--- a
+++ b/lymphoblasts.ipynb
@@ -0,0 +1,1993 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
+   },
+   "outputs": [],
+   "source": [
+    "# Import essential libraries\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "import itertools\n",
+    "import keras\n",
+    "import seaborn as sns\n",
+    "from glob import glob\n",
+    "from PIL import Image\n",
+    "np.random.seed(123)\n",
+    "from sklearn.preprocessing import label_binarize\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from keras.utils.np_utils import to_categorical\n",
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D\n",
+    "from keras.callbacks import ReduceLROnPlateau\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from keras import backend as K\n",
+    "from keras.layers.normalization import BatchNormalization\n",
+    "\n",
+    "# For Resnet\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from keras.models import Sequential\n",
+    "from keras import optimizers\n",
+    "from keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPooling2D\n",
+    "from keras.applications import ResNet50\n",
+    "from keras import regularizers\n",
+    "\n",
+    "# For snapshot Ensemble\n",
+    "from keras.callbacks import Callback\n",
+    "from keras import backend\n",
+    "from keras.models import load_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Im119_1': '../input/not healthy/Im119_1.tif',\n",
+       " 'Im124_1': '../input/not healthy/Im124_1.tif',\n",
+       " 'Im086_1': '../input/not healthy/Im086_1.tif',\n",
+       " 'Im097_1': '../input/not healthy/Im097_1.tif',\n",
+       " 'Im026_1': '../input/not healthy/Im026_1.tif',\n",
+       " 'Im115_1': '../input/not healthy/Im115_1.tif',\n",
+       " 'Im085_1': '../input/not healthy/Im085_1.tif',\n",
+       " 'Im107_1': '../input/not healthy/Im107_1.tif',\n",
+       " 'Im032_1': '../input/not healthy/Im032_1.tif',\n",
+       " 'Im025_1': '../input/not healthy/Im025_1.tif',\n",
+       " 'Im084_1': '../input/not healthy/Im084_1.tif',\n",
+       " 'Im029_1': '../input/not healthy/Im029_1.tif',\n",
+       " 'Im016_1': '../input/not healthy/Im016_1.tif',\n",
+       " 'Im126_1': '../input/not healthy/Im126_1.tif',\n",
+       " 'Im031_1': '../input/not healthy/Im031_1.tif',\n",
+       " 'Im057_1': '../input/not healthy/Im057_1.tif',\n",
+       " 'Im079_1': '../input/not healthy/Im079_1.tif',\n",
+       " 'Im096_1': '../input/not healthy/Im096_1.tif',\n",
+       " 'Im061_1': '../input/not healthy/Im061_1.tif',\n",
+       " 'Im076_1': '../input/not healthy/Im076_1.tif',\n",
+       " 'Im112_1': '../input/not healthy/Im112_1.tif',\n",
+       " 'Im039_1': '../input/not healthy/Im039_1.tif',\n",
+       " 'Im064_1': '../input/not healthy/Im064_1.tif',\n",
+       " 'Im098_1': '../input/not healthy/Im098_1.tif',\n",
+       " 'Im005_1': '../input/not healthy/Im005_1.tif',\n",
+       " 'Im022_1': '../input/not healthy/Im022_1.tif',\n",
+       " 'Im037_1': '../input/not healthy/Im037_1.tif',\n",
+       " 'Im027_1': '../input/not healthy/Im027_1.tif',\n",
+       " 'Im093_1': '../input/not healthy/Im093_1.tif',\n",
+       " 'Im070_1': '../input/not healthy/Im070_1.tif',\n",
+       " 'Im014_1': '../input/not healthy/Im014_1.tif',\n",
+       " 'Im036_1': '../input/not healthy/Im036_1.tif',\n",
+       " 'Im053_1': '../input/not healthy/Im053_1.tif',\n",
+       " 'Im028_1': '../input/not healthy/Im028_1.tif',\n",
+       " 'Im099_1': '../input/not healthy/Im099_1.tif',\n",
+       " 'Im080_1': '../input/not healthy/Im080_1.tif',\n",
+       " 'Im011_1': '../input/not healthy/Im011_1.tif',\n",
+       " 'Im035_1': '../input/not healthy/Im035_1.tif',\n",
+       " 'Im110_1': '../input/not healthy/Im110_1.tif',\n",
+       " 'Im051_1': '../input/not healthy/Im051_1.tif',\n",
+       " 'Im059_1': '../input/not healthy/Im059_1.tif',\n",
+       " 'Im063_1': '../input/not healthy/Im063_1.tif',\n",
+       " 'Im114_1': '../input/not healthy/Im114_1.tif',\n",
+       " 'Im052_1': '../input/not healthy/Im052_1.tif',\n",
+       " 'Im066_1': '../input/not healthy/Im066_1.tif',\n",
+       " 'Im121_1': '../input/not healthy/Im121_1.tif',\n",
+       " 'Im118_1': '../input/not healthy/Im118_1.tif',\n",
+       " 'Im001_1': '../input/not healthy/Im001_1.tif',\n",
+       " 'Im050_1': '../input/not healthy/Im050_1.tif',\n",
+       " 'Im103_1': '../input/not healthy/Im103_1.tif',\n",
+       " 'Im034_1': '../input/not healthy/Im034_1.tif',\n",
+       " 'Im091_1': '../input/not healthy/Im091_1.tif',\n",
+       " 'Im087_1': '../input/not healthy/Im087_1.tif',\n",
+       " 'Im019_1': '../input/not healthy/Im019_1.tif',\n",
+       " 'Im054_1': '../input/not healthy/Im054_1.tif',\n",
+       " 'Im013_1': '../input/not healthy/Im013_1.tif',\n",
+       " 'Im055_1': '../input/not healthy/Im055_1.tif',\n",
+       " 'Im083_1': '../input/not healthy/Im083_1.tif',\n",
+       " 'Im043_1': '../input/not healthy/Im043_1.tif',\n",
+       " 'Im023_1': '../input/not healthy/Im023_1.tif',\n",
+       " 'Im094_1': '../input/not healthy/Im094_1.tif',\n",
+       " 'Im062_1': '../input/not healthy/Im062_1.tif',\n",
+       " 'Im082_1': '../input/not healthy/Im082_1.tif',\n",
+       " 'Im040_1': '../input/not healthy/Im040_1.tif',\n",
+       " 'Im009_1': '../input/not healthy/Im009_1.tif',\n",
+       " 'Im116_1': '../input/not healthy/Im116_1.tif',\n",
+       " 'Im120_1': '../input/not healthy/Im120_1.tif',\n",
+       " 'Im008_1': '../input/not healthy/Im008_1.tif',\n",
+       " 'Im111_1': '../input/not healthy/Im111_1.tif',\n",
+       " 'Im003_1': '../input/not healthy/Im003_1.tif',\n",
+       " 'Im069_1': '../input/not healthy/Im069_1.tif',\n",
+       " 'Im048_1': '../input/not healthy/Im048_1.tif',\n",
+       " 'Im038_1': '../input/not healthy/Im038_1.tif',\n",
+       " 'Im075_1': '../input/not healthy/Im075_1.tif',\n",
+       " 'Im102_1': '../input/not healthy/Im102_1.tif',\n",
+       " 'Im041_1': '../input/not healthy/Im041_1.tif',\n",
+       " 'Im033_1': '../input/not healthy/Im033_1.tif',\n",
+       " 'Im074_1': '../input/not healthy/Im074_1.tif',\n",
+       " 'Im128_1': '../input/not healthy/Im128_1.tif',\n",
+       " 'Im106_1': '../input/not healthy/Im106_1.tif',\n",
+       " 'Im056_1': '../input/not healthy/Im056_1.tif',\n",
+       " 'Im127_1': '../input/not healthy/Im127_1.tif',\n",
+       " 'Im113_1': '../input/not healthy/Im113_1.tif',\n",
+       " 'Im104_1': '../input/not healthy/Im104_1.tif',\n",
+       " 'Im060_1': '../input/not healthy/Im060_1.tif',\n",
+       " 'Im020_1': '../input/not healthy/Im020_1.tif',\n",
+       " 'Im100_1': '../input/not healthy/Im100_1.tif',\n",
+       " 'Im018_1': '../input/not healthy/Im018_1.tif',\n",
+       " 'Im081_1': '../input/not healthy/Im081_1.tif',\n",
+       " 'Im002_1': '../input/not healthy/Im002_1.tif',\n",
+       " 'Im073_1': '../input/not healthy/Im073_1.tif',\n",
