CNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RadhaKrishna.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "O5m9rfgD6CS_",
        "EGZerI7C6GgX"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "metadata": {
        "id": "QiSIx3ArqJem",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Emotion Recognition CNN"
      ]
    },
    {
      "metadata": {
        "id": "WVR9whbiqN3S",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 1. Setup"
      ]
    },
    {
      "metadata": {
        "id": "RnrYu-UsqUtn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 1.1 Import Data from Drive"
      ]
    },
    {
      "metadata": {
        "id": "04SMa4qtqzV8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 1.1.1 Mount drive"
      ]
    },
    {
      "metadata": {
        "id": "tp7GB4Iuqws1",
        "colab_type": "code",
        "outputId": "63893bc1-0272-4e1c-d756-b0f732055da5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "from google.colab import files\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "drcjkN6zq4ma",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 1.1.2 List the contents of current working directory"
      ]
    },
    {
      "metadata": {
        "id": "9LiruWb_ijOT",
        "colab_type": "code",
        "outputId": "3b46f4cf-256f-4f53-8016-8477df18dc7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "drive  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KFxbMGPCr8np",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 1.1.3 Copy the data zip file from Drive to current working directory"
      ]
    },
    {
      "metadata": {
        "id": "vgNAfUCMWJ0X",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# !cp drive/'My Drive'/radhakrishna.tar.gz /content\n",
        "!cp drive/'My Drive'/krishna.tar.gz /content"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IneMYcYMsGQ0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 1.1.4 Unzip the data zip file"
      ]
    },
    {
      "metadata": {
        "id": "x0scdKUxWLbV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# !tar -xvzf radhakrishna.tar.gz\n",
        "!tar -xvzf krishna.tar.gz"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NJQ1XD8XsOIm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 1.1.5 List all the files in Aligned directory"
      ]
    },
    {
      "metadata": {
        "id": "nkvi8KcUXWLI",
        "colab_type": "code",
        "outputId": "31ecd4be-4a37-4ec9-fb35-d60b87c41afd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!cd Aligned/ && ls"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Negative  Neutral  Positive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gOvphCo8sYxX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 1.2 Import required libraries"
      ]
    },
    {
      "metadata": {
        "id": "4FZePxNaWSeb",
        "colab_type": "code",
        "outputId": "018a844c-6718-46e8-c999-0280a18847d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('always')\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Data Manipulation libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Scikit-learn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,confusion_matrix,roc_curve,roc_auc_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.utils import class_weight\n",
        "\n",
        "# Visualization libraries\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import style\n",
        " \n",
        "# Matplotlib configuration\n",
        "%matplotlib inline  \n",
        "style.use('fivethirtyeight')\n",
        "\n",
        "# Keras\n",
        "from keras import backend as K\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense\n",
        "from keras.optimizers import Adam,SGD,Adagrad,Adadelta,RMSprop\n",
        "from keras.utils import to_categorical\n",
        "from keras.callbacks import LambdaCallback, EarlyStopping\n",
        "from keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img\n",
        "from keras.layers import Dropout, Flatten, Activation, Reshape, Input\n",
        "from keras.layers import Conv2D, MaxPooling2D, BatchNormalization, ZeroPadding2D, AveragePooling2D, GlobalAveragePooling2D\n",
        "from keras.layers.advanced_activations import PReLU\n",
        "from keras.utils.data_utils import get_file\n",
        "\n",
        "# Tensorflow\n",
        "import tensorflow as tf\n",
        "\n",
        "# Image manipulation libraries\n",
        "import cv2                  \n",
        "from PIL import Image\n",
        "\n",
        "# Other libraries\n",
        "import random\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "import itertools\n",
        "\n",
        "# Set seed for random number generators\n",
        "np.random.seed(42)\n",
        "random.seed(42)\n",
        "tf.set_random_seed(42)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "yqAvSHWcuIeD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 2. Data Preprocessing"
