Distraction_Detection_MobileNetV2_LSTM_Main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = blue> Real-Time Driver Distraction Detection using TimeDistributed Convolutional LSTM Network for Mobile Platforms </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color = green> Author: Abhiram Gurijala </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color =blue> Import the required libraries </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from imageio import imread\n",
    "import cv2 as cv\n",
    "from cv2 import resize\n",
    "import datetime\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import random as rn\n",
    "from keras import backend as K\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, GRU, Flatten, TimeDistributed, Flatten, BatchNormalization, Activation, Dropout, LSTM\n",
    "from tensorflow.keras.layers import Conv2D, Conv3D, MaxPooling2D, MaxPooling3D, GlobalAveragePooling2D\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.optimizers import Adam, SGD\n",
    "from tensorflow.keras.applications import ResNet50, mobilenet_v2\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras import initializers\n",
    "from tensorflow.keras.models import load_model, Model\n",
    "from sklearn.metrics import confusion_matrix, f1_score, matthews_corrcoef, precision_score, recall_score\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the random seed\n",
    "np.random.seed(30)\n",
    "rn.seed(30)\n",
    "tf.random.set_seed(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Load the Training and Validation CSV files </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_doc = np.random.permutation(open('./Project_Data/train_cam_1_2_mv2_t6.csv').readlines())\n",
    "val_doc = np.random.permutation(open('./Project_Data/val_cam_1_2_mv2.csv').readlines())\n",
    "batch_size = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# training sequences = 1740\n",
      "# validation sequences = 60\n",
      "# epochs = 25\n"
     ]
    }
   ],
   "source": [
    "# Set the date and time variable\n",
    "curr_dt_time = datetime.datetime.now()\n",
    "\n",
    "train_path = 'Project_Data/train_cam_1_2_mv2_t6'\n",
    "val_path = 'Project_Data/val_cam_1_2_mv2'\n",
    "\n",
    "num_train_sequences = len(train_doc)\n",
    "print('# training sequences =', num_train_sequences)\n",
    "\n",
    "num_val_sequences = len(val_doc)\n",
    "print('# validation sequences =', num_val_sequences)\n",
    "\n",
    "# choose the number of epochs\n",
    "num_epochs = 25 \n",
    "print ('# epochs =', num_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Custom video data generator for training and validation datasets </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def video_generator(source_path, folder_list, batch_size):\n",
    "    print( 'Source path = ', source_path, '; batch size =', batch_size)\n",
    "    # Read every alternate frame of the video; So 10 frames will be used for Project\n",
    "    img_idx = [x for x in range(0,20,2) ] #create a list of image numbers you want to use for a particular video\n",
    "    \n",
    "    while True:\n",
    "        t = np.random.permutation(folder_list)\n",
    "        num_batches = len(folder_list) // batch_size # calculate the number of batches\n",
    "        for batch in range(num_batches): # we iterate over the number of batches\n",
    "            # We will use image size of (224 x 224) with 3 channels\n",
    "            batch_data = np.zeros((batch_size,len(img_idx),224,224,3)) # x is the number of images you use for each video, (y,z) is the final size of the input images and 3 is the number of channels RGB\n",
    "            batch_labels = np.zeros((batch_size,10)) # batch_labels is the one hot representation of the output\n",
    "            for folder in range(batch_size): # iterate over the batch_size\n",
    "                imgs = os.listdir(source_path+'/'+ t[folder + (batch*batch_size)].split(';')[0]) # read all the images in the folder\n",
    "                for idx,item in enumerate(img_idx): #  Iterate iver the frames/images of a folder to read them in\n",
    "                    img = image.load_img(source_path+'/'+ t[folder + (batch*batch_size)].strip().split(';')[0]+'/'+imgs[item], target_size=(224, 224))\n",
    "                    img_array = image.img_to_array(img)\n",
    "                    \n",
    "                    #Pre-process using mobilenetv2 pre-processor\n",
    "                    image_norm = tf.keras.applications.mobilenet_v2.preprocess_input(img_array)\n",
    "                    \n",
    "                    # Batch of 3 channel images normalized and fed into the batch_data array\n",
    "                    batch_data[folder,idx,:,:,0] = image_norm[:,:,0] #normalise and feed in the image\n",
    "                    batch_data[folder,idx,:,:,1] = image_norm[:,:,1] #normalise and feed in the image\n",
