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Generative Adversarial Networks Projects

You're reading from   Generative Adversarial Networks Projects Build next-generation generative models using TensorFlow and Keras

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136678
Length 316 pages
Edition 1st Edition
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Author (1):
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Kailash Ahirwar Kailash Ahirwar
Author Profile Icon Kailash Ahirwar
Kailash Ahirwar
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Table of Contents (11) Chapters Close

Preface 1. Introduction to Generative Adversarial Networks FREE CHAPTER 2. 3D-GAN - Generating Shapes Using GANs 3. Face Aging Using Conditional GAN 4. Generating Anime Characters Using DCGANs 5. Using SRGANs to Generate Photo-Realistic Images 6. StackGAN - Text to Photo-Realistic Image Synthesis 7. CycleGAN - Turn Paintings into Photos 8. Conditional GAN - Image-to-Image Translation Using Conditional Adversarial Networks 9. Predicting the Future of GANs 10. Other Books You May Enjoy

Setting up a project

The source code for the project is available on GitHub at the following link: https://github.com/PacktPublishing/Generative-Adversarial-Networks-Projects.

Run the following commands to set up the project:

  1. Start by navigating to the parent directory, as follows:
cd Generative-Adversarial-Networks-Projects
  1. Next, change directory from the current directory to the Chapter02 directory:
cd Chapter02
  1. Next, create a Python virtual environment for this project:
virtualenv venv
  1. After that, activate the virtual environment:
source venv/bin/activate
  1. Finally, install all the requirements that are indicated in the requirements.txt file:
pip install -r requirements.txt

We have now successfully set up the project. For more information, refer to the README.md file included with the code repository.

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