      ]
    },
    {
      "metadata": {
        "id": "ZU4XyV-6XfqA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# data\n",
        "X = []\n",
        "# labels\n",
        "X_labels = []\n",
        "\n",
        "# size of the image - 64x64\n",
        "IMG_SIZE = 64\n",
        "\n",
        "# assign variables for paths of each class\n",
        "POSITIVE_DIR = 'Aligned/Positive/'\n",
        "NEUTRAL_DIR = 'Aligned/Neutral/'\n",
        "NEGATIVE_DIR = 'Aligned/Negative/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "b2aIiroQ2RKS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2.1 Function to generate training data from given images and labels"
      ]
    },
    {
      "metadata": {
        "id": "KQXpLNa2xlx8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def generate_data_from_images(emotion_type, directory):\n",
        "  # tqdm used for progress bar\n",
        "  # for each image in the specified directory\n",
        "  for image in tqdm(os.listdir(directory)):\n",
        "    filename = os.fsdecode(image)\n",
        "    if filename.endswith(\".jpg\"): \n",
        "      # read the colored image\n",
        "      image = cv2.imread(os.path.join(directory, image))\n",
        "      # resize image to 64x64\n",
        "      image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))\n",
        "      \n",
        "      # append the image as a numpy array to X\n",
        "      X.append(np.array(image))\n",
        "      # append the label to Z\n",
        "      X_labels.append(emotion_type)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OWxPNH1o2aUe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2.2 Generate data for \"Positive\" images"
      ]
    },
    {
      "metadata": {
        "id": "BP2Q3BdQX8aB",
        "colab_type": "code",
        "outputId": "fb5b9152-0f5b-42ee-cbc3-3a8a0af8b8c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "generate_data_from_images('Positive', POSITIVE_DIR)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 16801/16801 [00:03<00:00, 4502.07it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "eMteW0Sr2mQ1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2.3 Generate data for \"Neutral\" images"
      ]
    },
    {
      "metadata": {
        "id": "NCKBZ7KwYBpL",
        "colab_type": "code",
        "outputId": "1de8d644-e0f2-41ad-b46a-3d24549e9eb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "generate_data_from_images('Neutral', NEUTRAL_DIR)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 9265/9265 [00:02<00:00, 4490.90it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "6FMJpIyN2qvW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2.4 Generate data for \"Negative\" images"
      ]
    },
    {
      "metadata": {
        "id": "3l2zv79oYHNt",
        "colab_type": "code",
        "outputId": "72a498a5-f60b-4f71-ecf9-6a7899713edf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "generate_data_from_images('Negative', NEGATIVE_DIR)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 8467/8467 [00:01<00:00, 4319.71it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "1LMXZpB34I-_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2.5 Check the size of the dataset"
      ]
    },
    {
      "metadata": {
        "id": "R9F85Sa04CwS",
        "colab_type": "code",
        "outputId": "7e54895b-e94f-4c59-96b8-12a6f2449149",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "len(X), len(X_labels)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(34530, 34530)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "35ZaM-Sk4VuV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2.6 Visualize some images from the dataset"
      ]
    },
    {
      "metadata": {
        "id": "EEeqPEaW9awi",
        "colab_type": "code",
        "outputId": "eb783c4f-62f3-4507-f8c2-40268a6f2f76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        }
      },
      "cell_type": "code",
      "source": [
        "cols = 3\n",
        "rows = 3\n",
        "fig, axis = plt.subplots(rows, cols)\n",
        "fig.set_size_inches(10, 10)\n",
        "for i in range(rows):\n",
        "  for j in range(cols):\n",
        "    image_index = random.randint(0, len(X))\n",
        "    axis[i, j].imshow(X[image_index])\n",
        "    axis[i, j].set_title(X_labels[image_index])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n/Y7rcV9Hx7LHlAmrW7VPJnsmbqMaDzz7/zxWtCdYFJm+nLik7Aw5NUSNC6LQ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\nYpwwdOQqAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "h0bOK8oAN0ct",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2.7 Label Encoding and One-Hot Encoding the labels (Convert categorical text data into model-understandable numerical data)\n",
        "Basically, we want to convert the 3 label classes \"positive\", \"neutral\" and \"negative\" into corresponding numerical classes - 2, 1 and 0."
