Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Implementing neural style transfer using PyTorch

Having discussed the internals of a neural style transfer system, we are all set to build one using PyTorch. In the form of an exercise, we will load a style and a content image. Then, we will load the pretrained VGG model. After defining which layers to compute the style and content loss on, we will trim the model so that it only retains the relevant layers. Finally, we will train the neural style transfer model to refine the generated image epoch by epoch.

Loading the content and style images

In this exercise, we will only show the important parts of the code for demonstration purposes. To access the full code, go to our GitHub repository [3]. Follow these steps:

  1. First, we need to import the necessary libraries:
    from PIL import Image
    import matplotlib.pyplot as plt
    import torch
    import torch.nn as nn
    import torch.optim as optim
    import torchvision
    dvc = torch.device("cuda" if torch.cuda.is_available...