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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 a StackGAN

In this section, we will learn how to train both the StackGANs. In the first subsection, we will train the Stage-I StackGAN. In the second subsection, we will train the Stage-II StackGAN.

Training the Stage-I StackGAN

Before starting the training, we need to specify the essential hyperparameters. Hyperparameters are values that don't change during the training. Let's do this first:

data_dir = "Specify your dataset directory here/Data/birds"
train_dir = data_dir + "/train"
test_dir = data_dir + "/test"
image_size = 64
batch_size = 64
z_dim = 100
stage1_generator_lr = 0.0002
stage1_discriminator_lr = 0.0002
stage1_lr_decay_step = 600
epochs = 1000
condition_dim = 128

embeddings_file_path_train...
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