      ]
    },
    {
      "metadata": {
        "id": "2SQaW6wCYQmk",
        "colab_type": "code",
        "outputId": "8e608c5d-8c62-48dc-e9be-7ea69f4fb590",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# initialize the label encoder class\n",
        "labelencoder = LabelEncoder()\n",
        "\n",
        "# Convert the 3 classes \"positive\", \"neutral\" and \"negative\" into corresponding \n",
        "# numerical classes - 2, 1 and 0.\n",
        "Y = labelencoder.fit_transform(X_labels)\n",
        "# Check the classes applied by the Label Encoder\n",
        "labelencoder_classes = dict(zip(labelencoder.classes_, labelencoder.transform(labelencoder.classes_)))\n",
        "print(labelencoder_classes)\n",
        "# Apply one-hot encoding to convert the label encoded categorical data\n",
        "# into multiple columns where each numerical class is replaced by 0 or 1\n",
        "Y = to_categorical(Y,3)\n",
        "\n",
        "# Convert X into a numpy array\n",
        "X = np.array(X)\n",
        "# Normalize all the pixel values for each image by diving by 255\n",
        "X = X/255"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'Negative': 0, 'Neutral': 1, 'Positive': 2}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FwDcQPiGCo_L",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 2.8 Split the data into training set and validation set"
      ]
    },
    {
      "metadata": {
        "id": "W0FzqWNUYaTt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gxo1VoxzEP_6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 3. Modelling"
      ]
    },
    {
      "metadata": {
        "id": "M2Dm1bqCEZ1s",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 3.1 Building the CNN Model"
      ]
    },
    {
      "metadata": {
        "id": "_gWBTFW6Ef_E",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 3.1.1 Model 1"
      ]
    },
    {
      "metadata": {
        "id": "terYvOc2debM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "# Convolution layer\n",
        "# (i) filters = 32 (the number of output filters i.e. number of feature maps \n",
        "# in the convolution)\n",
        "# (ii) kernel_size = 5x5 (the height and width of the 2D convolution kernel \n",
        "# (also known as a feature detector))\n",
        "# (iii) activation = relu\n",
        "# (iv) input_shape = 64x64x3 because our input images are 64x64 and rgb\n",
        "model.add(Conv2D(filters = 32, kernel_size = (5,5),padding = 'Same',activation ='relu', input_shape = (64,64,3)))\n",
        "# Max pooling layer\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same',activation ='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        " \n",
        "\n",
        "model.add(Conv2D(filters = 96, kernel_size = (3,3),padding = 'Same',activation ='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "model.add(Conv2D(filters = 96, kernel_size = (3,3),padding = 'Same',activation ='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "# fully connected layer\n",
        "model.add(Dense(512))\n",
        "\n",
        "# Relu activation layer\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "# Fully connected layer with softmax activation\n",
        "model.add(Dense(3, activation = \"softmax\"))\n",
        "\n",
        "model.compile(optimizer=Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FPAXn0A5ElM9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 3.1.2 Model 2"
      ]
    },
    {
      "metadata": {
        "id": "XebqxQ0sl2R2",
        "colab_type": "code",
        "outputId": "078cf650-6aa8-410b-91b0-7c2cc1b54220",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "# Adapted from https://github.com/a514514772/Real-Time-Facial-Expression-Recognition-with-DeepLearning\n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "# Zero-padding layer to add 1 pixel of padding to the height and width of the\n",
        "# image\n",
        "model.add(ZeroPadding2D((1,1), input_shape=(64,64,3)))\n",
        "# Convolution layer\n",
        "# (i) filters = 32 (the number of output filters i.e. number of feature maps \n",
        "# in the convolution)\n",
        "# (ii) kernel_size = 3x3 (the height and width of the 2D convolution kernel \n",
        "# (also known as a feature detector))\n",
        "# (iii) activation = relu\n",
        "model.add(Conv2D(32, 3, 3, activation='relu'))\n",
        "\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(32, 3, 3, activation='relu'))\n",
        "# max pooling layer\n",
        "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
        "\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(64, 3, 3, activation='relu'))\n",
        "\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(64, 3, 3, activation='relu'))\n",
        "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
        "\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(128, 3, 3, activation='relu'))\n",
        "\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(128, 3, 3, activation='relu'))\n",
        "\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(128, 3, 3, activation='relu'))\n",
