customModel_InceptionV3_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": [
    "## InceptionV3 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_30_38_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": {
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       "      <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",
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       "      <th>4</th>\n",
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       "      <td>1330</td>\n",
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       "      <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>./newtrain/pneumonia</td>\n",
       "      <td>1024</td>\n",
       "      <td>1024</td>\n",
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       "      <td>3145728</td>\n",
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       "    <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",
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       "    <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",
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       "<p>92 rows × 5 columns</p>\n",
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      ],
      "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": [
    {
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       "    <tr>\n",
       "      <th>./newtrain/normal</th>\n",
       "      <th>1024</th>\n",
       "      <th>1024</th>\n",
       "      <th>3</th>\n",
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       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>./newtrain/pneumonia</th>\n",
       "      <th>1024</th>\n",
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       "      <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": 35,
   "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": 37,
   "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 [==============================] - 112s 21s/step - loss: 1.2260 - accuracy: 0.4033 - val_loss: 1.2964 - val_accuracy: 0.2632\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.26316, saving model to best_model.h5\n",
      "Epoch 2/2\n",
      "4/4 [==============================] - 83s 21s/step - loss: 1.3526 - accuracy: 0.2516 - val_loss: 1.2812 - val_accuracy: 0.3158\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.26316 to 0.31579, 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=(imagesize, imagesize, 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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 256, 256, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 127, 127, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 127, 127, 32) 96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 127, 127, 32) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 125, 125, 32) 9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 125, 125, 32) 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 125, 125, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 125, 125, 64) 18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 125, 125, 64) 192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 125, 125, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 62, 62, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 62, 62, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 62, 62, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 62, 62, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 60, 60, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 60, 60, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 60, 60, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 29, 29, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 29, 29, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 29, 29, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 29, 29, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 29, 29, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 29, 29, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 29, 29, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 29, 29, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 29, 29, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 29, 29, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 29, 29, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 29, 29, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 29, 29, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 29, 29, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 29, 29, 32)   6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 29, 29, 64)   192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 29, 29, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 29, 29, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 29, 29, 32)   96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 29, 29, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 29, 29, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 29, 29, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 29, 29, 32)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 29, 29, 256)  0           activation_5[0][0]               \n",
      "                                                                 activation_7[0][0]               \n",
      "                                                                 activation_10[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 29, 29, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 29, 29, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 29, 29, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 29, 29, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 29, 29, 96)   55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 29, 29, 48)   144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 29, 29, 96)   288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 29, 29, 48)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 29, 29, 96)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 29, 29, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 29, 29, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 29, 29, 64)   76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 29, 29, 96)   82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 29, 29, 64)   16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 29, 29, 64)   192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 29, 29, 64)   192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 29, 29, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 29, 29, 64)   192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 29, 29, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 29, 29, 64)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 29, 29, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 29, 29, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 29, 29, 288)  0           activation_12[0][0]              \n",
      "                                                                 activation_14[0][0]              \n",
