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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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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

Keras adversarial GANs for forging MNIST

Keras adversarial (https://github.com/bstriner/keras-adversarial) is an open source Python package for building GANs developed by Ben Striner (https://github.com/bstriner and https://github.com/bstriner/keras-adversarial/blob/master/LICENSE.txt). Since Keras just recently moved to 2.0, I suggest downloading latest Keras adversarial package:

git clone --depth=50 --branch=master https://github.com/bstriner/keras-adversarial.git

And install setup.py:

python setup.py install

Note that compatibility with Keras 2.0 is tracked in this issue https://github.com/bstriner/keras-adversarial/issues/11.

 If the generator G and the discriminator D are based on the same model, M, then they can be combined into an adversarial model; it uses the same input, M, but separates targets and metrics for G and D. The library has the following API call:

adversarial_model...
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