        "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "# fully connected layer with ReLu activation\n",
        "model.add(Dense(1024, activation='relu'))\n",
        "# dropout\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "# fully connected layer with softmax activation\n",
        "model.add(Dense(3, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer=Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: 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 /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KWaSeOkFEnF3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 3.1.3 Model 3"
      ]
    },
    {
      "metadata": {
        "id": "V6hu02bB38iL",
        "colab_type": "code",
        "outputId": "c98840e5-2c65-44df-df8b-6a8944fcbffb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "# Block 1\n",
        "\n",
        "# Convolution layer\n",
        "# (i) filters = 64 (the number of output filters i.e. number of feature maps \n",
        "# in the convolution)\n",
        "# (ii) kernel_size = 5x5 (the height and width of the 2D convolution kernel \n",
        "# (also known as a feature detector))\n",
        "# (iii) border_mode = valid. This gives an output that is smaller than the \n",
        "# input because the convolution is only computed where the input and the filter \n",
        "# fully overlap.\n",
        "# (iv) input_shape = 64x64x3 because our input images are 64x64 and rgb\n",
        "model.add(Conv2D(64, 5, 5, border_mode='valid', input_shape=(64, 64, 3)))\n",
        "# Parametric Rectified Linear Unit activation layer\n",
        "model.add(PReLU(init='zero', weights=None))\n",
        "# Zero-padding layer to add 2 pixels of padding to the height and width of the\n",
        "# image\n",
        "model.add(ZeroPadding2D(padding=(2, 2), dim_ordering='tf'))\n",
        "# Max pooling layer\n",
        "model.add(MaxPooling2D(pool_size=(5, 5),strides=(2, 2)))\n",
        "# Zero-padding layer to add 1 pixel of padding to the height and width of the\n",
        "# image\n",
        "model.add(ZeroPadding2D(padding=(1, 1), dim_ordering='tf'))\n",
        "\n",
        "# Block 2\n",
        "model.add(Conv2D(64, 3, 3))\n",
        "model.add(PReLU(init='zero', weights=None))\n",
        "model.add(ZeroPadding2D(padding=(1, 1), dim_ordering='tf')) \n",
        "\n",
        "# Block 3\n",
        "model.add(Conv2D(64, 3, 3))\n",
        "model.add(PReLU(init='zero', weights=None))\n",
        "model.add(AveragePooling2D(pool_size=(3, 3),strides=(2, 2)))\n",
        "model.add(ZeroPadding2D(padding=(1, 1), dim_ordering='tf'))\n",
        "\n",
        "# Block 4\n",
        "model.add(Conv2D(128, 3, 3))\n",
        "model.add(PReLU(init='zero', weights=None))\n",
        "model.add(ZeroPadding2D(padding=(1, 1), dim_ordering='tf'))\n",
        "\n",
        "# Block 5\n",
        "model.add(Conv2D(128, 3, 3))\n",
        "model.add(PReLU(init='zero', weights=None))\n",
        "model.add(ZeroPadding2D(padding=(1, 1), dim_ordering='tf'))\n",
        "# Average pooling\n",
        "model.add(AveragePooling2D(pool_size=(3, 3),strides=(2, 2)))\n",
        "\n",
        "# Block 6\n",
        "model.add(Flatten())\n",
        "\n",
        "# Block 7\n",
        "# fully connected layer\n",
        "model.add(Dense(2048))\n",
        "model.add(PReLU(init='zero', weights=None))\n",
        "# dropout\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Block 8\n",
        "model.add(Dense(1024))\n",
        "model.add(PReLU(init='zero', weights=None))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Block 8\n",
        "model.add(Dense(512))\n",
        "model.add(PReLU(init='zero', weights=None))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Block 9\n",
        "model.add(Dense(3))\n",
        "\n",
        "# Block 10\n",
        "# softmax activation function\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "# optimizer = Adadelta(lr=0.1, rho=0.95, epsilon=1e-08)\n",
        "\n",
        "# optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)\n",
        "# optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
        "# optimizer = Adadelta(lr=1.0, rho=0.95, epsilon=None, decay=0.0)\n",
        "\n",
        "# after optimization\n",
        "optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=None, decay=0.0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: 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 /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "CsK31ODbEpVh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 3.1.4 Model 4"
      ]
    },
    {
      "metadata": {
        "id": "qKawa2yEs5eG",
        "colab_type": "code",
        "outputId": "464107c3-a409-4f53-bf88-e3b1a2c71c5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(ZeroPadding2D((1,1), input_shape=(64, 64, 3)))\n",
        "\n",
        "# Convolution layer\n",
        "# (i) filters = 32 (the number of output filters i.e. number of feature maps \n",
        "# in the convolution)\n",
        "# (ii) kernel_size = 3x3 (the height and width of the 2D convolution kernel \n",