      "                                                                 activation_17[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 29, 29, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 29, 29, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 29, 29, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 29, 29, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 29, 29, 96)   55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 29, 29, 48)   144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 29, 29, 96)   288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 29, 29, 48)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 29, 29, 96)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 29, 29, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 29, 29, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 29, 29, 64)   76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 29, 29, 96)   82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 29, 29, 64)   18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 29, 29, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 29, 29, 64)   192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 29, 29, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 29, 29, 64)   192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 29, 29, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 29, 29, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 29, 29, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 29, 29, 64)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 29, 29, 288)  0           activation_19[0][0]              \n",
      "                                                                 activation_21[0][0]              \n",
      "                                                                 activation_24[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 29, 29, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 29, 29, 64)   192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 29, 29, 64)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 29, 29, 96)   55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 29, 29, 96)   288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 29, 29, 96)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 14, 14, 384)  995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 14, 14, 96)   82944       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 14, 14, 384)  1152        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 14, 14, 96)   288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 14, 14, 384)  0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 14, 14, 96)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 14, 14, 288)  0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 14, 14, 768)  0           activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 14, 14, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 14, 14, 128)  384         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 14, 14, 128)  0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 14, 14, 128)  114688      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 14, 14, 128)  384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 14, 14, 128)  0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 14, 14, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 14, 14, 128)  114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 14, 14, 128)  384         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 14, 14, 128)  384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 14, 14, 128)  0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 14, 14, 128)  0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 14, 14, 128)  114688      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 14, 14, 128)  114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 14, 14, 128)  384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 14, 14, 128)  384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 14, 14, 128)  0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 14, 14, 128)  0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 14, 14, 768)  0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 14, 14, 192)  147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 14, 14, 192)  172032      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 14, 14, 192)  172032      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 14, 14, 192)  576         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 14, 14, 192)  576         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 14, 14, 192)  576         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 14, 14, 192)  576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 14, 14, 192)  0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 14, 14, 192)  0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 14, 14, 192)  0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 14, 14, 192)  0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 14, 14, 768)  0           activation_30[0][0]              \n",
      "                                                                 activation_33[0][0]              \n",
      "                                                                 activation_38[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 14, 14, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 14, 14, 160)  480         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 14, 14, 160)  0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 14, 14, 160)  179200      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 14, 14, 160)  480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 14, 14, 160)  0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 14, 14, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 14, 14, 160)  179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 14, 14, 160)  480         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 14, 14, 160)  480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 14, 14, 160)  0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 14, 14, 160)  0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 14, 14, 160)  179200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 14, 14, 160)  179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 14, 14, 160)  480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 14, 14, 160)  480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 14, 14, 160)  0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 14, 14, 160)  0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 14, 14, 768)  0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 14, 14, 192)  147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 14, 14, 192)  215040      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 14, 14, 192)  215040      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 14, 14, 192)  576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 14, 14, 192)  576         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 14, 14, 192)  576         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 14, 14, 192)  576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 14, 14, 192)  0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 14, 14, 192)  0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 14, 14, 192)  0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 