+       " 'Im108_1': '../input/not healthy/Im108_1.tif',\n",
+       " 'Im117_1': '../input/not healthy/Im117_1.tif',\n",
+       " 'Im006_1': '../input/not healthy/Im006_1.tif',\n",
+       " 'Im089_1': '../input/not healthy/Im089_1.tif',\n",
+       " 'Im077_1': '../input/not healthy/Im077_1.tif',\n",
+       " 'Im090_1': '../input/not healthy/Im090_1.tif',\n",
+       " 'Im030_1': '../input/not healthy/Im030_1.tif',\n",
+       " 'Im072_1': '../input/not healthy/Im072_1.tif',\n",
+       " 'Im042_1': '../input/not healthy/Im042_1.tif',\n",
+       " 'Im123_1': '../input/not healthy/Im123_1.tif',\n",
+       " 'Im047_1': '../input/not healthy/Im047_1.tif',\n",
+       " 'Im058_1': '../input/not healthy/Im058_1.tif',\n",
+       " 'Im101_1': '../input/not healthy/Im101_1.tif',\n",
+       " 'Im092_1': '../input/not healthy/Im092_1.tif',\n",
+       " 'Im109_1': '../input/not healthy/Im109_1.tif',\n",
+       " 'Im071_1': '../input/not healthy/Im071_1.tif',\n",
+       " 'Im130_1': '../input/not healthy/Im130_1.tif',\n",
+       " 'Im049_1': '../input/not healthy/Im049_1.tif',\n",
+       " 'Im088_1': '../input/not healthy/Im088_1.tif',\n",
+       " 'Im024_1': '../input/not healthy/Im024_1.tif',\n",
+       " 'Im015_1': '../input/not healthy/Im015_1.tif',\n",
+       " 'Im010_1': '../input/not healthy/Im010_1.tif',\n",
+       " 'Im078_1': '../input/not healthy/Im078_1.tif',\n",
+       " 'Im125_1': '../input/not healthy/Im125_1.tif',\n",
+       " 'Im046_1': '../input/not healthy/Im046_1.tif',\n",
+       " 'Im007_1': '../input/not healthy/Im007_1.tif',\n",
+       " 'Im067_1': '../input/not healthy/Im067_1.tif',\n",
+       " 'Im004_1': '../input/not healthy/Im004_1.tif',\n",
+       " 'Im044_1': '../input/not healthy/Im044_1.tif',\n",
+       " 'Im068_1': '../input/not healthy/Im068_1.tif',\n",
+       " 'Im045_1': '../input/not healthy/Im045_1.tif',\n",
+       " 'Im017_1': '../input/not healthy/Im017_1.tif',\n",
+       " 'Im065_1': '../input/not healthy/Im065_1.tif',\n",
+       " 'Im012_1': '../input/not healthy/Im012_1.tif',\n",
+       " 'Im095_1': '../input/not healthy/Im095_1.tif',\n",
+       " 'Im129_1': '../input/not healthy/Im129_1.tif',\n",
+       " 'Im105_1': '../input/not healthy/Im105_1.tif',\n",
+       " 'Im122_1': '../input/not healthy/Im122_1.tif',\n",
+       " 'Im021_1': '../input/not healthy/Im021_1.tif',\n",
+       " 'Im223_0': '../input/healthy/Im223_0.tif',\n",
+       " 'Im168_0': '../input/healthy/Im168_0.tif',\n",
+       " 'Im144_0': '../input/healthy/Im144_0.tif',\n",
+       " 'Im166_0': '../input/healthy/Im166_0.tif',\n",
+       " 'Im188_0': '../input/healthy/Im188_0.tif',\n",
+       " 'Im208_0': '../input/healthy/Im208_0.tif',\n",
+       " 'Im151_0': '../input/healthy/Im151_0.tif',\n",
+       " 'Im215_0': '../input/healthy/Im215_0.tif',\n",
+       " 'Im213_0': '../input/healthy/Im213_0.tif',\n",
+       " 'Im177_0': '../input/healthy/Im177_0.tif',\n",
+       " 'Im243_0': '../input/healthy/Im243_0.tif',\n",
+       " 'Im179_0': '../input/healthy/Im179_0.tif',\n",
+       " 'Im173_0': '../input/healthy/Im173_0.tif',\n",
+       " 'Im150_0': '../input/healthy/Im150_0.tif',\n",
+       " 'Im219_0': '../input/healthy/Im219_0.tif',\n",
+       " 'Im160_0': '../input/healthy/Im160_0.tif',\n",
+       " 'Im190_0': '../input/healthy/Im190_0.tif',\n",
+       " 'Im244_0': '../input/healthy/Im244_0.tif',\n",
+       " 'Im214_0': '../input/healthy/Im214_0.tif',\n",
+       " 'Im178_0': '../input/healthy/Im178_0.tif',\n",
+       " 'Im196_0': '../input/healthy/Im196_0.tif',\n",
+       " 'Im182_0': '../input/healthy/Im182_0.tif',\n",
+       " 'Im228_0': '../input/healthy/Im228_0.tif',\n",
+       " 'Im164_0': '../input/healthy/Im164_0.tif',\n",
+       " 'Im162_0': '../input/healthy/Im162_0.tif',\n",
+       " 'Im257_0': '../input/healthy/Im257_0.tif',\n",
+       " 'Im140_0': '../input/healthy/Im140_0.tif',\n",
+       " 'Im149_0': '../input/healthy/Im149_0.tif',\n",
+       " 'Im202_0': '../input/healthy/Im202_0.tif',\n",
+       " 'Im191_0': '../input/healthy/Im191_0.tif',\n",
+       " 'Im248_0': '../input/healthy/Im248_0.tif',\n",
+       " 'Im161_0': '../input/healthy/Im161_0.tif',\n",
+       " 'Im258_0': '../input/healthy/Im258_0.tif',\n",
+       " 'Im259_0': '../input/healthy/Im259_0.tif',\n",
+       " 'Im245_0': '../input/healthy/Im245_0.tif',\n",
+       " 'Im197_0': '../input/healthy/Im197_0.tif',\n",
+       " 'Im239_0': '../input/healthy/Im239_0.tif',\n",
+       " 'Im231_0': '../input/healthy/Im231_0.tif',\n",
+       " 'Im209_0': '../input/healthy/Im209_0.tif',\n",
+       " 'Im131_0': '../input/healthy/Im131_0.tif',\n",
+       " 'Im232_0': '../input/healthy/Im232_0.tif',\n",
+       " 'Im132_0': '../input/healthy/Im132_0.tif',\n",
+       " 'Im207_0': '../input/healthy/Im207_0.tif',\n",
+       " 'Im255_0': '../input/healthy/Im255_0.tif',\n",
+       " 'Im170_0': '../input/healthy/Im170_0.tif',\n",
+       " 'Im227_0': '../input/healthy/Im227_0.tif',\n",
+       " 'Im163_0': '../input/healthy/Im163_0.tif',\n",
+       " 'Im181_0': '../input/healthy/Im181_0.tif',\n",
+       " 'Im138_0': '../input/healthy/Im138_0.tif',\n",
+       " 'Im234_0': '../input/healthy/Im234_0.tif',\n",
+       " 'Im139_0': '../input/healthy/Im139_0.tif',\n",
+       " 'Im238_0': '../input/healthy/Im238_0.tif',\n",
+       " 'Im185_0': '../input/healthy/Im185_0.tif',\n",
+       " 'Im200_0': '../input/healthy/Im200_0.tif',\n",
+       " 'Im175_0': '../input/healthy/Im175_0.tif',\n",
+       " 'Im167_0': '../input/healthy/Im167_0.tif',\n",
+       " 'Im155_0': '../input/healthy/Im155_0.tif',\n",
+       " 'Im165_0': '../input/healthy/Im165_0.tif',\n",
+       " 'Im137_0': '../input/healthy/Im137_0.tif',\n",
+       " 'Im189_0': '../input/healthy/Im189_0.tif',\n",
+       " 'Im204_0': '../input/healthy/Im204_0.tif',\n",
+       " 'Im174_0': '../input/healthy/Im174_0.tif',\n",
+       " 'Im158_0': '../input/healthy/Im158_0.tif',\n",
+       " 'Im218_0': '../input/healthy/Im218_0.tif',\n",
+       " 'Im187_0': '../input/healthy/Im187_0.tif',\n",
+       " 'Im183_0': '../input/healthy/Im183_0.tif',\n",
+       " 'Im251_0': '../input/healthy/Im251_0.tif',\n",
+       " 'Im152_0': '../input/healthy/Im152_0.tif',\n",
+       " 'Im211_0': '../input/healthy/Im211_0.tif',\n",
+       " 'Im133_0': '../input/healthy/Im133_0.tif',\n",
+       " 'Im212_0': '../input/healthy/Im212_0.tif',\n",
+       " 'Im169_0': '../input/healthy/Im169_0.tif',\n",
+       " 'Im252_0': '../input/healthy/Im252_0.tif',\n",
+       " 'Im192_0': '../input/healthy/Im192_0.tif',\n",
+       " 'Im233_0': '../input/healthy/Im233_0.tif',\n",
+       " 'Im156_0': '../input/healthy/Im156_0.tif',\n",
+       " 'Im201_0': '../input/healthy/Im201_0.tif',\n",
+       " 'Im237_0': '../input/healthy/Im237_0.tif',\n",
+       " 'Im146_0': '../input/healthy/Im146_0.tif',\n",
+       " 'Im236_0': '../input/healthy/Im236_0.tif',\n",
