customModel_VGG16_BS_16_25Nov2021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COVID-19, normal (healthy), and pneumonia classification using Deep Learning Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## VGG16 with image size 256x256, categorical cross-entropy loss function and 16 batch size, trained over 1000 epochs to obtain the best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the necessary libraries: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25_Nov_2021--02_05_43_AM\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "dt=datetime.now().strftime(\"%d_%b_%Y--%I_%M_%S_%p\")\n",
    "print(dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary packages\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "#from tensorflow import keras\n",
    "from tensorflow.keras.layers import AveragePooling2D, Dropout, Flatten, Dense, Input\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras import optimizers, models, layers\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications import VGG16\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from imutils import paths\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2\n",
    "import os\n",
    "from tensorflow.keras.models import Sequential,Model\n",
    "from tensorflow.keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, Input, Reshape, Concatenate, GlobalAveragePooling2D, BatchNormalization, Dropout, Activation, GlobalMaxPooling2D\n",
    "from tensorflow.keras.utils import Sequence\n",
    "from tensorflow.keras.applications.densenet import DenseNet121\n",
    "#from keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.utils import plot_model\n",
    "#from keras.utils.vis_utils import pydot\n",
    "import re\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "import seaborn as sn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = os.path.join(base_dir, 'newtrain') # change here newtrain to train\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "\n",
    "train_COVID_dir = os.path.join(train_dir, 'covid')\n",
    "train_NORMAL_dir = os.path.join(train_dir, 'normal')\n",
    "train_PNEUMONIA_dir = os.path.join(train_dir, 'pneumonia')\n",
    "\n",
    "validation_COVID_dir = os.path.join(validation_dir, 'covid')\n",
    "validation_NORMAL_dir = os.path.join(validation_dir, 'normal')\n",
    "validation_PNEUMONIA_dir = os.path.join(validation_dir, 'pneumonia')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "newtrain\\covid\n",
      "newtrain\\normal\n",
      "newtrain\\pneumonia\n",
      "\n",
      "validation\\covid\n",
      "validation\\normal\n",
      "validation\\pneumonia\n"
     ]
    }
   ],
   "source": [
    "print(train_COVID_dir)\n",
    "print(train_NORMAL_dir)\n",
    "print(train_PNEUMONIA_dir)\n",
    "print()\n",
    "print(validation_COVID_dir)\n",
    "print(validation_NORMAL_dir)\n",
    "print(validation_PNEUMONIA_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cov_1110.jpg', 'cov_1111.jpg', 'cov_11110.jpg', 'cov_11111.jpg', 'cov_11112.jpg', 'cov_11113.jpg', 'cov_11114.jpg', 'cov_11115.jpg', 'cov_11116.jpg', 'cov_11117.jpg']\n",
      "\n",
      "['normal-0000.jpg', 'normal-0001.jpg', 'normal-00010.jpg', 'normal-00011.jpg', 'normal-00012.jpg', 'normal-00013.jpg', 'normal-00014.jpg', 'normal-00015.jpg', 'normal-00016.jpg', 'normal-00017.jpg']\n",
      "\n",
      "['pneumonia0000.jpg', 'pneumonia0001.jpg', 'pneumonia00010.jpg', 'pneumonia00011.jpg', 'pneumonia00012.jpg', 'pneumonia00013.jpg', 'pneumonia00014.jpg', 'pneumonia00015.jpg', 'pneumonia00016.jpg', 'pneumonia00017.jpg']\n",
      "total training COVID images : 33\n",
      "total training NORMAL images : 37\n",
      "total training PNEUMONIA images : 22\n",
      "total validation COVID images : 1432\n",
      "total validation NORMAL images : 1000\n",
      "total validation PNEUMONIA images : 1000\n"
     ]
    }
   ],
   "source": [
    "train_COVID_fnames = os.listdir( train_COVID_dir )\n",
    "train_NORMAL_fnames = os.listdir( train_NORMAL_dir )\n",
    "train_PNEUMONIA_fnames = os.listdir( train_PNEUMONIA_dir )\n",
    "\n",
    "print(train_COVID_fnames[:10])\n",
    "print()\n",
    "print(train_NORMAL_fnames[:10])\n",
    "print()\n",
    "print(train_PNEUMONIA_fnames[:10])\n",
    "\n",
    "print('total training COVID images :', len(os.listdir(      train_COVID_dir ) ))\n",
    "print('total training NORMAL images :', len(os.listdir(      train_NORMAL_dir ) ))\n",
    "print('total training PNEUMONIA images :', len(os.listdir(      train_PNEUMONIA_dir ) ))\n",
    "\n",
    "print('total validation COVID images :', len(os.listdir( validation_COVID_dir ) ))\n",
    "print('total validation NORMAL images :', len(os.listdir( validation_NORMAL_dir ) ))\n",
    "print('total validation PNEUMONIA images :', len(os.listdir( validation_PNEUMONIA_dir ) ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experimental parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set learning rate, epochs and batch size\n",
    "INIT_LR = 1e-5    # This value is specific to what model is chosen: Inception, VGG or ResNet etc.\n",
    "EPOCHS = 2 # changed to 1000\n",
    "BS = 16\n",
    "imagesize=256"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the sample image files provided here "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "give the path totraining dataset in this variable\n",
    "imagePath = \"./train/\"  # change to your local path for the sample images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading images...\n",
      "... ...  92 images loaded in multiple classes:\n",
      "['./newtrain/covid' './newtrain/normal' './newtrain/pneumonia']\n"
     ]
    }
   ],
   "source": [
    "print(\"Loading images...\")\n",
    "imagePath = \"./newtrain/\"  # change to your local path for the sample images\n",
    "#imagePath = \"./train/\"  # change to your local path for the sample images\n",
    "imagePaths = list(paths.list_images(imagePath))\n",
    "#print('point a')\n",
    "f = open(\"filesize.txt\", \"w\")\n",
    "data = []\n",
    "labels = []\n",
    "\n",
    "count = 1\n",
    "filesize=[]\n",
    "# read all X-Rays in the specified path, and resize them all to 256x256\n",
    "for imagePath in imagePaths:\n",
    "    #print(count)\n",
    "    label = imagePath.split(os.path.sep)[-2]\n",
    "    image = cv2.imread(imagePath)\n",
    "    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "    height, width, channels = image.shape\n",
    "    #print (label,' ',height,' ', width,' ', channels)\n",
    "    #f.write(label,' ',height,' ', width,' ', channels)\n",
    "    filesize.append([label,height,width,channels,height*width*channels])\n",
    "    image = cv2.resize(image, (imagesize, imagesize))\n",
    "    data.append(image)\n",
    "    labels.append(label)\n",
    "    count=count+1\n",
    "#print('point b')    \n",
    "#normalise pixel values to real numbers between 0.0 - 1.0\n",
    "data = np.array(data) / 255.0\n",
    "labels = np.array(labels)\n",
    "#print('point c')\n",
    "# perform one-hot encoding for a multi-class labeling\n",
    "label_encoder = LabelEncoder()\n",
    "integer_encoded = label_encoder.fit_transform(labels)\n",
    "labels = to_categorical(integer_encoded)\n",
    "#print('point d')\n",
    "print(\"... ... \", len(data), \"images loaded in multiple classes:\")\n",
    "print(label_encoder.classes_)\n",
    "#print('point e')\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "calculating the image size in training dataset and saving in a training_image_size.csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>channels</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./newtrain/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./newtrain/covid</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./newtrain/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./newtrain/covid</td>\n",
       "      <td>2539</td>\n",
       "      <td>3050</td>\n",
       "      <td>3</td>\n",
       "      <td>23231850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./newtrain/covid</td>\n",
       "      <td>1330</td>\n",
       "      <td>1796</td>\n",
       "      <td>3</td>\n",
       "      <td>7166040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>./newtrain/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>./newtrain/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>./newtrain/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>./newtrain/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>./newtrain/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>92 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   label  height  width  channels      size\n",
       "0       ./newtrain/covid     512    512         3    786432\n",
       "1       ./newtrain/covid    1024   1024         3   3145728\n",
       "2       ./newtrain/covid     512    512         3    786432\n",
       "3       ./newtrain/covid    2539   3050         3  23231850\n",
       "4       ./newtrain/covid    1330   1796         3   7166040\n",
       "..                   ...     ...    ...       ...       ...\n",
       "87  ./newtrain/pneumonia    1024   1024         3   3145728\n",
       "88  ./newtrain/pneumonia    1024   1024         3   3145728\n",
       "89  ./newtrain/pneumonia    1024   1024         3   3145728\n",
       "90  ./newtrain/pneumonia    1024   1024         3   3145728\n",
       "91  ./newtrain/pneumonia    1024   1024         3   3145728\n",
       "\n",
       "[92 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(filesize,columns=[\"label\",\"height\",\"width\",\"channels\",\"size\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label                 height  width  channels  size    \n",
       "./newtrain/normal     1024    1024   3         3145728     37\n",
       "./newtrain/pneumonia  1024    1024   3         3145728     22\n",
       "./newtrain/covid      512     512    3         786432       8\n",
       "                      1024    1024   3         3145728      7\n",
       "                      2000    2000   3         12000000     3\n",
       "                      554     561    3         932382       1\n",
       "                      597     737    3         1319967      1\n",
       "                      610     586    3         1072380      1\n",
       "                      695     849    3         1770165      1\n",
       "                      734     895    3         1970790      1\n",
       "                      992     1024   3         3047424      1\n",
       "                      366     446    3         489708       1\n",
       "                      1282    1210   3         4653660      1\n",
       "                      1330    1796   3         7166040      1\n",
       "                      1711    2038   3         10461054     1\n",
       "                      1828    2200   3         12064800     1\n",
       "                      1971    2498   3         14770674     1\n",
       "                      2539    3050   3         23231850     1\n",
       "                      462     497    3         688842       1\n",