14, 14, 192)  0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 14, 14, 768)  0           activation_40[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "                                                                 activation_48[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 14, 14, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 14, 14, 160)  480         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 14, 14, 160)  0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 14, 14, 160)  179200      activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 14, 14, 160)  480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 14, 14, 160)  0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 14, 14, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 14, 14, 160)  179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 14, 14, 160)  480         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 14, 14, 160)  480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 14, 14, 160)  0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 14, 14, 160)  0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 14, 14, 160)  179200      activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 14, 14, 160)  179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 14, 14, 160)  480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 14, 14, 160)  480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 14, 14, 160)  0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 14, 14, 160)  0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 14, 14, 768)  0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 14, 14, 192)  147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 14, 14, 192)  215040      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 14, 14, 192)  215040      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 14, 14, 192)  576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 14, 14, 192)  576         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 14, 14, 192)  576         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 14, 14, 192)  576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 14, 14, 192)  0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 14, 14, 192)  0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 14, 14, 192)  0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 14, 14, 192)  0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 14, 14, 768)  0           activation_50[0][0]              \n",
      "                                                                 activation_53[0][0]              \n",
      "                                                                 activation_58[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 14, 14, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 14, 14, 192)  576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 14, 14, 192)  0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 14, 14, 192)  258048      activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 14, 14, 192)  576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 14, 14, 192)  0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 14, 14, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 14, 14, 192)  258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 14, 14, 192)  576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 14, 14, 192)  576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 14, 14, 192)  0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 14, 14, 192)  0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 14, 14, 192)  258048      activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 14, 14, 192)  258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 14, 14, 192)  576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 14, 14, 192)  576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 14, 14, 192)  0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 14, 14, 192)  0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 14, 14, 768)  0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 14, 14, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 14, 14, 192)  258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 14, 14, 192)  258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 14, 14, 192)  147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 14, 14, 192)  576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 14, 14, 192)  576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 14, 14, 192)  576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 14, 14, 192)  576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 14, 14, 192)  0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 14, 14, 192)  0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 14, 14, 192)  0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 14, 14, 192)  0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 14, 14, 768)  0           activation_60[0][0]              \n",
      "                                                                 activation_63[0][0]              \n",
      "                                                                 activation_68[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 14, 14, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 14, 14, 192)  576         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 14, 14, 192)  0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 14, 14, 192)  258048      activation_72[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 14, 14, 192)  576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 14, 14, 192)  0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 14, 14, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 14, 14, 192)  258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 14, 14, 192)  576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 14, 14, 192)  576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 14, 14, 192)  0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 14, 14, 192)  0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 6, 6, 320)    552960      activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 6, 6, 192)    331776      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 6, 6, 320)    960         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 6, 6, 192)    576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 6, 6, 320)    0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 6, 6, 192)    0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 6, 6, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 6, 6, 1280)   0           activation_71[0][0]              \n",