+       " 'Im135_0': '../input/healthy/Im135_0.tif',\n",
+       " 'Im153_0': '../input/healthy/Im153_0.tif',\n",
+       " 'Im180_0': '../input/healthy/Im180_0.tif',\n",
+       " 'Im194_0': '../input/healthy/Im194_0.tif',\n",
+       " 'Im216_0': '../input/healthy/Im216_0.tif',\n",
+       " 'Im206_0': '../input/healthy/Im206_0.tif',\n",
+       " 'Im249_0': '../input/healthy/Im249_0.tif',\n",
+       " 'Im141_0': '../input/healthy/Im141_0.tif',\n",
+       " 'Im221_0': '../input/healthy/Im221_0.tif',\n",
+       " 'Im159_0': '../input/healthy/Im159_0.tif',\n",
+       " 'Im157_0': '../input/healthy/Im157_0.tif',\n",
+       " 'Im148_0': '../input/healthy/Im148_0.tif',\n",
+       " 'Im256_0': '../input/healthy/Im256_0.tif',\n",
+       " 'Im229_0': '../input/healthy/Im229_0.tif',\n",
+       " 'Im136_0': '../input/healthy/Im136_0.tif',\n",
+       " 'Im195_0': '../input/healthy/Im195_0.tif',\n",
+       " 'Im222_0': '../input/healthy/Im222_0.tif',\n",
+       " 'Im134_0': '../input/healthy/Im134_0.tif',\n",
+       " 'Im143_0': '../input/healthy/Im143_0.tif',\n",
+       " 'Im224_0': '../input/healthy/Im224_0.tif',\n",
+       " 'Im226_0': '../input/healthy/Im226_0.tif',\n",
+       " 'Im240_0': '../input/healthy/Im240_0.tif',\n",
+       " 'Im225_0': '../input/healthy/Im225_0.tif',\n",
+       " 'Im247_0': '../input/healthy/Im247_0.tif',\n",
+       " 'Im172_0': '../input/healthy/Im172_0.tif',\n",
+       " 'Im184_0': '../input/healthy/Im184_0.tif',\n",
+       " 'Im242_0': '../input/healthy/Im242_0.tif',\n",
+       " 'Im235_0': '../input/healthy/Im235_0.tif',\n",
+       " 'Im217_0': '../input/healthy/Im217_0.tif',\n",
+       " 'Im241_0': '../input/healthy/Im241_0.tif',\n",
+       " 'Im260_0': '../input/healthy/Im260_0.tif',\n",
+       " 'Im199_0': '../input/healthy/Im199_0.tif',\n",
+       " 'Im145_0': '../input/healthy/Im145_0.tif',\n",
+       " 'Im142_0': '../input/healthy/Im142_0.tif',\n",
+       " 'Im246_0': '../input/healthy/Im246_0.tif',\n",
+       " 'Im171_0': '../input/healthy/Im171_0.tif',\n",
+       " 'Im203_0': '../input/healthy/Im203_0.tif',\n",
+       " 'Im210_0': '../input/healthy/Im210_0.tif',\n",
+       " 'Im198_0': '../input/healthy/Im198_0.tif',\n",
+       " 'Im205_0': '../input/healthy/Im205_0.tif',\n",
+       " 'Im253_0': '../input/healthy/Im253_0.tif',\n",
+       " 'Im176_0': '../input/healthy/Im176_0.tif',\n",
+       " 'Im230_0': '../input/healthy/Im230_0.tif',\n",
+       " 'Im220_0': '../input/healthy/Im220_0.tif',\n",
+       " 'Im254_0': '../input/healthy/Im254_0.tif',\n",
+       " 'Im193_0': '../input/healthy/Im193_0.tif',\n",
+       " 'Im147_0': '../input/healthy/Im147_0.tif',\n",
+       " 'Im250_0': '../input/healthy/Im250_0.tif',\n",
+       " 'Im154_0': '../input/healthy/Im154_0.tif',\n",
+       " 'Im186_0': '../input/healthy/Im186_0.tif'}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lymphoblasts_directory = os.path.join('..', 'input/')\n",
+    "\n",
+    "# creating a directory for all images present with us and bringing them under same directory\n",
+    "image_directory = {os.path.splitext(os.path.basename(x))[0]: x\n",
+    "                     for x in glob(os.path.join(lymphoblasts_directory, '*', '*.tif'))}\n",
+    "image_directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Im223_0.tif\n",
+      "Im168_0.tif\n",
+      "Im144_0.tif\n",
+      "Im166_0.tif\n",
+      "Im188_0.tif\n",
+      "Im208_0.tif\n",
+      "Im151_0.tif\n",
+      "Im215_0.tif\n",
+      "Im213_0.tif\n",
+      "Im177_0.tif\n",
+      "Im243_0.tif\n",
+      "Im179_0.tif\n",
+      "Im173_0.tif\n",
+      "Im150_0.tif\n",
+      "Im219_0.tif\n",
+      "Im160_0.tif\n",
+      "Im190_0.tif\n",
+      "Im244_0.tif\n",
+      "Im214_0.tif\n",
+      "Im178_0.tif\n",
+      "Im196_0.tif\n",
+      "Im182_0.tif\n",
+      "Im228_0.tif\n",
+      "Im164_0.tif\n",
+      "Im162_0.tif\n",
+      "Im257_0.tif\n",
+      "Im140_0.tif\n",
+      "Im149_0.tif\n",
+      "Im202_0.tif\n",
+      "Im191_0.tif\n",
+      "Im248_0.tif\n",
+      "Im161_0.tif\n",
+      "Im258_0.tif\n",
+      "Im259_0.tif\n",
+      "Im245_0.tif\n",
+      "Im197_0.tif\n",
+      "Im239_0.tif\n",
+      "Im231_0.tif\n",
+      "Im209_0.tif\n",
+      "Im131_0.tif\n",
+      "Im232_0.tif\n",
+      "Im132_0.tif\n",
+      "Im207_0.tif\n",
+      "Im255_0.tif\n",
+      "Im170_0.tif\n",
+      "Im227_0.tif\n",
+      "Im163_0.tif\n",
+      "Im181_0.tif\n",
+      "Im138_0.tif\n",
+      "Im234_0.tif\n",
+      "Im139_0.tif\n",
+      "Im238_0.tif\n",
+      "Im185_0.tif\n",
+      "Im200_0.tif\n",
+      "Im175_0.tif\n",
+      "Im167_0.tif\n",
+      "Im155_0.tif\n",
+      "Im165_0.tif\n",
+      "Im137_0.tif\n",
+      "Im189_0.tif\n",
+      "Im204_0.tif\n",
+      "Im174_0.tif\n",
+      "Im158_0.tif\n",
+      "Im218_0.tif\n",
+      "Im187_0.tif\n",
+      "Im183_0.tif\n",
+      "Im251_0.tif\n",
+      "Im152_0.tif\n",
+      "Im211_0.tif\n",
+      "Im133_0.tif\n",
+      "Im212_0.tif\n",
+      "Im169_0.tif\n",
+      "Im252_0.tif\n",
+      "Im192_0.tif\n",
+      "Im233_0.tif\n",
+      "Im156_0.tif\n",
+      "Im201_0.tif\n",
+      "Im237_0.tif\n",
+      "Im146_0.tif\n",
+      "Im236_0.tif\n",
+      "Im135_0.tif\n",
+      "Im153_0.tif\n",
+      "Im180_0.tif\n",
+      "Im194_0.tif\n",
+      "Im216_0.tif\n",
+      "Im206_0.tif\n",
+      "Im249_0.tif\n",
+      "Im141_0.tif\n",
+      "Im221_0.tif\n",
+      "Im159_0.tif\n",
+      "Im157_0.tif\n",
+      "Im148_0.tif\n",
+      "Im256_0.tif\n",
+      "Im229_0.tif\n",
+      "Im136_0.tif\n",
+      "Im195_0.tif\n",
+      "Im222_0.tif\n",
+      "Im134_0.tif\n",
+      "Im143_0.tif\n",
+      "Im224_0.tif\n",
+      "Im226_0.tif\n",
+      "Im240_0.tif\n",
+      "Im225_0.tif\n",
+      "Im247_0.tif\n",
+      "Im172_0.tif\n",
+      "Im184_0.tif\n",
+      "Im242_0.tif\n",
+      "Im235_0.tif\n",
+      "Im217_0.tif\n",
+      "Im241_0.tif\n",
+      "Im260_0.tif\n",
+      "Im199_0.tif\n",
+      "Im145_0.tif\n",
+      "Im142_0.tif\n",
+      "Im246_0.tif\n",
+      "Im171_0.tif\n",
+      "Im203_0.tif\n",
+      "Im210_0.tif\n",
+      "Im198_0.tif\n",
+      "Im205_0.tif\n",
+      "Im253_0.tif\n",
+      "Im176_0.tif\n",
+      "Im230_0.tif\n",
+      "Im220_0.tif\n",
+      "Im254_0.tif\n",
+      "Im193_0.tif\n",
+      "Im147_0.tif\n",
+      "Im250_0.tif\n",
+      "Im154_0.tif\n",
+      "Im186_0.tif\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "['../input/healthy/Im223_0.tif',\n",
+       " '../input/healthy/Im168_0.tif',\n",
+       " '../input/healthy/Im144_0.tif',\n",
+       " '../input/healthy/Im166_0.tif',\n",
+       " '../input/healthy/Im188_0.tif',\n",
+       " '../input/healthy/Im208_0.tif',\n",
+       " '../input/healthy/Im151_0.tif',\n",
+       " '../input/healthy/Im215_0.tif',\n",
+       " '../input/healthy/Im213_0.tif',\n",
+       " '../input/healthy/Im177_0.tif',\n",
