Emotion_Detector_with_LittleVGG.ipynb

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    "colab": {
      "name": "Emotion Detector with LittleVGG.ipynb",
      "version": "0.3.2",
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      "include_colab_link": true
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pranaya-mathur/Deep-Learning-Projects/blob/master/Emotion_Detector_with_LittleVGG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IPs_fc1_DWJP",
        "colab_type": "text"
      },
      "source": [
        "# Using LittleVGG for Emotion Detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UUhABYdEPjW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "c8827d26-bc31-4758-f251-ea34959290bd"
      },
      "source": [
        "!curl --header 'Host: a.udemycdn.com' --user-agent 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:67.0) Gecko/20100101 Firefox/67.0' --header 'Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8' --header 'Accept-Language: en-US,en;q=0.5' --referer 'https://www.udemy.com/master-deep-learning-computer-visiontm-cnn-ssd-yolo-gans/learn/lecture/12584856' --header 'Upgrade-Insecure-Requests: 1' 'https://a.udemycdn.com/2018-11-18_20-05-03-baf92df8c11bcc4344eb9648100a9971/original.zip?nva=20190713015203&filename=fer2013.zip&download=True&token=062a2231956f4ad4de895' --output 'fer2013.zip'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  143M  100  143M    0     0  17.1M      0  0:00:08  0:00:08 --:--:-- 17.3M\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yo7hVVaEVXF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import zipfile\n",
        "zip_ref = zipfile.ZipFile('fer2013.zip', 'r')\n",
        "zip_ref.extractall(\"data\")\n",
        "zip_ref.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2Yuqch3DWJn",
        "colab_type": "text"
      },
      "source": [
        "### Training Emotion Detector"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYpYfJ3aDWJu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "2039af07-7554-4549-d26d-fac509676f1a"
      },
      "source": [
        "import keras\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "import os\n",
        "\n",
        "num_classes = 7\n",
        "img_rows, img_cols = 48, 48\n",
        "batch_size = 16\n",
        "\n",
        "train_data_dir = 'data/fer2013/train'\n",
        "validation_data_dir = 'data/fer2013/validation'\n",
        "\n",
        "# Let's use some data augmentaiton \n",
        "train_datagen = ImageDataGenerator(\n",
        "      rescale=1./255,\n",
        "      rotation_range=30,\n",
        "      shear_range=0.3,\n",
        "      zoom_range=0.3,\n",
        "      width_shift_range=0.4,\n",
        "      height_shift_range=0.4,\n",
        "      horizontal_flip=True,\n",
        "      fill_mode='nearest')\n",
        " \n",
        "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
        " \n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_data_dir,\n",
        "        color_mode = 'grayscale',\n",
        "        target_size=(img_rows, img_cols),\n",
        "        batch_size=batch_size,\n",
        "        class_mode='categorical',\n",
        "        shuffle=True)\n",
        " \n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "        validation_data_dir,\n",
        "        color_mode = 'grayscale',\n",
        "        target_size=(img_rows, img_cols),\n",
        "        batch_size=batch_size,\n",
        "        class_mode='categorical',\n",
        "        shuffle=True)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 28709 images belonging to 7 classes.\n",
            "Found 3589 images belonging to 7 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDmT8-68DWKY",
        "colab_type": "text"
      },
      "source": [
        "## Our Keras Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cse1AVeEDWKf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.layers.advanced_activations import ELU\n",
        "from keras.layers.core import Activation, Flatten, Dropout, Dense"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RCQg8cRoDWKz",
        "colab_type": "text"
      },
      "source": [
        "## Keras LittleVGG Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXTQUQlMDWK3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a1d71ddb-15c4-4be7-9cca-d19655183a97"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), padding = 'same', kernel_initializer=\"he_normal\",\n",
        "                 input_shape = (img_rows, img_cols, 1)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(32, (3, 3), padding = \"same\", kernel_initializer=\"he_normal\", \n",
        "                 input_shape = (img_rows, img_cols, 1)))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Block #2: second CONV => RELU => CONV => RELU => POOL\n",
        "# layer set\n",
        "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(64, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Block #3: third CONV => RELU => CONV => RELU => POOL\n",
        "# layer set\n",
        "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(128, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Block #4: third CONV => RELU => CONV => RELU => POOL\n",
        "# layer set\n",
        "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Conv2D(256, (3, 3), padding=\"same\", kernel_initializer=\"he_normal\"))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Block #5: first set of FC => RELU layers\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "# Block #6: second set of FC => RELU layers\n",
