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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

Training your first GAN

Training is the life blood of your model. You've created something, but without a way to train and show it to the world, it is meaningless. This class is going to provide your model with a way to train and a method for you to spot check the training.

Getting ready

Remember to complete all of the steps up until this point! Once you get that done, you need to create the train.py and run.py files in the full-gan folder. Also, if you haven't done so already, go ahead and create the data folder inside of the full-gan folder. Ensure that you have put the full-gan folder at the $HOME/full-gan location, or you might need to update your run.sh script.

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