        "# (also known as a feature detector))\n",
        "# (iii) activation = ReLu\n",
        "model.add(Conv2D(32, 3, 3, activation='relu'))\n",
        "# Zero-padding layer to add 1 pixel of padding to the height and width of the\n",
        "# image\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(32, 3, 3, activation='relu'))\n",
        "# max pooling layer\n",
        "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(64, 3, 3, activation='relu'))\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(64, 3, 3, activation='relu'))\n",
        "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(128, 3, 3, activation='relu'))\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(128, 3, 3, activation='relu'))\n",
        "model.add(ZeroPadding2D((1,1)))\n",
        "model.add(Conv2D(128, 3, 3, activation='relu'))\n",
        "model.add(MaxPooling2D((2,2), strides=(2,2)))\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "# fully connected layer with ReLu activation\n",
        "model.add(Dense(1024, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "# fully connected layer with softmax activation\n",
        "model.add(Dense(3, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer=Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: 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 /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "v6ajL3a3Esdh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 3.1.5 Model 5"
      ]
    },
    {
      "metadata": {
        "id": "W5xy6AYuUwVO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters = 64, kernel_size = (11,11),padding = 'Same',activation ='relu', input_shape = (64,64,3)))\n",
        "model.add(MaxPooling2D(pool_size=(3,3)))\n",
        "\n",
        "model.add(Conv2D(filters = 64, kernel_size = (5,5),padding = 'Same',activation ='relu'))\n",
        "model.add(MaxPooling2D(pool_size=(3,3)))\n",
        "\n",
        "model.add(Conv2D(filters = 128, kernel_size = (3,3),padding = 'Same',activation ='relu'))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dense(3, activation = \"softmax\"))\n",
        "\n",
        "model.compile(optimizer=Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "O5m9rfgD6CS_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 3.1.6 Model 6 (Transfer learning with VGGFace)"
      ]
    },
    {
      "metadata": {
        "id": "BFL5btkx7mJv",
        "colab_type": "code",
        "outputId": "60b4e00a-3850-4d10-800e-3d1cf0486dce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/rcmalli/keras-vggface.git"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/rcmalli/keras-vggface.git\n",
            "  Cloning https://github.com/rcmalli/keras-vggface.git to /tmp/pip-req-build-bfpbjoqu\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (1.14.6)\n",
            "Requirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (1.1.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (2.8.0)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (4.1.1)\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (2.2.4)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (1.11.0)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras-vggface==0.5) (3.13)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->keras-vggface==0.5) (0.46)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from keras->keras-vggface==0.5) (1.0.9)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from keras->keras-vggface==0.5) (1.0.7)\n",
            "Building wheels for collected packages: keras-vggface\n",
            "  Building wheel for keras-vggface (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Stored in directory: /tmp/pip-ephem-wheel-cache-yf26kr4b/wheels/36/07/46/06c25ce8e9cd396dabe151ea1d8a2bc28dafcb11321c1f3a6d\n",
            "Successfully built keras-vggface\n",
            "Installing collected packages: keras-vggface\n",
            "Successfully installed keras-vggface-0.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "I80FiOPF7oNq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "38851a0e-c891-4e96-eb1a-7c7e95811a85"
      },
      "cell_type": "code",
      "source": [
        "from keras.engine import Model\n",
        "from keras_vggface.vggface import VGGFace\n",
        "\n",
        "# initialize the VGGFace model\n",
        "# exclude the top layer and set input shape to the shape of our images\n",
        "vgg_model = VGGFace(include_top=False, input_shape=(64, 64, 3))\n",
        "# get the output of the last layer\n",
        "last_layer = vgg_model.get_layer('pool5').output\n",
        "# flatten the last layer\n",
        "x = Flatten(name='flatten')(last_layer)\n",
        "# add a fully connected layer with ReLu activation\n",
        "x = Dense(512, activation='relu', name='fc6')(x)\n",
        "# add another fully connected layer with ReLu activation\n",
        "x = Dense(512, activation='relu', name='fc7')(x)\n",
        "# add a final fully connected layer with 3 neurons corresponding to our 3 classes and \n",
        "# a softmax activation since this is a multiclass classification problem\n",
