shuffle=True,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
shuffle=False,
class_mode='categorical')
model_ft = tf.keras.models.load_model(args.model)
print(model_ft)
for i in range(2):
model_ft.layers.pop()
im_in = Input(shape=(img_width, img_height, 3))
if args.network == 'resnetV2':
base_model = resNetV2(img_width, img_height, include_top=False)
elif args.network == 'squeezenet':
base_model = squeezeNet(img_width, img_height, .2, include_top=False)
elif args.network == 'vgg16':
base_model = vgg16(img_width, img_height, include_top=False)
elif args.network == 'mesonet':
base_model = mesonet(img_width, img_height, include_top=False)
elif args.network == 'denseNet':
base_model = denseNet(img_width, img_height, include_top=False)
base_model.set_weights(model_ft.get_weights())
pt_output = base_model(im_in)