        "model.add(Dense(64, kernel_initializer=\"he_normal\"))\n",
        "model.add(Activation('elu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "# Block #7: softmax classifier\n",
        "model.add(Dense(num_classes, kernel_initializer=\"he_normal\"))\n",
        "model.add(Activation(\"softmax\"))\n",
        "\n",
        "print(model.summary())"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_17 (Conv2D)           (None, 48, 48, 32)        320       \n",
            "_________________________________________________________________\n",
            "activation_23 (Activation)   (None, 48, 48, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_21 (Batc (None, 48, 48, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv2d_18 (Conv2D)           (None, 48, 48, 32)        9248      \n",
            "_________________________________________________________________\n",
            "activation_24 (Activation)   (None, 48, 48, 32)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_22 (Batc (None, 48, 48, 32)        128       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 24, 24, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 24, 24, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 24, 24, 64)        18496     \n",
            "_________________________________________________________________\n",
            "activation_25 (Activation)   (None, 24, 24, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_23 (Batc (None, 24, 24, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 24, 24, 64)        36928     \n",
            "_________________________________________________________________\n",
            "activation_26 (Activation)   (None, 24, 24, 64)        0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_24 (Batc (None, 24, 24, 64)        256       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_14 (Dropout)         (None, 12, 12, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 12, 12, 128)       73856     \n",
            "_________________________________________________________________\n",
            "activation_27 (Activation)   (None, 12, 12, 128)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_25 (Batc (None, 12, 12, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 12, 12, 128)       147584    \n",
            "_________________________________________________________________\n",
            "activation_28 (Activation)   (None, 12, 12, 128)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_26 (Batc (None, 12, 12, 128)       512       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 6, 6, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 6, 6, 256)         295168    \n",
            "_________________________________________________________________\n",
            "activation_29 (Activation)   (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_27 (Batc (None, 6, 6, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv2d_24 (Conv2D)           (None, 6, 6, 256)         590080    \n",
            "_________________________________________________________________\n",
            "activation_30 (Activation)   (None, 6, 6, 256)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_28 (Batc (None, 6, 6, 256)         1024      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 3, 3, 256)         0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 2304)              0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 64)                147520    \n",
            "_________________________________________________________________\n",
            "activation_31 (Activation)   (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_29 (Batc (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "activation_32 (Activation)   (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_30 (Batc (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dropout_18 (Dropout)         (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 7)                 455       \n",
            "_________________________________________________________________\n",
            "activation_33 (Activation)   (None, 7)                 0         \n",
            "=================================================================\n",
            "Total params: 1,328,167\n",
            "Trainable params: 1,325,991\n",
            "Non-trainable params: 2,176\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eArx8TRJDWLS",
        "colab_type": "text"
      },
      "source": [
        "## Training our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eCGL1Ly5DWLZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 899
        },
        "outputId": "f7e82525-4e0c-4e44-95a6-363972819dae"
      },
      "source": [
        "from keras.optimizers import RMSprop, SGD, Adam\n",
        "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
        "\n",
        "                     \n",
        "checkpoint = ModelCheckpoint(\"emotion_little_vgg_3.h5\",\n",
        "                             monitor=\"val_loss\",\n",
        "                             mode=\"min\",\n",
        "                             save_best_only = True,\n",
        "                             verbose=1)\n",
        "\n",
        "earlystop = EarlyStopping(monitor = 'val_loss', \n",
        "                          min_delta = 0, \n",
