from keras.preprocessing import image

# augmentation processes for the train set
train_image_generator = image.ImageDataGenerator (  
        rescale = 1./255.,
        rotation_range = 40,
        width_shift_range = 0.2,
        height_shift_range = 0.2,
        zoom_range = 0.2,
        horizontal_flip = True,
        fill_mode = 'nearest'
    )

image_source = X_train[6].reshape(1,150,150,3) 

fig, axs = plt.subplots(1,8, figsize=(17,6))
i=-1
for batch in train_image_generator.flow(image_source, batch_size = 1):
    i = i + 1
    axs[i].imshow(batch[0]);    
    axs[i].axis('off')
    if i == 7:
        break
        
plt.show()
