Face Recognition (TL).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"IMPORTING ALL REQUIRED MODULES\"\"\"\n",
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.platform import gfile\n",
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_face(filename, required_size=(160, 160)):\n",
    "    # load image from file\n",
    "    image = cv2.imread(filename)#Read image file\n",
    "    \n",
    "    \"\"\"\n",
    "       Detecting face boundaries using Haar feature-based cascade classifiers wich an effective\n",
    "       object detection method proposed by Paul Viola and Michael Jones in their paper,\n",
    "       “Rapid Object Detection using a Boosted Cascade of Simple Features” in 2001.\n",
    "    \"\"\"\n",
    "    \n",
    "    face_det=cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
    "    faces = face_det.detectMultiScale(image, 1.3, 5)#detect faces\n",
    "    \n",
    "    if list(faces):#If face is detected\n",
    "            # extract the bounding box from the first face\n",
    "            x1, y1, width, height = faces[0]\n",
    "            x1, y1= abs(x1), abs(y1)\n",
    "            x2, y2= x1 + width, y1 + height\n",
    "            face=image[y1:y2, x1:x2]#extracting face\n",
    "            face_array=cv2.resize(face,required_size)\n",
    "            return face_array\n",
    "    else:\n",
    "        #print('Face Not Detected')\n",
    "        return []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_faces(directory):\n",
    "    \"\"\"Function to get get image vector of faces and add them in list \"\"\"\n",
    "    faces = list()\n",
    "    # enumerate files\n",
    "    for filename in os.listdir(directory):\n",
    "        path = directory + filename\n",
    "        # get face\n",
    "        face = extract_face(path)\n",
    "        if list(face):\n",
    "            faces.append(face)\n",
    "    return faces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dataset(directory):\n",
    "    X, y = list(), list()#declaring empty lists\n",
    "    for celebrities in os.listdir(directory):\n",
    "        path = directory + celebrities + '/'\n",
    "        # load all faces of celebrities\n",
    "        faces = load_faces(path)\n",
    "        # create labels\n",
    "        labels = [celebrities for _ in range(len(faces))]\n",
    "        # summarize progress\n",
    "        print('>loaded %d examples for class: %s' % (len(faces), celebrities))\n",
    "        # store\n",
    "        X.extend(faces)\n",
    "        y.extend(labels)\n",
    "    return np.asarray(X), np.asarray(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set\n",
      ">loaded 11 examples for class: ben_afflek\n",
      ">loaded 12 examples for class: madonna\n",
      ">loaded 7 examples for class: me\n",
      ">loaded 10 examples for class: elton_john\n",
      ">loaded 20 examples for class: mindy_kaling\n",
      ">loaded 21 examples for class: jerry_seinfeld\n",
      "Validation Set\n",
      ">loaded 6 examples for class: ben_afflek\n",
      ">loaded 6 examples for class: madonna\n",
      ">loaded 5 examples for class: me\n",
      ">loaded 5 examples for class: elton_john\n",
      ">loaded 6 examples for class: mindy_kaling\n",
      ">loaded 6 examples for class: jerry_seinfeld\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Creating Training and Testing set\"\"\"\n",
    "print(\"Training Set\")\n",
    "x_train,y_train=load_dataset('5-celebrity-faces-dataset(modified)/train/')\n",
    "print(\"Validation Set\")\n",
    "x_val,y_val=load_dataset('5-celebrity-faces-dataset(modified)/val/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Encoding y labels to be used in model \"\"\"\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "y_train=LabelEncoder().fit_transform(y_train.ravel())\n",
    "y_val=LabelEncoder().fit_transform(y_val.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1,\n",
       "       5, 5, 5, 5, 5, 5, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_batch(x,y):\n",
    "    \"\"\"Create batch for training and validation\"\"\"\n",
    "    batch_size=32 #size\n",
    "    n_examples=x.shape[0]#limit for extracting random images and labels\n",
    "    #Intilize batch data\n",
    "    label_batch=np.zeros((batch_size,))\n",
    "    image_batch=np.zeros((batch_size,160,160,3))\n",
    "    \n",
    "    #varible used for getting random images and labels\n",
    "    idx_1=np.random.choice(n_examples,batch_size,replace=False)\n",
    "    \n",
    "    #loop for creating batches\n",
    "    for i in range(batch_size):\n",
    "        image_batch[i,:,:,:]=x[idx_1[i]]\n",
    "        label_batch[i]=y[idx_1[i]]\n",
