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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

Configuring Keras

Keras has a very minimalist configuration file. Let's load it with a vi session. The parameters are very simple:

Parameters Values
image_dim_ordering Can be either tf for the TensorFlow image ordering or th for Theano image ordering
epsilon The epsilon value used during computation
floatx Can be either float32 or float64
backend Can be either tensorflow or theano

The image_dim_ordering of th value gives you a somewhat non-intuitive dimension ordering for images (depth, width, and height), instead of (width, height, and depth), for tf. The following are the default parameters in my machine:

If you install a GPU-enabled TensorFlow version, then Keras will automatically use your configured GPU when TensorFlow is selected as the backend.
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