    "                    batch_data[folder,idx,:,:,2] = image_norm[:,:,2] #normalise and feed in the image\n",
    "                    \n",
    "                batch_labels[folder, int(t[folder + (batch*batch_size)].strip().split(';')[1])] = 1\n",
    "            yield batch_data, batch_labels # yield the batch_data and the batch_labels, remember what does yield do\n",
    "\n",
    "        \n",
    "        # write the code for the remaining data points which are left after full batches\n",
    "        batch_size_rem = (len(folder_list) % batch_size)\n",
    "        if batch_size_rem != 0:\n",
    "            batch_data = np.zeros((batch_size_rem,len(img_idx),224,224,3))\n",
    "            batch_labels = np.zeros((batch_size_rem,10))\n",
    "            for folder in range(batch_size_rem):\n",
    "                imgs = os.listdir(source_path+'/'+ t[folder + (num_batches*batch_size)].split(';')[0]) # read all the images in the folder\n",
    "                # For each video, iterate through the images\n",
    "                for idx,item in enumerate(img_idx): #  Iterate iver the frames/images of a folder to read them in\n",
    "                    img = image.load_img(source_path+'/'+ t[folder + (num_batches*batch_size)].strip().split(';')[0]+'/'+imgs[item], target_size=(224, 224))\n",
    "                    img_array = image.img_to_array(img)\n",
    "                    \n",
    "                    #Pre-process using mobilenetv2 pre-processor\n",
    "                    image_norm = tf.keras.applications.mobilenet_v2.preprocess_input(img_array)\n",
    "                    \n",
    "                    # Batch of 3 channel images normalized and fed into the batch_data array\n",
    "                    batch_data[folder,idx,:,:,0] = image_norm[:,:,0] #normalise and feed in the image\n",
    "                    batch_data[folder,idx,:,:,1] = image_norm[:,:,1] #normalise and feed in the image\n",
    "                    batch_data[folder,idx,:,:,2] = image_norm[:,:,2] #normalise and feed in the image\n",
    "                    \n",
    "                batch_labels[folder, int(t[folder + (num_batches*batch_size)].strip().split(';')[1])] = 1\n",
    "            yield batch_data, batch_labels # yield the batch_data and the batch_labels, remember what does yield do"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Define the deep learning model </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the base model MobileNetV2\n",
    "def build_mobilenet(shape):\n",
    "    model = tf.keras.applications.MobileNetV2(\n",
    "        include_top=False,\n",
    "        input_shape=shape,\n",
    "        weights='imagenet')\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def distr_detect_model(shape, n_classes):\n",
    "\n",
    "    # Create the base model\n",
    "    mobinet = build_mobilenet(shape[1:])\n",
    "    \n",
    "    # Start creating the time-distribted model with Sequential\n",
    "    model = tf.keras.Sequential()\n",
    "\n",
    "    # Add base model and pooling layers\n",
    "    model.add(TimeDistributed(mobinet, input_shape=shape))\n",
    "    model.add(TimeDistributed(GlobalAveragePooling2D()))\n",
    "    model.add(BatchNormalization())\n",
    "\n",
    "    # Decision making layers\n",
    "    \n",
    "    # Fully Connected layer with 1024 neurons with Relu activation. Batch Normalized and Dropout at 0.25\n",
    "    model.add(Dense(1024, activation='relu',  kernel_initializer=\"he_normal\", bias_initializer='zeros'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    # Add a fully connected layer with 512 neurons and 25% connections dropped out\n",
    "    model.add(TimeDistributed(Dense(512, activation='relu')))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.25))\n",
    "    \n",
    "    # Add a fully connected layer with 256 neurons and 25% connections dropped out\n",
    "    model.add(TimeDistributed(Dense(256, activation='relu')))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    # Add a fully connected layer with 128 neurons and 25% connections dropped out\n",
    "    model.add(TimeDistributed(Dense(128, activation='relu')))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dropout(0.25))\n",
    "\n",
    "    # Add a LSTM layer with 512 neurons and 50% dropout\n",
    "    model.add(LSTM(512, return_sequences=False, dropout=0.5))\n",
    "\n",
    "    # Softmax layer for final 10-class output\n",
    "    model.add(Dense(n_classes, activation='softmax'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "time_distributed_5 (TimeDist (None, 10, 7, 7, 1280)    2257984   \n",
      "_________________________________________________________________\n",