       "                      277     375    3         311625       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(df.value_counts()).to_csv('training_image_size.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['label', 'height', 'width', 'channels', 'size', '0']\n",
      "['./newtrain/normal', '1024', '1024', '3', '3145728', '37']\n",
      "['./newtrain/pneumonia', '1024', '1024', '3', '3145728', '22']\n",
      "['./newtrain/covid', '512', '512', '3', '786432', '8']\n",
      "['./newtrain/covid', '1024', '1024', '3', '3145728', '7']\n",
      "['./newtrain/covid', '2000', '2000', '3', '12000000', '3']\n",
      "['./newtrain/covid', '554', '561', '3', '932382', '1']\n",
      "['./newtrain/covid', '597', '737', '3', '1319967', '1']\n",
      "['./newtrain/covid', '610', '586', '3', '1072380', '1']\n",
      "['./newtrain/covid', '695', '849', '3', '1770165', '1']\n",
      "['./newtrain/covid', '734', '895', '3', '1970790', '1']\n",
      "['./newtrain/covid', '992', '1024', '3', '3047424', '1']\n",
      "['./newtrain/covid', '366', '446', '3', '489708', '1']\n",
      "['./newtrain/covid', '1282', '1210', '3', '4653660', '1']\n",
      "['./newtrain/covid', '1330', '1796', '3', '7166040', '1']\n",
      "['./newtrain/covid', '1711', '2038', '3', '10461054', '1']\n",
      "['./newtrain/covid', '1828', '2200', '3', '12064800', '1']\n",
      "['./newtrain/covid', '1971', '2498', '3', '14770674', '1']\n",
      "['./newtrain/covid', '2539', '3050', '3', '23231850', '1']\n",
      "['./newtrain/covid', '462', '497', '3', '688842', '1']\n",
      "['./newtrain/covid', '277', '375', '3', '311625', '1']\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "with open('training_image_size.csv', newline='') as f:\n",
    "    reader = csv.reader(f)\n",
    "    for row in reader:\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th>height</th>\n",
       "      <th>width</th>\n",
       "      <th>channels</th>\n",
       "      <th>size</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>./newtrain/normal</th>\n",
       "      <th>1024</th>\n",
       "      <th>1024</th>\n",
       "      <th>3</th>\n",
       "      <th>3145728</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./newtrain/pneumonia</th>\n",
       "      <th>1024</th>\n",
       "      <th>1024</th>\n",
       "      <th>3</th>\n",
       "      <th>3145728</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"18\" valign=\"top\">./newtrain/covid</th>\n",
       "      <th>512</th>\n",
       "      <th>512</th>\n",
       "      <th>3</th>\n",
       "      <th>786432</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <th>1024</th>\n",
       "      <th>3</th>\n",
       "      <th>3145728</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <th>2000</th>\n",
       "      <th>3</th>\n",
       "      <th>12000000</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <th>561</th>\n",
       "      <th>3</th>\n",
       "      <th>932382</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <th>737</th>\n",
       "      <th>3</th>\n",
       "      <th>1319967</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <th>586</th>\n",
       "      <th>3</th>\n",
       "      <th>1072380</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <th>849</th>\n",
       "      <th>3</th>\n",
       "      <th>1770165</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>734</th>\n",
       "      <th>895</th>\n",
       "      <th>3</th>\n",
       "      <th>1970790</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <th>1024</th>\n",
       "      <th>3</th>\n",
       "      <th>3047424</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <th>446</th>\n",
       "      <th>3</th>\n",
       "      <th>489708</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <th>1210</th>\n",
       "      <th>3</th>\n",
       "      <th>4653660</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <th>1796</th>\n",
       "      <th>3</th>\n",
       "      <th>7166040</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1711</th>\n",
       "      <th>2038</th>\n",
       "      <th>3</th>\n",
       "      <th>10461054</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1828</th>\n",
       "      <th>2200</th>\n",
       "      <th>3</th>\n",
       "      <th>12064800</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <th>2498</th>\n",
       "      <th>3</th>\n",
       "      <th>14770674</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2539</th>\n",
       "      <th>3050</th>\n",
       "      <th>3</th>\n",
       "      <th>23231850</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <th>497</th>\n",
       "      <th>3</th>\n",
       "      <th>688842</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <th>375</th>\n",
       "      <th>3</th>\n",
       "      <th>311625</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      0\n",
       "label                height width channels size        \n",
       "./newtrain/normal    1024   1024  3        3145728   37\n",
       "./newtrain/pneumonia 1024   1024  3        3145728   22\n",
       "./newtrain/covid     512    512   3        786432     8\n",
       "                     1024   1024  3        3145728    7\n",
       "                     2000   2000  3        12000000   3\n",
       "                     554    561   3        932382     1\n",
       "                     597    737   3        1319967    1\n",
       "                     610    586   3        1072380    1\n",
       "                     695    849   3        1770165    1\n",
       "                     734    895   3        1970790    1\n",
       "                     992    1024  3        3047424    1\n",
       "                     366    446   3        489708     1\n",
       "                     1282   1210  3        4653660    1\n",
       "                     1330   1796  3        7166040    1\n",
       "                     1711   2038  3        10461054   1\n",
       "                     1828   2200  3        12064800   1\n",
       "                     1971   2498  3        14770674   1\n",
       "                     2539   3050  3        23231850   1\n",
       "                     462    497   3        688842     1\n",
       "                     277    375   3        311625     1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df.value_counts()).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['./newtrain/covid' './newtrain/normal' './newtrain/pneumonia']\n",
      "\n",
      "(3,)\n",
      "\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.str_'>\n"
     ]
    }
   ],
   "source": [
    "classes_of_data=label_encoder.classes_\n",
    "\n",
    "print(classes_of_data)\n",
    "print()\n",
    "print(classes_of_data.shape)\n",
    "print()\n",
    "print(type(classes_of_data))\n",
    "print(type(classes_of_data[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./newtrain/covid\n",
      "./newtrain/normal\n",
      "./newtrain/pneumonia\n",
      "['./newtrain/covid', './newtrain/normal', './newtrain/pneumonia']\n",
      "['covid', 'normal', 'pneumonia']\n"
     ]
    }
   ],
   "source": [
    "c1=[]\n",
    "c2=[]\n",
    "for i in range(len(classes_of_data)):\n",
    "    print(classes_of_data[i])\n",
    "    c1.append(classes_of_data[i])\n",
    "    c2.append(re.sub(r'^./newtrain/','',classes_of_data[i]))\n",
    "\n",
    "print(c1)\n",
    "print(c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(classes_of_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(type(c2))\n",
    "print(type(c2[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./newtrain/covid covid\n",
      "./newtrain/normal normal\n",
      "./newtrain/pneumonia pneumonia\n"
     ]
    }
   ],
   "source": [
    "for i in (classes_of_data):\n",
    "    print(i,re.sub(r'^./newtrain/','',i))\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 256, 256, 3)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(imagePath)\n",
    "#print((imagePaths))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading sample image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading sample image from training data set: the first image from covid, normal (healthy), and pneumonia class are loaded as a sample image, which is used in the research paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and display an image with Matplotlib\n",
    "from matplotlib import image\n",
    "from matplotlib import pyplot\n",
    "import cv2\n",
    "cov = image.imread('./newtrain/covid\\\\cov_1110.jpg')\n",
    "nor = image.imread('./newtrain/normal\\\\normal-0000.jpg')\n",
    "pne = image.imread('./newtrain/pneumonia\\\\pneumonia0000.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8\n",
      "(512, 512, 3)\n",
      "\n",
      "uint8\n",
      "(1024, 1024)\n",
      "\n",
      "uint8\n",
      "(1024, 1024)\n"
     ]
    }
   ],
   "source": [
    "# summarize shape of the pixel array\n",
    "print(cov.dtype)\n",
    "print(cov.shape)\n",
    "print()\n",
    "print(nor.dtype)\n",
    "print(nor.shape)\n",
    "print()\n",
    "print(pne.dtype)\n",
    "print(pne.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 960x800 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "\n",
    "figure(figsize=(12, 10), dpi=80)\n",
    "# plot all three images using matplotlib\n",
    "pyplot.subplot(131)\n",
    "pyplot.imshow(cov,cmap='gray')\n",
    "plt.title(\"COVID\")\n",
    "pyplot.subplot(132)\n",
    "pyplot.imshow(nor,cmap='gray')\n",
    "plt.title(\"Normal\")\n",
    "pyplot.subplot(133)\n",
    "pyplot.imshow(pne,cmap='gray')\n",
    "plt.title(\"Pneumonia\")\n",
    "\n",
    "plt.savefig( '{}_Sample_Images.png'.format(dt))\n",
    "plt.savefig(\"sample_images.pdf\")\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./newtrain/covid     ....0\n",
      "./newtrain/normal    ....1\n",
      "./newtrain/pneumonia ....2\n"
     ]
    }
   ],
   "source": [
    "list_of_classes=list(label_encoder.classes_)\n",
    "#print(label_encoder.transform(list_of_classes))\n",
    "for i ,j in zip(list_of_classes,label_encoder.transform(list_of_classes)):\n",
    "    print('{:20}'.format(i),'{:.>5}'.format(j))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(len(list_of_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./newtrain/covid     .......covid ....0\n",
      "./newtrain/normal    ......normal ....1\n",
      "./newtrain/pneumonia ...pneumonia ....2\n"
     ]
    }
   ],
   "source": [
    "for i ,j, k in zip(list_of_classes,label_encoder.transform(list_of_classes),c2):\n",
    "    print('{:20}'.format(i),'{:.>12}'.format(k),'{:.>5}'.format(j))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(92, 3)\n",
      "[[1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "print(labels.shape)\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " size of data:  18087936 \n",
      " No of rows:  92 \n",
      " No of labels:  92 \n",
      " Type:  <class 'numpy.ndarray'> \n",
      " rows:  92 \n",
      " columns:  256 \n",
      " total_length:  23552\n"
     ]
    }
   ],
   "source": [
    "print(' size of data: ',data.size, '\\n No of rows: ',len(data),'\\n No of labels: ',len(labels),'\\n Type: ',type(data),'\\n','rows: ',len(data),'\\n','columns: ',len(data[0]),'\\n','total_length: ',len(data)*len(data[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92 256 256 3\n"