      "                                                                 activation_75[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 6, 6, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 6, 6, 448)    1344        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 6, 6, 448)    0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 6, 6, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 6, 6, 384)    1548288     activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 6, 6, 384)    1152        conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 6, 6, 384)    1152        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 6, 6, 384)    0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 6, 6, 384)    0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 6, 6, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 6, 6, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 6, 6, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 6, 6, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, 6, 6, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 6, 6, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 6, 6, 384)    1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 6, 6, 384)    1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 6, 6, 384)    1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 6, 6, 384)    1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 6, 6, 192)    245760      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 6, 6, 320)    960         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 6, 6, 384)    0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 6, 6, 384)    0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 6, 6, 384)    0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 6, 6, 384)    0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 6, 6, 192)    576         conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 6, 6, 320)    0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 6, 6, 768)    0           activation_78[0][0]              \n",
      "                                                                 activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 6, 6, 768)    0           activation_82[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 6, 6, 192)    0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed9 (Concatenate)            (None, 6, 6, 2048)   0           activation_76[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate[0][0]                \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 6, 6, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 6, 6, 448)    1344        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 6, 6, 448)    0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 6, 6, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 6, 6, 384)    1548288     activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 6, 6, 384)    1152        conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 6, 6, 384)    1152        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 6, 6, 384)    0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 6, 6, 384)    0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 6, 6, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 6, 6, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 6, 6, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 6, 6, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, 6, 6, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 6, 6, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 6, 6, 384)    1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 6, 6, 384)    1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 6, 6, 384)    1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 6, 6, 384)    1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 6, 6, 192)    393216      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 6, 6, 320)    960         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 6, 6, 384)    0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 6, 6, 384)    0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 6, 6, 384)    0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 6, 6, 384)    0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 6, 6, 192)    576         conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 6, 6, 320)    0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 6, 6, 768)    0           activation_87[0][0]              \n",
      "                                                                 activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 6, 6, 768)    0           activation_91[0][0]              \n",
      "                                                                 activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 6, 6, 192)    0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 6, 6, 2048)   0           activation_85[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_9 (AveragePoo (None, 1, 1, 2048)   0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 2048)         0           average_pooling2d_9[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 64)           131136      flatten[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 64)           0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 3)            195         dropout[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 21,934,115\n",
      "Trainable params: 21,899,683\n",
      "Non-trainable params: 34,432\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": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  0.3287671208381653 , Test:  0.31578946113586426\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": 40,
   "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": 41,
   "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'], 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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 42,
     "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": 43,
   "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       0.00      0.00      0.00         7\n",
      "   ./newtrain/normal       0.42      0.62      0.50         8\n",
      "./newtrain/pneumonia       0.14      0.25      0.18         4\n",
      "\n",
      "            accuracy                           0.32        19\n",
      "           macro avg       0.19      0.29      0.23        19\n",
      "        weighted avg       0.21      0.32      0.25        19\n",
      "\n",
      "total:  19\n",
      "confusion matrix: \n",
      " [[0 4 3]\n",
      " [0 5 3]\n",
      " [0 3 1]]\n",
      "acc: 0.3158\n",
      "sensitivity: 0.0000\n",
      "specificity: 1.0000\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": 44,
   "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": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Test Dataset (17,958 CXR images) Confusion Matrix')]"
      ]
     },
     "execution_count": 45,
     "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": 46,
   "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": 47,
   "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": 48,
   "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": 49,
   "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": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ellipsis"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "...\n",
    "imagePathValidation = \"./validation_2/\"\n",
    "#imagePathValidation = \"./validation/\"\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./validation_2/\n"
     ]
    }
   ],
   "source": [
    "print(imagePathValidation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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": 53,
