Assignment_emotions.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Assignment_emotions.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3.7.7 64-bit",
      "metadata": {
        "interpreter": {
          "hash": "845d0d26b0e548194937396ebcaa41e34bef927157581abe6ae72f1d1a6fe485"
        }
      }
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kAFgTjOyElk"
      },
      "source": [
        "Import the required modules"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPFD3hZrUev3"
      },
      "source": [
        " !pip install tensorflow==1.13.2"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow==1.13.2\n",
            "  Downloading https://files.pythonhosted.org/packages/ee/89/5b4f6d1dc1d40307feff9068a03484e81d2441099d24a400c95127188783/tensorflow-1.13.2-cp37-cp37m-win_amd64.whl (63.1MB)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (1.34.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (1.1.2)\n",
            "Requirement already satisfied: tensorboard<1.14.0,>=1.13.0 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (1.13.1)\n",
            "Requirement already satisfied: tensorflow-estimator<1.14.0rc0,>=1.13.0 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (1.13.0)\n",
            "Requirement already satisfied: absl-py>=0.1.6 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (0.11.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (0.4.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (1.1.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (1.19.4)\n",
            "Requirement already satisfied: astor>=0.6.0 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (0.8.1)\n",
            "Requirement already satisfied: wheel>=0.26 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (0.36.1)\n",
            "Requirement already satisfied: six>=1.10.0 in c:\\users\\abdim\\appdata\\roaming\\python\\python37\\site-packages (from tensorflow==1.13.2) (1.15.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (1.0.8)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow==1.13.2) (3.14.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.2) (1.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.2) (3.3.3)\n",
            "Requirement already satisfied: mock>=2.0.0 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow==1.13.2) (4.0.3)\n",
            "Requirement already satisfied: h5py in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from keras-applications>=1.0.6->tensorflow==1.13.2) (3.1.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from markdown>=2.6.8->tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.2) (3.1.1)\n",
            "Requirement already satisfied: cached-property; python_version < \"3.8\" in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from h5py->keras-applications>=1.0.6->tensorflow==1.13.2) (1.5.2)\n",
            "Requirement already satisfied: zipp>=0.5 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.14.0,>=1.13.0->tensorflow==1.13.2) (3.4.0)\n",
            "Installing collected packages: tensorflow\n",
            "  Found existing installation: tensorflow 1.13.1\n",
            "    Uninstalling tensorflow-1.13.1:\n",
            "      Successfully uninstalled tensorflow-1.13.1\n",
            "Successfully installed tensorflow-1.13.2\n",
            "WARNING: You are using pip version 19.2.3, however version 20.3.1 is available.\n",
            "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_V3Kpjo1VrwD"
      },
      "source": [
        "pip install keras==2.3.0"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Note: you may need to restart the kernel to use updated packages.Requirement already satisfied: keras==2.3.0 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (2.3.0)\n",
            "WARNING: You are using pip version 19.2.3, however version 20.3.1 is available.\n",
            "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n",
            "\n",
            "Requirement already satisfied: pyyaml in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from keras==2.3.0) (5.3.1)\n",
            "Requirement already satisfied: numpy>=1.9.1 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from keras==2.3.0) (1.19.4)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from keras==2.3.0) (1.1.2)\n",
            "Requirement already satisfied: scipy>=0.14 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from keras==2.3.0) (1.5.4)\n",
            "Requirement already satisfied: six>=1.9.0 in c:\\users\\abdim\\appdata\\roaming\\python\\python37\\site-packages (from keras==2.3.0) (1.15.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from keras==2.3.0) (1.0.8)\n",
            "Requirement already satisfied: h5py in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from keras==2.3.0) (3.1.0)\n",
            "Requirement already satisfied: cached-property; python_version < \"3.8\" in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from h5py->keras==2.3.0) (1.5.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXyn6RjAveDJ"