      "time_distributed_6 (TimeDist (None, 10, 1280)          0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 10, 1280)          5120      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 10, 1024)          1311744   \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 10, 1024)          4096      \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 10, 1024)          0         \n",
      "_________________________________________________________________\n",
      "time_distributed_7 (TimeDist (None, 10, 512)           524800    \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 10, 512)           2048      \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 10, 512)           0         \n",
      "_________________________________________________________________\n",
      "time_distributed_8 (TimeDist (None, 10, 256)           131328    \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 10, 256)           1024      \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 10, 256)           0         \n",
      "_________________________________________________________________\n",
      "time_distributed_9 (TimeDist (None, 10, 128)           32896     \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 10, 128)           512       \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 10, 128)           0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 512)               1312768   \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 5,589,450\n",
      "Trainable params: 5,548,938\n",
      "Non-trainable params: 40,512\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Define the model input shape of 10 images with size (224,224,3)\n",
    "INSHAPE = (10, 224, 224, 3)\n",
    "\n",
    "# No. of target classes = 10\n",
    "n_classes = 10\n",
    "\n",
    "# Call the model function to load the model\n",
    "model = distr_detect_model(INSHAPE, n_classes)\n",
    "\n",
    "# Adam optimizer with initial learning rate = 0.0005\n",
    "optimiser =Adam(lr=5e-4)\n",
    "\n",
    "# Compile the model withh optimiser and loss parameters\n",
    "model.compile(optimizer=optimiser, loss='categorical_crossentropy', metrics=['categorical_accuracy'])\n",
    "\n",
    "# Print Model Summary\n",
    "print (model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Load the training and validation data using the generator </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_generator = video_generator(train_path, train_doc, batch_size)\n",
    "val_generator = video_generator(val_path, val_doc, batch_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Set the model training parameters, checkpoint and callbacks </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'model_init' + '_' + str(curr_dt_time).replace(' ','').replace(':','_') + '/'\n",
    "    \n",
    "if not os.path.exists(model_name):\n",
    "    os.mkdir(model_name)\n",
    "        \n",
    "filepath = model_name + 'model-{epoch:05d}-{loss:.5f}-{categorical_accuracy:.5f}-{val_loss:.5f}-{val_categorical_accuracy:.5f}.h5'\n",
    "\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_categorical_accuracy', verbose=1, save_best_only=True, save_weights_only=False, mode='auto', save_freq='epoch')\n",
    "\n",
    "# ReduceLROnplateau\n",
    "LR = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2, cooldown=1, verbose=1) \n",
    "callbacks_list = [checkpoint, LR]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Set the steps per epoch for both training and validation </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "if (num_train_sequences%batch_size) == 0:\n",
    "    steps_per_epoch = int(num_train_sequences/batch_size)\n",
    "else:\n",
    "    steps_per_epoch = (num_train_sequences//batch_size) + 1\n",
    "\n",
    "if (num_val_sequences%batch_size) == 0:\n",
    "    validation_steps = int(num_val_sequences/batch_size)\n",
    "else:\n",
    "    validation_steps = (num_val_sequences//batch_size) + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source path =  Project_Data/train_cam_1_2_mv2_t6 ; batch size = 10\n",
      "Epoch 1/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 2.0516 - categorical_accuracy: 0.2448Source path =  Project_Data/val_cam_1_2_mv2 ; batch size = 10\n",
      "\n",
      "Epoch 00001: val_categorical_accuracy improved from -inf to 0.26667, saving model to model_init_2021-02-1605_09_47.319278/model-00001-2.05159-0.24483-3.18863-0.26667.h5\n",
      "174/174 [==============================] - 231s 1s/step - loss: 2.0516 - categorical_accuracy: 0.2448 - val_loss: 3.1886 - val_categorical_accuracy: 0.2667 - lr: 5.0000e-04\n",
      "Epoch 2/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 1.1182 - categorical_accuracy: 0.6149\n",
      "Epoch 00002: val_categorical_accuracy improved from 0.26667 to 0.46667, saving model to model_init_2021-02-1605_09_47.319278/model-00002-1.11819-0.61494-2.11418-0.46667.h5\n",