   "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": 54,
   "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": 54,
     "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": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(df_v.value_counts()).to_csv('validation_image_size.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": 57,
   "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": 58,
   "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": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.07333405 0.776729   0.14993685]\n",
      " [0.14855182 0.35192582 0.49952236]\n",
      " [0.187334   0.43589884 0.37676722]\n",
      " [0.15709159 0.67892843 0.16397999]\n",
      " [0.5148722  0.10509276 0.3800351 ]\n",
      " [0.08795463 0.26990202 0.6421433 ]\n",
      " [0.02220069 0.91928923 0.05850998]\n",
      " [0.1305523  0.5302476  0.33920002]\n",
      " [0.21206695 0.5893646  0.19856852]\n",
      " [0.18488583 0.5371968  0.27791736]\n",
      " [0.20288418 0.31929752 0.47781825]\n",
      " [0.07863659 0.70703727 0.2143262 ]\n",
      " [0.12940031 0.4257759  0.44482383]\n",
      " [0.17599623 0.6129221  0.2110817 ]\n",
      " [0.13392882 0.34640744 0.5196637 ]\n",
      " [0.11700464 0.5288217  0.35417366]\n",
      " [0.18652244 0.50924677 0.30423075]\n",
      " [0.19219892 0.38245916 0.42534187]\n",
      " [0.17088433 0.4728053  0.35631037]\n",
      " [0.19417809 0.45387527 0.3519466 ]\n",
      " [0.14384025 0.48497686 0.3711828 ]\n",
      " [0.19235238 0.36585867 0.441789  ]\n",
      " [0.20093608 0.38499948 0.41406438]\n",
      " [0.17170414 0.58015716 0.24813871]\n",
      " [0.21311216 0.3674448  0.41944307]\n",
      " [0.11973225 0.72013503 0.16013277]\n",
      " [0.37370738 0.41838288 0.20790972]\n",
      " [0.11767618 0.62192935 0.26039442]\n",
      " [0.2018033  0.41587782 0.38231894]\n",
      " [0.11795944 0.4689584  0.41308215]\n",
      " [0.14643276 0.76604575 0.08752151]\n",
      " [0.14291658 0.6854073  0.17167611]\n",
      " [0.11720991 0.32342452 0.55936563]]\n",
      "[1 2 1 1 0 2 1 1 1 1 2 1 2 1 2 1 1 2 1 1 1 2 2 1 2 1 1 1 1 1 1 1 2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ellipsis"
      ]
     },
     "execution_count": 59,
     "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": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating the trained model ...\n",
      "                          precision    recall  f1-score   support\n",
      "\n",
      "    ./validation_2/covid       1.00      0.09      0.17        11\n",
      "   ./validation_2/normal       0.32      0.64      0.42        11\n",
      "./validation_2/pneumonia       0.30      0.27      0.29        11\n",
      "\n",
      "                accuracy                           0.33        33\n",
      "               macro avg       0.54      0.33      0.29        33\n",
      "            weighted avg       0.54      0.33      0.29        33\n",
      "\n",
      "[[1 7 3]\n",
      " [0 7 4]\n",
      " [0 8 3]]\n",
      "acc: 0.3333\n",
      "sensitivity: 0.0909\n",
      "specificity: 1.0000\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": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Validation Dataset (3,432 CXR images) Confusion Matrix')]"
      ]
     },
     "execution_count": 61,
     "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": 62,
   "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": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ellipsis"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "...\n",
    "imagePathTest = \"./test_set_2/\"\n",
    "..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./test_set_2/\n"
     ]
    }
   ],
   "source": [
    "print(imagePathTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 66,
   "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": 67,
   "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": 67,
     "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": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(df_test.value_counts()).to_csv('test_image_size.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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": 70,
   "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": 71,
   "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": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.14316902 0.44541714 0.4114138 ]\n",
      " [0.18559557 0.6597773  0.15462708]\n",
      " [0.23942584 0.27276927 0.48780495]\n",
      " [0.09765343 0.68255776 0.2197888 ]\n",
      " [0.14615014 0.5006881  0.35316175]\n",
      " [0.07101811 0.815976   0.11300588]\n",
      " [0.1065264  0.48400912 0.40946448]\n",
      " [0.08640505 0.7482078  0.16538715]\n",
      " [0.21369219 0.49500278 0.29130495]\n",
      " [0.1673301  0.6064472  0.22622268]\n",
      " [0.2681508  0.2957839  0.43606535]\n",
      " [0.21537493 0.4050916  0.37953347]\n",
      " [0.20053284 0.3895245  0.4099427 ]\n",
      " [0.29842862 0.27959228 0.4219791 ]\n",
      " [0.1957568  0.43281668 0.37142655]\n",
      " [0.07274283 0.6792429  0.24801418]\n",
      " [0.12886645 0.66360193 0.20753165]\n",
      " [0.19556317 0.36703557 0.43740124]\n",
      " [0.20098354 0.40783224 0.39118427]\n",
      " [0.2480462  0.47085914 0.28109464]\n",
      " [0.09249993 0.4949829  0.4125172 ]\n",
      " [0.22297926 0.511849   0.2651718 ]\n",
      " [0.03646095 0.7281204  0.23541865]\n",
      " [0.1319152  0.66177636 0.20630848]\n",
      " [0.24990582 0.28984421 0.46024996]\n",
      " [0.03490426 0.84190226 0.12319345]\n",
      " [0.25199807 0.35911742 0.38888445]\n",
      " [0.1478031  0.616062   0.23613495]\n",
      " [0.07247242 0.55571514 0.3718124 ]\n",
      " [0.19582778 0.38170648 0.42246583]\n",
      " [0.18805358 0.4176044  0.394342  ]\n",
      " [0.32529157 0.32324645 0.35146198]\n",
      " [0.06558286 0.50616413 0.42825302]\n",
      " [0.13888277 0.58089465 0.28022254]\n",
      " [0.26689827 0.32479513 0.40830663]\n",
      " [0.19044136 0.49935108 0.31020752]\n",
      " [0.1624322  0.49388173 0.34368604]\n",
      " [0.16842414 0.5435948  0.2879811 ]\n",
      " [0.12369471 0.55794734 0.3183579 ]\n",
      " [0.22972018 0.3353706  0.43490916]\n",
      " [0.24510927 0.4790187  0.27587208]\n",
      " [0.4641919  0.38247183 0.1533362 ]\n",
      " [0.23865633 0.3804502  0.3808935 ]\n",
      " [0.0757422  0.49868643 0.42557144]\n",
      " [0.31876135 0.3471249  0.3341138 ]\n",
      " [0.21083374 0.42891487 0.3602514 ]\n",
      " [0.25818312 0.51963514 0.22218172]\n",
      " [0.23552953 0.39024055 0.37422985]\n",
      " [0.18682367 0.3849678  0.4282085 ]\n",
      " [0.11478244 0.44406512 0.44115242]\n",
      " [0.20400035 0.3438604  0.45213923]\n",
      " [0.186501   0.5628483  0.25065073]]\n",
      "[1 1 2 1 1 1 1 1 1 1 2 1 2 2 1 1 1 2 1 1 1 1 1 1 2 1 2 1 1 2 1 2 1 1 2 1 1\n",
      " 1 1 2 1 0 2 1 1 1 1 1 2 1 2 1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Ellipsis"
      ]
     },
     "execution_count": 72,
     "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": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0\n",
      "0  1\n",