    "    im=tf.cast(image_batch,tf.float32)\n",
    "    return im,label_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=get_batch(x_train,y_train)\n",
    "val=get_batch(x_val,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<tf.Tensor 'Cast_6:0' shape=(32, 160, 160, 3) dtype=float32>,\n",
       " array([1., 2., 2., 5., 5., 5., 3., 2., 2., 2., 0., 5., 2., 2., 3., 0., 2.,\n",
       "        1., 0., 5., 5., 1., 1., 4., 2., 2., 4., 2., 2., 5., 3., 5.]))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"mobilenetv2_1.00_160\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            [(None, 160, 160, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_pad (ZeroPadding2D)       (None, 161, 161, 3)  0           input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "Conv1 (Conv2D)                  (None, 80, 80, 32)   864         Conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_Conv1 (BatchNormalization)   (None, 80, 80, 32)   128         Conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "Conv1_relu (ReLU)               (None, 80, 80, 32)   0           bn_Conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise (Depthw (None, 80, 80, 32)   288         Conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_BN (Bat (None, 80, 80, 32)   128         expanded_conv_depthwise[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_depthwise_relu (R (None, 80, 80, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project (Conv2D)  (None, 80, 80, 16)   512         expanded_conv_depthwise_relu[0][0\n",
      "__________________________________________________________________________________________________\n",
      "expanded_conv_project_BN (Batch (None, 80, 80, 16)   64          expanded_conv_project[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand (Conv2D)         (None, 80, 80, 96)   1536        expanded_conv_project_BN[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_BN (BatchNormali (None, 80, 80, 96)   384         block_1_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_1_expand_relu (ReLU)      (None, 80, 80, 96)   0           block_1_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_pad (ZeroPadding2D)     (None, 81, 81, 96)   0           block_1_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise (DepthwiseCon (None, 40, 40, 96)   864         block_1_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_BN (BatchNorm (None, 40, 40, 96)   384         block_1_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_1_depthwise_relu (ReLU)   (None, 40, 40, 96)   0           block_1_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project (Conv2D)        (None, 40, 40, 24)   2304        block_1_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_1_project_BN (BatchNormal (None, 40, 40, 24)   96          block_1_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand (Conv2D)         (None, 40, 40, 144)  3456        block_1_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_2_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_2_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_2_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise (DepthwiseCon (None, 40, 40, 144)  1296        block_2_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_BN (BatchNorm (None, 40, 40, 144)  576         block_2_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_2_depthwise_relu (ReLU)   (None, 40, 40, 144)  0           block_2_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project (Conv2D)        (None, 40, 40, 24)   3456        block_2_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_2_project_BN (BatchNormal (None, 40, 40, 24)   96          block_2_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_2_add (Add)               (None, 40, 40, 24)   0           block_1_project_BN[0][0]         \n",
      "                                                                 block_2_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand (Conv2D)         (None, 40, 40, 144)  3456        block_2_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_BN (BatchNormali (None, 40, 40, 144)  576         block_3_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_3_expand_relu (ReLU)      (None, 40, 40, 144)  0           block_3_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_pad (ZeroPadding2D)     (None, 41, 41, 144)  0           block_3_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise (DepthwiseCon (None, 20, 20, 144)  1296        block_3_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_BN (BatchNorm (None, 20, 20, 144)  576         block_3_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_3_depthwise_relu (ReLU)   (None, 20, 20, 144)  0           