LabelEncoder</label><div class=\"sk-toggleable__content\"><pre>LabelEncoder()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "le = LabelEncoder()\n",
        "le.fit(train['label'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "50ece229",
      "metadata": {
        "id": "50ece229"
      },
      "outputs": [],
      "source": [
        "y_train = le.transform(train['label'])\n",
        "y_test = le.transform(test['label'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "5e284299",
      "metadata": {
        "id": "5e284299"
      },
      "outputs": [],
      "source": [
        "y_train = to_categorical(y_train,num_classes = 7)\n",
        "y_test = to_categorical(y_test,num_classes = 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the model"
      ],
      "metadata": {
        "id": "u-RGoKuQU74c"
      },
      "id": "u-RGoKuQU74c"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-uE8ayVVAts",
        "outputId": "83824273-8c67-4ec0-afd4-5856950ec2e6"
      },
      "id": "Q-uE8ayVVAts",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (2.13.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow.keras as keras"
      ],
      "metadata": {
        "id": "dh2stSOTVENR"
      },
      "id": "dh2stSOTVENR",
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Conv2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import concatenate"
      ],
      "metadata": {
        "id": "Vgz-3qGMVHjb"
      },
      "id": "Vgz-3qGMVHjb",
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import plot_model\n",
        "from keras.models import Model\n",
        "from keras.layers import Input, Dense, Flatten, Dropout, BatchNormalization\n",
        "\n",
        "from keras.optimizers import Adam, SGD\n",
        "from keras.regularizers import l1, l2\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "metadata": {
        "id": "KCAN8NHfVIcj"
      },
      "id": "KCAN8NHfVIcj",
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ModelCheckpoint\n",
        "checkpoint_callback = ModelCheckpoint('best_model.h5', monitor='val_loss', save_best_only=True)"
      ],
      "metadata": {
        "id": "rxoB8GhNVUkq"
      },
      "id": "rxoB8GhNVUkq",
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "3977223a",
      "metadata": {
        "id": "3977223a"
      },
      "outputs": [],
      "source": [
        "    input_shape= (48, 48, 1)\n",
        "    # Create input layer\n",
        "    visible = Input(shape=input_shape, name='input')\n",
        "    num_classes = 7\n",
        "    #the 1-st block\n",
        "    conv1_1 = Conv2D(64, kernel_size=3, activation='relu', padding='same', name = 'conv1_1')(visible)\n",
        "    conv1_1 = BatchNormalization()(conv1_1)\n",
        "    conv1_2 = Conv2D(64, kernel_size=3, activation='relu', padding='same', name = 'conv1_2')(conv1_1)\n",
        "    conv1_2 = BatchNormalization()(conv1_2)\n",
        "    pool1_1 = MaxPooling2D(pool_size=(2,2), name = 'pool1_1')(conv1_2)\n",
        "    drop1_1 = Dropout(0.3, name = 'drop1_1')(pool1_1)#the 2-nd block\n",
        "    conv2_1 = Conv2D(128, kernel_size=3, activation='relu', padding='same', name = 'conv2_1')(drop1_1)\n",
        "    conv2_1 = BatchNormalization()(conv2_1)\n",
        "    conv2_2 = Conv2D(128, kernel_size=3, activation='relu', padding='same', name = 'conv2_2')(conv2_1)\n",
        "    conv2_2 = BatchNormalization()(conv2_2)\n",
        "    conv2_3 = Conv2D(128, kernel_size=3, activation='relu', padding='same', name = 'conv2_3')(conv2_2)\n",
        "    conv2_2 = BatchNormalization()(conv2_3)\n",
        "    pool2_1 = MaxPooling2D(pool_size=(2,2), name = 'pool2_1')(conv2_3)\n",
        "    drop2_1 = Dropout(0.3, name = 'drop2_1')(pool2_1)#the 3-rd block\n",
        "    conv3_1 = Conv2D(256, kernel_size=3, activation='relu', padding='same', name = 'conv3_1')(drop2_1)\n",
        "    conv3_1 = BatchNormalization()(conv3_1)\n",
        "    conv3_2 = Conv2D(256, kernel_size=3, activation='relu', padding='same', name = 'conv3_2')(conv3_1)\n",
        "    conv3_2 = BatchNormalization()(conv3_2)\n",
        "    conv3_3 = Conv2D(256, kernel_size=3, activation='relu', padding='same', name = 'conv3_3')(conv3_2)\n",
