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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 2. 3D-GAN - Generating Shapes Using GANs FREE CHAPTER 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

A Keras implementation of a 3D-GAN

In this section, we will implement the generator network and the discriminator network in the Keras framework. We need to create two Keras models. Both of the networks will have their own separate weights values. Let's start with the generator network.

The generator network

To implement the generator network, we need to create a Keras model and add the neural network layers. The steps required to implement the generator network are as follows:

  1. Start by specifying the values for different hyperparameters:
z_size = 200
gen_filters = [512, 256, 128, 64, 1]
gen_kernel_sizes = [4, 4, 4, 4, 4]
gen_strides = [1, 2, 2, 2, 2]
gen_input_shape = (1, 1, 1, z_size)
gen_activations = ['relu&apos...
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Generative Adversarial Networks Projects
Published in: Jan 2019
Publisher: Packt
ISBN-13: 9781789136678
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