preprocessing.py

from keras.applications.nasnet import Xception, preprocess_input
from keras.models import Sequential, Model
from keras.layers.core import Lambda
from keras.backend import tf as ktf

# Initialize a Xception model
Xception_model = Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)

# Any required pre-processing should be baked into the model
input_tensor = Input(shape=(None, None, 3))
x = Lambda(lambda image: ktf.image.resize_images(image, (299, 299)))(input_tensor)
x = Lambda(lambda image: preprocess_input(image))(x)
output_tensor = Xception_model(x)

final_Xception_model = Model(input_tensor, output_tensor)