      },
      "source": [
        "import keras\n",
        "import os"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using TensorFlow backend.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-Rv97u7zAeI"
      },
      "source": [
        "Defining Variables for emotion classes, the size of the images, and batch size\n",
        "COULD GET RID AND USE THEM IN RELEVANT CODE BLOCKS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1jk5i51zPll"
      },
      "source": [
        "num_emotion_classes=7\n",
        "img_row=48\n",
        "img_col=48\n",
        "batch_size=32"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iU1jIgp1X7B"
      },
      "source": [
        "loading the dataset. Found on kaggle. \n",
        "Training data: 28,709 images (combination of all 7 classes)\n",
        "Validation data: 7178 images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "zVKa6Qmu2lvk",
        "outputId": "ff5b027a-4f6d-4005-caf4-6de7f70cf72d"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-1d738d49-1d5f-4181-a6e0-bd2f1d27962f\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-1d738d49-1d5f-4181-a6e0-bd2f1d27962f\">\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 src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving emotion_dataset.zip to emotion_dataset.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ncit2kE-2_l1"
      },
      "source": [
        "unzip the folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q6JOliP92pT4"
      },
      "source": [
        "!unzip -q emotion_dataset.zip"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LGdgmb5UG7XA",
        "outputId": "73dc1b6e-21d2-4a72-e7ab-3c803ebbf8b7"
      },
      "source": [
        "# from google.colab import drive\n",
        "# drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CXhMKsM4YhV"
      },
      "source": [
        "#GDRIVEPATH='/content/drive/My Drive/'\n",
        "GDRIVEPATH=''"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JaqyWbF4soe"
      },
      "source": [
        "import os,shutil\n",
        "\n",
        "base_dir = GDRIVEPATH+'emotion_dataset'\n",
        "\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "test_dir = os.path.join(base_dir, 'test')\n",
        "\n",
        "train_angry_dir = os.path.join(train_dir, 'angry')\n",
        "train_disgust_dir = os.path.join(train_dir, 'disgust')\n",
        "train_fear_dir = os.path.join(train_dir, 'fear')\n",
        "train_happy_dir = os.path.join(train_dir, 'happy')\n",
        "train_neutral_dir = os.path.join(train_dir, 'neutral')\n",
        "train_sad_dir = os.path.join(train_dir, 'sad')\n",
        "train_suprise_dir = os.path.join(train_dir, 'suprise')\n",
        "\n",
        "\n",
        "test_angry_dir = os.path.join(test_dir, 'angry')\n",
        "test_disgust_dir = os.path.join(test_dir, 'disgust')\n",
        "test_fear_dir = os.path.join(test_dir, 'fear')\n",
        "test_happy_dir = os.path.join(test_dir, 'happy')\n",
        "test_neutral_dir = os.path.join(test_dir, 'neutral')\n",
        "test_sad_dir = os.path.join(test_dir, 'sad')\n",
        "test_suprise_dir = os.path.join(test_dir, 'suprise')\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgIi-iYQ2ADf",
        "outputId": "ff6b4bcd-0021-4f49-bacd-50e45a98e458"
      },
      "source": [
        "print('total training angry images:', len(os.listdir(train_angry_dir)))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total training angry images: 3995\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nz_sBPO3iRb9"
      },
      "source": [
        "Image Augmentation on the training and testing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tcwJ6osCELVD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c6390e5-d2a7-4b86-e162-0c564dbcc199"
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,\n",
        "        color_mode='grayscale',\n",
        "        target_size=(48, 48),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical',\n",
        "        shuffle=True) \n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "        test_dir,\n",
        "        color_mode='grayscale',\n",
        "        target_size=(48, 48),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical',\n",
        "        shuffle=True)\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 28709 images belonging to 7 classes.\n",
            "Found 7178 images belonging to 7 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZWYAGBtN3dx"
      },
      "source": [
        "datagen = ImageDataGenerator(\n",
        "      rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting matplotlib\n",
            "  Downloading https://files.pythonhosted.org/packages/24/02/202982cbc4ab7a5a87c7f8d16e51d464016af742d5fb2d2b6b23116caec5/matplotlib-3.3.3-cp37-cp37m-win_amd64.whl (8.5MB)\n",
            "Collecting kiwisolver>=1.0.1 (from matplotlib)\n",
