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Hands-On Generative Adversarial Networks with Keras

You're reading from   Hands-On Generative Adversarial Networks with Keras Your guide to implementing next-generation generative adversarial networks

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789538205
Length 272 pages
Edition 1st Edition
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Author (1):
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Rafael Valle Rafael Valle
Author Profile Icon Rafael Valle
Rafael Valle
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Toc

Table of Contents (14) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup
2. Deep Learning Basics and Environment Setup FREE CHAPTER 3. Introduction to Generative Models 4. Section 2: Training GANs
5. Implementing Your First GAN 6. Evaluating Your First GAN 7. Improving Your First GAN 8. Section 3: Application of GANs in Computer Vision, Natural Language Processing, and Audio
9. Progressive Growing of GANs 10. Generation of Discrete Sequences Using GANs 11. Text-to-Image Synthesis with GANs 12. TequilaGAN - Identifying GAN Samples 13. Whats next in GANs

Inference

The imports are shared among all inference experiments:

import matplotlib
matplotlib.use("Agg")
import matplotlib.pylab as plt
import numpy as np
from utils import get_data, iterate_minibatches, load_model

In the following subsections, we are going to sample our generator in four different manners. We are going to leverage the validation set to collect text embeddings.

Sampling the generator

We randomly sample the generator to informally evaluate image quality with respect to the constraints described in the text. Inference is straightforward and is described in the following code block:

def infer(data_filepath='data/flowers.hdf5', z_dim=128, out_dir='gan',
n_samples=5):
# we...
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