     ]
    }
   ],
   "source": [
    "print(len(data),len(data[0]),len(data[0][0]),len(data[0][0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Model: Add in basic data augment, re-compose the model, then train it  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "trainX: 73\n",
      "testX: 19\n",
      "trainY: 73\n",
      "testY: 19\n",
      "Compiling model...\n",
      "Training the full stack model...\n",
      "Epoch 1/2\n",
      "4/4 [==============================] - 360s 91s/step - loss: 1.2362 - accuracy: 0.3792 - val_loss: 1.0802 - val_accuracy: 0.3684\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.36842, saving model to best_model.h5\n",
      "Epoch 2/2\n",
      "4/4 [==============================] - 277s 69s/step - loss: 1.0868 - accuracy: 0.4112 - val_loss: 1.0343 - val_accuracy: 0.4737\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.36842 to 0.47368, saving model to best_model.h5\n"
     ]
    }
   ],
   "source": [
    "baseModel3 = VGG16(weights=\"imagenet\", include_top=False, input_tensor=Input(shape=(imagesize, imagesize, 3)))\n",
    "#baseModel3 = InceptionV3(weights=\"imagenet\", include_top=False, input_tensor=Input(shape=(256, 256, 3))\n",
    "#baseModel = VGG16(weights=\"imagenet\", include_top=False, input_tensor=Input(shape=(imagesize, imagesize, 3)))\n",
    "#baseModel = ResNet50(weights=\"imagenet\", include_top=False, input_tensor=Input(shape=(imagesize, imagesize, 3)))\n",
    "#baseModel = VGG16(weights=\"imagenet\", include_top=False, input_tensor=Input(shape=(256, 256, 3)))\n",
    "#baseModel = ResNet50(weights=\"imagenet\", include_top=False, input_tensor=Input(shape=(256, 256, 3)))\n",
    "\n",
    "# split the data between train and validation.\n",
    "(trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.20, stratify=labels, random_state=42)\n",
    "\n",
    "print(\"trainX:\",trainX.shape[0])\n",
    "print(\"testX:\",testX.shape[0])\n",
    "print(\"trainY:\",trainY.shape[0])\n",
    "print(\"testY:\",testY.shape[0])\n",
    "\n",
    "# add on a simple Augmentation. Note: too many Augumentation doesn't actually help in this case - I found during the test. \n",
    "trainAug = ImageDataGenerator(rotation_range=15, fill_mode=\"nearest\")\n",
    "\n",
    "\n",
    "#baseModel3 = DenseNet121(weights=\"imagenet\", include_top=False, input_tensor=Input(shape=(imagesize, imagesize, 3)))\n",
    "#Add on a couple of custom CNN layers on top of the Inception V3 model. \n",
    "headModel3 = baseModel3.output\n",
    "#tf.keras.layers.\n",
    "#headModel3 = GlobalAveragePooling2D()(headModel3)\n",
    "headModel3 = AveragePooling2D(pool_size=(4, 4))(headModel3)\n",
    "headModel3 = Flatten(name=\"flatten\")(headModel3)\n",
    "headModel3 = Dense(64, activation=\"relu\")(headModel3)\n",
    "headModel3 = Dropout(0.5)(headModel3)\n",
    "headModel3 = Dense(3, activation=\"softmax\")(headModel3) # as we have 3 classes: covid, normal and pneumonia \n",
    "\n",
    "# Compose the final model\n",
    "model = Model(inputs=baseModel3.input, \n",
    "              outputs=headModel3)\n",
    "\n",
    "# Unfreeze pre-trained Inception \"ImageNet\" weights for re-training since I got a Navidia T4 GPU to play with anyway\n",
    "#for layer in baseModel.layers:\n",
    "#    layer.trainable = False\n",
    "\n",
    "print(\"Compiling model...\")\n",
    "\n",
    "opt = Adam(lr=INIT_LR, \n",
    "           decay=INIT_LR / EPOCHS)\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", \n",
    "              optimizer=opt, \n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "# pip3 install pydot-ng\n",
    "# pip3 install graphviz\n",
    "#\n",
    "#plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n",
    "\n",
    "# simple early stopping\n",
    "# We can update the example and add very simple early stopping.\n",
    "# As soon as the loss of the model begins to increase on the test dataset, we will stop training.\n",
    "# monitor: allows you to specify the performance measure to monitor in order to end training.\n",
    "# mode: we would seek a maximum for validation accuracy.\n",
    "# verbose = 1: Once stopped, the callback will print the epoch number.\n",
    "# min_delta: for 0.5% for accuracy. \n",
    "# baseline: \n",
    "#es = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', mode='max', verbose=1, min_delta=0.5)#, baseline=0.4)\n",
    "#from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
    "\n",
    "es = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "        monitor='val_accuracy', # monitor: quantity to be monitored.\n",
    "        factor=0.001, # factor: factor by which the learning rate will be reduced. new_lr = lr * factor.\n",
    "        patience=30, # patience: number of epochs with no improvement after which learning rate will be reduced.\n",
    "        mode='auto', # mode: one of {'auto', 'min', 'max'}  in 'auto' mode, the direction is automatically inferred \n",
    "        #                                                                   from the name of the monitored quantity.\n",
    "        min_delta=0.05, # min_delta: threshold for measuring the new optimum, to only focus on significant changes; \n",
    "        cooldown=5, # cooldown: number of epochs to wait before resuming normal operation after lr has been reduced.\n",
    "        min_lr=INIT_LR, # min_lr: lower bound on the learning rate.\n",
    "        verbose=1, # displays epoch number\n",
    "    )\n",
    "\n",
    "mc = tf.keras.callbacks.ModelCheckpoint('best_model.h5', \n",
    "                                        monitor='val_accuracy', \n",
    "                                        mode='max', \n",
    "                                        verbose=1, \n",
    "                                        save_best_only=True)\n",
    "\n",
    "# We can improve the trigger for early stopping by waiting a while before stopping.\n",
    "# patient early stopping: In this case, we will wait 200 epochs before training is stopped. \n",
    "#          Specifically, this means that we will allow training to continue for up to an additional 5 epochs \n",
    "#          after the point that validation loss started to degrade, giving the training process an opportunity \n",
    "#          to get across flat spots or find some additional improvement.\n",
    "# es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)\n",
    "\n",
    "# train the full model, since we unfroze the pre-trained weights above\n",
    "print(\"Training the full stack model...\")\n",
    "H = model.fit( trainAug.flow(trainX, trainY, batch_size=BS), \n",
    "                        steps_per_epoch=len(trainX) // (BS),\n",
    "                        validation_data=(testX, testY), \n",
    "                        validation_steps=len(testX) // (BS), \n",
    "                        epochs=EPOCHS,\n",
    "                        callbacks=[es,mc]                        \n",
    "                       )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 256, 256, 3)]     0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         \n",
      "_________________________________________________________________\n",
      "average_pooling2d (AveragePo (None, 2, 2, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 2048)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                131136    \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 3)                 195       \n",
      "=================================================================\n",
      "Total params: 14,846,019\n",
      "Trainable params: 14,846,019\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# load the saved model\n",
    "saved_model = tf.keras.models.load_model('best_model.h5')\n",
    "saved_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.5479452013969421 , Test:  0.4736842215061188\n"
     ]
    }
   ],
   "source": [
    "#trainX, testX, trainY, testY\n",
    "# evaluate the model\n",
    "_,train_acc = saved_model.evaluate(trainX, trainY, verbose=0)\n",
    "_,test_acc = saved_model.evaluate(testX, testY, verbose=0)\n",
    "print('Train: ',train_acc,', Test: ' ,  test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy', 'lr'])\n"
     ]
    }
   ],
   "source": [
    "history_dict = H.history\n",
    "print(history_dict.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot of training model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "H.history.keys()\n",
    "plt.plot(H.history['accuracy'])\n",
    "plt.plot(H.history['val_accuracy'])\n",
    "plt.title('Accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "\n",
    "plt.savefig( '{}_{}_epochs_{}_BS_{}_Accuracy_plot.png'.format(os.path.dirname(imagePaths[0]).split('/')[1],EPOCHS,BS,dt))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shows epoches value\n",
    "len(H.history['val_accuracy']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot confusion matrix for the TRAINING DATA results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating the trained model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\installed_program\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      precision    recall  f1-score   support\n",
      "\n",
      "    ./newtrain/covid       0.46      0.86      0.60         7\n",
      "   ./newtrain/normal       0.50      0.38      0.43         8\n",
      "./newtrain/pneumonia       0.00      0.00      0.00         4\n",
      "\n",
      "            accuracy                           0.47        19\n",
      "           macro avg       0.32      0.41      0.34        19\n",
      "        weighted avg       0.38      0.47      0.40        19\n",
      "\n",
      "total:  19\n",
      "confusion matrix: \n",
      " [[6 1 0]\n",
      " [5 3 0]\n",
      " [2 2 0]]\n",
      "acc: 0.4737\n",
      "sensitivity: 0.8571\n",
      "specificity: 0.4167\n"
     ]
    }
   ],
   "source": [
    "print(\"Evaluating the trained model ...\")\n",
    "predIdxs = saved_model.predict(testX, batch_size=BS)\n",
    "\n",
    "predIdxs = np.argmax(predIdxs, axis=1)\n",
    "\n",
    "print(classification_report(testY.argmax(axis=1), predIdxs, target_names=label_encoder.classes_))\n",
    "\n",
    "# calculate a basic confusion matrix\n",
    "cm = confusion_matrix(testY.argmax(axis=1), predIdxs)\n",
    "total = sum(sum(cm))\n",
    "print('total: ',total)\n",
    "acc = (cm[0, 0] + cm[1, 1] + cm[2, 2]) / total\n",
    "sensitivity = cm[0, 0] / (cm[0, 0] + cm[0, 1] + cm[0, 2])\n",
    "specificity = (cm[1, 1] + cm[1, 2] + cm[2, 1] + cm[2, 2]) / (cm[1, 0] + cm[1, 1] + cm[1, 2] + cm[2, 0] + cm[2, 1] + cm[2, 2])\n",
    "\n",
    "# show the confusion matrix, accuracy, sensitivity, and specificity\n",
    "print('confusion matrix: \\n',cm)\n",
    "print(\"acc: {:.4f}\".format(acc))\n",
    "print(\"sensitivity: {:.4f}\".format(sensitivity))\n",
    "print(\"specificity: {:.4f}\".format(specificity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./newtrain/covid     .......covid ....0 ./newtrain/covid\n",
      "./newtrain/normal    ......normal ....1 ./newtrain/normal\n",
      "./newtrain/pneumonia ...pneumonia ....2 ./newtrain/pneumonia\n"
     ]
    }
   ],
   "source": [