+       " '../input/healthy/Im243_0.tif',\n",
+       " '../input/healthy/Im179_0.tif',\n",
+       " '../input/healthy/Im173_0.tif',\n",
+       " '../input/healthy/Im150_0.tif',\n",
+       " '../input/healthy/Im219_0.tif',\n",
+       " '../input/healthy/Im160_0.tif',\n",
+       " '../input/healthy/Im190_0.tif',\n",
+       " '../input/healthy/Im244_0.tif',\n",
+       " '../input/healthy/Im214_0.tif',\n",
+       " '../input/healthy/Im178_0.tif',\n",
+       " '../input/healthy/Im196_0.tif',\n",
+       " '../input/healthy/Im182_0.tif',\n",
+       " '../input/healthy/Im228_0.tif',\n",
+       " '../input/healthy/Im164_0.tif',\n",
+       " '../input/healthy/Im162_0.tif',\n",
+       " '../input/healthy/Im257_0.tif',\n",
+       " '../input/healthy/Im140_0.tif',\n",
+       " '../input/healthy/Im149_0.tif',\n",
+       " '../input/healthy/Im202_0.tif',\n",
+       " '../input/healthy/Im191_0.tif',\n",
+       " '../input/healthy/Im248_0.tif',\n",
+       " '../input/healthy/Im161_0.tif',\n",
+       " '../input/healthy/Im258_0.tif',\n",
+       " '../input/healthy/Im259_0.tif',\n",
+       " '../input/healthy/Im245_0.tif',\n",
+       " '../input/healthy/Im197_0.tif',\n",
+       " '../input/healthy/Im239_0.tif',\n",
+       " '../input/healthy/Im231_0.tif',\n",
+       " '../input/healthy/Im209_0.tif',\n",
+       " '../input/healthy/Im131_0.tif',\n",
+       " '../input/healthy/Im232_0.tif',\n",
+       " '../input/healthy/Im132_0.tif',\n",
+       " '../input/healthy/Im207_0.tif',\n",
+       " '../input/healthy/Im255_0.tif',\n",
+       " '../input/healthy/Im170_0.tif',\n",
+       " '../input/healthy/Im227_0.tif',\n",
+       " '../input/healthy/Im163_0.tif',\n",
+       " '../input/healthy/Im181_0.tif',\n",
+       " '../input/healthy/Im138_0.tif',\n",
+       " '../input/healthy/Im234_0.tif',\n",
+       " '../input/healthy/Im139_0.tif',\n",
+       " '../input/healthy/Im238_0.tif',\n",
+       " '../input/healthy/Im185_0.tif',\n",
+       " '../input/healthy/Im200_0.tif',\n",
+       " '../input/healthy/Im175_0.tif',\n",
+       " '../input/healthy/Im167_0.tif',\n",
+       " '../input/healthy/Im155_0.tif',\n",
+       " '../input/healthy/Im165_0.tif',\n",
+       " '../input/healthy/Im137_0.tif',\n",
+       " '../input/healthy/Im189_0.tif',\n",
+       " '../input/healthy/Im204_0.tif',\n",
+       " '../input/healthy/Im174_0.tif',\n",
+       " '../input/healthy/Im158_0.tif',\n",
+       " '../input/healthy/Im218_0.tif',\n",
+       " '../input/healthy/Im187_0.tif',\n",
+       " '../input/healthy/Im183_0.tif',\n",
+       " '../input/healthy/Im251_0.tif',\n",
+       " '../input/healthy/Im152_0.tif',\n",
+       " '../input/healthy/Im211_0.tif',\n",
+       " '../input/healthy/Im133_0.tif',\n",
+       " '../input/healthy/Im212_0.tif',\n",
+       " '../input/healthy/Im169_0.tif',\n",
+       " '../input/healthy/Im252_0.tif',\n",
+       " '../input/healthy/Im192_0.tif',\n",
+       " '../input/healthy/Im233_0.tif',\n",
+       " '../input/healthy/Im156_0.tif',\n",
+       " '../input/healthy/Im201_0.tif',\n",
+       " '../input/healthy/Im237_0.tif',\n",
+       " '../input/healthy/Im146_0.tif',\n",
+       " '../input/healthy/Im236_0.tif',\n",
+       " '../input/healthy/Im135_0.tif',\n",
+       " '../input/healthy/Im153_0.tif',\n",
+       " '../input/healthy/Im180_0.tif',\n",
+       " '../input/healthy/Im194_0.tif',\n",
+       " '../input/healthy/Im216_0.tif',\n",
+       " '../input/healthy/Im206_0.tif',\n",
+       " '../input/healthy/Im249_0.tif',\n",
+       " '../input/healthy/Im141_0.tif',\n",
+       " '../input/healthy/Im221_0.tif',\n",
+       " '../input/healthy/Im159_0.tif',\n",
+       " '../input/healthy/Im157_0.tif',\n",
+       " '../input/healthy/Im148_0.tif',\n",
+       " '../input/healthy/Im256_0.tif',\n",
+       " '../input/healthy/Im229_0.tif',\n",
+       " '../input/healthy/Im136_0.tif',\n",
+       " '../input/healthy/Im195_0.tif',\n",
+       " '../input/healthy/Im222_0.tif',\n",
+       " '../input/healthy/Im134_0.tif',\n",
+       " '../input/healthy/Im143_0.tif',\n",
+       " '../input/healthy/Im224_0.tif',\n",
+       " '../input/healthy/Im226_0.tif',\n",
+       " '../input/healthy/Im240_0.tif',\n",
+       " '../input/healthy/Im225_0.tif',\n",
+       " '../input/healthy/Im247_0.tif',\n",
+       " '../input/healthy/Im172_0.tif',\n",
+       " '../input/healthy/Im184_0.tif',\n",
+       " '../input/healthy/Im242_0.tif',\n",
+       " '../input/healthy/Im235_0.tif',\n",
+       " '../input/healthy/Im217_0.tif',\n",
+       " '../input/healthy/Im241_0.tif',\n",
+       " '../input/healthy/Im260_0.tif',\n",
+       " '../input/healthy/Im199_0.tif',\n",
+       " '../input/healthy/Im145_0.tif',\n",
+       " '../input/healthy/Im142_0.tif',\n",
+       " '../input/healthy/Im246_0.tif',\n",
+       " '../input/healthy/Im171_0.tif',\n",
+       " '../input/healthy/Im203_0.tif',\n",
+       " '../input/healthy/Im210_0.tif',\n",
+       " '../input/healthy/Im198_0.tif',\n",
+       " '../input/healthy/Im205_0.tif',\n",
+       " '../input/healthy/Im253_0.tif',\n",
+       " '../input/healthy/Im176_0.tif',\n",
+       " '../input/healthy/Im230_0.tif',\n",
+       " '../input/healthy/Im220_0.tif',\n",
+       " '../input/healthy/Im254_0.tif',\n",
+       " '../input/healthy/Im193_0.tif',\n",
+       " '../input/healthy/Im147_0.tif',\n",
+       " '../input/healthy/Im250_0.tif',\n",
+       " '../input/healthy/Im154_0.tif',\n",
+       " '../input/healthy/Im186_0.tif']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "healthyPaths = []\n",
+    "for dirname, _, filenames in os.walk(os.path.join(lymphoblasts_directory, 'healthy')):\n",
+    "    for filename in filenames:\n",
+    "        print(filename)\n",
+    "        if (filename[-3:] == 'tif'):\n",
+    "            healthyPaths.append(os.path.join(dirname, filename))\n",
+    "healthyPaths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['../input/not healthy/Im119_1.tif',\n",
+       " '../input/not healthy/Im124_1.tif',\n",
+       " '../input/not healthy/Im086_1.tif',\n",
+       " '../input/not healthy/Im097_1.tif',\n",
+       " '../input/not healthy/Im026_1.tif',\n",
+       " '../input/not healthy/Im115_1.tif',\n",
+       " '../input/not healthy/Im085_1.tif',\n",
+       " '../input/not healthy/Im107_1.tif',\n",
+       " '../input/not healthy/Im032_1.tif',\n",
+       " '../input/not healthy/Im025_1.tif',\n",
+       " '../input/not healthy/Im084_1.tif',\n",
+       " '../input/not healthy/Im029_1.tif',\n",
+       " '../input/not healthy/Im016_1.tif',\n",
+       " '../input/not healthy/Im126_1.tif',\n",
+       " '../input/not healthy/Im031_1.tif',\n",
+       " '../input/not healthy/Im057_1.tif',\n",
+       " '../input/not healthy/Im079_1.tif',\n",
+       " '../input/not healthy/Im096_1.tif',\n",
+       " '../input/not healthy/Im061_1.tif',\n",
