You're reading from Generative Adversarial Networks Projects

Product typeBook

Published inJan 2019

Reading LevelIntermediate

PublisherPackt

ISBN-139781789136678

Edition1st Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Deep Learning

Author (1)

Kailash Ahirwar

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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Generative Adversarial Networks Projects

Published in: Jan 2019Publisher: PacktISBN-13: 9781789136678

Author (1)

Kailash Ahirwar

Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence (AI), ranging from natural language processing and computer vision to generative modeling using GANs. He is a co-founder and CTO of Mate Labs. He uses GANs to build different models, such as turning paintings into photos and controlling deep image synthesis with texture patches. He is super optimistic about AGI and believes that AI is going to be the workhorse of human evolution.
Read more about Kailash Ahirwar

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Author (1)

Hands-On Generative Adversarial Networks with PyTorch 1.x

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