Update main.py

main.py

@@ -1,13 +1,13 @@
 import argparse
 import numpy as np
-from utils.generate_csv import generate_csv 
-from utils.k_fold_splits import k_fold_splits 
-from utils.k_fold_separate import k_fold_separate 
+from utils.generate_csv import generate_csv
+from utils.k_fold_splits import k_fold_splits
+from utils.k_fold_separate import k_fold_separate
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--num_epochs', type=int, default = 70,
                     help='Number of Epochs for training')
-parser.add_argument('--path', type=str, default = './', 
+parser.add_argument('--path', type=str, default = './',
                     help='Path where the image data is stored')
 parser.add_argument('--batch_size', type=int, default = 16,
                     help='Batch Size for Mini Batch Training')
@@ -18,21 +18,21 @@
 df = generate_csv(args.path)
 
 y = np.array(list(df["class"]))
-x = np.array(list( df["path"])) 
+x = np.array(list( df["path"]))
 
 files_for_train_x = []
-files_for_validation_x = [] 
+files_for_validation_x = []
 files_for_train_y = []
 files_for_validation_y = []
 
 k_fold_splits(x,y, files_for_train_x ,  files_for_validation_x ,
-              files_for_train_y , files_for_validation_y ) # n_splits = 5 
+              files_for_train_y , files_for_validation_y ) # n_splits = 5
 
 #n is the number of folds
-n = len(files_for_train_x)
-for i in range(0,n):
+N = len(files_for_train_x)
+for i in range(0,N):
     k_fold_separate(files_for_train_x[i] , files_for_train_y[i] ,
                     files_for_validation_x[i] ,files_for_validation_y[i] ,
                     "InceptionV3" , "MobileNetV2" ,"InceptionResNetV2" ,i+1 ,
                     NUM_EPOCHS = args.num_epochs , train_batch=args.batch_size ,
-                    validation_batch = args.batch_size, lr=args.lr) 
+                    validation_batch = args.batch_size, lr=args.lr)