    "#prediction_decoded = label_encoder.inverse_transform(predClasses)\n",
    "for i ,j, k, l in zip(list_of_classes,label_encoder.transform(list_of_classes),c2,label_encoder.classes_):\n",
    "    print('{:20}'.format(i),'{:.>12}'.format(k),'{:.>5}'.format(j),'{:.>5}'.format(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Test Dataset (17,958 CXR images) Confusion Matrix')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix using sklearn\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=c2)\n",
    "disp.plot()\n",
    "disp.ax_.set(\n",
    "                title='Test Dataset (17,958 CXR images) Confusion Matrix')#, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix using seaborn \n",
    "## The Seaborn Library provides data visualization. In this example, it plots\n",
    "## the Confusion Matrix\n",
    "import seaborn as sns\n",
    "## Call the heatmap() function from the Seaborn Library.\n",
    "## annot=True annotates cells.\n",
    "## fmt='g' disables scientific notation.\n",
    "## The heatmap() function returns a Matplotlib Axes Object.\n",
    "ax = sns.heatmap(cm, annot=True, fmt='g');\n",
    "## Modify the Axes Object directly to set various attributes such as the\n",
    "## Title, X/Y Labels.\n",
    "ax.set_title('Test Dataset (17,958 CXR images) Confusion Matrix');\n",
    "ax.set_xlabel('Predicted Label')\n",
    "ax.set_ylabel('Actual Label');\n",
    "## For the Tick Labels, the labels should be in Alphabetical order\n",
    "ax.xaxis.set_ticklabels(c2)\n",
    "ax.yaxis.set_ticklabels(c2)\n",
    "## Finally call the matplotlib show() function to display the visualization\n",
    "## of the Confusion Matrix.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and validation accuracy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_training(history,EPOCHS,BS,dt):\n",
    "  acc = history.history['accuracy']\n",
    "  val_acc = history.history['val_accuracy']\n",
    "  loss = history.history['loss']\n",
    "  val_loss = history.history['val_loss']\n",
    "  epochs = range(len(acc))\n",
    "  \n",
    "  plt.plot(epochs, acc, 'r.')\n",
    "  plt.plot(epochs, val_acc, 'r')\n",
    "  plt.title('Training and validation accuracy')\n",
    "  #plt.savefig(\"./customDenseNet121_plot00a.png\")\n",
    "  plt.savefig( '{}_{}_epochs_{}_BS_{}_Training&validation accuracyplot.png'.format(os.path.dirname(imagePaths[0]).split('/')[1],EPOCHS,BS,dt))\n",
    "  plt.figure()\n",
    "  plt.plot(epochs, loss, 'r.')\n",
    "  plt.plot(epochs, val_loss, 'r-')\n",
    "  plt.title('Training and validation loss')\n",
    "  #plt.savefig(\"./customDenseNet121_plot00b.png\")\n",
    "  plt.savefig( '{}_{}_epochs_{}_BS_{}_Training&validationLossplot.png'.format(os.path.dirname(imagePaths[0]).split('/')[1],EPOCHS,BS,dt))\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "plot_training(H,EPOCHS,BS,dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot confusion matrix for testing some real X-rays "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# plot the training loss and accuracy\n",
    "N = EPOCHS \n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\",c ='aqua')\n",
    "plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\",c ='lime')\n",
    "plt.plot(np.arange(0, N), H.history[\"accuracy\"], label=\"train_accuracy\",c ='red')\n",
    "plt.plot(np.arange(0, N), H.history[\"val_accuracy\"], label=\"val_accuracy\",c ='b')\n",
    "plt.title(\"Training Loss and Accuracy on COVID-19 Training Dataset\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy\")\n",
    "#plt.legend(bbox_to_anchor =(1.2, 1.25), ncol =4 )\n",
    "#plt.savefig(\"./customDenseNet121_plot01.png\")\n",
    "#plt.legend(loc=\"lower left\")\n",
    "plt.legend('loc=best')\n",
    "plt.savefig( '{}_{}_epochs_{}_BS_{}_plot.png'.format(os.path.dirname(imagePaths[0]).split('/')[1],EPOCHS,BS,dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validation Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ellipsis"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "...\n",
    "imagePathValidation = \"./validation_2/\"\n",
    "#imagePathValidation = \"./validation/\"\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./validation_2/\n"
     ]
    }
   ],
   "source": [
    "print(imagePathValidation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] loading untrained test images...\n",
      "33\n"
     ]
    }
   ],
   "source": [
    "print(\"[INFO] loading untrained test images...\")\n",
    "#imagePathTest = \"./validation\"\n",
    "imagePathsValidation = list(paths.list_images(imagePathValidation))\n",
    "print(len(imagePathsValidation))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "dataValidation = []\n",
    "labelsValidation = []\n",
    "\n",
    "counter2 = 1\n",
    "filesize_v=[]\n",
    "# loop over the image paths\n",
    "for imagePathValidation in imagePathsValidation:\n",
    "    #print(counter2)\n",
    "    # extract the class label from the filename\n",
    "    labelValidation = imagePathValidation.split(os.path.sep)[-2]\n",
    "  \n",
    "\t# load the image, swap color channels, and resize it to be a fixed\n",
    "\t# 256x256 pixels while ignoring aspect ratio\n",
    "    imageValidation = cv2.imread(imagePathValidation)\n",
    "    imageValidation = cv2.cvtColor(imageValidation, cv2.COLOR_BGR2RGB)\n",
    "    height_v, width_v, channels_v = imageValidation.shape\n",
    "    filesize_v.append([labelValidation,height_v,width_v,channels_v,height_v*width_v*channels_v])\n",
    "    imageValidation = cv2.resize(imageValidation, (imagesize, imagesize))\n",
    "\n",
    "\t# update the data and labels lists, respectively\n",
    "    dataValidation.append(imageValidation)\n",
    "    labelsValidation.append(labelValidation)\n",
    "    counter2=counter2+1\n",
    "    \n",
    "# convert the data and labels to NumPy arrays while scaling the pixel\n",
    "# intensities to the range [0, 255]\n",
    "dataValidation = np.array(dataValidation) / 255.0\n",
    "labelsValidation = np.array(labelsValidation)\n",
    "\n",
    "\n",
    "# perform one-hot encoding on the labels\n",
    "#from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "#label_encoder2 = LabelEncoder()\n",
    "integer_encoded2 = label_encoder.fit_transform(labelsValidation)\n",
    "labelsValidation = to_categorical(integer_encoded2)\n",
    "\n",
    "print(labelsValidation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label_v</th>\n",
       "      <th>height_v</th>\n",
       "      <th>width_v</th>\n",
       "      <th>channels_v</th>\n",
       "      <th>size_v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./validation_2/covid</td>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>./validation_2/normal</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>./validation_2/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     label_v  height_v  width_v  channels_v   size_v\n",
       "0       ./validation_2/covid       512      512           3   786432\n",
       "1       ./validation_2/covid       512      512           3   786432\n",
       "2       ./validation_2/covid       512      512           3   786432\n",
       "3       ./validation_2/covid       512      512           3   786432\n",
       "4       ./validation_2/covid       512      512           3   786432\n",
       "5       ./validation_2/covid       512      512           3   786432\n",
       "6       ./validation_2/covid       512      512           3   786432\n",
       "7       ./validation_2/covid       512      512           3   786432\n",
       "8       ./validation_2/covid       512      512           3   786432\n",
       "9       ./validation_2/covid       512      512           3   786432\n",
       "10      ./validation_2/covid       512      512           3   786432\n",
       "11     ./validation_2/normal      1024     1024           3  3145728\n",
       "12     ./validation_2/normal      1024     1024           3  3145728\n",
       "13     ./validation_2/normal      1024     1024           3  3145728\n",
       "14     ./validation_2/normal      1024     1024           3  3145728\n",
       "15     ./validation_2/normal      1024     1024           3  3145728\n",
       "16     ./validation_2/normal      1024     1024           3  3145728\n",
       "17     ./validation_2/normal      1024     1024           3  3145728\n",
       "18     ./validation_2/normal      1024     1024           3  3145728\n",
       "19     ./validation_2/normal      1024     1024           3  3145728\n",
       "20     ./validation_2/normal      1024     1024           3  3145728\n",
       "21     ./validation_2/normal      1024     1024           3  3145728\n",
       "22  ./validation_2/pneumonia      1024     1024           3  3145728\n",
       "23  ./validation_2/pneumonia      1024     1024           3  3145728\n",
       "24  ./validation_2/pneumonia      1024     1024           3  3145728\n",
       "25  ./validation_2/pneumonia      1024     1024           3  3145728\n",
       "26  ./validation_2/pneumonia      1024     1024           3  3145728\n",
       "27  ./validation_2/pneumonia      1024     1024           3  3145728\n",
       "28  ./validation_2/pneumonia      1024     1024           3  3145728\n",
       "29  ./validation_2/pneumonia      1024     1024           3  3145728\n",
       "30  ./validation_2/pneumonia      1024     1024           3  3145728\n",
       "31  ./validation_2/pneumonia      1024     1024           3  3145728\n",
       "32  ./validation_2/pneumonia      1024     1024           3  3145728"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_v=pd.DataFrame(filesize_v,columns=[\"label_v\",\"height_v\",\"width_v\",\"channels_v\",\"size_v\"])\n",
    "df_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(df_v.value_counts()).to_csv('validation_image_size.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['label_v', 'height_v', 'width_v', 'channels_v', 'size_v', '0']\n",
      "['./validation_2/covid', '512', '512', '3', '786432', '11']\n",
      "['./validation_2/normal', '1024', '1024', '3', '3145728', '11']\n",
      "['./validation_2/pneumonia', '1024', '1024', '3', '3145728', '11']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "with open('validation_image_size.csv', newline='') as f:\n",
    "    reader = csv.reader(f)\n",
    "    for row in reader:\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " size of data:  6488064 \n",
      " No of rows:  33 \n",
      " No of labels:  33 \n",
      " Type:  <class 'numpy.ndarray'> \n",
      " rows:  33 \n",
      " columns:  256 \n",
      " total_length:  8448\n"
     ]
    }
   ],
   "source": [
    "print(' size of data: ',dataValidation.size, '\\n No of rows: ',len(dataValidation),'\\n No of labels: ',len(labelsValidation),'\\n Type: ',type(dataValidation),'\\n','rows: ',len(dataValidation),'\\n','columns: ',len(dataValidation[0]),'\\n','total_length: ',len(dataValidation)*len(dataValidation[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33 256 256 3\n"
     ]
    }
   ],
   "source": [
    "print(len(dataValidation),len(dataValidation[0]),len(dataValidation[0][0]),len(dataValidation[0][0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.38345867 0.3885201  0.22802123]\n",
