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Generative Adversarial Networks Projects

You're reading from  Generative Adversarial Networks Projects

Product type Book
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136678
Pages 316 pages
Edition 1st Edition
Languages
Author (1):
Kailash Ahirwar Kailash Ahirwar
Profile icon Kailash Ahirwar

Table of Contents (11) Chapters

Preface 1. Introduction to Generative Adversarial Networks 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

Training the CycleGAN

We have already covered the training objective function in the An Introduction to CycleGANs section. We have also created the respective Keras models for both networks. Training the CycleGAN is a multi-step process. We will perform the following steps to train the network:

  1. Loading the dataset
  2. Creating the generator and the discriminator networks
  3. Training the network for a specified number of epochs
  4. Plotting the losses
  5. Generating new images

Let's define the essential variables before starting to train the network, as follows:

data_dir = "/Path/to/dataset/directory/*.*"
batch_size = 1
epochs = 500

Loading the dataset

Before doing anything else, load the dataset by performing the following...

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