+       " '../input/not healthy/Im076_1.tif',\n",
+       " '../input/not healthy/Im112_1.tif',\n",
+       " '../input/not healthy/Im039_1.tif',\n",
+       " '../input/not healthy/Im064_1.tif',\n",
+       " '../input/not healthy/Im098_1.tif',\n",
+       " '../input/not healthy/Im005_1.tif',\n",
+       " '../input/not healthy/Im022_1.tif',\n",
+       " '../input/not healthy/Im037_1.tif',\n",
+       " '../input/not healthy/Im027_1.tif',\n",
+       " '../input/not healthy/Im093_1.tif',\n",
+       " '../input/not healthy/Im070_1.tif',\n",
+       " '../input/not healthy/Im014_1.tif',\n",
+       " '../input/not healthy/Im036_1.tif',\n",
+       " '../input/not healthy/Im053_1.tif',\n",
+       " '../input/not healthy/Im028_1.tif',\n",
+       " '../input/not healthy/Im099_1.tif',\n",
+       " '../input/not healthy/Im080_1.tif',\n",
+       " '../input/not healthy/Im011_1.tif',\n",
+       " '../input/not healthy/Im035_1.tif',\n",
+       " '../input/not healthy/Im110_1.tif',\n",
+       " '../input/not healthy/Im051_1.tif',\n",
+       " '../input/not healthy/Im059_1.tif',\n",
+       " '../input/not healthy/Im063_1.tif',\n",
+       " '../input/not healthy/Im114_1.tif',\n",
+       " '../input/not healthy/Im052_1.tif',\n",
+       " '../input/not healthy/Im066_1.tif',\n",
+       " '../input/not healthy/Im121_1.tif',\n",
+       " '../input/not healthy/Im118_1.tif',\n",
+       " '../input/not healthy/Im001_1.tif',\n",
+       " '../input/not healthy/Im050_1.tif',\n",
+       " '../input/not healthy/Im103_1.tif',\n",
+       " '../input/not healthy/Im034_1.tif',\n",
+       " '../input/not healthy/Im091_1.tif',\n",
+       " '../input/not healthy/Im087_1.tif',\n",
+       " '../input/not healthy/Im019_1.tif',\n",
+       " '../input/not healthy/Im054_1.tif',\n",
+       " '../input/not healthy/Im013_1.tif',\n",
+       " '../input/not healthy/Im055_1.tif',\n",
+       " '../input/not healthy/Im083_1.tif',\n",
+       " '../input/not healthy/Im043_1.tif',\n",
+       " '../input/not healthy/Im023_1.tif',\n",
+       " '../input/not healthy/Im094_1.tif',\n",
+       " '../input/not healthy/Im062_1.tif',\n",
+       " '../input/not healthy/Im082_1.tif',\n",
+       " '../input/not healthy/Im040_1.tif',\n",
+       " '../input/not healthy/Im009_1.tif',\n",
+       " '../input/not healthy/Im116_1.tif',\n",
+       " '../input/not healthy/Im120_1.tif',\n",
+       " '../input/not healthy/Im008_1.tif',\n",
+       " '../input/not healthy/Im111_1.tif',\n",
+       " '../input/not healthy/Im003_1.tif',\n",
+       " '../input/not healthy/Im069_1.tif',\n",
+       " '../input/not healthy/Im048_1.tif',\n",
+       " '../input/not healthy/Im038_1.tif',\n",
+       " '../input/not healthy/Im075_1.tif',\n",
+       " '../input/not healthy/Im102_1.tif',\n",
+       " '../input/not healthy/Im041_1.tif',\n",
+       " '../input/not healthy/Im033_1.tif',\n",
+       " '../input/not healthy/Im074_1.tif',\n",
+       " '../input/not healthy/Im128_1.tif',\n",
+       " '../input/not healthy/Im106_1.tif',\n",
+       " '../input/not healthy/Im056_1.tif',\n",
+       " '../input/not healthy/Im127_1.tif',\n",
+       " '../input/not healthy/Im113_1.tif',\n",
+       " '../input/not healthy/Im104_1.tif',\n",
+       " '../input/not healthy/Im060_1.tif',\n",
+       " '../input/not healthy/Im020_1.tif',\n",
+       " '../input/not healthy/Im100_1.tif',\n",
+       " '../input/not healthy/Im018_1.tif',\n",
+       " '../input/not healthy/Im081_1.tif',\n",
+       " '../input/not healthy/Im002_1.tif',\n",
+       " '../input/not healthy/Im073_1.tif',\n",
+       " '../input/not healthy/Im108_1.tif',\n",
+       " '../input/not healthy/Im117_1.tif',\n",
+       " '../input/not healthy/Im006_1.tif',\n",
+       " '../input/not healthy/Im089_1.tif',\n",
+       " '../input/not healthy/Im077_1.tif',\n",
+       " '../input/not healthy/Im090_1.tif',\n",
+       " '../input/not healthy/Im030_1.tif',\n",
+       " '../input/not healthy/Im072_1.tif',\n",
+       " '../input/not healthy/Im042_1.tif',\n",
+       " '../input/not healthy/Im123_1.tif',\n",
+       " '../input/not healthy/Im047_1.tif',\n",
+       " '../input/not healthy/Im058_1.tif',\n",
+       " '../input/not healthy/Im101_1.tif',\n",
+       " '../input/not healthy/Im092_1.tif',\n",
+       " '../input/not healthy/Im109_1.tif',\n",
+       " '../input/not healthy/Im071_1.tif',\n",
+       " '../input/not healthy/Im130_1.tif',\n",
+       " '../input/not healthy/Im049_1.tif',\n",
+       " '../input/not healthy/Im088_1.tif',\n",
+       " '../input/not healthy/Im024_1.tif',\n",
+       " '../input/not healthy/Im015_1.tif',\n",
+       " '../input/not healthy/Im010_1.tif',\n",
+       " '../input/not healthy/Im078_1.tif',\n",
+       " '../input/not healthy/Im125_1.tif',\n",
+       " '../input/not healthy/Im046_1.tif',\n",
+       " '../input/not healthy/Im007_1.tif',\n",
+       " '../input/not healthy/Im067_1.tif',\n",
+       " '../input/not healthy/Im004_1.tif',\n",
+       " '../input/not healthy/Im044_1.tif',\n",
+       " '../input/not healthy/Im068_1.tif',\n",
+       " '../input/not healthy/Im045_1.tif',\n",
+       " '../input/not healthy/Im017_1.tif',\n",
+       " '../input/not healthy/Im065_1.tif',\n",
+       " '../input/not healthy/Im012_1.tif',\n",
+       " '../input/not healthy/Im095_1.tif',\n",
+       " '../input/not healthy/Im129_1.tif',\n",
+       " '../input/not healthy/Im105_1.tif',\n",
+       " '../input/not healthy/Im122_1.tif',\n",
+       " '../input/not healthy/Im021_1.tif']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nothealthyPaths = []\n",
+    "for dirname, _, filenames in os.walk(os.path.join(lymphoblasts_directory, 'not healthy')):\n",
+    "    for filename in filenames:\n",
+    "        if (filename[-3:] == 'tif'):\n",
+    "            nothealthyPaths.append(os.path.join(dirname, filename))\n",
+    "nothealthyPaths"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(columns = ['path', 'label'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# labels for healthy:0, not healthy:1\n",
+    "\n",
+    "for index1 in healthyPaths:\n",
+    "    df=df.append({'path' : str(index1) , 'label' : 0} , ignore_index=True)\n",
+    "for index2 in nothealthyPaths:\n",
+    "    df=df.append({'path' : str(index2) , 'label' : 1} , ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>path</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>../input/healthy/Im223_0.tif</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>../input/healthy/Im168_0.tif</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>../input/healthy/Im144_0.tif</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>../input/healthy/Im166_0.tif</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>../input/healthy/Im188_0.tif</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           path label\n",
+       "0  ../input/healthy/Im223_0.tif     0\n",
