train_datagen = image.ImageDataGenerator(
  	rescale = 1./255,  # to normalize bigger values. Convert from 0-255 to 0-1 range.
    shear_range = 0.2,
    zoom_range = 0.2,
    horizontal_flip = True
)


# only rescaling done on test dataset
test_datagen = image.ImageDataGenerator(
    rescale = 1./255
)

train_generator = train_datagen.flow_from_directory(
    directory=TRAIN_PATH,
    target_size=(224,224),
    batch_size=32,
    class_mode='binary',
    save_to_dir = SAVE_TRAIN_PATH,
    save_prefix='',
    save_format='png'
)

validation_generator = test_datagen.flow_from_directory(
    VAL_PATH,
    target_size = (224,224),
    batch_size = 32,
    class_mode = 'binary'
)

# 
train_generator.class_indices
validation_generator.class_indices

# generate augmented images and save into the directory
for i in range(5):
  train_generator.next()