      " [0.4134685  0.40004537 0.18648612]\n",
      " [0.47302914 0.31011814 0.21685265]\n",
      " [0.3574782  0.41549945 0.22702233]\n",
      " [0.39198393 0.39487728 0.21313874]\n",
      " [0.35730553 0.42795718 0.21473725]\n",
      " [0.42125946 0.33391798 0.24482252]\n",
      " [0.3723099  0.39435092 0.2333392 ]\n",
      " [0.39635065 0.36020496 0.24344443]\n",
      " [0.38624665 0.3375963  0.27615708]\n",
      " [0.39123392 0.40227875 0.20648731]\n",
      " [0.3598285  0.4203684  0.21980311]\n",
      " [0.34357572 0.40606076 0.25036347]\n",
      " [0.45405903 0.3257882  0.22015278]\n",
      " [0.36167943 0.43893874 0.19938187]\n",
      " [0.37451613 0.43877733 0.18670662]\n",
      " [0.34915543 0.4524486  0.19839597]\n",
      " [0.35843858 0.47967505 0.16188642]\n",
      " [0.37254873 0.4520373  0.17541392]\n",
      " [0.33933854 0.47910362 0.18155792]\n",
      " [0.34262386 0.45829666 0.19907947]\n",
      " [0.35018888 0.44375613 0.20605503]\n",
      " [0.33349454 0.4367659  0.22973958]\n",
      " [0.38996202 0.42016354 0.18987444]\n",
      " [0.40191495 0.34654278 0.25154233]\n",
      " [0.4124729  0.33829752 0.24922968]\n",
      " [0.4358307  0.33838743 0.2257819 ]\n",
      " [0.39762118 0.3269914  0.27538738]\n",
      " [0.44641712 0.30221257 0.25137025]\n",
      " [0.3907219  0.4147998  0.19447826]\n",
      " [0.40456387 0.42273194 0.17270426]\n",
      " [0.424156   0.3233768  0.25246713]\n",
      " [0.4132768  0.3268198  0.2599034 ]]\n",
      "[1 0 0 1 1 1 0 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 1 1 0 0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ellipsis"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predValidation = saved_model.predict(dataValidation, batch_size=BS)\n",
    "\n",
    "print(predValidation)\n",
    "\n",
    "predValidationClasses = predValidation.argmax(axis=-1)\n",
    "print(predValidationClasses)\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating the trained model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\installed_program\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          precision    recall  f1-score   support\n",
      "\n",
      "    ./validation_2/covid       0.38      0.45      0.42        11\n",
      "   ./validation_2/normal       0.50      0.91      0.65        11\n",
      "./validation_2/pneumonia       0.00      0.00      0.00        11\n",
      "\n",
      "                accuracy                           0.45        33\n",
      "               macro avg       0.29      0.45      0.35        33\n",
      "            weighted avg       0.29      0.45      0.35        33\n",
      "\n",
      "[[ 5  6  0]\n",
      " [ 1 10  0]\n",
      " [ 7  4  0]]\n",
      "acc: 0.4545\n",
      "sensitivity: 0.4545\n",
      "specificity: 0.6364\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testX_v = dataValidation\n",
    "testY_v = labelsValidation\n",
    "\n",
    "print(\"Evaluating the trained model ...\")\n",
    "predIdxs_v = saved_model.predict(testX_v, batch_size=BS)\n",
    "\n",
    "predIdxs_v = np.argmax(predIdxs_v, axis=1)\n",
    "\n",
    "print(classification_report(testY_v.argmax(axis=1), predIdxs_v, target_names=label_encoder.classes_))\n",
    "\n",
    "# calculate a basic confusion matrix\n",
    "cm2 = confusion_matrix(testY_v.argmax(axis=1), predIdxs_v)\n",
    "total2 = sum(sum(cm2))\n",
    "acc2 = (cm2[0, 0] + cm2[1, 1] + cm2[2, 2]) / total2\n",
    "sensitivity2 = cm2[0, 0] / (cm2[0, 0] + cm2[0, 1] + cm2[0, 2])\n",
    "specificity2 = (cm2[1, 1] + cm2[1, 2] + cm2[2, 1] + cm2[2, 2]) / (cm2[1, 0] + cm2[1, 1] + cm2[1, 2] + cm2[2, 0] + cm2[2, 1] + cm2[2, 2])\n",
    "\n",
    "# show the confusion matrix, accuracy, sensitivity, and specificity\n",
    "print(cm2)\n",
    "print(\"acc: {:.4f}\".format(acc2))\n",
    "print(\"sensitivity: {:.4f}\".format(sensitivity2))\n",
    "print(\"specificity: {:.4f}\".format(specificity2))\n",
    "\n",
    "# plot the training loss and accuracy\n",
    "N = EPOCHS \n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\",c ='aqua')\n",
    "plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\",c ='lime')\n",
    "plt.plot(np.arange(0, N), H.history[\"accuracy\"], label=\"train_accuracy\",c ='red')\n",
    "plt.plot(np.arange(0, N), H.history[\"val_accuracy\"], label=\"val_accuracy\",c ='b')\n",
    "\n",
    "#plt.plot(np.arange(0, N), H.history[\"loss\"], label=\"train_loss\")\n",
    "#plt.plot(np.arange(0, N), H.history[\"val_loss\"], label=\"val_loss\")\n",
    "#plt.plot(np.arange(0, N), H.history[\"acc\"], label=\"train_acc\")\n",
    "#plt.plot(np.arange(0, N), H.history[\"val_acc\"], label=\"val_acc\")\n",
    "plt.title(\"Training Loss and Accuracy on COVID-19 Validation Dataset\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy\")\n",
    "plt.legend(loc='best')\n",
    "#plt.savefig(\"./Validation_Data.png\")\n",
    "plt.savefig( '{}_{}_epochs_{}_BS_{}plot.png'.format(os.path.dirname(imagePathsValidation[0]).split('/')[1],EPOCHS,BS,dt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Validation Dataset (3,432 CXR images) Confusion Matrix')]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix of validation dataset from sklearn\n",
    "disp3 = ConfusionMatrixDisplay(confusion_matrix=cm2,display_labels=c2)\n",
    "disp3.plot()\n",
    "disp3.ax_.set(\n",
    "                title='Validation Dataset (3,432 CXR images) Confusion Matrix')#, \n",
    "                #xlabel='Predicted Fruits', \n",
    "                #ylabel='Actual Fruits')\n",
    "#disp.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix of validation dataset from seaborn\n",
    "## The Seaborn Library provides data visualization. In this example, it plots\n",
    "## the Confusion Matrix\n",
    "import seaborn as sns\n",
    "## Call the heatmap() function from the Seaborn Library.\n",
    "## annot=True annotates cells.\n",
    "## fmt='g' disables scientific notation.\n",
    "## The heatmap() function returns a Matplotlib Axes Object.\n",
    "ax = sns.heatmap(cm2, annot=True, fmt='g');\n",
    "## Modify the Axes Object directly to set various attributes such as the\n",
    "## Title, X/Y Labels.\n",
    "ax.set_title('Validation Dataset (3,432 CXR images) Confusion Matrix');\n",
    "ax.set_xlabel('Predicted Label')\n",
    "ax.set_ylabel('Actual Label');\n",
    "## For the Tick Labels, the labels should be in Alphabetical order\n",
    "ax.xaxis.set_ticklabels(c2)\n",
    "ax.yaxis.set_ticklabels(c2)\n",
    "## Finally call the matplotlib show() function to display the visualization\n",
    "## of the Confusion Matrix.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ellipsis"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "...\n",
    "imagePathTest = \"./test_set_2/\"\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./test_set_2/\n"
     ]
    }
   ],
   "source": [
    "print(imagePathTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] loading untrained test images...\n",
      "52\n"
     ]
    }
   ],
   "source": [
    "print(\"[INFO] loading untrained test images...\")\n",
    "#imagePathTest = \"./validation\"\n",
    "imagePathsTest = list(paths.list_images(imagePathTest))\n",
    "print(len(imagePathsTest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTest = []\n",
    "labelsTest = []\n",
    "\n",
    "counter2 = 1\n",
    "filesize_test=[]\n",
    "# loop over the image paths\n",
    "for imagePathTest in imagePathsTest:\n",
    "    #print(counter2)\n",
    "    # extract the class label from the filename\n",
    "  #  labelTest = imagePathTest.split(os.path.sep)[-2]\n",
    "  \n",
    "\t# load the image, swap color channels, and resize it to be a fixed\n",
    "\t# 256x256 pixels while ignoring aspect ratio\n",
    "    imageTest = cv2.imread(imagePathTest)\n",
    "    imageTest = cv2.cvtColor(imageTest, cv2.COLOR_BGR2RGB)\n",
    "    height_test, width_test, channels_test = imageTest.shape\n",
    "    filesize_test.append([height_test,width_test,channels_test,height_test*width_test*channels_test])\n",
    "    imageTest = cv2.resize(imageTest, (imagesize, imagesize))\n",
    "\n",
    "\t# update the data and labels lists, respectively\n",
    "    dataTest.append(imageTest)\n",
    "   # labelsTest.append(labelTest)\n",
    "    counter2=counter2+1\n",
    "    \n",
    "# convert the data and labels to NumPy arrays while scaling the pixel\n",
    "# intensities to the range [0, 255]\n",
    "dataTest = np.array(dataTest) / 255.0\n",
    "#labelsTest = np.array(labelsTest)\n",
    "\n",
    "\n",
    "# perform one-hot encoding on the labels\n",
    "#from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "#label_encoder2 = LabelEncoder()\n",
    "#integer_encoded2 = label_encoder2.fit_transform(labelsTest)\n",
    "#labelsTest = to_categorical(integer_encoded2)\n",
    "\n",
    "#print(labelsTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height_test</th>\n",
       "      <th>width_test</th>\n",
       "      <th>channels_test</th>\n",
       "      <th>size_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>512</td>\n",
       "      <td>512</td>\n",
       "      <td>3</td>\n",
       "      <td>786432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
       "      <td>3</td>\n",
       "      <td>3145728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    height_test  width_test  channels_test  size_test\n",
       "0          1024        1024              3    3145728\n",
       "1          1024        1024              3    3145728\n",
       "2          1024        1024              3    3145728\n",
       "3          1024        1024              3    3145728\n",
       "4           512         512              3     786432\n",
       "5          1024        1024              3    3145728\n",
       "6           512         512              3     786432\n",
       "7          1024        1024              3    3145728\n",
       "8           512         512              3     786432\n",
       "9          1024        1024              3    3145728\n",
       "10          512         512              3     786432\n",
       "11         1024        1024              3    3145728\n",
       "12         1024        1024              3    3145728\n",
       "13          512         512              3     786432\n",
       "14          512         512              3     786432\n",
       "15         1024        1024              3    3145728\n",
       "16          512         512              3     786432\n",
       "17         1024        1024              3    3145728\n",
       "18         1024        1024              3    3145728\n",
       "19         1024        1024              3    3145728\n",
       "20         1024        1024              3    3145728\n",
       "21          512         512              3     786432\n",
       "22          512         512              3     786432\n",
       "23         1024        1024              3    3145728\n",
       "24          512         512              3     786432\n",
       "25         1024        1024              3    3145728\n",
       "26         1024        1024              3    3145728\n",
       "27         1024        1024              3    3145728\n",
