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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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Table of Contents (14) Chapters Close

Preface 1. Section 1: Introduction and Environment Setup FREE CHAPTER
2. Deep Learning Basics and Environment Setup 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

In this section, we are going to consider both our experimental setups: one where the generator and discriminator predict and discriminate sequences of words, and another where the models predict and discriminate sequences on characters. Note that in both cases, there is no difference between the representation of a word or a character; they are just vectors in multidimensional space.

Assuming the same sequence length, the task of predicting a sequence of characters is harder than the task of predicting a sequence of words. First, because in the character case the model has to perform more predictions. Second, because overall entropy or uncertainty when predicting characters is higher than predicting words, as it implies predicting a sequence of characters that form a word and predicting another sequence of characters that forms another word, and that is likely to be...

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