FER_Keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FER-Keras.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "BROJzKZcFnWj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D,BatchNormalization\n",
        "import numpy as np\n",
        "import os\n",
        "from keras.preprocessing import image\n",
        "from keras.utils import to_categorical\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "\n",
        "x = []\n",
        "y = []\n",
        "\n",
        "### loading data\n",
        "# labels = {\"Neutral\":0,\"Happy\":1,\"Sad\":2,\"Surprise\":3,\"Angry\":4}\n",
        "# for label in labels:\n",
        "# \tprint(label)\n",
        "# \tfor filename in os.listdir(\"test/\"+label):\n",
        "# \t\tif filename.split(\".\").pop().lower() == \"jpg\" or filename.split(\".\").pop().lower() == \"png\" or filename.split(\".\").pop().lower() == \"jpeg\":\n",
        "# \t\t\timg = image.load_img(\"test/\"+label+\"/\"+filename, target_size=(48,48,1), grayscale=True)\n",
        "# \t\t\timg = image.img_to_array(img)\n",
        "# \t\t\timg = img/255.0\n",
        "# \t\t\tx.append(img)\n",
        "# \t\t\ty.append(labels[label])\n",
        "# x = np.array(x)\n",
        "\n",
        "\n",
        "# # Split the data\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, shuffle= True)\n",
        "\n",
        "y_train = to_categorical(y_train)\n",
        "y_test = to_categorical(y_test)\n",
        "\n",
        "  \n",
        "model = Sequential()\n",
        "model.add(Conv2D(64, (3, 3), input_shape=(48,48,1)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(128, (3, 3)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(256, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Conv2D(256, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors\n",
        "\n",
        "model.add(Dense(128))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Dense(64))\n",
        "model.add(Activation('relu'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "model.add(Dense(5))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "            optimizer='adam',\n",
        "            metrics=['accuracy'])\n",
        "\n",
        "model.fit(x_train, y_train, epochs=15, batch_size=64)\n",
        "scores = model.evaluate(x_test, y_test)\n",
        "print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n",
        "model.save('expression.model')\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}