+       "1  ../input/healthy/Im168_0.tif     0\n",
+       "2  ../input/healthy/Im144_0.tif     0\n",
+       "3  ../input/healthy/Im166_0.tif     0\n",
+       "4  ../input/healthy/Im188_0.tif     0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rgb2gray(rgb):\n",
+    "\n",
+    "    r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]\n",
+    "    #gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n",
+    "    gray = r\n",
+    "    \n",
+    "    return gray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['image'] = df['path'].map(lambda x: np.asarray(Image.open(x).resize((224,224))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for index in range(len(df['image'])):\n",
+    "    img = Image.fromarray(df['image'][index])\n",
+    "    if img.mode == 'RGB':\n",
+    "        df['image'][index] = rgb2gray(df['image'][index])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "\n",
+    "for i in range(len(df['image'])):\n",
+    "    gray = df['image'][i]\n",
+    "    df['image'][i] = cv2.merge([gray,gray,gray])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>path</th>\n",
+       "      <th>label</th>\n",
+       "      <th>image</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>../input/healthy/Im223_0.tif</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[[[156, 156, 156], [156, 156, 156], [156, 156,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>../input/healthy/Im168_0.tif</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[[[156, 156, 156], [158, 158, 158], [159, 159,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>../input/healthy/Im144_0.tif</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[[[169, 169, 169], [167, 167, 167], [165, 165,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>../input/healthy/Im166_0.tif</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[[[160, 160, 160], [159, 159, 159], [161, 161,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>../input/healthy/Im188_0.tif</td>\n",
+       "      <td>0</td>\n",
+       "      <td>[[[177, 177, 177], [177, 177, 177], [175, 175,...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           path label  \\\n",
+       "0  ../input/healthy/Im223_0.tif     0   \n",
+       "1  ../input/healthy/Im168_0.tif     0   \n",
+       "2  ../input/healthy/Im144_0.tif     0   \n",
+       "3  ../input/healthy/Im166_0.tif     0   \n",
+       "4  ../input/healthy/Im188_0.tif     0   \n",
+       "\n",
+       "                                               image  \n",
+       "0  [[[156, 156, 156], [156, 156, 156], [156, 156,...  \n",
+       "1  [[[156, 156, 156], [158, 158, 158], [159, 159,...  \n",
+       "2  [[[169, 169, 169], [167, 167, 167], [165, 165,...  \n",
+       "3  [[[160, 160, 160], [159, 159, 159], [161, 161,...  \n",
+       "4  [[[177, 177, 177], [177, 177, 177], [175, 175,...  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1440x432 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Printing Sample images for each  type\n",
+    "n_samples = 5\n",
+    "num_classes = 2\n",
+    "fig, m_axs = plt.subplots(num_classes, n_samples, figsize = (4*n_samples, 3*num_classes))\n",
+    "for n_axs, (type_name, type_rows) in zip(m_axs, \n",
+    "                                         df.sort_values(['label']).groupby('label')):\n",
+    "    n_axs[0].set_title(type_name)\n",
+    "    for c_ax, (_, c_row) in zip(n_axs, type_rows.sample(n_samples, random_state=1234).iterrows()):\n",
+    "        c_ax.imshow(c_row['image'])\n",
+    "        c_ax.axis('off')\n",
+    "fig.savefig('category_samples.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_df, test_df = train_test_split(df, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(224, 224, 3)    260\n",
+       "Name: image, dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['image'].map(lambda x: x.shape).value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    130\n",
+       "0    130\n",
+       "Name: label, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Print no. of instances of each class present to balace the data\n",
+    "df['label'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    1430\n",
+       "0    1430\n",
+       "Name: label, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Copy fewer class to balance the number of 7 classes\n",
+    "data_aug_rate = [11,11] # These values are calculated to balance the data\n",
+    "for i in range(num_classes):\n",
+    "    if data_aug_rate[i]:\n",
+    "        df=df.append([df.loc[df['label'] == i,:]]*(data_aug_rate[i]-1), ignore_index=True)\n",
+    "df['label'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features=df.drop(columns=['label'],axis=1)\n",
+    "target=df['label']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = np.asarray(df['image'].tolist())\n",
+    "x_test = np.asarray(test_df['image'].tolist())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalizing the data\n",
+    "x_train_mean = np.mean(x_train)\n",
+    "x_train_std = np.std(x_train)\n",
+    "\n",
+    "x_test_mean = np.mean(x_test)\n",
+    "x_test_std = np.std(x_test)\n",
+    "\n",
+    "x_train = (x_train - x_train_mean)/x_train_std\n",
+    "x_test = (x_test - x_test_mean)/x_test_std"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_train = to_categorical(df['label'], num_classes = num_classes)\n",
+    "y_test = to_categorical(test_df['label'], num_classes = num_classes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Splitting training data and validation data\n",
+    "x_train, x_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size = 0.20, random_state = 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = x_train.reshape(x_train.shape[0], *(224, 224, 3))\n",
+    "x_test = x_test.reshape(x_test.shape[0], *(224, 224, 3))\n",
+    "x_validate = x_validate.reshape(x_validate.shape[0], *(224, 224, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2288, 224, 224, 3)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(52, 224, 224, 3)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(572, 224, 224, 3)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_validate.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to avoid overfitting problem, we need to expand artificially our dataset. We can make your existing dataset even larger. The idea is to alter the training data with small transformations to reproduce the variations. Approaches that alter the training data in ways that change the array representation while keeping the label the same are known as data augmentation techniques. Some popular augmentations people use are grayscales, horizontal flips, vertical flips, random crops, color jitters, translations, rotations, and much more. By applying just a couple of these transformations to our training data, we can easily double or triple the number of training examples and create a very robust model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# With data augmentation to prevent overfitting and handling the imbalance in dataset\n",