      "1  1\n",
      "2  2\n",
      "3  1\n",
      "4  1\n",
      "(52,)\n",
      "52\n",
      "<class 'numpy.ndarray'>\n",
      "\n",
      "1    37\n",
      "2    14\n",
      "0     1\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": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./test_set_2/0001.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0002.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0003.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0004.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0005.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0006.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0007.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0008.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0009.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0010.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0011.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0012.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0013.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0014.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0015.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0016.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0017.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0018.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0019.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0020.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0021.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0022.jpg  : 1 :  ./validation_2/normal\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  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0026.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0027.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0028.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0029.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0030.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0031.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0032.jpg  : 2 :  ./validation_2/pneumonia\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  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0036.jpg  : 1 :  ./validation_2/normal\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  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0040.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0041.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0042.jpg  : 0 :  ./validation_2/covid\n",
      "./test_set_2/0043.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0044.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0045.jpg  : 1 :  ./validation_2/normal\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  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0049.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0050.jpg  : 1 :  ./validation_2/normal\n",
      "./test_set_2/0051.jpg  : 2 :  ./validation_2/pneumonia\n",
      "./test_set_2/0052.jpg  : 1 :  ./validation_2/normal\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": 75,
   "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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./test_set_2/0002.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./test_set_2/0003.jpg</td>\n",
       "      <td>2</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>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./test_set_2/0008.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./test_set_2/0009.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./test_set_2/0010.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./test_set_2/0011.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./test_set_2/0012.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./test_set_2/0013.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./test_set_2/0014.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./test_set_2/0015.jpg</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>./test_set_2/0018.jpg</td>\n",
       "      <td>2</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>1</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>1</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>./test_set_2/0026.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>./test_set_2/0027.jpg</td>\n",
       "      <td>2</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>./test_set_2/0030.jpg</td>\n",
       "      <td>2</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>2</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>./test_set_2/0036.jpg</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>./test_set_2/0040.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>./test_set_2/0041.jpg</td>\n",
       "      <td>1</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>./test_set_2/0044.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>./test_set_2/0045.jpg</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>./test_set_2/0049.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>./test_set_2/0050.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>./test_set_2/0051.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>./test_set_2/0052.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     case  class\n",
       "0   ./test_set_2/0001.jpg      1\n",
       "1   ./test_set_2/0002.jpg      1\n",
       "2   ./test_set_2/0003.jpg      2\n",
       "3   ./test_set_2/0004.jpg      1\n",
       "4   ./test_set_2/0005.jpg      1\n",
       "5   ./test_set_2/0006.jpg      1\n",
       "6   ./test_set_2/0007.jpg      1\n",
       "7   ./test_set_2/0008.jpg      1\n",
       "8   ./test_set_2/0009.jpg      1\n",
       "9   ./test_set_2/0010.jpg      1\n",
       "10  ./test_set_2/0011.jpg      2\n",
       "11  ./test_set_2/0012.jpg      1\n",
       "12  ./test_set_2/0013.jpg      2\n",
       "13  ./test_set_2/0014.jpg      2\n",
       "14  ./test_set_2/0015.jpg      1\n",
       "15  ./test_set_2/0016.jpg      1\n",
       "16  ./test_set_2/0017.jpg      1\n",
       "17  ./test_set_2/0018.jpg      2\n",
       "18  ./test_set_2/0019.jpg      1\n",
       "19  ./test_set_2/0020.jpg      1\n",
       "20  ./test_set_2/0021.jpg      1\n",
       "21  ./test_set_2/0022.jpg      1\n",
       "22  ./test_set_2/0023.jpg      1\n",
       "23  ./test_set_2/0024.jpg      1\n",
       "24  ./test_set_2/0025.jpg      2\n",
       "25  ./test_set_2/0026.jpg      1\n",
       "26  ./test_set_2/0027.jpg      2\n",
       "27  ./test_set_2/0028.jpg      1\n",
       "28  ./test_set_2/0029.jpg      1\n",
       "29  ./test_set_2/0030.jpg      2\n",
       "30  ./test_set_2/0031.jpg      1\n",
       "31  ./test_set_2/0032.jpg      2\n",
       "32  ./test_set_2/0033.jpg      1\n",
       "33  ./test_set_2/0034.jpg      1\n",
       "34  ./test_set_2/0035.jpg      2\n",
       "35  ./test_set_2/0036.jpg      1\n",
       "36  ./test_set_2/0037.jpg      1\n",
       "37  ./test_set_2/0038.jpg      1\n",
       "38  ./test_set_2/0039.jpg      1\n",
       "39  ./test_set_2/0040.jpg      2\n",
       "40  ./test_set_2/0041.jpg      1\n",
       "41  ./test_set_2/0042.jpg      0\n",
       "42  ./test_set_2/0043.jpg      2\n",
       "43  ./test_set_2/0044.jpg      1\n",
