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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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Table of Contents (10) Chapters Close

Preface 1. What Is a Generative Adversarial Network? FREE CHAPTER 2. Data First, Easy Environment, and Data Prep 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

Training

Have you got all the pieces? We're ready to go, right? WRONG! We need to understand the best a strategy for how we can train this type of architecture.

How to do it...

The GAN model relies on so-called adversarial training. You'll notice in the following diagram that there are two seemingly conflicting error functions being minimized/maximized.

How it works...

We've talked about the MiniMax problem at work here. By sampling two mini-batches at every epoch, the GAN architecture is able to simultaneously maximize the error to the generator and minimize the error to the discriminator:

Architecture diagram updated to show the backpropagation step in training the GAN model

In each chapter, we'll revisit what it means to train a GAN. Generative models are notoriously difficult to train to get good results. GANs are no different in this respect. There are tips and tricks that you will learn throughout this book in order to get your models to converge and produce results.

You have been reading a chapter from
Generative Adversarial Networks Cookbook
Published in: Dec 2018
Publisher: Packt
ISBN-13: 9781789139907
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