+    "\n",
+    "datagen = ImageDataGenerator(\n",
+    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
+    "        samplewise_center=False,  # set each sample mean to 0\n",
+    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
+    "        samplewise_std_normalization=False,  # divide each input by its std\n",
+    "        zca_whitening=False,  # apply ZCA whitening\n",
+    "        rotation_range = 30,  # randomly rotate images in the range (degrees, 0 to 180)\n",
+    "        zoom_range = 0.2, # Randomly zoom image \n",
+    "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
+    "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
+    "        horizontal_flip = True,  # randomly flip images\n",
+    "        vertical_flip=False)  # randomly flip images\n",
+    "\n",
+    "\n",
+    "datagen.fit(x_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the data augmentation, i choosed to :\n",
+    "\n",
+    "1-Randomly rotate some training images by 30 degrees\n",
+    "\n",
+    "2-Randomly Zoom by 20% some training images\n",
+    "\n",
+    "3-Randomly shift images horizontally by 10% of the width\n",
+    "\n",
+    "4-Randomly shift images vertically by 10% of the height\n",
+    "\n",
+    "5-Randomly flip images horizontally. Once our model is ready, we fit the training dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
+      "94773248/94765736 [==============================] - 2s 0us/step\n",
+      "Model: \"model\"\n",
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
+      "                                                                 conv2_block1_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
+      "                                                                 conv2_block2_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
+      "                                                                 conv2_block3_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
+      "                                                                 conv3_block1_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
+      "                                                                 conv3_block2_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
+      "                                                                 conv3_block3_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
+      "                                                                 conv3_block4_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
+      "                                                                 conv4_block1_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
+      "                                                                 conv4_block2_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
+      "                                                                 conv4_block3_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
+      "                                                                 conv4_block4_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
+      "                                                                 conv4_block5_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
+      "                                                                 conv4_block6_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
+      "                                                                 conv5_block1_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
+      "                                                                 conv5_block2_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
+      "                                                                 conv5_block3_3_bn[0][0]          \n",
+      "__________________________________________________________________________________________________\n",
+      "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "global_average_pooling2d (Globa (None, 2048)         0           conv5_block3_out[0][0]           \n",
+      "__________________________________________________________________________________________________\n",
+      "dense (Dense)                   (None, 1000)         2049000     global_average_pooling2d[0][0]   \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_1 (Dense)                 (None, 2)            2002        dense[0][0]                      \n",
+      "==================================================================================================\n",
+      "Total params: 25,638,714\n",
+      "Trainable params: 25,585,594\n",
+      "Non-trainable params: 53,120\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.applications.resnet50 import ResNet50\n",
+    "from keras.layers import Conv2D, MaxPooling2D, MaxPooling1D, GlobalAveragePooling2D, Dense, Dropout, Flatten, Input, LSTM, TimeDistributed\n",
+    "import keras\n",
+    "#import keras.metrics\n",
+    "from keras.models import Sequential,Input,Model\n",
+    "input_tensor = Input(shape=(224,224,3))\n",
+    "base_model = ResNet50(input_tensor = input_tensor, include_top = False, pooling = 'average')\n",
+    "x = base_model.output\n",
+    "x = GlobalAveragePooling2D()(x)\n",
+    "x = Dense(1000, activation = 'relu')(x)\n",
+    "x = Dense(2, activation = 'softmax')(x)\n",
+    "model = Model(base_model.input,x)\n",
+    "model.compile(optimizer='adam', loss = 'categorical_crossentropy', metrics=['accuracy'])\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = Adam(lr = 0.0001)\n",
+    "model.compile(loss=\"categorical_crossentropy\", metrics=[\"accuracy\"], optimizer=optimizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2288, 224, 224, 3)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', patience = 2, verbose=1,factor=0.3, min_lr=0.000001)\n",
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/20\n",
+      "229/228 [==============================] - 39s 170ms/step - loss: 0.1234 - accuracy: 0.9489 - val_loss: 0.9827 - val_accuracy: 0.4196 - lr: 1.0000e-04\n",
+      "Epoch 2/20\n",
+      "229/228 [==============================] - 38s 167ms/step - loss: 0.0581 - accuracy: 0.9873 - val_loss: 1.8720 - val_accuracy: 0.4948 - lr: 1.0000e-04\n",
+      "Epoch 3/20\n",
+      "229/228 [==============================] - 38s 164ms/step - loss: 0.0485 - accuracy: 0.9843 - val_loss: 1.9706 - val_accuracy: 0.5664 - lr: 1.0000e-04\n",
+      "Epoch 4/20\n",
+      "229/228 [==============================] - 37s 164ms/step - loss: 0.0160 - accuracy: 0.9939 - val_loss: 0.0772 - val_accuracy: 0.9808 - lr: 1.0000e-04\n",
+      "Epoch 5/20\n",
+      "229/228 [==============================] - 38s 166ms/step - loss: 0.0367 - accuracy: 0.9869 - val_loss: 0.0669 - val_accuracy: 0.9790 - lr: 1.0000e-04\n",