       "28          512         512              3     786432\n",
       "29         1024        1024              3    3145728\n",
       "30         1024        1024              3    3145728\n",
       "31         1024        1024              3    3145728\n",
       "32         1024        1024              3    3145728\n",
       "33         1024        1024              3    3145728\n",
       "34         1024        1024              3    3145728\n",
       "35          512         512              3     786432\n",
       "36          512         512              3     786432\n",
       "37         1024        1024              3    3145728\n",
       "38         1024        1024              3    3145728\n",
       "39         1024        1024              3    3145728\n",
       "40          512         512              3     786432\n",
       "41          512         512              3     786432\n",
       "42          512         512              3     786432\n",
       "43         1024        1024              3    3145728\n",
       "44          512         512              3     786432\n",
       "45         1024        1024              3    3145728\n",
       "46         1024        1024              3    3145728\n",
       "47          512         512              3     786432\n",
       "48         1024        1024              3    3145728\n",
       "49          512         512              3     786432\n",
       "50         1024        1024              3    3145728\n",
       "51         1024        1024              3    3145728"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test=pd.DataFrame(filesize_test,columns=[\"height_test\",\"width_test\",\"channels_test\",\"size_test\"])\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(df_test.value_counts()).to_csv('test_image_size.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['height_test', 'width_test', 'channels_test', 'size_test', '0']\n",
      "['1024', '1024', '3', '3145728', '33']\n",
      "['512', '512', '3', '786432', '19']\n"
     ]
    }
   ],
   "source": [
    "with open('test_image_size.csv', newline='') as f:\n",
    "    reader = csv.reader(f)\n",
    "    for row in reader:\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " size of data:  10223616 \n",
      " No of rows:  52 \n",
      " Type:  <class 'numpy.ndarray'> \n",
      " rows:  52 \n",
      " columns:  256 \n",
      " total_length:  13312\n"
     ]
    }
   ],
   "source": [
    "print(' size of data: ',dataTest.size, '\\n No of rows: ',len(dataTest),\n",
    "      #'\\n No of labels: ',len(labelsTest),\n",
    "      '\\n Type: ',type(dataTest),'\\n','rows: ',len(dataTest),'\\n','columns: ',len(dataTest[0]),'\\n','total_length: ',len(dataTest)*len(dataTest[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52 256 256 3\n"
     ]
    }
   ],
   "source": [
    "print(len(dataTest),len(dataTest[0]),len(dataTest[0][0]),len(dataTest[0][0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.38420635 0.38267764 0.23311602]\n",
      " [0.40468922 0.3414443  0.25386643]\n",
      " [0.40706518 0.35266736 0.24026741]\n",
      " [0.37519464 0.39883226 0.22597304]\n",
      " [0.37944698 0.3357012  0.28485182]\n",
      " [0.39831948 0.43207356 0.16960701]\n",
      " [0.41621977 0.34824747 0.2355327 ]\n",
      " [0.4174476  0.33477613 0.24777615]\n",
      " [0.4487765  0.36745322 0.18377027]\n",
      " [0.4444096  0.3180003  0.23759013]\n",
      " [0.41431618 0.36490887 0.22077495]\n",
      " [0.3920055  0.37254703 0.23544745]\n",
      " [0.38663974 0.3705884  0.24277183]\n",
      " [0.42379987 0.3925201  0.18368007]\n",
      " [0.3934056  0.36978918 0.23680523]\n",
      " [0.40810975 0.42380166 0.16808861]\n",
      " [0.3970163  0.36499903 0.2379846 ]\n",
      " [0.38236165 0.37435678 0.24328157]\n",
      " [0.3719482  0.42860678 0.19944498]\n",
      " [0.42953882 0.32705253 0.24340864]\n",
      " [0.37518713 0.40133622 0.22347654]\n",
      " [0.3898955  0.34982067 0.26028383]\n",
      " [0.35639408 0.4181571  0.22544885]\n",
      " [0.3935196  0.40781224 0.19866812]\n",
      " [0.39878634 0.36181203 0.2394016 ]\n",
      " [0.43326592 0.34906942 0.21766466]\n",
      " [0.38914222 0.44181114 0.16904664]\n",
      " [0.32234153 0.47113085 0.20652758]\n",
      " [0.3750033  0.32181993 0.30317676]\n",
      " [0.40763572 0.4173388  0.17502548]\n",
      " [0.35625604 0.4678788  0.17586522]\n",
      " [0.39638612 0.40464604 0.19896781]\n",
      " [0.37493783 0.4069967  0.2180655 ]\n",
      " [0.35219872 0.4630233  0.18477792]\n",
      " [0.35717076 0.45081565 0.19201365]\n",
      " [0.41032103 0.376877   0.21280195]\n",
      " [0.3685225  0.4232585  0.208219  ]\n",
      " [0.33303863 0.47727722 0.18968412]\n",
      " [0.4239071  0.42153403 0.15455878]\n",
      " [0.41787383 0.31711397 0.2650122 ]\n",
      " [0.47551933 0.2847255  0.2397552 ]\n",
      " [0.44987148 0.3516147  0.19851375]\n",
      " [0.39952794 0.41156477 0.18890728]\n",
      " [0.4279207  0.33763847 0.2344408 ]\n",
      " [0.3802491  0.32612202 0.29362887]\n",
      " [0.38248777 0.41995248 0.19755965]\n",
      " [0.37156606 0.41098064 0.21745333]\n",
      " [0.39590013 0.3914146  0.21268526]\n",
      " [0.39714414 0.44366828 0.15918761]\n",
      " [0.4032057  0.37050164 0.22629267]\n",
      " [0.39802805 0.39570168 0.20627029]\n",
      " [0.4184685  0.32507247 0.25645906]]\n",
      "[0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 1 1 0 0 1 1 0 1 1 1 1 1 1 0 1\n",
      " 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ellipsis"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predTest = saved_model.predict(dataTest, batch_size=BS)\n",
    "\n",
    "print(predTest)\n",
    "\n",
    "predClasses = predTest.argmax(axis=-1)\n",
    "print(predClasses)\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0\n",
      "0  0\n",
      "1  0\n",
      "2  0\n",
      "3  1\n",
      "4  0\n",
      "(52,)\n",
      "52\n",
      "<class 'numpy.ndarray'>\n",
      "\n",
      "0    31\n",
      "1    21\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "print(pd.DataFrame(predClasses).head())\n",
    "print(predClasses.shape)\n",
    "print(len(predClasses))\n",
    "print(type(predClasses))\n",
    "print()\n",
    "print(pd.DataFrame(predClasses).value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Final File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./test_set_2/0001.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0002.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0003.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0004.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0005.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0006.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0007.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0008.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0009.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0010.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0011.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0012.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0013.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0014.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0015.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0016.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0017.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0018.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0019.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0020.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0021.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0022.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0023.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0024.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0025.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0026.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0027.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0028.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0029.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0030.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0031.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0032.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0033.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0034.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0035.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0036.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0037.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0038.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0039.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0040.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0041.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0042.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0043.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0044.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0045.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0046.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0047.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0048.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0049.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0050.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0051.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0052.jpg  : 0 :  ./validation_2/covid\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "import re\n",
    "# name of csv file \n",
    "fn=\"submission2_\"\n",
    "filename = fn+dt+\".csv\"\n",
    "# open the file in the write mode\n",
    "f = open('/csv_file', 'w')\n",
    "prediction_decoded = label_encoder.inverse_transform(predClasses)\n",
    "\n",
    "#prediction_decoded2 = labels.inverse_transform(predClasses)\n",
    "#print(prediction_decoded)\n",
    "# writing to csv file \n",
    "with open(filename, 'w', newline='') as csvfile: \n",
    "    # creating a csv writer object \n",
    "    csvwriter = csv.writer(csvfile) \n",
    "    csvwriter.writerow(['case', 'class'])     \n",
    "    for i,j,k in zip(imagePathsTest,predClasses,prediction_decoded):\n",
    "        print(re.sub(r'^./test_set/','',i),' :',j,': ',re.sub(r'^./newtrain/','',k))\n",
    "        # writing the fields \n",
    "        #csvwriter.writerow((re.sub(r'^./test_set/','',i),re.sub(r'^./newtrain/','',k))) \n",