            "  Downloading https://files.pythonhosted.org/packages/b2/55/6681ac2cc8de9bb612b1a777606e5beef240bf63aaa6cb03f44af5f42a77/kiwisolver-1.3.1-cp37-cp37m-win_amd64.whl (51kB)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\abdim\\appdata\\roaming\\python\\python37\\site-packages (from matplotlib) (2.8.1)\n",
            "Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 (from matplotlib)\n",
            "  Using cached https://files.pythonhosted.org/packages/8a/bb/488841f56197b13700afd5658fc279a2025a39e22449b7cf29864669b15d/pyparsing-2.4.7-py2.py3-none-any.whl\n",
            "Collecting pillow>=6.2.0 (from matplotlib)\n",
            "  Downloading https://files.pythonhosted.org/packages/2b/65/e4a5130b4162d20ed99ff096549a04d18f050cfcdb16fe1643ac751c0181/Pillow-8.0.1-cp37-cp37m-win_amd64.whl (2.1MB)\n",
            "Collecting cycler>=0.10 (from matplotlib)\n",
            "  Downloading https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: numpy>=1.15 in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (from matplotlib) (1.19.4)\n",
            "Requirement already satisfied: six>=1.5 in c:\\users\\abdim\\appdata\\roaming\\python\\python37\\site-packages (from python-dateutil>=2.1->matplotlib) (1.15.0)\n",
            "Installing collected packages: kiwisolver, pyparsing, pillow, cycler, matplotlib\n",
            "Successfully installed cycler-0.10.0 kiwisolver-1.3.1 matplotlib-3.3.3 pillow-8.0.1 pyparsing-2.4.7\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "WARNING: You are using pip version 19.2.3, however version 20.3.1 is available.\n",
            "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
          ]
        }
      ],
      "source": [
        "pip install matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pillow in c:\\users\\abdim\\appdata\\local\\programs\\python\\python37\\lib\\site-packages (8.0.1)Note: you may need to restart the kernel to use updated packages.\n",
            "\n",
            "WARNING: You are using pip version 19.2.3, however version 20.3.1 is available.\n",
            "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
          ]
        }
      ],
      "source": [
        "pip install pillow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "q9QvH_xhN8z4",
        "outputId": "02e6d0f5-0f26-454c-e2e7-1159adc63574"
      },
      "source": [
        "from keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fnames = [os.path.join(train_sad_dir, fname) for\n",
        "     fname in os.listdir(train_sad_dir)]\n",
        "\n",
        "#take one image\n",
        "img_path = fnames[3]\n",
        "\n",
        "#load image and resize\n",
        "img = image.load_img(img_path, target_size=(48, 48))\n",
        "\n",
        "#Converts it to a Numpy array with shape (48, 48, 1)\n",
        "x = image.img_to_array(img)\n",
        "#Reshapes it to (1, 48, 48, 1)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "#Generates batches of randomly transformed images.\n",
        "i = 0\n",
        "fig = plt.figure(figsize=(20, 15))\n",
        "fig.subplots_adjust(hspace=0.1, wspace=0.1)\n",
        "for batch in datagen.flow(x, batch_size=1):\n",
        "    ax = fig.add_subplot(5, 7, i+1)\n",
        "    ax.axis('off')\n",
        "    ax.imshow(image.array_to_img(batch[0]))\n",
        "    i += 1\n",
        "    if i % 21 == 0:\n",
        "        break\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1440x1080 with 21 Axes>",
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"493.442105pt\" version=\"1.1\" viewBox=\"0 0 1130.4 493.442105\" width=\"1130.4pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <metadata>\r\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\r\n   <cc:Work>\r\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\r\n    <dc:date>2020-12-10T12:23:06.548287</dc:date>\r\n    <dc:format>image/svg+xml</dc:format>\r\n    <dc:creator>\r\n     <cc:Agent>\r\n      <dc:title>Matplotlib v3.3.3, https://matplotlib.org/</dc:title>\r\n     </cc:Agent>\r\n    </dc:creator>\r\n   </cc:Work>\r\n  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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MGWHEYvgi4T9"
      },
      "source": [
        "CREATING THE MODEL\n",
        "1. 11 layers\n",
        "2. using elu as activation function for hidden layers\n",
        "3. softmax as activation function for the output as it is for mutliclass classfication"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5mVPHEFMGHB"
      },
      "source": [
        "#*************** MODEL V1 *********************\r\n",
        "# from keras import layers\r\n",
        "# from keras import models\r\n",
        "# from keras import optimizers\r\n",
        "\r\n",
        "# model = models.Sequential()\r\n",
        "\r\n",
        "# model.add(layers.Conv2D(32, (3, 3), activation='relu',\r\n",
        "#                         input_shape=(48, 48, 1)))\r\n",
        "# model.add(layers.MaxPooling2D((2, 2)))\r\n",