        "    conv3_3 = BatchNormalization()(conv3_3)\n",
        "    conv3_4 = Conv2D(256, kernel_size=3, activation='relu', padding='same', name = 'conv3_4')(conv3_3)\n",
        "    conv3_4 = BatchNormalization()(conv3_4)\n",
        "    pool3_1 = MaxPooling2D(pool_size=(2,2), name = 'pool3_1')(conv3_4)\n",
        "    drop3_1 = Dropout(0.3, name = 'drop3_1')(pool3_1)#the 4-th block\n",
        "    conv4_1 = Conv2D(256, kernel_size=3, activation='relu', padding='same', name = 'conv4_1')(drop3_1)\n",
        "    conv4_1 = BatchNormalization()(conv4_1)\n",
        "    conv4_2 = Conv2D(256, kernel_size=3, activation='relu', padding='same', name = 'conv4_2')(conv4_1)\n",
        "    conv4_2 = BatchNormalization()(conv4_2)\n",
        "    conv4_3 = Conv2D(256, kernel_size=3, activation='relu', padding='same', name = 'conv4_3')(conv4_2)\n",
        "    conv4_3 = BatchNormalization()(conv4_3)\n",
        "    conv4_4 = Conv2D(256, kernel_size=3, activation='relu', padding='same', name = 'conv4_4')(conv4_3)\n",
        "    conv4_4 = BatchNormalization()(conv4_4)\n",
        "    pool4_1 = MaxPooling2D(pool_size=(2,2), name = 'pool4_1')(conv4_4)\n",
        "    drop4_1 = Dropout(0.3, name = 'drop4_1')(pool4_1)\n",
        "\n",
        "    #the 5-th block\n",
        "    conv5_1 = Conv2D(512, kernel_size=3, activation='relu', padding='same', name = 'conv5_1')(drop4_1)\n",
        "    conv5_1 = BatchNormalization()(conv5_1)\n",
        "    conv5_2 = Conv2D(512, kernel_size=3, activation='relu', padding='same', name = 'conv5_2')(conv5_1)\n",
        "    conv5_2 = BatchNormalization()(conv5_2)\n",
        "    conv5_3 = Conv2D(512, kernel_size=3, activation='relu', padding='same', name = 'conv5_3')(conv5_2)\n",
        "    conv5_3 = BatchNormalization()(conv5_3)\n",
        "    conv5_4 = Conv2D(512, kernel_size=3, activation='relu', padding='same', name = 'conv5_4')(conv5_3)\n",
        "    conv5_3 = BatchNormalization()(conv5_3)\n",
        "    pool5_1 = MaxPooling2D(pool_size=(2,2), name = 'pool5_1')(conv5_4)\n",
        "    drop5_1 = Dropout(0.3, name = 'drop5_1')(pool5_1)#Flatten and output\n",
        "    flatten = Flatten(name = 'flatten')(drop5_1)\n",
        "    ouput = Dense(num_classes, activation='softmax', name = 'output')(flatten)# create model\n",
        "    model= Model(inputs =visible, outputs = ouput)\n",
        "    model.compile(loss=\"categorical_crossentropy\",optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), # lr is 10x lower than before for fine-tuning\n",
        "                metrics=[\"accuracy\"])\n",
        "    # summary layers\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model upto 70 epochs to get val accuracy upto 65%"
      ],
      "metadata": {
        "id": "lm84sJX_ZNip"
      },
      "id": "lm84sJX_ZNip"
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "5cd0b130",
      "metadata": {
        "id": "5cd0b130",
        "outputId": "fab9fefd-d1fa-4eda-8106-ee4a2809bb74",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "226/226 [==============================] - 61s 159ms/step - loss: 2.0820 - accuracy: 0.2062 - val_loss: 1.8563 - val_accuracy: 0.2583\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7e6b532e5a80>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "model.fit(x= x_train,y = y_train, batch_size = 128, epochs = 1, validation_data = (x_test,y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "3932bbe8",
      "metadata": {
        "id": "3932bbe8"
      },
      "outputs": [],
      "source": [
        "label = ['angry','disgust','fear','happy','neutral','sad','surprise']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "b0ccbacf",
      "metadata": {
        "id": "b0ccbacf"
      },
      "outputs": [],
      "source": [
        "def ef(image):\n",
        "    img = load_img(image,grayscale =  True )\n",
        "    feature = np.array(img)\n",
        "    feature = feature.reshape(1,48,48,1)\n",
        "    return feature/255.0\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "wp4bKTR_aAoW"
      },
      "id": "wp4bKTR_aAoW",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "5882c78e",