       "44  ./test_set_2/0045.jpg      1\n",
       "45  ./test_set_2/0046.jpg      1\n",
       "46  ./test_set_2/0047.jpg      1\n",
       "47  ./test_set_2/0048.jpg      1\n",
       "48  ./test_set_2/0049.jpg      2\n",
       "49  ./test_set_2/0050.jpg      1\n",
       "50  ./test_set_2/0051.jpg      2\n",
       "51  ./test_set_2/0052.jpg      1"
      ]
     },
     "execution_count": 75,
     "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": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     case  class\n",
      "0   ./test_set_2/0001.jpg      1\n",
      "1   ./test_set_2/0002.jpg      1\n",
      "2   ./test_set_2/0003.jpg      2\n",
      "3   ./test_set_2/0004.jpg      1\n",
      "4   ./test_set_2/0005.jpg      1\n",
      "5   ./test_set_2/0006.jpg      1\n",
      "6   ./test_set_2/0007.jpg      1\n",
      "7   ./test_set_2/0008.jpg      1\n",
      "8   ./test_set_2/0009.jpg      1\n",
      "9   ./test_set_2/0010.jpg      1\n",
      "10  ./test_set_2/0011.jpg      2\n",
      "11  ./test_set_2/0012.jpg      1\n",
      "12  ./test_set_2/0013.jpg      2\n",
      "13  ./test_set_2/0014.jpg      2\n",
      "14  ./test_set_2/0015.jpg      1\n",
      "15  ./test_set_2/0016.jpg      1\n",
      "16  ./test_set_2/0017.jpg      1\n",
      "17  ./test_set_2/0018.jpg      2\n",
      "18  ./test_set_2/0019.jpg      1\n",
      "19  ./test_set_2/0020.jpg      1\n",
      "20  ./test_set_2/0021.jpg      1\n",
      "21  ./test_set_2/0022.jpg      1\n",
      "22  ./test_set_2/0023.jpg      1\n",
      "23  ./test_set_2/0024.jpg      1\n",
      "24  ./test_set_2/0025.jpg      2\n",
      "25  ./test_set_2/0026.jpg      1\n",
      "26  ./test_set_2/0027.jpg      2\n",
      "27  ./test_set_2/0028.jpg      1\n",
      "28  ./test_set_2/0029.jpg      1\n",
      "29  ./test_set_2/0030.jpg      2\n",
      "30  ./test_set_2/0031.jpg      1\n",
      "31  ./test_set_2/0032.jpg      2\n",
      "32  ./test_set_2/0033.jpg      1\n",
      "33  ./test_set_2/0034.jpg      1\n",
      "34  ./test_set_2/0035.jpg      2\n",
      "35  ./test_set_2/0036.jpg      1\n",
      "36  ./test_set_2/0037.jpg      1\n",
      "37  ./test_set_2/0038.jpg      1\n",
      "38  ./test_set_2/0039.jpg      1\n",
      "39  ./test_set_2/0040.jpg      2\n",
      "40  ./test_set_2/0041.jpg      1\n",
      "41  ./test_set_2/0042.jpg      0\n",
      "42  ./test_set_2/0043.jpg      2\n",
      "43  ./test_set_2/0044.jpg      1\n",
      "44  ./test_set_2/0045.jpg      1\n",
      "45  ./test_set_2/0046.jpg      1\n",
      "46  ./test_set_2/0047.jpg      1\n",
      "47  ./test_set_2/0048.jpg      1\n",
      "48  ./test_set_2/0049.jpg      2\n",
      "49  ./test_set_2/0050.jpg      1\n",
      "50  ./test_set_2/0051.jpg      2\n",
      "51  ./test_set_2/0052.jpg      1\n"
     ]
    }
   ],
   "source": [
    "file2=fn+dt+\".csv\"\n",
    "df = pd.read_csv(file2)\n",
    "print(df)"
   ]
  },
  {
   "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>./test_set_2/0001.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./test_set_2/0002.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./test_set_2/0003.jpg</td>\n",
       "      <td>2</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>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./test_set_2/0008.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./test_set_2/0009.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./test_set_2/0010.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./test_set_2/0011.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./test_set_2/0012.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./test_set_2/0013.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./test_set_2/0014.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./test_set_2/0015.jpg</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>./test_set_2/0018.jpg</td>\n",
       "      <td>2</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>1</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>1</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>./test_set_2/0026.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>./test_set_2/0027.jpg</td>\n",
       "      <td>2</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>./test_set_2/0030.jpg</td>\n",
       "      <td>2</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>2</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>./test_set_2/0036.jpg</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>./test_set_2/0040.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>./test_set_2/0041.jpg</td>\n",
       "      <td>1</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>./test_set_2/0044.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>./test_set_2/0045.jpg</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>./test_set_2/0049.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>./test_set_2/0050.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>./test_set_2/0051.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>./test_set_2/0052.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     case  class\n",
       "0   ./test_set_2/0001.jpg      1\n",
       "1   ./test_set_2/0002.jpg      1\n",
       "2   ./test_set_2/0003.jpg      2\n",
       "3   ./test_set_2/0004.jpg      1\n",
       "4   ./test_set_2/0005.jpg      1\n",
       "5   ./test_set_2/0006.jpg      1\n",
       "6   ./test_set_2/0007.jpg      1\n",
       "7   ./test_set_2/0008.jpg      1\n",
       "8   ./test_set_2/0009.jpg      1\n",
       "9   ./test_set_2/0010.jpg      1\n",
       "10  ./test_set_2/0011.jpg      2\n",
       "11  ./test_set_2/0012.jpg      1\n",
       "12  ./test_set_2/0013.jpg      2\n",
       "13  ./test_set_2/0014.jpg      2\n",
       "14  ./test_set_2/0015.jpg      1\n",
       "15  ./test_set_2/0016.jpg      1\n",
       "16  ./test_set_2/0017.jpg      1\n",
       "17  ./test_set_2/0018.jpg      2\n",
       "18  ./test_set_2/0019.jpg      1\n",
       "19  ./test_set_2/0020.jpg      1\n",
       "20  ./test_set_2/0021.jpg      1\n",
       "21  ./test_set_2/0022.jpg      1\n",
       "22  ./test_set_2/0023.jpg      1\n",
       "23  ./test_set_2/0024.jpg      1\n",
       "24  ./test_set_2/0025.jpg      2\n",
       "25  ./test_set_2/0026.jpg      1\n",
       "26  ./test_set_2/0027.jpg      2\n",
       "27  ./test_set_2/0028.jpg      1\n",
       "28  ./test_set_2/0029.jpg      1\n",
       "29  ./test_set_2/0030.jpg      2\n",
       "30  ./test_set_2/0031.jpg      1\n",
       "31  ./test_set_2/0032.jpg      2\n",
       "32  ./test_set_2/0033.jpg      1\n",
       "33  ./test_set_2/0034.jpg      1\n",
       "34  ./test_set_2/0035.jpg      2\n",
       "35  ./test_set_2/0036.jpg      1\n",
       "36  ./test_set_2/0037.jpg      1\n",
       "37  ./test_set_2/0038.jpg      1\n",
       "38  ./test_set_2/0039.jpg      1\n",
       "39  ./test_set_2/0040.jpg      2\n",
       "40  ./test_set_2/0041.jpg      1\n",
       "41  ./test_set_2/0042.jpg      0\n",
       "42  ./test_set_2/0043.jpg      2\n",
       "43  ./test_set_2/0044.jpg      1\n",
       "44  ./test_set_2/0045.jpg      1\n",
       "45  ./test_set_2/0046.jpg      1\n",