block_3_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project (Conv2D)        (None, 20, 20, 32)   4608        block_3_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_3_project_BN (BatchNormal (None, 20, 20, 32)   128         block_3_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand (Conv2D)         (None, 20, 20, 192)  6144        block_3_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_4_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_4_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_4_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_4_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_4_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_4_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_4_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project (Conv2D)        (None, 20, 20, 32)   6144        block_4_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_4_project_BN (BatchNormal (None, 20, 20, 32)   128         block_4_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_4_add (Add)               (None, 20, 20, 32)   0           block_3_project_BN[0][0]         \n",
      "                                                                 block_4_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand (Conv2D)         (None, 20, 20, 192)  6144        block_4_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_5_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_5_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_5_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise (DepthwiseCon (None, 20, 20, 192)  1728        block_5_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_BN (BatchNorm (None, 20, 20, 192)  768         block_5_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_5_depthwise_relu (ReLU)   (None, 20, 20, 192)  0           block_5_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project (Conv2D)        (None, 20, 20, 32)   6144        block_5_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_5_project_BN (BatchNormal (None, 20, 20, 32)   128         block_5_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_5_add (Add)               (None, 20, 20, 32)   0           block_4_add[0][0]                \n",
      "                                                                 block_5_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand (Conv2D)         (None, 20, 20, 192)  6144        block_5_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_BN (BatchNormali (None, 20, 20, 192)  768         block_6_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_6_expand_relu (ReLU)      (None, 20, 20, 192)  0           block_6_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_pad (ZeroPadding2D)     (None, 21, 21, 192)  0           block_6_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise (DepthwiseCon (None, 10, 10, 192)  1728        block_6_pad[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_BN (BatchNorm (None, 10, 10, 192)  768         block_6_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_6_depthwise_relu (ReLU)   (None, 10, 10, 192)  0           block_6_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project (Conv2D)        (None, 10, 10, 64)   12288       block_6_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_6_project_BN (BatchNormal (None, 10, 10, 64)   256         block_6_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand (Conv2D)         (None, 10, 10, 384)  24576       block_6_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_7_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_7_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_7_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_7_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_7_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_7_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_7_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project (Conv2D)        (None, 10, 10, 64)   24576       block_7_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_7_project_BN (BatchNormal (None, 10, 10, 64)   256         block_7_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_7_add (Add)               (None, 10, 10, 64)   0           block_6_project_BN[0][0]         \n",
      "                                                                 block_7_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand (Conv2D)         (None, 10, 10, 384)  24576       block_7_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_8_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_8_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_8_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_8_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_8_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_8_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_8_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project (Conv2D)        (None, 10, 10, 64)   24576       block_8_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_8_project_BN (BatchNormal (None, 10, 10, 64)   256         block_8_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_8_add (Add)               (None, 10, 10, 64)   0           block_7_add[0][0]                \n",
