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

How to work with training data

As with every architecture we train throughout this book, understanding the structure of the data and the development environment is important to overall success. So, in this section, we'll set up the development environment and download the data inside the Docker container.

Getting ready

You'll need to create a folder at the $HOME directory level of your Linux machine with the following directory structure (which can be checked using the tree function):

├── docker
│   ├──
│   ├──
│   ├── Dockerfile
│   └── kaggle.json
├── out
└── src

How to do it...

In this chapter, we're going to introduce the Kaggle API so we can grab the necessary data for the SimGAN training architecture. Using the Kaggle API will require you to set up a Kaggle account and get API token access.

Kaggle and its API is a popular online site that holds machine learning (ML) competitions and discussions. Kaggle also supplies an API for accessing...