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Generative Adversarial Networks Cookbook

You're reading from   Generative Adversarial Networks Cookbook Over 100 recipes to build generative models using Python, TensorFlow, and Keras

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789139907
Length 268 pages
Edition 1st Edition
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Author (1):
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Josh Kalin Josh Kalin
Author Profile Icon Josh Kalin
Josh Kalin
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Toc

Table of Contents (10) Chapters Close

Preface 1. What Is a Generative Adversarial Network? 2. Data First, Easy Environment, and Data Prep FREE CHAPTER 3. My First GAN in Under 100 Lines 4. Dreaming of New Outdoor Structures Using DCGAN 5. Pix2Pix Image-to-Image Translation 6. Style Transfering Your Image Using CycleGAN 7. Using Simulated Images To Create Photo-Realistic Eyeballs with SimGAN 8. From Image to 3D Models Using GANs 9. Other Books You May Enjoy

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
│   ├── build.sh
│   ├── clean.sh
│   ├── Dockerfile
│   └── kaggle.json
├── out
├── README.md
├── run.sh
└── 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

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

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