       "46  ./test_set_2/0047.jpg      1\n",
       "47  ./test_set_2/0048.jpg      1\n",
       "48  ./test_set_2/0049.jpg      2\n",
       "49  ./test_set_2/0050.jpg      1\n",
       "50  ./test_set_2/0051.jpg      2\n",
       "51  ./test_set_2/0052.jpg      1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./test_set_2/0001.jpg'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['case'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0001.jpg'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.path.basename(df['case'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0003.jpg</td>\n",
       "      <td>2</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>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0008.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0009.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0010.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0011.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0012.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0013.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0014.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0015.jpg</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0018.jpg</td>\n",
       "      <td>2</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>1</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>1</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0026.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0027.jpg</td>\n",
       "      <td>2</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0030.jpg</td>\n",
       "      <td>2</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>2</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0036.jpg</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0040.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0041.jpg</td>\n",
       "      <td>1</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0044.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0045.jpg</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0049.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0050.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0051.jpg</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>0052.jpg</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        case  class\n",
       "0   0001.jpg      1\n",
       "1   0002.jpg      1\n",
       "2   0003.jpg      2\n",
       "3   0004.jpg      1\n",
       "4   0005.jpg      1\n",
       "5   0006.jpg      1\n",
       "6   0007.jpg      1\n",
       "7   0008.jpg      1\n",
       "8   0009.jpg      1\n",
       "9   0010.jpg      1\n",
       "10  0011.jpg      2\n",
       "11  0012.jpg      1\n",
       "12  0013.jpg      2\n",
       "13  0014.jpg      2\n",
       "14  0015.jpg      1\n",
       "15  0016.jpg      1\n",
       "16  0017.jpg      1\n",
       "17  0018.jpg      2\n",
       "18  0019.jpg      1\n",
       "19  0020.jpg      1\n",
       "20  0021.jpg      1\n",
       "21  0022.jpg      1\n",
       "22  0023.jpg      1\n",
       "23  0024.jpg      1\n",
       "24  0025.jpg      2\n",
       "25  0026.jpg      1\n",
       "26  0027.jpg      2\n",
       "27  0028.jpg      1\n",
       "28  0029.jpg      1\n",
       "29  0030.jpg      2\n",
       "30  0031.jpg      1\n",
       "31  0032.jpg      2\n",
       "32  0033.jpg      1\n",
       "33  0034.jpg      1\n",
       "34  0035.jpg      2\n",
       "35  0036.jpg      1\n",
       "36  0037.jpg      1\n",
       "37  0038.jpg      1\n",
       "38  0039.jpg      1\n",
       "39  0040.jpg      2\n",
       "40  0041.jpg      1\n",
       "41  0042.jpg      0\n",
       "42  0043.jpg      2\n",
       "43  0044.jpg      1\n",
       "44  0045.jpg      1\n",
       "45  0046.jpg      1\n",
       "46  0047.jpg      1\n",
       "47  0048.jpg      1\n",
       "48  0049.jpg      2\n",
       "49  0050.jpg      1\n",
       "50  0051.jpg      2\n",
       "51  0052.jpg      1"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['case']=df['case'].map(lambda a:os.path.basename(a))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_file=fn+dt+\".csv\"\n",
    "df.to_csv(final_file,index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        case  class\n",
      "0   0001.jpg      1\n",
      "1   0002.jpg      1\n",
      "2   0003.jpg      2\n",
      "3   0004.jpg      1\n",
      "4   0005.jpg      1\n",
      "5   0006.jpg      1\n",
      "6   0007.jpg      1\n",
      "7   0008.jpg      1\n",
      "8   0009.jpg      1\n",
      "9   0010.jpg      1\n",
      "10  0011.jpg      2\n",
      "11  0012.jpg      1\n",
      "12  0013.jpg      2\n",
      "13  0014.jpg      2\n",
      "14  0015.jpg      1\n",
      "15  0016.jpg      1\n",
      "16  0017.jpg      1\n",
      "17  0018.jpg      2\n",
      "18  0019.jpg      1\n",
      "19  0020.jpg      1\n",
      "20  0021.jpg      1\n",
      "21  0022.jpg      1\n",
      "22  0023.jpg      1\n",
      "23  0024.jpg      1\n",
      "24  0025.jpg      2\n",
      "25  0026.jpg      1\n",
      "26  0027.jpg      2\n",
      "27  0028.jpg      1\n",
      "28  0029.jpg      1\n",
      "29  0030.jpg      2\n",
      "30  0031.jpg      1\n",
      "31  0032.jpg      2\n",
      "32  0033.jpg      1\n",
      "33  0034.jpg      1\n",
      "34  0035.jpg      2\n",
      "35  0036.jpg      1\n",
      "36  0037.jpg      1\n",
      "37  0038.jpg      1\n",
      "38  0039.jpg      1\n",
      "39  0040.jpg      2\n",
      "40  0041.jpg      1\n",
      "41  0042.jpg      0\n",
      "42  0043.jpg      2\n",
      "43  0044.jpg      1\n",
      "44  0045.jpg      1\n",
      "45  0046.jpg      1\n",
      "46  0047.jpg      1\n",
      "47  0048.jpg      1\n",
      "48  0049.jpg      2\n",
      "49  0050.jpg      1\n",
      "50  0051.jpg      2\n",
      "51  0052.jpg      1\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": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "\n",
      "       case\n",
      "class      \n",
      "0         1\n",
      "1        37\n",
      "2        14\n",
      "\n",
      "   class\n",
      "1     37\n",
      "2     14\n",
      "0      1\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": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1   ./validation_2/normal\n",
      "2   ./validation_2/pneumonia\n",
      "0   ./validation_2/covid\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": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1   37   ./validation_2/normal\n",
      "2   14   ./validation_2/pneumonia\n",
      "0   1   ./validation_2/covid\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": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(class  class\n",
       " 0      0         1\n",
       " 1      1        37\n",
       " 2      2        14\n",
       " Name: class, dtype: int64,\n",
       " array([1, 2, 0], dtype=int64),\n",
       " array(['./validation_2/normal', './validation_2/pneumonia',\n",
       "        './validation_2/covid'], dtype='<U24'))"
      ]
     },
     "execution_count": 86,
     "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": []
  }
 ],
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