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

Environment preparation

This recipe will show you how to develop the Docker container needed to run this 3D model generator and encoder. This chapter relies on the previous chapter, where you used the Kaggle API to download data. We'll use the same API to download data for this chapter.

Getting ready

As with previous chapters, we'll need to make sure that we have a folder created in the home directory on your Ubuntu machine. Check to make sure your directory structure appears as follows:

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

How to do it...

The Docker container is the heart of how we run these recipes—this chapter will start by building the infrastructure and will end with building the Docker container.

Creating the Docker container

This is how to create the Docker container for this chapter:

  1. Create a file under the docker folder called Dockerfile and place the following text into the file:
FROM base_image