      "174/174 [==============================] - 230s 1s/step - loss: 1.1182 - categorical_accuracy: 0.6149 - val_loss: 2.1142 - val_categorical_accuracy: 0.4667 - lr: 5.0000e-04\n",
      "Epoch 3/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.7346 - categorical_accuracy: 0.7534\n",
      "Epoch 00003: val_categorical_accuracy did not improve from 0.46667\n",
      "174/174 [==============================] - 229s 1s/step - loss: 0.7346 - categorical_accuracy: 0.7534 - val_loss: 3.2436 - val_categorical_accuracy: 0.3500 - lr: 5.0000e-04\n",
      "Epoch 4/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.5864 - categorical_accuracy: 0.8075\n",
      "Epoch 00004: val_categorical_accuracy did not improve from 0.46667\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "174/174 [==============================] - 230s 1s/step - loss: 0.5864 - categorical_accuracy: 0.8075 - val_loss: 3.0060 - val_categorical_accuracy: 0.4167 - lr: 5.0000e-04\n",
      "Epoch 5/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.2798 - categorical_accuracy: 0.9080\n",
      "Epoch 00005: val_categorical_accuracy improved from 0.46667 to 0.53333, saving model to model_init_2021-02-1605_09_47.319278/model-00005-0.27981-0.90805-2.15833-0.53333.h5\n",
      "174/174 [==============================] - 230s 1s/step - loss: 0.2798 - categorical_accuracy: 0.9080 - val_loss: 2.1583 - val_categorical_accuracy: 0.5333 - lr: 2.5000e-04\n",
      "Epoch 6/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.2002 - categorical_accuracy: 0.9362\n",
      "Epoch 00006: val_categorical_accuracy improved from 0.53333 to 0.63333, saving model to model_init_2021-02-1605_09_47.319278/model-00006-0.20023-0.93621-1.53271-0.63333.h5\n",
      "174/174 [==============================] - 229s 1s/step - loss: 0.2002 - categorical_accuracy: 0.9362 - val_loss: 1.5327 - val_categorical_accuracy: 0.6333 - lr: 2.5000e-04\n",
      "Epoch 7/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.1880 - categorical_accuracy: 0.9437\n",
      "Epoch 00007: val_categorical_accuracy did not improve from 0.63333\n",
      "174/174 [==============================] - 225s 1s/step - loss: 0.1880 - categorical_accuracy: 0.9437 - val_loss: 1.8611 - val_categorical_accuracy: 0.5500 - lr: 2.5000e-04\n",
      "Epoch 8/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.1726 - categorical_accuracy: 0.9494\n",
      "Epoch 00008: val_categorical_accuracy improved from 0.63333 to 0.73333, saving model to model_init_2021-02-1605_09_47.319278/model-00008-0.17262-0.94943-1.28859-0.73333.h5\n",
      "174/174 [==============================] - 227s 1s/step - loss: 0.1726 - categorical_accuracy: 0.9494 - val_loss: 1.2886 - val_categorical_accuracy: 0.7333 - lr: 2.5000e-04\n",
      "Epoch 9/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.1773 - categorical_accuracy: 0.9414\n",
      "Epoch 00009: val_categorical_accuracy did not improve from 0.73333\n",
      "174/174 [==============================] - 226s 1s/step - loss: 0.1773 - categorical_accuracy: 0.9414 - val_loss: 1.4363 - val_categorical_accuracy: 0.7000 - lr: 2.5000e-04\n",
      "Epoch 10/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.1758 - categorical_accuracy: 0.9489\n",
      "Epoch 00010: val_categorical_accuracy did not improve from 0.73333\n",
      "\n",
      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
      "174/174 [==============================] - 227s 1s/step - loss: 0.1758 - categorical_accuracy: 0.9489 - val_loss: 1.6931 - val_categorical_accuracy: 0.7000 - lr: 2.5000e-04\n",
      "Epoch 11/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.1010 - categorical_accuracy: 0.9667\n",
      "Epoch 00011: val_categorical_accuracy improved from 0.73333 to 0.81667, saving model to model_init_2021-02-1605_09_47.319278/model-00011-0.10103-0.96667-1.23550-0.81667.h5\n",
      "174/174 [==============================] - 226s 1s/step - loss: 0.1010 - categorical_accuracy: 0.9667 - val_loss: 1.2355 - val_categorical_accuracy: 0.8167 - lr: 1.2500e-04\n",
      "Epoch 12/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0755 - categorical_accuracy: 0.9753\n",
      "Epoch 00012: val_categorical_accuracy did not improve from 0.81667\n",
      "174/174 [==============================] - 229s 1s/step - loss: 0.0755 - categorical_accuracy: 0.9753 - val_loss: 1.4410 - val_categorical_accuracy: 0.7333 - lr: 1.2500e-04\n",
      "Epoch 13/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0624 - categorical_accuracy: 0.9805\n",
      "Epoch 00013: val_categorical_accuracy did not improve from 0.81667\n",
      "\n",
      "Epoch 00013: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "174/174 [==============================] - 226s 1s/step - loss: 0.0624 - categorical_accuracy: 0.9805 - val_loss: 1.5856 - val_categorical_accuracy: 0.7333 - lr: 1.2500e-04\n",
      "Epoch 14/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0501 - categorical_accuracy: 0.9862\n",
