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Deep Learning with Keras

You're reading from   Deep Learning with Keras Implementing deep learning models and neural networks with the power of Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787128422
Length 318 pages
Edition 1st Edition
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Authors (2):
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Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
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Toc

Table of Contents (10) Chapters Close

Preface 1. Neural Networks Foundations FREE CHAPTER 2. Keras Installation and API 3. Deep Learning with ConvNets 4. Generative Adversarial Networks and WaveNet 5. Word Embeddings 6. Recurrent Neural Network — RNN 7. Additional Deep Learning Models 8. AI Game Playing 9. Conclusion

What is a GAN?

The key intuition of GAN can be easily considered as analogous to art forgery, which is the process of creating works of art (https://en.wikipedia.org/wiki/Art) that are falsely credited to other, usually more famous, artists. GANs train two neural nets simultaneously, as shown in the next diagram. The generator G(Z) makes the forgery, and the discriminator D(Y) can judge how realistic the reproductions based on its observations of authentic pieces of arts and copies are. D(Y) takes an input, Y, (for instance, an image) and expresses a vote to judge how real the input is--in general, a value close to zero denotes real and a value close to one denotes forgery. G(Z) takes an input from a random noise, Z, and trains itself to fool D into thinking that whatever G(Z) produces is real. So, the goal of training the discriminator D(Y) is to maximize D(Y) for...

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