      "                                                                 block_8_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand (Conv2D)         (None, 10, 10, 384)  24576       block_8_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_BN (BatchNormali (None, 10, 10, 384)  1536        block_9_expand[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "block_9_expand_relu (ReLU)      (None, 10, 10, 384)  0           block_9_expand_BN[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise (DepthwiseCon (None, 10, 10, 384)  3456        block_9_expand_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_BN (BatchNorm (None, 10, 10, 384)  1536        block_9_depthwise[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "block_9_depthwise_relu (ReLU)   (None, 10, 10, 384)  0           block_9_depthwise_BN[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project (Conv2D)        (None, 10, 10, 64)   24576       block_9_depthwise_relu[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "block_9_project_BN (BatchNormal (None, 10, 10, 64)   256         block_9_project[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_9_add (Add)               (None, 10, 10, 64)   0           block_8_add[0][0]                \n",
      "                                                                 block_9_project_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand (Conv2D)        (None, 10, 10, 384)  24576       block_9_add[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_BN (BatchNormal (None, 10, 10, 384)  1536        block_10_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_10_expand_relu (ReLU)     (None, 10, 10, 384)  0           block_10_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise (DepthwiseCo (None, 10, 10, 384)  3456        block_10_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_BN (BatchNor (None, 10, 10, 384)  1536        block_10_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_10_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           block_10_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project (Conv2D)       (None, 10, 10, 96)   36864       block_10_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_10_project_BN (BatchNorma (None, 10, 10, 96)   384         block_10_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand (Conv2D)        (None, 10, 10, 576)  55296       block_10_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_11_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_11_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_11_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_11_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_11_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_11_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_11_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project (Conv2D)       (None, 10, 10, 96)   55296       block_11_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_11_project_BN (BatchNorma (None, 10, 10, 96)   384         block_11_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_11_add (Add)              (None, 10, 10, 96)   0           block_10_project_BN[0][0]        \n",
      "                                                                 block_11_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand (Conv2D)        (None, 10, 10, 576)  55296       block_11_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_12_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_12_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_12_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise (DepthwiseCo (None, 10, 10, 576)  5184        block_12_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_BN (BatchNor (None, 10, 10, 576)  2304        block_12_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_12_depthwise_relu (ReLU)  (None, 10, 10, 576)  0           block_12_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project (Conv2D)       (None, 10, 10, 96)   55296       block_12_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_12_project_BN (BatchNorma (None, 10, 10, 96)   384         block_12_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_12_add (Add)              (None, 10, 10, 96)   0           block_11_add[0][0]               \n",
