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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

Customizing Keras


Just as composing our basic building blocks into larger architectures enables us to build interesting deep learning models, sometimes we need to look at the other end of the spectrum. Keras has a lot of functionality built in already, so it is very likely that you can build all your models with the provided components and not feel the need for customization at all. In case you do need customization, Keras has you covered.

As you will recall, Keras is a high level API that delegates to either a TensorFlow or Theano backend for the computational heavy lifting. Any code you build for your customization will call out to one of these backends. In order to keep your code portable across the two backends, your custom code should use the Keras backend API (https://keras.io/backend/), which provides a set of functions that act like a facade over your chosen backend. Depending on the backend selected, the call to the backend facade will translate to the appropriate TensorFlow or Theano...

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