        "out = Dense(3, activation='softmax', name='fc8')(x)\n",
        "# set the input and output for the model\n",
        "model = Model(vgg_model.input, out)\n",
        "\n",
        "# compile the model\n",
        "model.compile(optimizer=Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: 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",
            "Downloading data from https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_notop_vgg16.h5\n",
            "58916864/58909280 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "EGZerI7C6GgX",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 3.1.7 Model 7 (Transfer learning with VGG-16)"
      ]
    },
    {
      "metadata": {
        "id": "fpuNlu3K-cAW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# set the paths for the weights file of VGG16\n",
        "WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels.h5'\n",
        "WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
        "\n",
        "# define the model\n",
        "def VGG16():\n",
        "\n",
        "    img_input = Input(shape=(64, 64, 3))\n",
        "    # Block 1\n",
        "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)\n",
        "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)\n",
        "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n",
        "\n",
        "    # Block 2\n",
        "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)\n",
        "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)\n",
        "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n",
        "\n",
        "    # Block 3\n",
        "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)\n",
        "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)\n",
        "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)\n",
        "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n",
        "\n",
        "    # Block 4\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)\n",
        "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n",
        "\n",
        "    # Block 5\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)\n",
        "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)\n",
        "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n",
        "\n",
        "    x = GlobalAveragePooling2D()(x)\n",
        "\n",
        "    top = Dense(256, activation='relu', name='fc')(x)\n",
        "    top = Dropout(0.5)(top)\n",
        "    out = Dense(3, activation='softmax', name='predictions')(top)\n",
        "\n",
        "    model = Model(img_input, out, name='vgg16')\n",
        "\n",
        "    weights_path = get_file('vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5',\n",
        "                            WEIGHTS_PATH_NO_TOP,\n",
        "                            cache_subdir='models')\n",
        "    model.load_weights(weights_path, by_name=True)\n",
        "    \n",
        "    return model\n",
        "\n",
        "\n",
        "lr = 0.0001\n",
        "\n",
        "optimizer = Adagrad(lr, decay=1e-4)\n",
        "model = VGG16()\n",
        "model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RuWrAvHhEzeR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 3.2 Set Model Training Parameters"
      ]
    },
    {
      "metadata": {
        "id": "w5RGS371YkwK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "batch_size=128\n",
        "# batch_size = 64\n",
        "# epochs=10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4hyUKZWAFJDL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 3.3 Data Augmentation"
      ]
    },
    {
      "metadata": {
        "id": "HK_v48BQYrDA",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# define the data generator\n",
        "datagen = ImageDataGenerator(\n",
        "        featurewise_center=False,\n",
        "        samplewise_center=False,\n",
        "        featurewise_std_normalization=False,\n",
        "        samplewise_std_normalization=False,\n",
        "        zca_whitening=False,\n",
        "        zoom_range = 0.1, \n",
        "        vertical_flip=False)\n",
        "\n",
        "# fit on our training set\n",
        "datagen.fit(X_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_JNwOb_dFRDR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 3.4 Model Summary"
      ]
    },
    {
      "metadata": {
        "id": "qOhddG8_Yvcx",
        "colab_type": "code",
        "outputId": "079c4372-5b88-43d0-eb1f-5d9e8a467b27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 918
        }
      },
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 64, 64, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv1_1 (Conv2D)             (None, 64, 64, 64)        1792      \n",
            "_________________________________________________________________\n",
            "conv1_2 (Conv2D)             (None, 64, 64, 64)        36928     \n",
            "_________________________________________________________________\n",
            "pool1 (MaxPooling2D)         (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2_1 (Conv2D)             (None, 32, 32, 128)       73856     \n",
