thusfor the overall process of neural style transferwe have an overall loss functionwhich can be defined as combination of content and style loss functions lstyle transfer argming(alcontent(cgblstyle(sg)where and are weights used to control the impact of content and style components on the overall loss the loss function we will try to minimize consists of three parts namelythe content lossthe style lossand the total variation losswhich we will be talking about later the beauty of deep learning is that by leveraging architectures like deep convolutional neural networks (cnns)we can mathematically define the above-mentioned style and content functions we will be using principles of transfer learning in building our system for neural style transfer we introduced the concept of transfer learning using pre-trained deep cnn model like vgg- we will be leveraging the same pre-trained model for the task of neural style transfer the main steps are outlined as follows leverage vgg- to help compute layer activations for the stylecontentand generated image use these activations to define specific loss functions mentioned earlier finallyuse gradient descent to minimize the overall loss we recommend you follow along this section with the notebook titled neural style transfer ipynbwhich contains step-by-step details of the style transfer process we would also like to give special mention and thanks to francois chollet as well as harish narayanan for providing some excellent resources on style transfer details on the same will be mentioned later we also recommend you check out the following papers (detailed links shared later ona neural algorithm of artistic style by leon gatysalexander eckerand matthias bethge perceptual losses for real-time style transfer and super-resolution by justin johnsonalexandre alahiand li fei-fei preprocessing the first and foremost step toward implementation of such network is to preprocess the data or images in this case the following are quick utilities to preprocess images for size and channel adjustments import numpy as np from keras applications import vgg from keras preprocessing image import load_imgimg_to_array def preprocess_image(image_pathheight=nonewidth=none)height if not height else height width width if width else int(width height heightimg load_img(image_pathtarget_size=(heightwidth)img img_to_array(imgimg np expand_dims(imgaxis= img vgg preprocess_input(imgreturn img def deprocess_image( )remove zero-center by mean pixel [:: +