        "# model.add(layers.Conv2D(64, (3, 3), activation='relu'))\r\n",
        "# model.add(layers.MaxPooling2D((2, 2)))\r\n",
        "# model.add(layers.Conv2D(128, (3, 3), activation='relu'))\r\n",
        "# model.add(layers.MaxPooling2D((2, 2)))\r\n",
        "# model.add(layers.Conv2D(128, (3, 3), activation='relu'))\r\n",
        "# model.add(layers.MaxPooling2D((2, 2)))\r\n",
        "# model.add(layers.Flatten())\r\n",
        "# model.add(layers.Dense(512, activation='relu'))\r\n",
        "\r\n",
        "\r\n",
        "# #output layer start\r\n",
        "# model.add(layers.Dense(7, activation='softmax'))\r\n",
        "#end output layer\r\n"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-GZyCSJO_Yg"
      },
      "source": [
        "#see what the first model looks like\r\n",
        "# model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7gprbaiQKuI"
      },
      "source": [
        "#compile the model\r\n",
        "# model.compile(loss='categorical_crossentropy',\r\n",
        "#               optimizer = optimizers.Adam(learning_rate=0.001),\r\n",
        "#               metrics=['accuracy'])\r\n"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HSD3QTlRPUmp",
        "outputId": "1153871a-f587-490a-eea7-85de096f6c63"
      },
      "source": [
        "#train the first model\r\n",
        "# train_datagen = ImageDataGenerator(\r\n",
        "#     rescale=1./255,\r\n",
        "#     rotation_range=40,\r\n",
        "#     width_shift_range=0.2,\r\n",
        "#     height_shift_range=0.2,\r\n",
        "#     shear_range=0.2,\r\n",
        "#     zoom_range=0.2,\r\n",
        "#     horizontal_flip=True,)\r\n",
        "\r\n",
        "# test_datagen = ImageDataGenerator(rescale=1./255)\r\n",
        "\r\n",
        "# train_generator = train_datagen.flow_from_directory(\r\n",
        "#         train_dir,\r\n",
        "#         target_size=(48, 48),\r\n",
        "#         color_mode='grayscale',\r\n",
        "#         batch_size=32,\r\n",
        "#         class_mode='categorical',\r\n",
        "#         shuffle=True)\r\n",
        "\r\n",
        "# validation_generator = test_datagen.flow_from_directory(\r\n",
        "#         test_dir,\r\n",
        "#         color_mode='grayscale',\r\n",
        "#         target_size=(48, 48),\r\n",
        "#         batch_size=32,\r\n",
        "#         class_mode='categorical',\r\n",
        "#         shuffle=True)\r\n"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 28709 images belonging to 7 classes.\n",
            "Found 7178 images belonging to 7 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IqHe4-5jU1Ab",
        "outputId": "fd58f2cb-caf8-4a6d-914d-9acb4d9b7a59"
      },
      "source": [
        "for data_batch, labels_batch in train_generator:\r\n",
        "     print('data batch shape:', data_batch.shape)\r\n",
        "     print('labels batch shape:', labels_batch.shape)\r\n",
        "     break"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data batch shape: (32, 48, 48, 1)\nlabels batch shape: (32, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "heXB9g_bUsEI"
      },
      "source": [
        "#compile the model\r\n",
        "# history = model.fit_generator(\r\n",
        "#       train_generator,\r\n",
        "#       steps_per_epoch=100,\r\n",
        "#       epochs=100,\r\n",
        "#       validation_data=validation_generator,\r\n",
        "#       validation_steps=50)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GCv-fgD1i8gk"
      },
      "source": [
        "#*********** MODEL V2 ***********************\n",
        "from keras import layers\n",
        "from keras import models\n",
        "from keras import optimizers\n",
        "\n",
        "model = models.Sequential()\n",
        "\n",
        "#conv2D 32 start\n",
        "model.add(layers.Conv2D(32,(3,3),padding='same',kernel_initializer='he_normal',input_shape=(48,48,1)))\n",
        "model.add(layers.Activation('elu'))\n",
        "model.add(layers.BatchNormalization())\n",
        "\n",
        "model.add(layers.Conv2D(32,(3,3), padding='same',kernel_initializer='he_normal',input_shape=(48,48,1)))\n",
        "model.add(layers.Activation('elu'))\n",
        "model.add(layers.BatchNormalization())\n",
        "model.add(layers.MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(layers.Dropout(0.2))\n",
        "#conv2D 32 end\n",
        "\n",
        "#conv2D 64 start\n",
        "model.add(layers.Conv2D(64,(3,3), padding='same',kernel_initializer='he_normal'))\n",
        "model.add(layers.Activation('elu'))\n",
        "model.add(layers.BatchNormalization())\n",
        "\n",
        "model.add(layers.Conv2D(64,(3,3), padding='same',kernel_initializer='he_normal'))\n",
        "model.add(layers.Activation('elu'))\n",
        "model.add(layers.BatchNormalization())\n",
        "model.add(layers.MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(layers.Dropout(0.2))\n",
