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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

Code implementation – discriminator

The discriminator is simpler in comparison to the generator. Deep convolutional networks are commonplace in classification research. The key thing to remember with GANs, however, is that the training should be adversarial—simply grabbing state-of-the-art classification techniques may not give the generator the ability to learn. In essence, there is a balancing act to structuring your discriminator.

Getting ready

As always, keep track of your directory and make sure that you are placing newly developed structures in the right place, as follows:

DCGAN
├── data
├── docker
├── README.md
├── run...
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