      "Epoch 00014: val_categorical_accuracy did not improve from 0.81667\n",
      "174/174 [==============================] - 227s 1s/step - loss: 0.0501 - categorical_accuracy: 0.9862 - val_loss: 1.4688 - val_categorical_accuracy: 0.7333 - lr: 6.2500e-05\n",
      "Epoch 15/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0328 - categorical_accuracy: 0.9862\n",
      "Epoch 00015: val_categorical_accuracy did not improve from 0.81667\n",
      "\n",
      "Epoch 00015: ReduceLROnPlateau reducing learning rate to 3.125000148429535e-05.\n",
      "174/174 [==============================] - 227s 1s/step - loss: 0.0328 - categorical_accuracy: 0.9862 - val_loss: 1.5402 - val_categorical_accuracy: 0.7333 - lr: 6.2500e-05\n",
      "Epoch 16/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0303 - categorical_accuracy: 0.9931\n",
      "Epoch 00016: val_categorical_accuracy did not improve from 0.81667\n",
      "174/174 [==============================] - 227s 1s/step - loss: 0.0303 - categorical_accuracy: 0.9931 - val_loss: 1.3247 - val_categorical_accuracy: 0.7667 - lr: 3.1250e-05\n",
      "Epoch 17/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0271 - categorical_accuracy: 0.9931\n",
      "Epoch 00017: val_categorical_accuracy did not improve from 0.81667\n",
      "\n",
      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "174/174 [==============================] - 230s 1s/step - loss: 0.0271 - categorical_accuracy: 0.9931 - val_loss: 1.2390 - val_categorical_accuracy: 0.7833 - lr: 3.1250e-05\n",
      "Epoch 18/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0230 - categorical_accuracy: 0.9937\n",
      "Epoch 00018: val_categorical_accuracy did not improve from 0.81667\n",
      "174/174 [==============================] - 229s 1s/step - loss: 0.0230 - categorical_accuracy: 0.9937 - val_loss: 1.1660 - val_categorical_accuracy: 0.8000 - lr: 1.5625e-05\n",
      "Epoch 19/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0228 - categorical_accuracy: 0.9925\n",
      "Epoch 00019: val_categorical_accuracy improved from 0.81667 to 0.83333, saving model to model_init_2021-02-1605_09_47.319278/model-00019-0.02285-0.99253-1.03887-0.83333.h5\n",
      "174/174 [==============================] - 228s 1s/step - loss: 0.0228 - categorical_accuracy: 0.9925 - val_loss: 1.0389 - val_categorical_accuracy: 0.8333 - lr: 1.5625e-05\n",
      "Epoch 20/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0166 - categorical_accuracy: 0.9966\n",
      "Epoch 00020: val_categorical_accuracy improved from 0.83333 to 0.85000, saving model to model_init_2021-02-1605_09_47.319278/model-00020-0.01655-0.99655-1.01868-0.85000.h5\n",
      "174/174 [==============================] - 230s 1s/step - loss: 0.0166 - categorical_accuracy: 0.9966 - val_loss: 1.0187 - val_categorical_accuracy: 0.8500 - lr: 1.5625e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 21/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0169 - categorical_accuracy: 0.9966\n",
      "Epoch 00021: val_categorical_accuracy did not improve from 0.85000\n",
      "174/174 [==============================] - 227s 1s/step - loss: 0.0169 - categorical_accuracy: 0.9966 - val_loss: 1.0600 - val_categorical_accuracy: 0.8333 - lr: 1.5625e-05\n",
      "Epoch 22/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0131 - categorical_accuracy: 0.9960\n",
      "Epoch 00022: val_categorical_accuracy did not improve from 0.85000\n",
      "174/174 [==============================] - 226s 1s/step - loss: 0.0131 - categorical_accuracy: 0.9960 - val_loss: 0.9726 - val_categorical_accuracy: 0.8500 - lr: 1.5625e-05\n",
      "Epoch 23/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0124 - categorical_accuracy: 0.9971\n",
      "Epoch 00023: val_categorical_accuracy did not improve from 0.85000\n",
      "174/174 [==============================] - 227s 1s/step - loss: 0.0124 - categorical_accuracy: 0.9971 - val_loss: 1.0705 - val_categorical_accuracy: 0.8333 - lr: 1.5625e-05\n",
      "Epoch 24/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0118 - categorical_accuracy: 0.9960\n",
      "Epoch 00024: val_categorical_accuracy did not improve from 0.85000\n",
      "\n",
      "Epoch 00024: ReduceLROnPlateau reducing learning rate to 7.812500371073838e-06.\n",
      "174/174 [==============================] - 227s 1s/step - loss: 0.0118 - categorical_accuracy: 0.9960 - val_loss: 1.0651 - val_categorical_accuracy: 0.8500 - lr: 1.5625e-05\n",
      "Epoch 25/25\n",
      "174/174 [==============================] - ETA: 0s - loss: 0.0168 - categorical_accuracy: 0.9954\n",
      "Epoch 00025: val_categorical_accuracy did not improve from 0.85000\n",
      "174/174 [==============================] - 228s 1s/step - loss: 0.0168 - categorical_accuracy: 0.9954 - val_loss: 1.0896 - val_categorical_accuracy: 0.8500 - lr: 7.8125e-06\n"