        "                          patience = 3,\n",
        "                          verbose = 1,\n",
        "                          restore_best_weights = True)\n",
        "\n",
        "reduce_lr = ReduceLROnPlateau(monitor = 'val_loss', factor = 0.2, patience = 3, verbose = 1, min_delta = 0.0001)\n",
        "\n",
        "# we put our call backs into a callback list\n",
        "callbacks = [earlystop, checkpoint, reduce_lr]\n",
        "\n",
        "# We use a very small learning rate \n",
        "model.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer = Adam(lr=0.001),\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "nb_train_samples = 28273\n",
        "nb_validation_samples = 3534\n",
        "epochs = 50\n",
        "\n",
        "history = model.fit_generator(\n",
        "    train_generator,\n",
        "    steps_per_epoch = nb_train_samples // batch_size,\n",
        "    epochs = epochs,\n",
        "    callbacks = callbacks,\n",
        "    validation_data = validation_generator,\n",
        "    validation_steps = nb_validation_samples // batch_size)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "1767/1767 [==============================] - 48s 27ms/step - loss: 2.0639 - acc: 0.2004 - val_loss: 1.7936 - val_acc: 0.2420\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 1.79357, saving model to emotion_little_vgg_3.h5\n",
            "Epoch 2/50\n",
            "1767/1767 [==============================] - 43s 24ms/step - loss: 1.8081 - acc: 0.2417 - val_loss: 1.7736 - val_acc: 0.2647\n",
            "\n",
            "Epoch 00002: val_loss improved from 1.79357 to 1.77355, saving model to emotion_little_vgg_3.h5\n",
            "Epoch 3/50\n",
            "1767/1767 [==============================] - 41s 23ms/step - loss: 1.7916 - acc: 0.2479 - val_loss: 1.7661 - val_acc: 0.2696\n",
            "\n",
            "Epoch 00003: val_loss improved from 1.77355 to 1.76614, saving model to emotion_little_vgg_3.h5\n",
            "Epoch 4/50\n",
            "1767/1767 [==============================] - 41s 23ms/step - loss: 1.7692 - acc: 0.2688 - val_loss: 1.6946 - val_acc: 0.3178\n",
            "\n",
            "Epoch 00004: val_loss improved from 1.76614 to 1.69462, saving model to emotion_little_vgg_3.h5\n",
            "Epoch 5/50\n",
            "1767/1767 [==============================] - 41s 23ms/step - loss: 1.7109 - acc: 0.3065 - val_loss: 1.8944 - val_acc: 0.3280\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 1.69462\n",
            "Epoch 6/50\n",
            "1767/1767 [==============================] - 41s 23ms/step - loss: 1.6423 - acc: 0.3408 - val_loss: 1.5286 - val_acc: 0.3961\n",
            "\n",
            "Epoch 00006: val_loss improved from 1.69462 to 1.52860, saving model to emotion_little_vgg_3.h5\n",
            "Epoch 7/50\n",
            "1767/1767 [==============================] - 41s 23ms/step - loss: 1.5697 - acc: 0.3861 - val_loss: 1.5029 - val_acc: 0.4184\n",
            "\n",
            "Epoch 00007: val_loss improved from 1.52860 to 1.50293, saving model to emotion_little_vgg_3.h5\n",
            "Epoch 8/50\n",
            "1767/1767 [==============================] - 40s 23ms/step - loss: 1.5202 - acc: 0.4095 - val_loss: 1.4574 - val_acc: 0.4517\n",
            "\n",
            "Epoch 00008: val_loss improved from 1.50293 to 1.45744, saving model to emotion_little_vgg_3.h5\n",
            "Epoch 9/50\n",
            "1767/1767 [==============================] - 40s 23ms/step - loss: 1.4817 - acc: 0.4284 - val_loss: 1.5305 - val_acc: 0.4386\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 1.45744\n",
            "Epoch 10/50\n",
            "1767/1767 [==============================] - 39s 22ms/step - loss: 1.4616 - acc: 0.4365 - val_loss: 1.5578 - val_acc: 0.4517\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 1.45744\n",
            "Epoch 11/50\n",
            "1767/1767 [==============================] - 39s 22ms/step - loss: 1.4374 - acc: 0.4484 - val_loss: 1.4588 - val_acc: 0.4625\n",
            "Restoring model weights from the end of the best epoch\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 1.45744\n",
            "\n",
            "Epoch 00011: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n",
            "Epoch 00011: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLbQ9F4xDWLu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 920
        },
        "outputId": "483ad753-2484-44d0-a6c8-6a32106378e5"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import sklearn\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import numpy as np\n",
        "\n",
        "nb_train_samples = 28709\n",
        "\n",
        "nb_validation_samples = 3589\n",
        "\n",
        "# We need to recreate our validation generator with shuffle = false\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "        validation_data_dir,\n",
        "        color_mode = 'grayscale',\n",
        "        target_size=(img_rows, img_cols),\n",
        "        batch_size=batch_size,\n",
        "        class_mode='categorical',\n",
        "        shuffle=False)\n",
        "\n",
        "class_labels = validation_generator.class_indices\n",
        "class_labels = {v: k for k, v in class_labels.items()}\n",
        "classes = list(class_labels.values())\n",
        "\n",
        "#Confution Matrix and Classification Report\n",
