Facial_Expression _Training_50_epochs.ipynb

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    "outputId": "0d6fa386-b4ae-492c-b374-95f79c7edc7e"
   },
   "outputs": [],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "outputId": "c48df142-ed07-45c6-c7bb-127940637a75"
   },
   "outputs": [],
   "source": [
    "!ls \"/content/gdrive/My Drive\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zRL1eSegZ5RA"
   },
   "outputs": [],
   "source": [
    "!unzip -q \"/content/gdrive/My Drive/FACIAL EXPRESSION RECOG/dopISneuSBGKSEp3rhgRew_d24af2d5210545f89c6639f42e84c1b3_Project.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "outputId": "7f503b95-494b-4570-e800-f4f35fc6a47e"
   },
   "outputs": [],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0ldKvRWQaT0T"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"/content/Project\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SwsRjCmTHx0h"
   },
   "outputs": [],
   "source": [
    "!pip install livelossplot --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "colab_type": "code",
    "id": "xyOKiPjbHx3U",
    "outputId": "1fee5a84-da02-4b55-c826-61b28137aae0"
   },
   "outputs": [],
   "source": [
    "!pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "colab_type": "code",
    "id": "O82CyhH2alA3",
    "outputId": "c3ce2d50-5f13-411f-be8d-9d76fbb12260"
   },
   "outputs": [],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 110
    },
    "colab_type": "code",
    "id": "LaPj3UX8BWAG",
    "outputId": "573af6bc-9d74-4ccc-f842-0bfe75a325a6"
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import utils\n",
    "import os\n",
    "%matplotlib inline\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.layers import Dense, Input, Dropout,Flatten, Conv2D\n",
    "from tensorflow.keras.layers import BatchNormalization, Activation, MaxPooling2D\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
    "from tensorflow.keras.utils import plot_model\n",
    "\n",
    "from IPython.display import SVG, Image\n",
    "from livelossplot import PlotLossesKerasTF\n",
    "import tensorflow as tf\n",
    "\n",
    "print(\"Tensorflow version:\", tf.__version__)\n",
    "device_name = tf.test.gpu_device_name()\n",
    "if device_name != '/device:GPU:0':\n",
    "  raise SystemError('GPU device not found')\n",
    "print('Found GPU at: {}'.format(device_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Db4SSiFDJodO",
    "outputId": "1a59f677-6493-44a5-9a5d-d31777c16092"
   },
   "outputs": [],
   "source": [
    "utils.datasets.fer.plot_example_images(plt).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145
    },
    "colab_type": "code",
    "id": "-pmMzz9fJohA",
    "outputId": "2aaa427b-e818-417f-bf72-2f4003a04ef5"
   },
   "outputs": [],
   "source": [
    "for expression in os.listdir(\"train/\"):\n",
    "    print(str(len(os.listdir(\"train/\" + expression))) + \" \" + expression + \" images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "Zi5dqPDNJokm",
    "outputId": "13830c27-28e0-4812-dd1d-24404f2e0f95"
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   "outputs": [],
   "source": [
    "img_size = 48\n",
    "batch_size = 64\n",
    "\n",
    "datagen_train = ImageDataGenerator(horizontal_flip=True)\n",
    "\n",
    "train_generator = datagen_train.flow_from_directory(\"train/\",\n",
    "                                                    target_size=(img_size,img_size),\n",
    "                                                    color_mode=\"grayscale\",\n",
    "                                                    batch_size=batch_size,\n",
    "                                                    class_mode='categorical',\n",
    "                                                    shuffle=True)\n",
    "\n",
    "datagen_validation = ImageDataGenerator(horizontal_flip=True)\n",
    "validation_generator = datagen_validation.flow_from_directory(\"test/\",\n",
    "                                                    target_size=(img_size,img_size),\n",
    "                                                    color_mode=\"grayscale\",\n",
    "                                                    batch_size=batch_size,\n",
    "                                                    class_mode='categorical',\n",
    "                                                    shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
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   "outputs": [],
   "source": [
    "# Initialising the CNN\n",
    "model = Sequential()\n",
    "\n",
    "# 1 - Convolution\n",
    "model.add(Conv2D(64,(3,3), padding='same', input_shape=(48, 48,1)))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# 2nd Convolution layer\n",
    "model.add(Conv2D(128,(5,5), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# 3rd Convolution layer\n",
    "model.add(Conv2D(512,(3,3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# 4th Convolution layer\n",
    "model.add(Conv2D(512,(3,3), padding='same'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# Flattening\n",
    "model.add(Flatten())\n",
    "\n",
    "# Fully connected layer 1st layer\n",
    "model.add(Dense(256))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "# Fully connected layer 2nd layer\n",
    "model.add(Dense(512))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Dense(7, activation='softmax'))\n",
    "\n",
    "opt = Adam(lr=0.0005)\n",
    "model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "colab_type": "code",
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   "outputs": [],
   "source": [
    "os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
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   "source": [
    "%%time\n",
    "with tf.device('/device:GPU:0'):\n",
    "\n",
    "  epochs = 50\n",
    "  steps_per_epoch = train_generator.n//train_generator.batch_size\n",
    "  validation_steps = validation_generator.n//validation_generator.batch_size\n",
    "\n",
    "  reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1,\n",
    "                              patience=2, min_lr=0.00001, mode='auto')\n",
    "  checkpoint = ModelCheckpoint(\"model_weights.h5\", monitor='val_accuracy',\n",
    "                             save_weights_only=True, mode='max', verbose=1)\n",
    "  callbacks = [PlotLossesKerasTF(), checkpoint, reduce_lr]\n",
    "\n",
    "  history = model.fit(\n",
    "    x=train_generator,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    epochs=epochs,\n",
    "    validation_data = validation_generator,\n",
    "    validation_steps = validation_steps,\n",
    "    callbacks=callbacks\n",
    "  )"
   ]
  },
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    "id": "9e-vFYXFJou-"
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   "source": []
  }
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