     ]
    }
   ],
   "source": [
    "H5 = model.fit(train_generator, steps_per_epoch=steps_per_epoch, epochs=num_epochs, verbose=1, \n",
    "                    callbacks=callbacks_list, validation_data=val_generator, \n",
    "                    validation_steps=validation_steps, class_weight=None, workers=-1, initial_epoch=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Plot the model training and validation accuracy & loss over 25 epochs </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the training loss and accuracy\n",
    "N = num_epochs\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure(figsize=(15,5))\n",
    "\n",
    "# summarize history for Accuracy\n",
    "plt.subplot(121)\n",
    "plt.plot(np.arange(1, N+1), H5.history[\"categorical_accuracy\"], label=\"train_acc\")\n",
    "plt.plot(np.arange(1, N+1), H5.history[\"val_categorical_accuracy\"], label=\"val_acc\")\n",
    "plt.title(\"MobileNet V2:Training and Validation Accuracy on Dataset\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "# summarize history for loss\n",
    "plt.subplot(122)\n",
    "plt.plot(np.arange(1, N+1), H5.history['loss'], label=\"train_loss\")\n",
    "plt.plot(np.arange(1, N+1), H5.history['val_loss'], label=\"validation_loss\")\n",
    "plt.title(\"MobileNet V2:Training and Validation Loss on Dataset\")\n",
    "plt.xlabel('Epoch #')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Create the test data video generator </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_video_generator(source_path, folder_list, batch_size):\n",
    "    print( 'Source path = ', source_path, '; batch size =', batch_size)\n",
    "\n",
    "    # Read first 10 frames\n",
    "    img_idx = [x for x in range(0,10) ] #create a list of image numbers you want to use for a particular video\n",
    "\n",
    "    # Read next 10 frames\n",
    "    img_idx_2 = [y for y in range(10,20) ]\n",
    "    \n",
    "    while True:\n",
    "        t = np.random.permutation(folder_list)\n",
    "        num_batches = len(folder_list)*2 // batch_size # calculate the number of batches\n",
    "        for batch in range(num_batches): # we iterate over the number of batches\n",
    "            # We will use image size of (224 x 224) with 3 channels\n",
    "            batch_data = np.zeros((batch_size,len(img_idx),224,224,3)) # x is the number of images you use for each video, (y,z) is the final size of the input images and 3 is the number of channels RGB\n",
    "            batch_labels = np.zeros((batch_size,10)) # batch_labels is the one hot representation of the output\n",
    "            for folder in range(batch_size//2): # iterate over the batch_size\n",
    "                imgs = os.listdir(source_path+'/'+ t[folder + (batch*batch_size//2)].split(';')[0]) # read all the images in the folder\n",
    "                for idx,item in enumerate(img_idx): #  Iterate iver the frames/images of a folder to read them in\n",
    "                    img = image.load_img(source_path+'/'+ t[folder + (batch*batch_size//2)].strip().split(';')[0]+'/'+imgs[item], target_size=(224, 224))\n",
    "                    img_array = image.img_to_array(img)\n",
    "                    \n",
    "                    #Pre-process using mobilenetv2 pre-processor\n",
    "                    image_norm = tf.keras.applications.mobilenet_v2.preprocess_input(img_array)\n",
    "                    \n",
    "                    # Batch of 3 channel images normalized and fed into the batch_data array\n",
    "                    batch_data[folder*2,idx,:,:,0] = image_norm[:,:,0] #normalise and feed in the image\n",
    "                    batch_data[folder*2,idx,:,:,1] = image_norm[:,:,1] #normalise and feed in the image\n",
    "                    batch_data[folder*2,idx,:,:,2] = image_norm[:,:,2] #normalise and feed in the image\n",
    "                    \n",
    "                batch_labels[folder*2, int(t[folder + (batch*batch_size//2)].strip().split(';')[1])] = 1\n",
    "\n",
    "                for idx,item in enumerate(img_idx_2): #  Iterate iver the frames/images of a folder to read them in\n",
    "                    img = image.load_img(source_path+'/'+ t[folder + (batch*batch_size//2)].strip().split(';')[0]+'/'+imgs[item], target_size=(224, 224))\n",
    "                    img_array = image.img_to_array(img)\n",
    "                    \n",
    "                    #Pre-process using mobilenetv2 pre-processor\n",
    "                    image_norm = tf.keras.applications.mobilenet_v2.preprocess_input(img_array)\n",
    "                    \n",
    "                    # Batch of 3 channel images normalized and fed into the batch_data array\n",
    "                    batch_data[folder*2 + 1,idx,:,:,0] = image_norm[:,:,0] #normalise and feed in the image\n",