        "#conv2D 64 end\n",
        "\n",
        "#conv2D 128 start\n",
        "model.add(layers.Conv2D(128,(3,3), padding='same',kernel_initializer='he_normal'))\n",
        "model.add(layers.Activation('elu'))\n",
        "model.add(layers.BatchNormalization())\n",
        "\n",
        "model.add(layers.Conv2D(128,(3,3), padding='same',kernel_initializer='he_normal'))\n",
        "model.add(layers.Activation('elu'))\n",
        "model.add(layers.BatchNormalization())\n",
        "model.add(layers.MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(layers.Dropout(0.2))\n",
        "#conv2D 128 end\n",
        "\n",
        "#conv2D 256 start\n",
        "model.add(layers.Conv2D(256,(3,3), padding='same',kernel_initializer='he_normal'))\n",
        "model.add(layers.Activation('elu'))\n",
        "model.add(layers.BatchNormalization())\n",
        "\n",
        "model.add(layers.Conv2D(256,(3,3), padding='same',kernel_initializer='he_normal'))\n",
        "model.add(layers.Activation('elu'))\n",
        "model.add(layers.BatchNormalization())\n",
        "model.add(layers.MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(layers.Dropout(0.2))\n",
        "#conv2D 256 end\n",
        "\n",
        "#dense 64\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(64,kernel_initializer='he_normal'))\n",
        "model.add(layers.Activation('elu'))\n",
        "model.add(layers.BatchNormalization())\n",
        "model.add(layers.Dropout(0.5))\n",
        "\n",
        "model.add(layers.Dense(64,kernel_initializer='he_normal'))\n",
        "model.add(layers.Activation('elu'))\n",
        "model.add(layers.BatchNormalization())\n",
        "model.add(layers.Dropout(0.5))\n",
        "#end dense 64\n",
        "\n",
        "#output layer start\n",
        "model.add(layers.Dense(7,kernel_initializer='he_normal'))\n",
        "model.add(layers.Activation('softmax'))\n",
        "#end output layer\n"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxIFKIUnAeFW",
        "outputId": "d47f3352-7730-4129-be16-eb3f0d6d0f08"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nconv2d_9 (Conv2D)            (None, 48, 48, 32)        320       \n_________________________________________________________________\nactivation_12 (Activation)   (None, 48, 48, 32)        0         \n_________________________________________________________________\nbatch_normalization_11 (Batc (None, 48, 48, 32)        128       \n_________________________________________________________________\nconv2d_10 (Conv2D)           (None, 48, 48, 32)        9248      \n_________________________________________________________________\nactivation_13 (Activation)   (None, 48, 48, 32)        0         \n_________________________________________________________________\nbatch_normalization_12 (Batc (None, 48, 48, 32)        128       \n_________________________________________________________________\nmax_pooling2d_5 (MaxPooling2 (None, 24, 24, 32)        0         \n_________________________________________________________________\ndropout_7 (Dropout)          (None, 24, 24, 32)        0         \n_________________________________________________________________\nconv2d_11 (Conv2D)           (None, 24, 24, 64)        18496     \n_________________________________________________________________\nactivation_14 (Activation)   (None, 24, 24, 64)        0         \n_________________________________________________________________\nbatch_normalization_13 (Batc (None, 24, 24, 64)        256       \n_________________________________________________________________\nconv2d_12 (Conv2D)           (None, 24, 24, 64)        36928     \n_________________________________________________________________\nactivation_15 (Activation)   (None, 24, 24, 64)        0         \n_________________________________________________________________\nbatch_normalization_14 (Batc (None, 24, 24, 64)        256       \n_________________________________________________________________\nmax_pooling2d_6 (MaxPooling2 (None, 12, 12, 64)        0         \n_________________________________________________________________\ndropout_8 (Dropout)          (None, 12, 12, 64)        0         \n_________________________________________________________________\nconv2d_13 (Conv2D)           (None, 12, 12, 128)       73856     \n_________________________________________________________________\nactivation_16 (Activation)   (None, 12, 12, 128)       0         \n_________________________________________________________________\nbatch_normalization_15 (Batc (None, 12, 12, 128)       512       \n_________________________________________________________________\nconv2d_14 (Conv2D)           (None, 12, 12, 128)       147584    \n_________________________________________________________________\nactivation_17 (Activation)   (None, 12, 12, 128)       0         \n_________________________________________________________________\nbatch_normalization_16 (Batc (None, 12, 12, 128)       512       \n_________________________________________________________________\nmax_pooling2d_7 (MaxPooling2 (None, 6, 6, 128)         0         \n_________________________________________________________________\ndropout_9 (Dropout)          (None, 6, 6, 128)         0         \n_________________________________________________________________\nconv2d_15 (Conv2D)           (None, 6, 6, 256)         295168    \n_________________________________________________________________\nactivation_18 (Activation)   (None, 6, 6, 256)         0         \n_________________________________________________________________\nbatch_normalization_17 (Batc (None, 6, 6, 256)         1024      \n_________________________________________________________________\nconv2d_16 (Conv2D)           (None, 6, 6, 256)         590080    \n_________________________________________________________________\nactivation_19 (Activation)   (None, 6, 6, 256)         0         \n_________________________________________________________________\nbatch_normalization_18 (Batc (None, 6, 6, 256)         1024      \n_________________________________________________________________\nmax_pooling2d_8 (MaxPooling2 (None, 3, 3, 256)         0         \n_________________________________________________________________\ndropout_10 (Dropout)         (None, 3, 3, 256)         0         \n_________________________________________________________________\nflatten_2 (Flatten)          (None, 2304)              0         \n_________________________________________________________________\ndense_4 (Dense)              (None, 64)                147520    \n_________________________________________________________________\nactivation_20 (Activation)   (None, 64)                0         \n_________________________________________________________________\nbatch_normalization_19 (Batc (None, 64)                256       \n_________________________________________________________________\ndropout_11 (Dropout)         (None, 64)                0         \n_________________________________________________________________\ndense_5 (Dense)              (None, 64)                4160      \n_________________________________________________________________\nactivation_21 (Activation)   (None, 64)                0         \n_________________________________________________________________\nbatch_normalization_20 (Batc (None, 64)                256       \n_________________________________________________________________\ndropout_12 (Dropout)         (None, 64)                0         \n_________________________________________________________________\ndense_6 (Dense)              (None, 7)                 455       \n_________________________________________________________________\nactivation_22 (Activation)   (None, 7)                 0         \n=================================================================\nTotal params: 1,328,167\nTrainable params: 1,325,991\nNon-trainable params: 2,176\n_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ShuwyxVYQA6W"
      },
      "source": [
        "Compiling the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKZw7-41RHbx"
      },
      "source": [
        "from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\n",
        "\n",
        "# earlyStopping = EarlyStopping(monitor='val_loss',\n",
        "#                               min_delta=0,\n",
        "#                               patience=3,\n",
        "#                               verbose=1,\n",
        "#                               restore_best_weights=True)\n",
        "\n",
        "checkpoint = ModelCheckpoint('EmotionModel.h5',\n",
        "                             monitor='val_loss',\n",
        "                             mode='min',\n",
        "                             save_best_only=True,\n",
        "                             verbose=1)\n",
        "reduce_lrOplateau = ReduceLROnPlateau(monitor='val_loss',\n",
        "                                      factor=0.2,\n",
        "                                      patience=3,\n",
        "                                      verbose=1,\n",
        "                                      min_delta=0.0001)\n",
        "\n",
        "callbacks= [checkpoint,reduce_lrOplateau]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YilbwpgPCTXF"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        " optimizer = optimizers.Adam(lr=0.001),\n",
        " metrics=['accuracy'])\n",
        "nb_train_samples = 24176\n",
        "nb_validation_samples = 3006\n",
        "epochs=25\n",
        "\n"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MSyTVrBIfXyu",
        "outputId": "40fd1fdc-eb5c-42c4-c5b3-434064918e7a"
      },
      "source": [
        "history=model.fit_generator(\r\n",
        " train_generator,\r\n",
        " steps_per_epoch=nb_train_samples//batch_size,\r\n",
        " epochs=epochs,\r\n",
        " callbacks=callbacks,\r\n",
        " validation_data=test_generator,\r\n",