      "                                                                 block_12_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand (Conv2D)        (None, 10, 10, 576)  55296       block_12_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_BN (BatchNormal (None, 10, 10, 576)  2304        block_13_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_13_expand_relu (ReLU)     (None, 10, 10, 576)  0           block_13_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_pad (ZeroPadding2D)    (None, 11, 11, 576)  0           block_13_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise (DepthwiseCo (None, 5, 5, 576)    5184        block_13_pad[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_BN (BatchNor (None, 5, 5, 576)    2304        block_13_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_13_depthwise_relu (ReLU)  (None, 5, 5, 576)    0           block_13_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project (Conv2D)       (None, 5, 5, 160)    92160       block_13_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_13_project_BN (BatchNorma (None, 5, 5, 160)    640         block_13_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand (Conv2D)        (None, 5, 5, 960)    153600      block_13_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_14_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_14_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_14_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_14_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_14_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_14_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_14_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project (Conv2D)       (None, 5, 5, 160)    153600      block_14_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_14_project_BN (BatchNorma (None, 5, 5, 160)    640         block_14_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_14_add (Add)              (None, 5, 5, 160)    0           block_13_project_BN[0][0]        \n",
      "                                                                 block_14_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand (Conv2D)        (None, 5, 5, 960)    153600      block_14_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_15_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_15_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_15_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_15_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_15_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_15_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_15_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project (Conv2D)       (None, 5, 5, 160)    153600      block_15_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_15_project_BN (BatchNorma (None, 5, 5, 160)    640         block_15_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block_15_add (Add)              (None, 5, 5, 160)    0           block_14_add[0][0]               \n",
      "                                                                 block_15_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand (Conv2D)        (None, 5, 5, 960)    153600      block_15_add[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_BN (BatchNormal (None, 5, 5, 960)    3840        block_16_expand[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block_16_expand_relu (ReLU)     (None, 5, 5, 960)    0           block_16_expand_BN[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise (DepthwiseCo (None, 5, 5, 960)    8640        block_16_expand_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_BN (BatchNor (None, 5, 5, 960)    3840        block_16_depthwise[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block_16_depthwise_relu (ReLU)  (None, 5, 5, 960)    0           block_16_depthwise_BN[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project (Conv2D)       (None, 5, 5, 320)    307200      block_16_depthwise_relu[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "block_16_project_BN (BatchNorma (None, 5, 5, 320)    1280        block_16_project[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1 (Conv2D)                 (None, 5, 5, 1280)   409600      block_16_project_BN[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "Conv_1_bn (BatchNormalization)  (None, 5, 5, 1280)   5120        Conv_1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "out_relu (ReLU)                 (None, 5, 5, 1280)   0           Conv_1_bn[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 2,257,984\n",
      "Trainable params: 0\n",
      "Non-trainable params: 2,257,984\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 5, 5, 1280)\n"