            "_________________________________________________________________\n",
            "conv2_2 (Conv2D)             (None, 32, 32, 128)       147584    \n",
            "_________________________________________________________________\n",
            "pool2 (MaxPooling2D)         (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv3_1 (Conv2D)             (None, 16, 16, 256)       295168    \n",
            "_________________________________________________________________\n",
            "conv3_2 (Conv2D)             (None, 16, 16, 256)       590080    \n",
            "_________________________________________________________________\n",
            "conv3_3 (Conv2D)             (None, 16, 16, 256)       590080    \n",
            "_________________________________________________________________\n",
            "pool3 (MaxPooling2D)         (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv4_1 (Conv2D)             (None, 8, 8, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "conv4_2 (Conv2D)             (None, 8, 8, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv4_3 (Conv2D)             (None, 8, 8, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "pool4 (MaxPooling2D)         (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv5_1 (Conv2D)             (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv5_2 (Conv2D)             (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "conv5_3 (Conv2D)             (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "pool5 (MaxPooling2D)         (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "fc6 (Dense)                  (None, 512)               1049088   \n",
            "_________________________________________________________________\n",
            "fc7 (Dense)                  (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "fc8 (Dense)                  (None, 3)                 1539      \n",
            "=================================================================\n",
            "Total params: 16,027,971\n",
            "Trainable params: 16,027,971\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "66WV9UJeFbS5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 3.5 Estimate class weights since our dataset is unbalanced"
      ]
    },
    {
      "metadata": {
        "id": "VjqWJai2amI3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# get the position of the largest value for each label in y_train\n",
        "# eg - argmax([0., 0., 1.]) = 2\n",
        "y_ints = [y.argmax() for y in y_train]\n",
        "class_weights = class_weight.compute_class_weight('balanced', np.unique(y_ints), y_ints)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VbJyZkKxFeQ2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 3.6  Fit the model on the Training set and making predictions on the Validation set"
      ]
    },
    {
      "metadata": {
        "id": "j4aF0cPLYxIL",
        "colab_type": "code",
        "outputId": "20074fa3-bef3-43df-d934-c5a990f66291",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "cell_type": "code",
      "source": [
        "callbacks = []\n",
        "earlystop = EarlyStopping(monitor='val_loss', patience=5, verbose=1)\n",
        "callbacks.append(earlystop)\n",
        "\n",
        "# history = model.fit(X_train,y_train, nb_epoch=400, \\\n",
        "#             batch_size=batch_size, \\\n",
        "#             validation_split=0.2, \\\n",
        "#             shuffle=True, verbose=1, \\\n",
        "#             callbacks=callbacks, class_weight=class_weights)\n",
        "\n",
        "history = model.fit_generator(datagen.flow(X_train,y_train, batch_size=batch_size),\n",
        "                              epochs = 400, validation_data = (X_test,y_test),\n",
        "                              verbose = 1, steps_per_epoch=X_train.shape[0] // batch_size, callbacks=callbacks, class_weight=class_weights)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/400\n",
            "215/215 [==============================] - 79s 368ms/step - loss: 0.8287 - acc: 0.6047 - val_loss: 0.7799 - val_acc: 0.6397\n",
            "Epoch 2/400\n",
            "215/215 [==============================] - 75s 348ms/step - loss: 0.7535 - acc: 0.6566 - val_loss: 0.7560 - val_acc: 0.6499\n",
            "Epoch 3/400\n",
            "215/215 [==============================] - 72s 335ms/step - loss: 0.6965 - acc: 0.6864 - val_loss: 0.7463 - val_acc: 0.6613\n",
            "Epoch 4/400\n",
            "215/215 [==============================] - 72s 335ms/step - loss: 0.6209 - acc: 0.7248 - val_loss: 0.7749 - val_acc: 0.6565\n",
            "Epoch 5/400\n",
            "215/215 [==============================] - 72s 335ms/step - loss: 0.5102 - acc: 0.7825 - val_loss: 0.8740 - val_acc: 0.6493\n",
            "Epoch 6/400\n",
            "215/215 [==============================] - 72s 334ms/step - loss: 0.3787 - acc: 0.8463 - val_loss: 0.9422 - val_acc: 0.6410\n",
            "Epoch 7/400\n",
            "215/215 [==============================] - 72s 334ms/step - loss: 0.2725 - acc: 0.8944 - val_loss: 1.1820 - val_acc: 0.6402\n",