      "metadata": {
        "id": "5882c78e",
        "outputId": "81f279dc-4675-4ea2-bb8e-74184f91529a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original image is of sad\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "model prediction is  happy\n"
          ]
        }
      ],
      "source": [
        "image = 'images/train/sad/42.jpg'\n",
        "print(\"original image is of sad\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c83af9e1",
      "metadata": {
        "id": "c83af9e1",
        "outputId": "b6b772b5-e24d-4616-fb0a-438d353d0cf9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "original image is of sad\n",
            "1/1 [==============================] - 0s 55ms/step\n",
            "model prediction is  sad\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x16abfe14e80>"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "image = 'images/train/sad/42.jpg'\n",
        "print(\"original image is of sad\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5adb7b7a",
      "metadata": {
        "id": "5adb7b7a",
        "outputId": "43de50ad-0d1f-42aa-ca12-a12c0bf72a8c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "original image is of fear\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "model prediction is  sad\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x16abfe99060>"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "image = 'images/train/fear/2.jpg'\n",
        "print(\"original image is of fear\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5ea96ba",
      "metadata": {
        "id": "e5ea96ba",
        "outputId": "c421dfe4-1e64-4724-f35d-aa5a2724d763"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "original image is of disgust\n",
            "1/1 [==============================] - 0s 57ms/step\n",
            "model prediction is  disgust\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x16abfef4d90>"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "image = 'images/train/disgust/299.jpg'\n",
        "print(\"original image is of disgust\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1c4befa7",
      "metadata": {
        "id": "1c4befa7",
        "outputId": "20bd259a-e600-4069-dee9-cdffbc32d788"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "original image is of happy\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "model prediction is  happy\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x16ac00a8970>"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "image = 'images/train/happy/7.jpg'\n",
        "print(\"original image is of happy\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8d6bb2fa",
      "metadata": {
        "id": "8d6bb2fa",
        "outputId": "203ae000-8a5b-4b30-baaa-05822c95633d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "original image is of surprise\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "model prediction is  surprise\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x16abfdd6cb0>"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "image = 'images/train/surprise/15.jpg'\n",
        "print(\"original image is of surprise\")\n",
        "img = ef(image)\n",
        "pred = model.predict(img)\n",
        "pred_label = label[pred.argmax()]\n",
        "print(\"model prediction is \",pred_label)\n",
        "plt.imshow(img.reshape(48,48),cmap='gray')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Saving and loading the model"
      ],
      "metadata": {
        "id": "7YRaYyx1WiYD"
      },
      "id": "7YRaYyx1WiYD"
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "b005764f",
      "metadata": {
        "id": "b005764f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70562508-f5fb-4701-d081-743a024a67eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ],
      "source": [
        "model.save(\"FED.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1= tf.keras.models.load_model('/content/FED.h5')"
      ],
      "metadata": {
        "id": "DX1eGyKfWgNe"
      },
      "id": "DX1eGyKfWgNe",
      "execution_count": 37,
      "outputs": []
    }
  ],
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.7"
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