     ]
    }
   ],
   "source": [
    "#creating the Model\n",
    "IMG_SHAPE=(160,160,3)#Same to our image shape\n",
    "\n",
    "\"\"\"The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the \n",
    "   residual block are thin bottleneck layers opposite to traditional residual models which use expanded \n",
    "   representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in \n",
    "   the intermediate expansion layer.\n",
    "\"\"\"\n",
    "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
    "                                               include_top=False,\n",
    "                                               weights='imagenet')\n",
    "#We freeze all the trainble weights \n",
    "base_model.trainable = False \n",
    "base_model.summary()#Descirbe the architecture of the MobileNetV2\n",
    "\n",
    "feature_batch = base_model(train)\n",
    "print(feature_batch.shape)#Extracted faeture shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 1280)\n",
      "(32, 1)\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "mobilenetv2_1.00_160 (Model) (None, 5, 5, 1280)        2257984   \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d_3 ( (None, 1280)              0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 1281      \n",
      "=================================================================\n",
      "Total params: 2,259,265\n",
      "Trainable params: 1,281\n",
      "Non-trainable params: 2,257,984\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Adding our classification layer\n",
    "\n",
    "#Pooling Layer\n",
    "global_average_layer=tf.keras.layers.GlobalAveragePooling2D()\n",
    "feature_batch_average=global_average_layer(feature_batch)\n",
    "print(feature_batch_average.shape)\n",
    "#Prediction Layer\n",
    "prediction_layer = tf.keras.layers.Dense(1)\n",
    "prediction_batch = prediction_layer(feature_batch_average)\n",
    "print(prediction_batch.shape)\n",
    "\n",
    "#addind our layer on top base_model\n",
    "model = tf.keras.Sequential([\n",
    "  base_model,\n",
    "  global_average_layer,\n",
    "  prediction_layer])\n",
    "\n",
    "base_learning_rate = 0.0001\n",
    "#Compile Model\n",
    "model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=base_learning_rate),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "model.summary()#Our model description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "5/5 [==============================] - 14s 3s/step - loss: 9.3241 - acc: 0.1937\n",
      "Epoch 2/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 2.9979 - acc: 0.4625\n",
      "Epoch 3/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.5171 - acc: 0.6875\n",
      "Epoch 4/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.3507 - acc: 0.7437\n",
      "Epoch 5/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.2787 - acc: 0.8562\n",
      "Epoch 6/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.2174 - acc: 0.8875\n",
      "Epoch 7/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.1688 - acc: 0.9250\n",
      "Epoch 8/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.1269 - acc: 0.9563\n",
      "Epoch 9/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.0903 - acc: 1.0000\n",
      "Epoch 10/15\n",
      "5/5 [==============================] - 9s 2s/step - loss: 0.0602 - acc: 1.0000\n",
      "Epoch 11/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.0409 - acc: 1.0000\n",
      "Epoch 12/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.0309 - acc: 1.0000\n",
      "Epoch 13/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.0229 - acc: 1.0000\n",
      "Epoch 14/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.0146 - acc: 1.0000\n",
      "Epoch 15/15\n",
      "5/5 [==============================] - 8s 2s/step - loss: 0.0082 - acc: 1.0000\n"
     ]
    }
   ],
   "source": [
    "#Training\n",
    "initial_epochs =15\n",
    "history = model.fit(train,epochs=initial_epochs,steps_per_epoch=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a8b59e4a8>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9BbhOUiUwC7ilqR1JuknSAkkLamtrk8hqOerqG7hl+iK27N7PPdefS59uHdOOZGYJSrIQNHUI2XiC5GnATyOiFJgMPCDpLZki4t6IKIuIsn79/BBT0r71m1U898oWvvq+sYwd0ivtOGaWsCQLQSUwNGe5lLd2/XwceAQgIuYBnYG+CWayFjy1tJp7/rCW684fxofKhrb8DWZW8JIsBPOBUZJGSupI5mLwzEZt1gMXA0g6g0whcN9PSlZv3MnnH13C2cN6c8cVY9KOY2bHSWKFICLqgJuBOcBKMncHLZd0p6Qp2WafBf5R0hJgOvCxiGjcfWTHwY69B/inBxbStWN7fviRc+nY3s8amhWLRB8oi4hZZC4C5667I+f1CuCCJDNYyxoags8+soTXtu7h5ze+jYG9PMewWTHxYZ/xwz+8wm9XbOSLk8/gbSedmHYcMzvOXAiK3B9eruXbv1nF1PGD+fsLRqQdx8xS4EJQxCq27uHW6Ys4bUAPvn7VOI8oalakXAiK1Bv76/mnBxYSEdxz/bl07ejxB82Klf/6i1BE8MXHyllZs4P7bjiP4Sd2SzuSmaXIZwRF6IHnX+NXizbwzxefyntO7592HDNLmQtBkVmwbit3/noFF5/en1suOiXtOGbWCrgQFJFlG7bzyYdepPSELnzn6vG084iiZoavERSFPfvr+O5vX+Ynf36VE7t34p7ry+jVxdNNmlmGC0Eb94eXa/niY+VUvv4G0yYM47aJp9Orq4uAmb3JhaCN2rxrH//+5AqeWFzFSf268fBN5/upYTNrkgtBGxMRPLqwkv+YtZLd++r49MWj+F/vOZlO7UvSjmZmrZQLQRuybvNu/uWxcp57ZQtlw0/g61eNY9SAHmnHMrNWzoWgDThQ38C9f1zLXXNX07GkHV9931iunTDMdwWZWV5cCArcovWvc/uvynmpZicTxwzk36aOYUBPDyNtZvlzIShQu/bV8e05q7h/3joG9OjMPdefy2VjBqYdy8wKUKKFQNJE4HtACfDjiPhGo+3fBd6TXewK9I+I3klmagt+t2IjX3piGTU79nL9+cP5/GWn0aOzbwk1syOTWCGQVALcDVxKZiL7+ZJmZmclAyAiPpPT/hbg7KTytAWbduzl3369gqfKqzl1QHd+cO07OHf4CWnHMrMCl+QZwQRgTUSsBZA0A5gKrGim/TTgywnmKVgNDcHDCyr42qyV7Ktr4HPvPZWb3nWy5xU2s2MiyUIwBKjIWa4E3tZUQ0nDgZHA75vZfhNwE8CwYcOObcpWbs2mXfzLr8p5Yd1Wzj+pD197/zhO6tc97Vhm1oYkWQiauncxmml7DfBoRNQ3tTEi7gXuBSgrK2tuH23Kvrp6fvjsK/zfZ16hS8cSvvmBM/lQWalnETOzYy7JQlAJDM1ZLgWqmml7DfCpBLMUlPqG4MM/mseSyu1cedZg7rhiNP16dEo7lpm1UUkWgvnAKEkjgQ1kPuyvbdxI0mnACcC8BLMUlD++XMuSyu189X1jue784WnHMbM2LrGrjRFRB9wMzAFWAo9ExHJJd0qaktN0GjAjIoqiyycf019Yz4ndOvLhsqEtNzYzO0otnhFIuhl4KCJeP9ydR8QsYFajdXc0Wv7K4e63Ldu0Yy9zX9rEje8c6buCzOy4yOeTZiCZZwAekTRRvlqZqF8srKS+Ibj6PJ8NmNnx0WIhiIh/BUYBPwE+BqyW9DVJJyecreg0NAQPz69gwsg+vkXUzI6bvPoesv33NdmvOjIXdx+V9M0EsxWdeWu3sH7rHqZN8NmAmR0/+VwjuBW4AdgM/Bj4fEQckNQOWA38n2QjFo8Z8yvo2bk9k8YOSjuKmRWRfG4f7QtcFRGv5a6MiAZJVyQTq/hs3b2fOctquPZtw+jcwbOJmdnxk0/X0Cxg68EFST0kvQ0gIlYmFazY/OrFSvbXN3CNu4XM7DjLpxD8ENiVs7w7u86OkYhgxvwKxg/tzekDe6Ydx8yKTD6FQLkPe0VEA57Q5pha+NrrrNm0yxeJzSwV+RSCtZJuldQh+/VpYG3SwYrJ9Bcq6NaxhCvOHJx2FDMrQvkUgk8A7yAzXtDBoaRvSjJUMdn+xgGeKq9iyvghdOvkEy0zO/5a/OSJiE1kBoyzBMxcvIG9Bxq4xk8Sm1lK8nmOoDPwcWAM0Png+oj4hwRzFY0Z8ys4Y1BPziztlXYUMytS+XQNPUBmvKHLgD+QmVdgZ5KhikV55XaWV+1g2oShnnDGzFKTTyE4JSK+BOyOiPuBy4FxycYqDtPnr6dzh3ZMHT8k7ShmVsTyKQQHsv/dJmks0AsYkViiIrF7Xx0zF1cxedwgenXpkHYcMyti+RSCeyWdAPwrMBNYAfxnPjvPDlu9StIaSbc10+bDklZIWi7p53knL3BPLa1m1746pk0YlnYUMytyh7xYnB1Ybkd2Upo/Aiflu2NJJcDdwKVkbjudL2lmRKzIaTMKuB24ICJel9T/CH6GgjR9/npO6d+dsuEnpB3FzIrcIc8Isk8R33yE+54ArImItRGxH5gBTG3U5h+Buw/Ofpa9VbXNW1Wzk0Xrt3HNeb5IbGbpy6dr6LeSPidpqKQ+B7/y+L4hQEXOcmV2Xa5TgVMl/UXS85ImNrUjSTdJWiBpQW1tbR5v3bpNf2E9HUvacdU5pWlHMTPLa8ygg88LfCpnXdByN1FTh7qNJ6hvT2b2swvJ3Jb6J0ljI2Lb33xTxL3AvQBlZWUFPcn93gP1PLZoA+8dM4A+3TqmHcfMLK8ni0ce4b4rgdzHZUuBqibaPB8RB4BXJa0iUxjmH+F7tnpzltew/Y0DXHOeLxKbWeuQz5PFH21qfUT8rIVvnQ+MkjSSzDhF1wDXNmrzODAN+KmkvmS6itr0gHbTX1jP0D5deMfJJ6YdxcwMyK9r6Lyc152Bi4EXgUMWgoiok3QzMAcoAe6LiOWS7gQWRMTM7Lb3SloB1JOZBnPLEfwcBeHVzbt5fu1WPn/ZabRr54vEZtY65NM1dEvusqReZIadaFFEzCIzw1nuujtyXgfwv7Nfbd6M+espaSc+dK4vEptZ65HPXUON7SHTj2+HYX9dA79cWMlFp/enf8/OLX+Dmdlxks81gl/z5t0+7YDRwCNJhmqL5q7cyOZd+z0LmZm1OvlcI/h2zus64LWIqEwoT5s1fX4Fg3p15t2nFs3D02ZWIPIpBOuB6ojYCyCpi6QREbEu0WRtSMXWPfxpdS23XDSKEl8kNrNWJp9rBL8AGnKW67PrLE+/WJg5gfpwmS8Sm1nrk08haJ8dKwiA7Gs/Epun+obgFwsqeNeofpSe0DXtOGZmb5FPIaiVNOXggqSpwObkIrUtf3h5E9Xb93pOYjNrtfK5RvAJ4CFJP8guVwJNPm1sbzX9hQr6du/IxWcMSDuKmVmT8nmg7BXgfEndAUWE5yvO06Yde/n9S5u48e9G0rH9kTyyYWaWvBY/nSR9TVLviNgVETslnSDpq8cjXKH7xcJK6hvCA8yZWauWz2HqpNxhobOTyExOLlLb0NAQzJi/nvNP6sPIvt3SjmNm1qx8CkGJpE4HFyR1ATodor0Bz72yhYqtb3hOYjNr9fK5WPwgMFfS/8su/z1wf3KR2obp89fTu2sHLhszMO0oZmaHlM/F4m9KWgpcQmbWsaeB4UkHK2Rbd+/nN8truO784XTuUJJ2HDOzQ8r3VpYaMk8Xf4DMfAQrE0vUBvzqxUoO1PsisZkVhmbPCCSdSmZWsWnAFuBhMrePvuc4ZStIEcH0F9Zz9rDenDawR9pxzMxadKgzgpfIHP1fGRHvjIjvkxlnKG+SJkpaJWmNpNua2P4xSbWSFme/bjy8+K3Pgtde55Xa3Uzz2YCZFYhDXSP4AJkzgmckPQ3MIHONIC+SSoC7gUvJPI08X9LMiFjRqOnDEXHz4cVuvaa/sJ7undpzxVmD0o5iZpaXZs8IIuKxiLgaOB14FvgMMEDSDyW9N499TwDWRMTa7EB1M4CpxyBzq7X9jQPMKq9myvjBdO2Yzw1ZZmbpa/FicUTsjoiHIuIKoBRYDLylm6cJQ4CKnOXK7LrGPiBpqaRHJTU5MpukmyQtkLSgtrY2j7dOxxOLN7D3QIO7hcysoBzWADgRsTUi7omIi/Jo3lQ3UjRa/jUwIiLOBH5HM88nRMS9EVEWEWX9+vU7nMjHTeYicQVjBvdkXGmvtOOYmeUtyZHQKoHcI/xSoCq3QURsiYh92cX/Bs5NME+iyjdsZ2X1Dq7xk8RmVmCSLATzgVGSRkrqSObC88zcBpJyr6hOoYCfT5j+QgVdOpQwdfzgtKOYmR2WxK5oRkSdpJuBOUAJcF9ELJd0J7AgImYCt2YnvakDtgIfSypPknbvq2Pm4g1cfuYgenbukHYcM7PDkuitLRExC5jVaN0dOa9vB25PMsPx8OTSKnbvr2faBM9CZmaFx7OlHAPTX6jglP7dOWfYCWlHMTM7bC4ER+mlmh0srtjGNecNRcr7eTszs1bDheAozXihgo4l7bjqnNK0o5iZHREXgqNQ3xA8ubSai8/oT59uHdOOY2Z2RFwIjsL8dVvZvGsfl5/pcYXMrHC5EByF2eXVdO7Qjvec1j/tKGZmR8yF4Ag1NASzl9Vw4an96dbJA8yZWeFyIThCC9e/zqad+5jsbiEzK3AuBEfoqaXVdGzfjotOd7eQmRU2F4Ij0NAQPL2shgtP7Ud3dwuZWYFzITgCiypep2bHXiaPc7eQmRU+F4IjMKu8ho4l7bjoDHcLmVnhcyE4TA0Nwezyat51al+PNGpmbYILwWFaUrmNqu17mTTW3UJm1ja4EBym2ctq6FAiLhk9IO0oZmbHRKKFQNJESaskrZHU7IT3kj4oKSSVJZnnaEUETy2t5p2n9KVXF3cLmVnbkFghkFQC3A1MAkYD0ySNbqJdD+BW4K9JZTlWyjdsZ8O2N3y3kJm1KUmeEUwA1kTE2ojYD8wApjbR7t+BbwJ7E8xyTDxVXk37duJSdwuZWRuSZCEYAlTkLFdm1/0PSWcDQyPiyUPtSNJNkhZIWlBbW3vsk+YhIphdXsMFp/Sld1cPOW1mbUeShaCp6brifzZK7YDvAp9taUcRcW9ElEVEWb9+/Y5hxPwtr9rB+q17mDxuYCrvb2aWlCQLQSWQO5t7KVCVs9wDGAs8K2kdcD4ws7VeMJ5VXk1JO/He0S4EZta2JFkI5gOjJI2U1BG4Bph5cGNEbI+IvhExIiJGAM8DUyJiQYKZjkhEMKu8mnecfCIneCYyM2tjEisEEVEH3AzMAVYCj0TEckl3SpqS1PsmYWX1TtZt2eO