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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? 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

Putting all the GAN pieces together

We've got a generator and a discriminator—that's all we need, right? Not so fast. We need to actually create the adversarial model. Also, there is an open ended question about why are we not focusing more on the loss function. In this case, each of the loss functions are built into the Keras library, so we aren't going to focus heavily on that aspect right now. When we cover more complex models, the loss functions will need to be customized, and that will require more explanation. For now, let's keep our focus on how to structure a basic GAN and how we can train it in an adversarial manner.

Getting ready

All of this code will be put into the gan.py file under...

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