cross

import cv2
import glob
train_x_covid = [cv2.imread(file) for file in glob.glob("/content/drive/My Drive/trainn/train/Covid/*.png")]
train_x_noncovid=[cv2.imread(file) for file in glob.glob("/content/drive/My Drive/trainn/train/Non-Covid/*.png")]
train_x_covid=np.array(train_x_covid)
train_x_noncovid=np.array(train_x_noncovid)

train_y_covid=[1] * 500
train_y_noncovid=[0]*500

train_x=np.concatenate((train_x_covid,train_x_noncovid))
train_y=np.concatenate((train_y_covid,train_y_noncovid))



from sklearn.model_selection import StratifiedKFold

kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
cvscores = []
for train, test in kfold.split(train_x, train_y):
  # create model
  model= keras.Sequential()
  for layer in vgg16_model.layers[:-1]:
    model.add(layer)
  #freazing the layers
  for layer in model.layers:
    layer.trainable=False
    
#covid or non-covid
  model.add(Dense(units=2,activation='softmax'))
  model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
#fit our model
  model.fit(train_x[train], train_x[train], epochs=15, batch_size=10, verbose=0)
	# evaluate the model
	scores = model.evaluate(train_x[test], train_x[test], verbose=0)
	print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
	cvscores.append(scores[1] * 100)
print("%.2f%% (+/- %.2f%%)" % (numpy.mean(cvscores), numpy.std(cvscores)))