        "Y_pred = model.predict_generator(validation_generator, nb_validation_samples // batch_size+1)\n",
        "y_pred = np.argmax(Y_pred, axis=1)\n",
        "\n",
        "print('Confusion Matrix')\n",
        "print(confusion_matrix(validation_generator.classes, y_pred))\n",
        "print('Classification Report')\n",
        "target_names = list(class_labels.values())\n",
        "print(classification_report(validation_generator.classes, y_pred, target_names=target_names))\n",
        "\n",
        "plt.figure(figsize=(8,8))\n",
        "cnf_matrix = confusion_matrix(validation_generator.classes, y_pred)\n",
        "\n",
        "plt.imshow(cnf_matrix, interpolation='nearest')\n",
        "plt.colorbar()\n",
        "tick_marks = np.arange(len(classes))\n",
        "_ = plt.xticks(tick_marks, classes, rotation=90)\n",
        "_ = plt.yticks(tick_marks, classes)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 3589 images belonging to 7 classes.\n",
            "Confusion Matrix\n",
            "[[190   0  16  40  89 124  32]\n",
            " [ 35   0   1   4   4   8   3]\n",
            " [144   0  15  40 136  98  95]\n",
            " [ 24   0   4 726  51  52  22]\n",
            " [134   0   9 147 169  96  71]\n",
            " [121   0   6  48 159 251   9]\n",
            " [ 18   0  14  31  64   7 282]]\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       Angry       0.29      0.39      0.33       491\n",
            "     Disgust       0.00      0.00      0.00        55\n",
            "        Fear       0.23      0.03      0.05       528\n",
            "       Happy       0.70      0.83      0.76       879\n",
            "     Neutral       0.25      0.27      0.26       626\n",
            "         Sad       0.39      0.42      0.41       594\n",
            "    Surprise       0.55      0.68      0.61       416\n",
            "\n",
            "    accuracy                           0.46      3589\n",
            "   macro avg       0.34      0.37      0.34      3589\n",
            "weighted avg       0.42      0.46      0.42      3589\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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Sc5JckOTVbfuyJKclOS/JhUkOa9v3SnJpkuOAi4A9u4xfkqT5stCV9LZJzm/Xv19VzwOO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\nJGlCmaQlSZpQ/z9kGzRkhuqkLAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Viu7RrB9FuLr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab.patches import cv2_imshow"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWaLWQdNDWMF",
        "colab_type": "text"
      },
      "source": [
        "### Loading our saved model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "erF2L8W-DWMM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "classifier = load_model('emotion_little_vgg_3.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6k1mqVcDWMd",
        "colab_type": "text"
      },
      "source": [
        "### Get our class labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dS1vkx7hDWMi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "0aedd467-92ce-4a81-f7bc-3c89519edeec"
      },
      "source": [
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "        validation_data_dir,\n",
        "        color_mode = 'grayscale',\n",
        "        target_size=(img_rows, img_cols),\n",
        "        batch_size=batch_size,\n",
        "        class_mode='categorical',\n",
        "        shuffle=False)\n",
        "\n",
        "class_labels = validation_generator.class_indices\n",
        "class_labels = {v: k for k, v in class_labels.items()}\n",
        "classes = list(class_labels.values())\n",
        "print(class_labels)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 3589 images belonging to 7 classes.\n",
            "{0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Neutral', 5: 'Sad', 6: 'Surprise'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBUCHDYjDWM0",
        "colab_type": "text"
      },
      "source": [
        "### Let's test on some of validation images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8CYAOdnfDWM6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4e9bcf78-5fc1-4a24-cec7-acf91edc34a7"
      },
      "source": [
        "from keras.models import load_model\n",
        "from keras.optimizers import RMSprop, SGD, Adam\n",
        "from keras.preprocessing import image\n",
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "import numpy as np\n",
        "from os import listdir\n",
        "from os.path import isfile, join\n",
        "import re\n",
        "\n",
        "def draw_test(name,pred, im, true_label):\n",
        "    BLACK = [0,0,0]\n",
        "    expanded_image = cv2.copyMakeBorder(im, 160, 0, 0, 300 ,cv2.BORDER_CONSTANT,value=BLACK)\n",
        "    cv2.putText(expanded_image, \"predited - \"+ pred, (20, 60) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,0,255), 2)\n",
        "    cv2.putText(expanded_image, \"true - \"+ true_label, (20, 120) , cv2.FONT_HERSHEY_SIMPLEX,1, (0,255,0), 2)\n",
        "    cv2_imshow(expanded_image)\n",
        "\n",
        "\n",
        "def getRandomImage(path, img_width, img_height):\n",
        "    \"\"\"function loads a random images from a random folder in our test path \"\"\"\n",
        "    folders = list(filter(lambda x: os.path.isdir(os.path.join(path, x)), os.listdir(path)))\n",
        "    random_directory = np.random.randint(0,len(folders))\n",
        "    path_class = folders[random_directory]\n",