        " validation_steps=nb_validation_samples//batch_size)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "755/755 [==============================] - 715s 947ms/step - loss: 0.9610 - accuracy: 0.6565 - val_loss: 0.8740 - val_accuracy: 0.5964\n",
            "\n",
            "Epoch 00001: val_loss did not improve from 0.63514\n",
            "Epoch 2/2\n",
            "755/755 [==============================] - 707s 936ms/step - loss: 0.9514 - accuracy: 0.6597 - val_loss: 1.1006 - val_accuracy: 0.6011\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 0.63514\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "Iz76eXsnGZVO",
        "outputId": "0b1a118a-1cd1-43eb-e96e-db297c3542b1"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "acc = history.history['accuracy']\r\n",
        "val_acc = history.history['val_accuracy']\r\n",
        "loss = history.history['loss']\r\n",
        "val_loss = history.history['val_loss']\r\n",
        "\r\n",
        "epochs = range(1, len(acc) + 1)\r\n",
        "\r\n",
        "plt.plot(epochs, acc, 'bo', label='Training acc')\r\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\r\n",
        "plt.title('Training and validation accuracy')\r\n",
        "plt.legend()\r\n",
        "\r\n",
        "plt.figure()\r\n",
        "\r\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\r\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\r\n",
        "plt.title('Training and validation loss')\r\n",
        "plt.legend()\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
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          "output_type": "display_data",
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<module 'h5py.version' from 'C:\\\\Users\\\\Abdim\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python37\\\\lib\\\\site-packages\\\\h5py\\\\version.py'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "h5py.version"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhrKEkL_I87O"
      },
      "source": [
        "from keras.models import load_model\r\n",
        "from keras.preprocessing.image import img_to_array\r\n",
        "from keras.preprocessing import image\r\n",
        "import cv2\r\n",
        "import numpy as np\r\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using TensorFlow backend.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From C:\\Users\\Abdim\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From C:\\Users\\Abdim\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n"
          ]
        }
      ],
      "source": [
        "face_classifier=cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n",
        "\n",
        "classifier = load_model('EmotionModel.h5')"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SmP2-tBBOGCi"
      },
      "source": [
        "class_labels=['Angry','Disgusted','Afraid','Happy','Neutral','Sad','Surprised']\n"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# cap=cv2.VideoCapture(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [],
      "source": [
        "cap = cv2.VideoCapture(0)\n",
        "\n",
        "if not cap.isOpened():\n",
        "    print(\"Cannot open camera\")\n",
        "    exit()\n",
        "\n",
        "while True:\n",
        "    ret, frame = cap.read()\n",
        "    labels=[]\n",
        "\n",
        "    if ret == False:\n",
        "        print(\"Can't receive frame (stream end?). Exiting ...\")\n",
        "        break\n",
        "\n",
        "    gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
        "    faces=face_classifier.detectMultiScale(gray,1.3,5)\n",
        "\n",
        "    for (x,y,w,h) in faces:\n",
        "        cv2.rectangle(frame, (x,y), (x+w, y+h), (0,250,0),2)\n",
        "        cv2.rectangle(frame, (x,y-40), (x+w, y), (0,200,0),-1)\n",
        "        roi_gray=gray[y:y+h,x:x+w]\n",
        "        roi_gray=cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)\n",
        "\n",
        "        if np.sum([roi_gray]) !=0:\n",
        "            roi=roi_gray.astype('float')/255.0\n",
        "            roi=img_to_array(roi)\n",
        "            roi=np.expand_dims(roi,axis=0)\n",
        "\n",
        "            preds=classifier.predict(roi)[0]\n",
        "            label=class_labels[preds.argmax()]\n",
        "            label_position=(x,y)\n",
        "            cv2.putText(frame,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,1.5,(255,255,255),2)\n",
        "        else:\n",
        "            cv2.putText(frame,'No face was found',(200,200),cv2.FONT_HERSHEY_SIMPLEX,2,(0,0,255),2)\n",
        "            \n",
        "\n",
        "        \n",
        "\n",
        "\n",
        "\n",
        "    cv2.imshow('Emotion Detection', frame)\n",
        "\n",
        "    if cv2.waitKey(1) & 0xFF == ord('q'):\n",
        "        break\n",
        "\n",
        "cap.release()\n",
        "cv2.destroyAllWindows()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}