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

Parsing the CycleGAN dataset

You'll get tired of hearing how important data is to us—but honestly, it can make or break your development. In our case, we are going to simply use the same datasets that the original CycleGAN authors used in their development. This has two use cases: we can compare our results to theirs and we can take advantage of their small curated datasets.

Getting ready

So far, we've focused on just reviewing the structure of how we will solve the problem. As with every one of these chapters, we need to spend a few minutes collecting training data for our experiments. Replicate the directory structure with files, as seen as follows:

├── data
│   ├── 
├── docker
│   ├──
│   ├──
│   └── Dockerfile
├── scripts
│   └──
├── src
│   ├── 

We'll go and introduce the files you'll need to build so you can have a development environment and data to work with on CycleGAN.

How to do it...

This should start to become a habit by now...