        "    file_path = path + path_class\n",
        "    file_names = [f for f in listdir(file_path) if isfile(join(file_path, f))]\n",
        "    random_file_index = np.random.randint(0,len(file_names))\n",
        "    image_name = file_names[random_file_index]\n",
        "    final_path = file_path + \"/\" + image_name\n",
        "    return image.load_img(final_path, target_size = (img_width, img_height),grayscale=True), final_path, path_class\n",
        "\n",
        "# dimensions of our images\n",
        "img_width, img_height = 48, 48\n",
        "\n",
        "# We use a very small learning rate \n",
        "model.compile(loss = 'categorical_crossentropy',\n",
        "              optimizer = RMSprop(lr = 0.001),\n",
        "              metrics = ['accuracy'])\n",
        "\n",
        "files = []\n",
        "predictions = []\n",
        "true_labels = []\n",
        "\n",
        "# predicting images\n",
        "for i in range(0, 10):\n",
        "    path = 'data/fer2013/validation/' \n",
        "    img, final_path, true_label = getRandomImage(path, img_width, img_height)\n",
        "    files.append(final_path)\n",
        "    true_labels.append(true_label)\n",
        "    x = image.img_to_array(img)\n",
        "    x = x * 1./255\n",
        "    x = np.expand_dims(x, axis=0)\n",
        "    images = np.vstack([x])\n",
        "    classes = model.predict_classes(images, batch_size = 10)\n",
        "    predictions.append(classes)\n",
        "    \n",
        "for i in range(0, len(files)):\n",
        "    image = cv2.imread((files[i]))\n",
        "    image = cv2.resize(image, None, fx=3, fy=3, interpolation = cv2.INTER_CUBIC)\n",
        "    draw_test(\"Prediction\", class_labels[predictions[i][0]], image, true_labels[i])\n",
        "#     cv2.waitKey(0)\n",
        "\n",
        "# cv2.destroyAllWindows()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9t0uKDHsse4ddVpLFhaeuRQjxmnxPBB0OB5SRLxaL1WpFxUyMKYoRqtYHg8Hl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\nOWxtPD/gO2P4E3JEOCgqQFlbygoqTJe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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=444x304 at 0x7FDCCD0CB278>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DSm9oR6vVGo1Gr169Go/HSKfM5/PLy8vz8/PpdMqjVVUFe9OFlDqdelevK0LQ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\nCY2+PIFtSA+d8cqqqtbrNYxNDXS6EP3kaHkJ6o/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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=444x304 at 0x7FDCCD0CB240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1JcOBNOPEyCAvmMcJdSXSLwAsmYs1IK+urtDhc319rdWXKL3MZGxwVQ8mUjRZ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\n89i4XVUV5XK5XGLKOr1gjCxCtBHChy0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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=444x304 at 0x7FDCCD0CB2B0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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u2pB0t4OUiJMqTbbhj6prpT7qR/rwRylQx3obGhxkW18eP0qeij47AqN3d3d/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\nGoLthnrsmFYcC6nQR2Oax1E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psIaT6oSc0FVelc7tdhtSNRLTL9S+ID04IJqbzWa9XmN6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\nxpgL4f8BpJjPMOqM020AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=444x304 at 0x7FDCCD0CB358>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ssTFGFoRLS5b1QyZJsNBAk1dIuzjrnuP7AMWY+H8URfC+IdnlcjlOuqEAho/0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\nfag4WhRFo9Go2+12u93xeFxIekYZ7bVppGLyewu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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=444x304 at 0x7FDCCD0CB240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hy3SqktWASzOKw8ZUhKRGnGZFek89FmQf9UY2OT6N1tgXhciMzV6B5ju1rJWG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\nTtpsNvC+IZ0xRgRDMc5ms1k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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=444x304 at 0x7FDCCD0CB2B0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7PRwOXZzW6+LycxQkwnRCpphtL5juQMYcOR+EMtmviGfUOlBdzgiDjooZYhgx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\n1Tjn8D7WqkO5kK/X1ZZ5BJZ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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=444x304 at 0x7FDCCD0CB320>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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Jq6oCl2FVPli0Xq9xHKY38fvlcok3RL/fH4/HP//883a7vbu7W6/Xf/zxR1EUEMgk7hjj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\nbl/wXt7MdJ/XhpnG5DZFUaCTSIzR14mb83uaC4u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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=444x304 at 0x7FDCCD0CB240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QwtrKYsM1dYRuKeOeMNbg1YYQ4HG32230t+giwLAomaPXLkZdEoPW5SaW1uPt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\nuLTcn/xvbxjGI8iZTknCfJyKBtsNghV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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=444x304 at 0x7FDCCD0CB2B0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P1t6hW4rSJbil0+kU2Z71eo1f+75frVZMUmOvmDsqQb+m0ykk5urqarlcQn0g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\njpNKd/I73NqkmuNWl/mcNap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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=444x304 at 0x7FDCCD0CB278>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}