            "Epoch 8/400\n",
            "215/215 [==============================] - 72s 334ms/step - loss: 0.1926 - acc: 0.9276 - val_loss: 1.3245 - val_acc: 0.6413\n",
            "Epoch 00008: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "z1wUJMWBFqXf",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 3.7 Save the Model and weights"
      ]
    },
    {
      "metadata": {
        "id": "aTltZcB5CilC",
        "colab_type": "code",
        "outputId": "697f369c-8113-4cc5-9bbc-441fb2ce4db2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# serialize model to JSON\n",
        "model_json = model.to_json()\n",
        "with open(\"vggface_adam.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "model.save_weights(\"vggface_adam.h5\")\n",
        "print(\"Saved model to disk\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved model to disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kdtHDOzBJDVG",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 4. Evaluate Model Performance"
      ]
    },
    {
      "metadata": {
        "id": "Glle635wJK91",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 4.1 Loss vs Epochs Graph"
      ]
    },
    {
      "metadata": {
        "id": "yKX5fiHPYzxS",
        "colab_type": "code",
        "outputId": "6017476f-8c4a-4a6a-b54b-ffccdf0a4572",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model Loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.legend(['train', 'test'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CB/sLS3l9TylBeoWrYo0VQRbjLx8eoo6cDrS/bEG/YSXaHRtRnI6am3eIw5E8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\nJYQQQlH+AYm1HTRsDIyxAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "iNR5nmN_JPl5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 4.2 Accuracy vs Epochs Graph"
      ]
    },
    {
      "metadata": {
        "id": "ru_kOvkIY2Di",
        "colab_type": "code",
        "outputId": "0acbd3ec-4bc4-4f52-c7d3-4d27ce731713",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.legend(['train', 'test'])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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N11xzDT169CA6OppZs2axfPlyLBYLzz77rDurLITwEIqi8PZeEzN+K3TY1j/M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\ng1pcZivMYkaVk/VnC+tcK0t1+gRqY36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I6SnMRETE9BRmIiJiev1m1fz+oq+7WttKSUkJ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\ni4b8wi0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "EVyOoxG4JdMA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 4.3 Confusion Matrix"
      ]
    },
    {
      "metadata": {
        "id": "qXQOO6kbJjLP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 4.3.1 Get the predictions on the validation set"
      ]
    },
    {
      "metadata": {
        "id": "BSnIfwMbY3ri",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n",
        "y_pred_classes = np.argmax(y_pred,axis = 1) \n",
        "y_true = np.argmax(y_test,axis = 1) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WwnCfIt9JnCH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 4.3.2 Function to plot the confusion matrix"
      ]
    },
    {
      "metadata": {
        "id": "HG1GhzfNUlCr",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    Credit: https://github.com/scikit-learn/scikit-learn/blob/master/examples/model_selection/plot_confusion_matrix.py\n",
        "    \"\"\"\n",
        "    np.set_printoptions(precision=2)\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\n",
        "\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H8AupnBZJtJZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#### 4.3.3 Plot the Confusion Matrix"
      ]
    },
    {
      "metadata": {
        "id": "OlPgpobVSvT4",
        "colab_type": "code",
        "outputId": "445ce004-850c-4bb1-a673-7b7887faa480",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_true, y_pred_classes)\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cm, classes=labelencoder.classes_)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[ 863  484  326]\n",
            " [ 521  989  348]\n",
            " [ 327  471 2577]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yUVKS5Pjkx7t371i/fj1r1qyhbNmyQOZY29mzZwHw8/OjTZs2GBsbExISQnx8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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "gJoCnLvOJykp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### 4.4 Calculate the Accuracy on the Validation Set"
      ]
    },
    {
      "metadata": {
        "id": "SAVEupIjSzkG",
        "colab_type": "code",
        "outputId": "4fc34480-ee1a-4e03-e30a-4479abe3a90b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "accuracy_score(y_true, y_pred_classes)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6413263828554879"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "metadata": {
        "id": "Dd5zSWS34hkF",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "---"
      ]
    },
    {
      "metadata": {
        "id": "KDNm_s1j4ibS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}