+      "Epoch 6/20\n",
+      "229/228 [==============================] - 37s 161ms/step - loss: 0.0087 - accuracy: 0.9983 - val_loss: 2.5923e-04 - val_accuracy: 1.0000 - lr: 1.0000e-04\n",
+      "Epoch 7/20\n",
+      "229/228 [==============================] - 38s 167ms/step - loss: 0.0053 - accuracy: 0.9983 - val_loss: 0.0288 - val_accuracy: 0.9878 - lr: 1.0000e-04\n",
+      "Epoch 8/20\n",
+      "229/228 [==============================] - ETA: 0s - loss: 0.0355 - accuracy: 0.9895\n",
+      "Epoch 00008: ReduceLROnPlateau reducing learning rate to 2.9999999242136255e-05.\n",
+      "229/228 [==============================] - 38s 166ms/step - loss: 0.0355 - accuracy: 0.9895 - val_loss: 0.0397 - val_accuracy: 0.9878 - lr: 1.0000e-04\n",
+      "Epoch 9/20\n",
+      "229/228 [==============================] - 37s 163ms/step - loss: 0.0143 - accuracy: 0.9965 - val_loss: 7.8456e-04 - val_accuracy: 1.0000 - lr: 3.0000e-05\n",
+      "Epoch 10/20\n",
+      "229/228 [==============================] - ETA: 0s - loss: 0.0041 - accuracy: 0.9987\n",
+      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 8.999999772640877e-06.\n",
+      "229/228 [==============================] - 38s 165ms/step - loss: 0.0041 - accuracy: 0.9987 - val_loss: 0.0029 - val_accuracy: 0.9983 - lr: 3.0000e-05\n",
+      "Epoch 11/20\n",
+      "229/228 [==============================] - 38s 165ms/step - loss: 0.0037 - accuracy: 0.9996 - val_loss: 2.5705e-04 - val_accuracy: 1.0000 - lr: 9.0000e-06\n",
+      "Epoch 12/20\n",
+      "229/228 [==============================] - ETA: 0s - loss: 0.0028 - accuracy: 0.9991\n",
+      "Epoch 00012: ReduceLROnPlateau reducing learning rate to 2.6999998226528985e-06.\n",
+      "229/228 [==============================] - 37s 162ms/step - loss: 0.0028 - accuracy: 0.9991 - val_loss: 2.2044e-04 - val_accuracy: 1.0000 - lr: 9.0000e-06\n",
+      "Epoch 13/20\n",
+      "229/228 [==============================] - 38s 166ms/step - loss: 0.0036 - accuracy: 0.9987 - val_loss: 1.4592e-04 - val_accuracy: 1.0000 - lr: 2.7000e-06\n",
+      "Epoch 14/20\n",
+      "229/228 [==============================] - ETA: 0s - loss: 0.0019 - accuracy: 0.9996\n",
+      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 1e-06.\n",
+      "229/228 [==============================] - 37s 161ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 1.5966e-04 - val_accuracy: 1.0000 - lr: 2.7000e-06\n",
+      "Epoch 15/20\n",
+      "229/228 [==============================] - 37s 164ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 2.1170e-04 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
+      "Epoch 16/20\n",
+      "229/228 [==============================] - 38s 167ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 1.1949e-04 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
+      "Epoch 17/20\n",
+      "229/228 [==============================] - 37s 161ms/step - loss: 0.0030 - accuracy: 0.9987 - val_loss: 1.0432e-04 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
+      "Epoch 18/20\n",
+      "229/228 [==============================] - 38s 165ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 1.1810e-04 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
+      "Epoch 19/20\n",
+      "229/228 [==============================] - 38s 164ms/step - loss: 0.0010 - accuracy: 0.9996 - val_loss: 2.3247e-04 - val_accuracy: 1.0000 - lr: 1.0000e-06\n",
+      "Epoch 20/20\n",
+      "229/228 [==============================] - 37s 161ms/step - loss: 5.1334e-04 - accuracy: 1.0000 - val_loss: 0.0020 - val_accuracy: 1.0000 - lr: 1.0000e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "history = model.fit_generator(\n",
+    "    datagen.flow(x_train, y_train, batch_size=10),\n",
+    "    steps_per_epoch=len(x_train) / 10,\n",
+    "    epochs=20,\n",
+    "    verbose=1,\n",
+    "    callbacks=[learning_rate_reduction],\n",
+    "    validation_data= datagen.flow(x_validate, y_validate)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2/2 [==============================] - 0s 108ms/step - loss: 2.2447e-04 - accuracy: 1.0000\n",
+      "Loss of the model is -  0.00022447215451393276\n",
+      "2/2 [==============================] - 0s 22ms/step - loss: 2.2447e-04 - accuracy: 1.0000\n",
+      "Accuracy of the model is -  100.0 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Loss of the model is - \" , model.evaluate(x_test,y_test)[0])\n",
+    "print(\"Accuracy of the model is - \" , model.evaluate(x_test,y_test)[1]*100 , \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1440x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "epochs = [i for i in range(20)]\n",
+    "fig , ax = plt.subplots(1,2)\n",
+    "train_acc = history.history['accuracy']\n",
+    "train_loss = history.history['loss']\n",
+    "val_acc = history.history['val_accuracy']\n",
+    "val_loss = history.history['val_loss']\n",
+    "fig.set_size_inches(20,10)\n",
+    "\n",
+    "ax[0].plot(epochs , train_acc , 'go-' , label = 'Training Accuracy')\n",
+    "ax[0].plot(epochs , val_acc , 'ro-' , label = 'Validation Accuracy')\n",
+    "ax[0].set_title('Training & Validation Accuracy')\n",
+    "ax[0].legend()\n",
+    "ax[0].set_xlabel(\"Epochs\")\n",
+    "ax[0].set_ylabel(\"Accuracy\")\n",
+    "\n",
+    "ax[1].plot(epochs , train_loss , 'g-o' , label = 'Training Loss')\n",
+    "ax[1].plot(epochs , val_loss , 'r-o' , label = 'Validation Loss')\n",
+    "ax[1].set_title('Testing Accuracy & Loss')\n",
+    "ax[1].legend()\n",
+    "ax[1].set_xlabel(\"Epochs\")\n",
+    "ax[1].set_ylabel(\"Training & Validation Loss\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00       283\n",
+      "           1       1.00      1.00      1.00       289\n",
+      "\n",
+      "    accuracy                           1.00       572\n",
+      "   macro avg       1.00      1.00      1.00       572\n",
+      "weighted avg       1.00      1.00      1.00       572\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "pred = model.predict(x_validate)\n",
+    "print(classification_report(np.argmax(y_validate, axis = 1),np.argmax(pred, axis = 1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[283,   0],\n",
+       "       [  0, 289]])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cm = confusion_matrix(np.argmax(y_validate, axis = 1),np.argmax(pred, axis = 1))\n",
+    "cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cm = pd.DataFrame(cm , index = ['0','1'] , columns = ['0','1'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sn\n",
+    "array =[[283,0],\n",
+    "        [0,289]]\n",
+    "df_cm = pd.DataFrame(array, index = [i for i in [\"healthy\", \"not healthy\"]],\n",
+    "                  columns = [i for i in [\"healthy\", \"not healthy\"]])\n",
+    "plt.figure(figsize = (10,7))\n",
+    "sn.heatmap(df_cm, annot=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}