    "                    batch_data[folder*2 + 1,idx,:,:,1] = image_norm[:,:,1] #normalise and feed in the image\n",
    "                    batch_data[folder*2 + 1,idx,:,:,2] = image_norm[:,:,2] #normalise and feed in the image\n",
    "                    \n",
    "                batch_labels[folder*2 + 1, int(t[folder + (batch*batch_size//2)].strip().split(';')[1])] = 1\n",
    "                \n",
    "            yield batch_data, batch_labels # yield the batch_data and the batch_labels, remember what does yield do\n",
    "\n",
    "        \n",
    "        # write the code for the remaining data points which are left after full batches\n",
    "        batch_size_rem = (len(folder_list)*2 % batch_size)\n",
    "        if batch_size_rem != 0:\n",
    "            batch_data = np.zeros((batch_size_rem,len(img_idx),224,224,3))\n",
    "            batch_labels = np.zeros((batch_size_rem,10))\n",
    "            for folder in range(batch_size_rem//2):\n",
    "                imgs = os.listdir(source_path+'/'+ t[folder + (num_batches*batch_size//2)].split(';')[0]) # read all the images in the folder\n",
    "                # For each video, iterate through the images\n",
    "                for idx,item in enumerate(img_idx): #  Iterate iver the frames/images of a folder to read them in\n",
    "                    img = image.load_img(source_path+'/'+ t[folder + (num_batches*batch_size//2)].strip().split(';')[0]+'/'+imgs[item], target_size=(224, 224))\n",
    "                    img_array = image.img_to_array(img)\n",
    "                    \n",
    "                    #Pre-process using mobilenetv2 pre-processor                    \n",
    "                    image_norm = tf.keras.applications.mobilenet_v2.preprocess_input(img_array)\n",
    "                    \n",
    "                    # Batch of 3 channel images normalized and fed into the batch_data array\n",
    "                    batch_data[folder*2,idx,:,:,0] = image_norm[:,:,0] #normalise and feed in the image\n",
    "                    batch_data[folder*2,idx,:,:,1] = image_norm[:,:,1] #normalise and feed in the image\n",
    "                    batch_data[folder*2,idx,:,:,2] = image_norm[:,:,2] #normalise and feed in the image\n",
    "                    \n",
    "                batch_labels[folder*2, int(t[folder + (num_batches*batch_size//2)].strip().split(';')[1])] = 1\n",
    "\n",
    "                for idx,item in enumerate(img_idx_2): #  Iterate iver the frames/images of a folder to read them in\n",
    "                    img = image.load_img(source_path+'/'+ t[folder + (num_batches*batch_size//2)].strip().split(';')[0]+'/'+imgs[item], target_size=(224, 224))\n",
    "                    img_array = image.img_to_array(img)\n",
    "                    \n",
    "                    #Pre-process using mobilenetv2 pre-processor                    \n",
    "                    image_norm = tf.keras.applications.mobilenet_v2.preprocess_input(img_array)\n",
    "                    \n",
    "                    # Batch of 3 channel images normalized and fed into the batch_data array\n",
    "                    batch_data[folder*2 + 1,idx,:,:,0] = image_norm[:,:,0] #normalise and feed in the image\n",
    "                    batch_data[folder*2 + 1,idx,:,:,1] = image_norm[:,:,1] #normalise and feed in the image\n",
    "                    batch_data[folder*2 + 1,idx,:,:,2] = image_norm[:,:,2] #normalise and feed in the image\n",
    "                    \n",
    "                batch_labels[folder*2 + 1, int(t[folder + (num_batches*batch_size//2)].strip().split(';')[1])] = 1\n",
    "                \n",
    "            yield batch_data, batch_labels # yield the batch_data and the batch_labels, remember what does yield do"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Start loading the best model \".h5\" file and evaluate the model performance on test data </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Testing sequences = 182\n",
      "Model loaded.\n",
      "Number of batches = 18\n"
     ]
    }
   ],
   "source": [
    "#######\n",
    "    \n",
    "model_name = 'model_init_2021-02-1605_09_47.319278/model-00020-0.01655-0.99655-1.01868-0.85000.h5'\n",
    "    \n",
    "#######\n",
    "    \n",
    "test_doc = open('Project_Data/test_cam_1_2_t1_mv2.csv').readlines()\n",
    "test_path = 'Project_Data/test_cam_1_2_t1_mv2'\n",
    "num_test_sequences = len(test_doc)*2\n",
    "print ('# Testing sequences =', num_test_sequences)\n",
    "test_datagen = test_video_generator(test_path, test_doc, batch_size)\n",
    "model = load_model(model_name)\n",
    "print(\"Model loaded.\")\n",
    "model_func = Model(inputs=[model.input], outputs=[model.output])\n",
    "    \n",
    "acc = 0\n",
    "    \n",
    "num_batches = int(num_test_sequences/batch_size)\n",