    "        csvwriter.writerow((re.sub(r'^./test_set/','',i),j)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./test_set_2/0001.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./test_set_2/0002.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./test_set_2/0003.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./test_set_2/0004.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./test_set_2/0005.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>./test_set_2/0006.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>./test_set_2/0007.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./test_set_2/0008.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./test_set_2/0009.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./test_set_2/0010.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./test_set_2/0011.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./test_set_2/0012.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./test_set_2/0013.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./test_set_2/0014.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./test_set_2/0015.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>./test_set_2/0016.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>./test_set_2/0017.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>./test_set_2/0018.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>./test_set_2/0019.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>./test_set_2/0020.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>./test_set_2/0021.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>./test_set_2/0022.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>./test_set_2/0023.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>./test_set_2/0024.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>./test_set_2/0025.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>./test_set_2/0026.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>./test_set_2/0027.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>./test_set_2/0028.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>./test_set_2/0029.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>./test_set_2/0030.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>./test_set_2/0031.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>./test_set_2/0032.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>./test_set_2/0033.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>./test_set_2/0034.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>./test_set_2/0035.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>./test_set_2/0036.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>./test_set_2/0037.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>./test_set_2/0038.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>./test_set_2/0039.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>./test_set_2/0040.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>./test_set_2/0041.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>./test_set_2/0042.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>./test_set_2/0043.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>./test_set_2/0044.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>./test_set_2/0045.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>./test_set_2/0046.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>./test_set_2/0047.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>./test_set_2/0048.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>./test_set_2/0049.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>./test_set_2/0050.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>./test_set_2/0051.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>./test_set_2/0052.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     case  class\n",
       "0   ./test_set_2/0001.jpg      0\n",
       "1   ./test_set_2/0002.jpg      0\n",
       "2   ./test_set_2/0003.jpg      0\n",
       "3   ./test_set_2/0004.jpg      1\n",
       "4   ./test_set_2/0005.jpg      0\n",
       "5   ./test_set_2/0006.jpg      1\n",
       "6   ./test_set_2/0007.jpg      0\n",
       "7   ./test_set_2/0008.jpg      0\n",
       "8   ./test_set_2/0009.jpg      0\n",
       "9   ./test_set_2/0010.jpg      0\n",
       "10  ./test_set_2/0011.jpg      0\n",
       "11  ./test_set_2/0012.jpg      0\n",
       "12  ./test_set_2/0013.jpg      0\n",
       "13  ./test_set_2/0014.jpg      0\n",
       "14  ./test_set_2/0015.jpg      0\n",
       "15  ./test_set_2/0016.jpg      1\n",
       "16  ./test_set_2/0017.jpg      0\n",
       "17  ./test_set_2/0018.jpg      0\n",
       "18  ./test_set_2/0019.jpg      1\n",
       "19  ./test_set_2/0020.jpg      0\n",
       "20  ./test_set_2/0021.jpg      1\n",
       "21  ./test_set_2/0022.jpg      0\n",
       "22  ./test_set_2/0023.jpg      1\n",
       "23  ./test_set_2/0024.jpg      1\n",
       "24  ./test_set_2/0025.jpg      0\n",
       "25  ./test_set_2/0026.jpg      0\n",
       "26  ./test_set_2/0027.jpg      1\n",
       "27  ./test_set_2/0028.jpg      1\n",
       "28  ./test_set_2/0029.jpg      0\n",
       "29  ./test_set_2/0030.jpg      1\n",
       "30  ./test_set_2/0031.jpg      1\n",
       "31  ./test_set_2/0032.jpg      1\n",
       "32  ./test_set_2/0033.jpg      1\n",
       "33  ./test_set_2/0034.jpg      1\n",
       "34  ./test_set_2/0035.jpg      1\n",
       "35  ./test_set_2/0036.jpg      0\n",
       "36  ./test_set_2/0037.jpg      1\n",
       "37  ./test_set_2/0038.jpg      1\n",
       "38  ./test_set_2/0039.jpg      0\n",
       "39  ./test_set_2/0040.jpg      0\n",
       "40  ./test_set_2/0041.jpg      0\n",
       "41  ./test_set_2/0042.jpg      0\n",
       "42  ./test_set_2/0043.jpg      1\n",
       "43  ./test_set_2/0044.jpg      0\n",
       "44  ./test_set_2/0045.jpg      0\n",
       "45  ./test_set_2/0046.jpg      1\n",
       "46  ./test_set_2/0047.jpg      1\n",
       "47  ./test_set_2/0048.jpg      0\n",
       "48  ./test_set_2/0049.jpg      1\n",
       "49  ./test_set_2/0050.jpg      0\n",
       "50  ./test_set_2/0051.jpg      0\n",
       "51  ./test_set_2/0052.jpg      0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pandas.read_csv('submission.csv')\n",
    "df = pd.read_csv(filename)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     case  class\n",
      "0   ./test_set_2/0001.jpg      0\n",
      "1   ./test_set_2/0002.jpg      0\n",
      "2   ./test_set_2/0003.jpg      0\n",
      "3   ./test_set_2/0004.jpg      1\n",
      "4   ./test_set_2/0005.jpg      0\n",
      "5   ./test_set_2/0006.jpg      1\n",
      "6   ./test_set_2/0007.jpg      0\n",
      "7   ./test_set_2/0008.jpg      0\n",
      "8   ./test_set_2/0009.jpg      0\n",
      "9   ./test_set_2/0010.jpg      0\n",
      "10  ./test_set_2/0011.jpg      0\n",
      "11  ./test_set_2/0012.jpg      0\n",
      "12  ./test_set_2/0013.jpg      0\n",
      "13  ./test_set_2/0014.jpg      0\n",
      "14  ./test_set_2/0015.jpg      0\n",
      "15  ./test_set_2/0016.jpg      1\n",
      "16  ./test_set_2/0017.jpg      0\n",
      "17  ./test_set_2/0018.jpg      0\n",
      "18  ./test_set_2/0019.jpg      1\n",
      "19  ./test_set_2/0020.jpg      0\n",
      "20  ./test_set_2/0021.jpg      1\n",
      "21  ./test_set_2/0022.jpg      0\n",
      "22  ./test_set_2/0023.jpg      1\n",
      "23  ./test_set_2/0024.jpg      1\n",
      "24  ./test_set_2/0025.jpg      0\n",
      "25  ./test_set_2/0026.jpg      0\n",
      "26  ./test_set_2/0027.jpg      1\n",
      "27  ./test_set_2/0028.jpg      1\n",
      "28  ./test_set_2/0029.jpg      0\n",
      "29  ./test_set_2/0030.jpg      1\n",
      "30  ./test_set_2/0031.jpg      1\n",
      "31  ./test_set_2/0032.jpg      1\n",
      "32  ./test_set_2/0033.jpg      1\n",
      "33  ./test_set_2/0034.jpg      1\n",
      "34  ./test_set_2/0035.jpg      1\n",
      "35  ./test_set_2/0036.jpg      0\n",
      "36  ./test_set_2/0037.jpg      1\n",
      "37  ./test_set_2/0038.jpg      1\n",
      "38  ./test_set_2/0039.jpg      0\n",
      "39  ./test_set_2/0040.jpg      0\n",
      "40  ./test_set_2/0041.jpg      0\n",
      "41  ./test_set_2/0042.jpg      0\n",
      "42  ./test_set_2/0043.jpg      1\n",
      "43  ./test_set_2/0044.jpg      0\n",
      "44  ./test_set_2/0045.jpg      0\n",
      "45  ./test_set_2/0046.jpg      1\n",
      "46  ./test_set_2/0047.jpg      1\n",
      "47  ./test_set_2/0048.jpg      0\n",
      "48  ./test_set_2/0049.jpg      1\n",
      "49  ./test_set_2/0050.jpg      0\n",
      "50  ./test_set_2/0051.jpg      0\n",
      "51  ./test_set_2/0052.jpg      0\n"
     ]
    }
   ],
   "source": [
    "file2=fn+dt+\".csv\"\n",
    "df = pd.read_csv(file2)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./test_set_2/0001.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./test_set_2/0002.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./test_set_2/0003.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./test_set_2/0004.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./test_set_2/0005.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>./test_set_2/0006.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>./test_set_2/0007.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./test_set_2/0008.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./test_set_2/0009.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./test_set_2/0010.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./test_set_2/0011.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./test_set_2/0012.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./test_set_2/0013.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./test_set_2/0014.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./test_set_2/0015.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>./test_set_2/0016.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>./test_set_2/0017.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>./test_set_2/0018.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>./test_set_2/0019.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>./test_set_2/0020.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>./test_set_2/0021.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>./test_set_2/0022.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>./test_set_2/0023.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>./test_set_2/0024.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>./test_set_2/0025.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>./test_set_2/0026.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>./test_set_2/0027.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>./test_set_2/0028.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>./test_set_2/0029.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>./test_set_2/0030.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>./test_set_2/0031.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>./test_set_2/0032.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>./test_set_2/0033.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>./test_set_2/0034.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>./test_set_2/0035.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>./test_set_2/0036.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>./test_set_2/0037.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>./test_set_2/0038.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>./test_set_2/0039.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>./test_set_2/0040.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>./test_set_2/0041.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>./test_set_2/0042.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>./test_set_2/0043.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>./test_set_2/0044.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>./test_set_2/0045.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>./test_set_2/0046.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>./test_set_2/0047.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>./test_set_2/0048.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>./test_set_2/0049.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>./test_set_2/0050.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>./test_set_2/0051.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>./test_set_2/0052.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     case  class\n",
