Book Image

Generative Adversarial Networks Cookbook

By : Josh Kalin
Book Image

Generative Adversarial Networks Cookbook

By: Josh Kalin

Overview of this book

Developing Generative Adversarial Networks (GANs) is a complex task, and it is often hard to find code that is easy to understand. This book leads you through eight different examples of modern GAN implementations, including CycleGAN, simGAN, DCGAN, and 2D image to 3D model generation. Each chapter contains useful recipes to build on a common architecture in Python, TensorFlow and Keras to explore increasingly difficult GAN architectures in an easy-to-read format. The book starts by covering the different types of GAN architecture to help you understand how the model works. This book also contains intuitive recipes to help you work with use cases involving DCGAN, Pix2Pix, and so on. To understand these complex applications, you will take different real-world data sets and put them to use. By the end of this book, you will be equipped to deal with the challenges and issues that you may face while working with GAN models, thanks to easy-to-follow code solutions that you can implement right away.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
About Packt
Dedication
Contributors
Preface
Dedication2
Index

Training


This is the step that brings it all together: training! In this recipe, you'll learn how to put all of these networks together and train your Pix2Pix network to do a style transfer.

Getting ready

Spot check! Make sure you have the following files in your working directory:

├── docker
│   ├── build.sh
│   ├── clean.sh
│   └── Dockerfile
├── README.md
├── run.sh
└── src
|   ├── generator.py
|   ├── discriminator.py
|   ├── gan.py
|   ├── train.py

Make sure you have the generator, discriminator, and GAN networks all built—otherwise, nothing in the training script will work!

How to do it...

This is how we start training our models - we need to create the right connections to each of the networks and inputs in the class instantiation,. build the training method that allows up to train this network, and finally understand the helper functions that allow us to make all of this code possible.

Setting up the class

Follow these steps to set up your class and initialize your training method:

  1. The imports...