7hcysTUp06MyImAXMarTujmbaXphklqPxZreQ7xYys7bHTxa34GC30Pkn9eHE7p3SjmNmdsy5ELTg5Y27WLt5t8cWMrM2y4WgBU+VV9NOcNkY3y1kZm2TC0ELZpdXM2FkH/r1cLeQmbVNLgSHsHrjTlZv2uW7hcysTXMhOIRZ5TVIMNHdQmbWhrkQHMKs8mrOG96H/j07px3FzCwxLgTNWLNpF6s27vTYQmbW5rkQNGN2eTUAE33bqJm1cS4EzZi1rIay4ScwsJe7hcysbXMhaMKrm3ezsnoHk3y3kJkVAReCJszKdgtNGuvrA2bW9rkQNGFWeTVnD+vN4N5d0o5iZpY4F4JGXtuym+VVO7jc3UJmViRcCBqZvawGgInuFjKzIuFC0Mis8mrOGtqb0hO6ph3FzOy4SLQQSJooaZWkNZJua2L7JySVS1os6c+SRieZpyUVW/ewtHI7k302YGZFJLFCIKkEuBuYBIwGpjXxQf/ziBgXEeOBbwLfSSpPPmYvy9wt5EHmzKyYJHlGMAFYExFrI2I/MAOYmtsgInbkLHYDIsE8LZpVXsO4Ib0Y2sfdQmZWPJIsBEOAipzlyuy6vyHpU5JeIXNGcGtTO5J0k6QFkhbU1tYmEnbDtjdYXLGNSR5byMyKTJKFQE2se8sRf0TcHREnA18A/rWpHUXEvRFRFhFl/fr1O8YxMw6OLTTZYwuZWZFJshBUAkNzlkuBqkO0nwG8L8E8hzSrvJrRg3oyom+3tCKYmaUiyUIwHxglaaSkjsA1wMzcBpJG5SxeDqxOME+zqre/wYvrt6lY/foAAAjfSURBVHH5mT4bMLPi0z6pHUdEnaSbgTlACXBfRCyXdCewICJmAjdLugQ4ALwO3JBUnkOZXZ55iMxjC5lZMUqsEABExCxgVqN1d+S8/nSS75+v2cuqOX1gD07q1z3tKGZmx13RP1m8ccdeFrz2up8dMLOiVfSF4OllNUT4ITIzK15FXwhmlVdz6oDunNLf3UJmVpyKuhBs2rmXF9Zt9dmAmRW1oi4Ec5ZvdLeQmRW9oi4Es5ZWc0r/7pw6oEfaUczMUlO0hWDzrn389dUtHnLazIpe0RaCOctraAiY7KeJzazIFW0hmF1ew0l9u3Gau4XMrMgVZSHYuns/89ZuYdK4gUhNDZJqZlY8irIQ/GZ5DfUN4buFzMwo0kLwVHk1w0/syuhBPdOOYmaWuqIrBK/v3s9zr2xh8rhB7hYyM6MIC8FvV27MdAt5JjIzM6AIC8Gs8mqG9unC2CHuFjIzg4QLgaSJklZJWiPptia2/29JKyQtlTRX0vAk82zfc4C/rNnM5LHuFjIzOyixQiCpBLgbmASMBqZJGt2o2SKgLCLOBB4FvplUHsh0Cx2o991CZma5kjwjmACsiYi1EbGfzOT0U3MbRMQzEbEnu/g8mQnuEzO7vJohvbtwZmmvJN/GzKygJFkIhgAVOcuV2XXN+Tgwu6kNkm6StEDSgtra2iMKs2PvAf60ejOT/RCZmdnfSLIQNPVpG002lK4DyoBvNbU9Iu6NiLKIKOvXr98RhZm7ciP76xuY5G4hM7O/keTk9ZXA0JzlUqCqcSNJlwBfBN4dEfuSCtO9UwcuHT2As4f2TuotzMwKUpKFYD4wStJIYANwDXBtbgNJZwP3ABMjYlOCWbh09AAuHT0gybcwMytIiXUNRUQdcDMwB1gJPBIRyyXdKWlKttm3gO7ALyQtljQzqTxmZta0JM8IiIhZwKxG6+7IeX1Jku9vZmYtK7oni83M7G+5EJiZFTkXAjOzIudCYGZW5FwIzMyKnAuBmVmRU0SToz60WpJqgdeO8Nv7ApuPYZykFVLeQsoKhZW3kLJCYeUtpKxwdHmHR0STY/QUXCE4GpIWRERZ2jnyVUh5CykrFFbeQsoKhZW3kLJCcnndNWRmVuRcCMzMilyxFYJ70w5wmAopbyFlhcLKW0hZobDyFlJWSChvUV0jMDOztyq2MwIzM2vEhcDMrMgVTSGQNFHSKklrJN2Wdp7mSBoq6RlJKyUtl/TptDPlQ1KJpEWSnkw7y6FI6i3pUUkvZX/Hb08706FI+kz238EySdMldU47Uy5J90naJGlZzro+kn4raXX2vyekmfGgZrJ+K/tvYamkxyS1iikMm8qas+1zkkJS32P1fkVRCCSVAHcDk4DRwDRJo9NN1aw64LMRcQZwPvCpVpw116fJTEDU2n0PeDoiTgfOohVnljQEuBUoi4ixQAmZmf5ak58CExutuw2YGxGjgLnZ5dbgp7w162+BsRFxJvAycPvxDtWMn/LWrEgaClwKrD+Wb1YUhQCYAKyJiLURsR+YAUxNOVOTIqI6Il7Mvt5J5oNqSLqpDk1SKXA58OO0sxyKpJ7Au4CfAETE/ojYlm6qFrUHukhqD3SliXm/0xQRfwS2Nlo9Fbg/+/p+4H3HNVQzmsoaEb/JzqYI8DyZudVT18zvFeC7wP8BjuldPsVSCIYAFTnLlbTyD1cASSOAs4G/ppukRf9F5h9nQ9pBWnASUAv8v2w31o8ldUs7VHMiYgPwbTJHf9XA9oj4Tbqp8jIgIqohc2AD9E85T77+AZiddojmZKf43RARS471voulEKiJda36vllJ3YFfAv8cETvSztMcSVcAmyJiYdpZ8tAeOAf4YUScDeym9XRbvEW2b30qMBIYDHSTdF26qdomSV8k0y37UNpZmiKpK/BF4I6W2h6JYikElcDQnOVSWtkpdi5JHcgUgYci4ldp52nBBcAUSevIdLldJOnBdCM1qxKojIiDZ1iPkikMrdUlwKsRURsRB4BfAe9IOVM+NkoaBJD976aU8xySpBuAK4CPROt9sOpkMgcES7J/a6XAi5IGHoudF0shmA+MkjRSUkcyF9xmppypSZJEpg97ZUR8J+08LYmI2yOiNCJGkPm9/j4iWuVRa0TUABWSTsuuuhhYkWKklqwHzpfUNfvv4mJa8cXtHDOBG7KvbwCeSDHLIUmaCHwBmBIRe9LO05yIKI+I/hExIvu3Vgmck/03fdSKohBkLwbdDMwh84f0SEQsTzdVsy4AridzZL04+zU57VBtyC3AQ5KWAuOBr6Wcp1nZM5dHgReBcjJ/r61qSARJ04F5wGmSKiV9HPgGcKmk1WTucPlGmhkPaibrD4AewG+zf2s/SjVkVjNZk3u/1nsmZGZmx0NRnBGYmVnzXAjMzIqcC4GZWZFzITAzK3IuBGZmRc6FwCxLUn3OLbuLj+UotZJGNDWSpFlr0D7tAGatyBsRMT7tEGbHm88IzFogaZ2k/5T0QvbrlOz64ZLmZseynytpWHb9gOzY9kuyXweHhSiR9N/Z+QV+I6lLtv2tklZk9zMjpR/TipgLgdmbujTqGro6Z9uOiJhA5knU/8qu+wHws+xY9g8Bd2XX3wX8ISLOIjOW0cGn2EcBd0fEGGAb8IHs+tuAs7P7+URSP5xZc/xksVmWpF0R0b2J9euAiyJibXZAwJqIOFHSZmBQRBzIrq+OiL6SaoHSiNiXs48RwG+zk7Ug6QtAh4j4qqSngV3A48DjEbEr4R/V7G/4jMAsP9HM6+baNGVfzut63rxGdzmZGfTOBRZmJ6ExO25cCMzyc3XOf+dlXz/Hm1NHfgT4c/b1XOCT8D9zOfdsbqeS2gFDI+IZMpP79AbeclZiliQfeZi9qYukxTnLT0fEwVtIO0n6K5mDp2nZdbcC90n6PJmZz/4+u/7TwL3ZESPryRSF6mbeswR4UFIvMhMofbcAps+0NsbXCMxakL1GUBYRm9POYpYEdw2ZmRU5nxGYmRU5nxGYmRU5FwIzsyLnQmBmVuRcCMzMipwLgZlZkfv/KnItfKtCm14AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history.history['acc']\n",
    "loss = history.history['loss']\n",
    "\n",
    "\n",
    "plt.title('Training Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.plot(acc, label='Training Accuracy')#plotting accuracy\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a5657bf28>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Training Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.plot(loss, label='Training Loss') #plotting loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-20.745548, 0.8645833]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Testing data on validation set gave us accuracy of 86%\n",
    "\n",
    "model.test_on_batch(val[0], y=val[1], sample_weight=None, reset_metrics=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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