    "print('Number of batches =',num_batches)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Start making batch predictions on the test data </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source path =  Project_Data/test_cam_1_2_t1_mv2 ; batch size = 10\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (10, 10, 224, 224, 3) and shape of true_labels: (10, 10)\n",
      "shape of x: (2, 10, 224, 224, 3) and shape of true_labels: (2, 10)\n"
     ]
    }
   ],
   "source": [
    "acc = 0\n",
    "actual_list = []\n",
    "pred_list = []\n",
    "for i in range(num_batches):\n",
    "    x,true_labels = test_datagen.__next__()\n",
    "    print (\"shape of x:\", x.shape, \"and shape of true_labels:\", true_labels.shape)\n",
    "    pred_idx = np.argmax(model_func.predict_on_batch(x), axis=1)\n",
    "    actual_list.append(np.where(true_labels==1)[1])\n",
    "    pred_list.append(pred_idx)\n",
    "    for j,k in enumerate(pred_idx):\n",
    "        if true_labels[j,k] == 1:\n",
    "            acc += 1\n",
    "            \n",
    "if (num_test_sequences%batch_size) != 0:\n",
    "    x,true_labels = test_datagen.__next__()\n",
    "    print (\"shape of x:\", x.shape, \"and shape of true_labels:\", true_labels.shape)\n",
    "    pred_idx = np.argmax(model_func.predict_on_batch(x), axis=1)\n",
    "    actual_list.append(np.where(true_labels==1)[1])\n",
    "    pred_list.append(pred_idx)\n",
    "    for j,k in enumerate(pred_idx):\n",
    "        if true_labels[j,k] == 1:\n",
    "            acc += 1            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Check Test Accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy = 95.6 %\n"
     ]
    }
   ],
   "source": [
    "print('Test Accuracy =', round((acc/num_test_sequences)*100,2),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Check Test F1-Score </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weighted F1-score on test data =  95.22 %\n"
     ]
    }
   ],
   "source": [
    "test_weighted_f1 = f1_score(\n",
    "    np.concatenate(actual_list),    \n",
    "    np.concatenate(pred_list), average='weighted'\n",
    ")\n",
    "\n",
    "print(\"Weighted F1-score on test data = \", round(test_weighted_f1*100,2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Micro F1-score on test data =  95.6 %\n"
     ]
    }
   ],
   "source": [
    "test_micro_f1 = f1_score(\n",
    "    np.concatenate(actual_list),    \n",
    "    np.concatenate(pred_list), average='micro'\n",
    ")\n",
    "\n",
    "print(\"Micro F1-score on test data = \", round(test_micro_f1*100,2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macro F1-score on test data =  94.86 %\n"
     ]
    }
   ],
   "source": [
    "test_macro_f1 = f1_score(\n",
    "    np.concatenate(actual_list),    \n",
    "    np.concatenate(pred_list), average='macro'\n",
    ")\n",
    "\n",
    "print(\"Macro F1-score on test data = \", round(test_macro_f1*100,2),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Check Test Precision and Recall Scores </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision score on test data =  96.12 %\n"
     ]
    }
   ],
   "source": [
    "test_precision_score = precision_score(\n",
    "    np.concatenate(actual_list),    \n",
    "    np.concatenate(pred_list), average='macro'\n",
    ")\n",
    "\n",
    "print(\"Precision score on test data = \", round(test_precision_score*100,2),'%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall score on test data =  95.12 %\n"
     ]
    }
   ],
   "source": [
    "test_recall_score = recall_score(\n",
    "    np.concatenate(actual_list),    \n",
    "    np.concatenate(pred_list), average='macro'\n",
    ")\n",
    "\n",
    "print(\"Recall score on test data = \", round(test_recall_score*100,2),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = blue> Create Confusion Matrix of 10-class accuracy </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_matrix = confusion_matrix(\n",
    "       np.concatenate(actual_list),    \n",
    "    np.concatenate(pred_list)\n",
    "\n",
    ")\n",
    "categories= [ 'Drive_Safe','Text_Right','Talk_Right','Text_Left','Talk_Left','Adjust_Radio','Drink','Reach_Behind','Hair_Makeup','Talk_Passenger' ]\n",
    "sns.set(rc={'figure.figsize':(12,8)})\n",
    "sns.heatmap(cf_matrix, annot=True, linewidths=.5, cmap=\"YlGnBu\",cbar=False,xticklabels=categories,yticklabels=categories)\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <font color = Blue> Display accuracy percentages in confusion matrix </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(12,8)})\n",
    "sns.heatmap(cf_matrix / np.sum(cf_matrix, axis =1 ).reshape(10,-1), annot=True, \n",
    "            fmt='.2%', linewidths=.5, cmap=\"YlGnBu\",cbar=False,xticklabels=categories,yticklabels=categories)\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = blue> End of Notebook </font>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}