neural style transfer preserve style by maintaining similar correlations within activations for both lowlevel layers and high-level layers feature correlations capture textures the generated image and the style-reference image should share the same textures at different spatial scales nowlet' look at keras implementation of the original neural style transfer algorithm as you'll seeit shares many similarities with the deepdream implementation developed in the previous section neural style transfer in keras neural style transfer can be implemented using any pretrained convnet hereyou'll use the vgg network used by gatys et al vgg is simple variant of the vgg network introduced in with three more convolutional layers this is the general process set up network that computes vgg layer activations for the style-reference imagethe target imageand the generated image at the same time use the layer activations computed over these three images to define the loss function described earlierwhich you'll minimize in order to achieve style transfer set up gradient-descent process to minimize this loss function let' start by defining the paths to the style-reference image and the target image to make sure that the processed images are similar size (widely different sizes make style transfer more difficult)you'll later resize them all to shared height of px listing defining initial variables from keras preprocessing image import load_imgimg_to_array target_image_path 'img/portrait jpgstyle_reference_image_path 'img/transfer_style_reference jpgwidthheight load_img(target_image_pathsize img_height img_width int(width img_height heightpath to the image you want to transform path to the style image dimensions of the generated picture you need some auxiliary functions for loadingpreprocessingand postprocessing the images that go in and out of the vgg convnet listing auxiliary funct ions import numpy as np from keras applications import vgg def preprocess_image(image_path)img load_img(image_pathtarget_size=(img_heightimg_width)img img_to_array(imgimg np expand_dims(imgaxis= img vgg preprocess_input(imgreturn img licensed to