Hands-On Transfer Learning with Python

Image preprocessing methodology

The first and foremost step towards implementation of such a network is to preprocess the data or images in this case. The following code snippet shows some quick utilities to preprocess and postprocess images for size and channel adjustments:

import numpy as npfrom keras.applications import vgg16from keras.preprocessing.image import load_img, img_to_arraydef preprocess_image(image_path, height=None, width=None):    height = 400 if not height else height    width = width if width else int(width * height / height)    img = load_img(image_path, target_size=(height, width))    img = img_to_array(img)    img = np.expand_dims(img, axis=0)    img = vgg16.preprocess_input(img)    return imgdef deprocess_image(x): # Remove zero-center ...