model.py

import numpy as np
from keras.models import load_model
from keras.applications.resnet50 import preprocess_input


def channel_axis(self):
	return 3


def predictions(self, image):
	
	image=image[:,:,::-1]
	prediction = np.argmax(self.predictions(image))
        	
        return prediction

def bounds(self):
        	
	return (0, 255)


def create_fmodel():
	model = keras.load_model('/home/shikhar/notebooks/resnet18_tiny_imagenet_standard.h5')
	preprocessing = (np.array([104, 116, 123]), 1)
	fmodel=foolbox.models.KerasModel(model, bounds=(0, 255), preprocessing=preprocessing)
	return fmodel