       "0   ./test_set_2/0001.jpg      0\n",
       "1   ./test_set_2/0002.jpg      0\n",
       "2   ./test_set_2/0003.jpg      0\n",
       "3   ./test_set_2/0004.jpg      1\n",
       "4   ./test_set_2/0005.jpg      0\n",
       "5   ./test_set_2/0006.jpg      1\n",
       "6   ./test_set_2/0007.jpg      0\n",
       "7   ./test_set_2/0008.jpg      0\n",
       "8   ./test_set_2/0009.jpg      0\n",
       "9   ./test_set_2/0010.jpg      0\n",
       "10  ./test_set_2/0011.jpg      0\n",
       "11  ./test_set_2/0012.jpg      0\n",
       "12  ./test_set_2/0013.jpg      0\n",
       "13  ./test_set_2/0014.jpg      0\n",
       "14  ./test_set_2/0015.jpg      0\n",
       "15  ./test_set_2/0016.jpg      1\n",
       "16  ./test_set_2/0017.jpg      0\n",
       "17  ./test_set_2/0018.jpg      0\n",
       "18  ./test_set_2/0019.jpg      1\n",
       "19  ./test_set_2/0020.jpg      0\n",
       "20  ./test_set_2/0021.jpg      1\n",
       "21  ./test_set_2/0022.jpg      0\n",
       "22  ./test_set_2/0023.jpg      1\n",
       "23  ./test_set_2/0024.jpg      1\n",
       "24  ./test_set_2/0025.jpg      0\n",
       "25  ./test_set_2/0026.jpg      0\n",
       "26  ./test_set_2/0027.jpg      1\n",
       "27  ./test_set_2/0028.jpg      1\n",
       "28  ./test_set_2/0029.jpg      0\n",
       "29  ./test_set_2/0030.jpg      1\n",
       "30  ./test_set_2/0031.jpg      1\n",
       "31  ./test_set_2/0032.jpg      1\n",
       "32  ./test_set_2/0033.jpg      1\n",
       "33  ./test_set_2/0034.jpg      1\n",
       "34  ./test_set_2/0035.jpg      1\n",
       "35  ./test_set_2/0036.jpg      0\n",
       "36  ./test_set_2/0037.jpg      1\n",
       "37  ./test_set_2/0038.jpg      1\n",
       "38  ./test_set_2/0039.jpg      0\n",
       "39  ./test_set_2/0040.jpg      0\n",
       "40  ./test_set_2/0041.jpg      0\n",
       "41  ./test_set_2/0042.jpg      0\n",
       "42  ./test_set_2/0043.jpg      1\n",
       "43  ./test_set_2/0044.jpg      0\n",
       "44  ./test_set_2/0045.jpg      0\n",
       "45  ./test_set_2/0046.jpg      1\n",
       "46  ./test_set_2/0047.jpg      1\n",
       "47  ./test_set_2/0048.jpg      0\n",
       "48  ./test_set_2/0049.jpg      1\n",
       "49  ./test_set_2/0050.jpg      0\n",
       "50  ./test_set_2/0051.jpg      0\n",
       "51  ./test_set_2/0052.jpg      0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./test_set_2/0001.jpg'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['case'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0001.jpg'"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.path.basename(df['case'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0001.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0004.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0005.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0006.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0007.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0008.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0009.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0010.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0011.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0012.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0013.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0014.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0015.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0016.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0017.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0018.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0019.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0020.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0021.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0022.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0023.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0024.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0025.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0026.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0027.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0028.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0029.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0030.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0031.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0032.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0033.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0034.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0035.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0036.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0037.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0038.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0039.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0040.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0041.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0042.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0043.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0044.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0045.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0046.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0047.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0048.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0049.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0050.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0051.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0052.jpg</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        case  class\n",
       "0   0001.jpg      0\n",
       "1   0002.jpg      0\n",
       "2   0003.jpg      0\n",
       "3   0004.jpg      1\n",
       "4   0005.jpg      0\n",
       "5   0006.jpg      1\n",
       "6   0007.jpg      0\n",
       "7   0008.jpg      0\n",
       "8   0009.jpg      0\n",
       "9   0010.jpg      0\n",
       "10  0011.jpg      0\n",
       "11  0012.jpg      0\n",
       "12  0013.jpg      0\n",
       "13  0014.jpg      0\n",
       "14  0015.jpg      0\n",
       "15  0016.jpg      1\n",
       "16  0017.jpg      0\n",
       "17  0018.jpg      0\n",
       "18  0019.jpg      1\n",
       "19  0020.jpg      0\n",
       "20  0021.jpg      1\n",
       "21  0022.jpg      0\n",
       "22  0023.jpg      1\n",
       "23  0024.jpg      1\n",
       "24  0025.jpg      0\n",
       "25  0026.jpg      0\n",
       "26  0027.jpg      1\n",
       "27  0028.jpg      1\n",
       "28  0029.jpg      0\n",
       "29  0030.jpg      1\n",
       "30  0031.jpg      1\n",
       "31  0032.jpg      1\n",
       "32  0033.jpg      1\n",
       "33  0034.jpg      1\n",
       "34  0035.jpg      1\n",
       "35  0036.jpg      0\n",
       "36  0037.jpg      1\n",
       "37  0038.jpg      1\n",
       "38  0039.jpg      0\n",
       "39  0040.jpg      0\n",
       "40  0041.jpg      0\n",
       "41  0042.jpg      0\n",
       "42  0043.jpg      1\n",
       "43  0044.jpg      0\n",
       "44  0045.jpg      0\n",
       "45  0046.jpg      1\n",
       "46  0047.jpg      1\n",
       "47  0048.jpg      0\n",
       "48  0049.jpg      1\n",
       "49  0050.jpg      0\n",
       "50  0051.jpg      0\n",
       "51  0052.jpg      0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['case']=df['case'].map(lambda a:os.path.basename(a))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_file=fn+dt+\".csv\"\n",
    "df.to_csv(final_file,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        case  class\n",
      "0   0001.jpg      0\n",
      "1   0002.jpg      0\n",
      "2   0003.jpg      0\n",
      "3   0004.jpg      1\n",
      "4   0005.jpg      0\n",
      "5   0006.jpg      1\n",
      "6   0007.jpg      0\n",
      "7   0008.jpg      0\n",
      "8   0009.jpg      0\n",
      "9   0010.jpg      0\n",
      "10  0011.jpg      0\n",
      "11  0012.jpg      0\n",
      "12  0013.jpg      0\n",
      "13  0014.jpg      0\n",
      "14  0015.jpg      0\n",
      "15  0016.jpg      1\n",
      "16  0017.jpg      0\n",
      "17  0018.jpg      0\n",
      "18  0019.jpg      1\n",
      "19  0020.jpg      0\n",
      "20  0021.jpg      1\n",
      "21  0022.jpg      0\n",
      "22  0023.jpg      1\n",
      "23  0024.jpg      1\n",
      "24  0025.jpg      0\n",
      "25  0026.jpg      0\n",
      "26  0027.jpg      1\n",
      "27  0028.jpg      1\n",
      "28  0029.jpg      0\n",
      "29  0030.jpg      1\n",
      "30  0031.jpg      1\n",
      "31  0032.jpg      1\n",
      "32  0033.jpg      1\n",
      "33  0034.jpg      1\n",
      "34  0035.jpg      1\n",
      "35  0036.jpg      0\n",
      "36  0037.jpg      1\n",
      "37  0038.jpg      1\n",
      "38  0039.jpg      0\n",
      "39  0040.jpg      0\n",
      "40  0041.jpg      0\n",
      "41  0042.jpg      0\n",
      "42  0043.jpg      1\n",
      "43  0044.jpg      0\n",
      "44  0045.jpg      0\n",
      "45  0046.jpg      1\n",
      "46  0047.jpg      1\n",
      "47  0048.jpg      0\n",
      "48  0049.jpg      1\n",
      "49  0050.jpg      0\n",
      "50  0051.jpg      0\n",
      "51  0052.jpg      0\n"
     ]
    }
   ],
   "source": [
    "#import pandas\n",
    "df2 = pandas.read_csv(final_file)\n",
    "#df = pandas.read_csv(filename)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "\n",
      "       case\n",
      "class      \n",
      "0        31\n",
      "1        21\n",
      "\n",
      "   class\n",
      "0     31\n",
      "1     21\n"
     ]
    }
   ],
   "source": [
    "# label_encoder.inverse_transform(predClasses)\n",
    "print(type(df2))\n",
    "print()\n",
    "print(pandas.DataFrame(df2.groupby('class').count()))\n",
    "print()\n",
    "print(pandas.DataFrame(df2['class'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   ./validation_2/covid\n",
      "1   ./validation_2/normal\n"
     ]
    }
   ],
   "source": [
    "for i,j in zip(df2['class'].unique(),label_encoder.inverse_transform(df2['class'].unique())):\n",
    "    print(i,' ',j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   31   ./validation_2/covid\n",
      "1   21   ./validation_2/normal\n"
     ]
    }
   ],
   "source": [
    "for i,j,k in zip(df['class'].unique(),df['class'].value_counts(),label_encoder.inverse_transform(df['class'].unique())):\n",
    "    print(i,' ',j,' ',k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(class  class\n",
       " 0      0        31\n",
       " 1      1        21\n",
       " Name: class, dtype: int64,\n",
       " array([0, 1], dtype=int64),\n",
       " array(['./validation_2/covid', './validation_2/normal'], dtype='<U24'))"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('class')['class'].value_counts(),df['class'].unique(),label_encoder.inverse_transform(df['class'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": 4
}