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Mastering TensorFlow 1.x

You're reading from   Mastering TensorFlow 1.x Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788292061
Length 474 pages
Edition 1st Edition
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Toc

Table of Contents (21) Chapters Close

Preface 1. TensorFlow 101 FREE CHAPTER 2. High-Level Libraries for TensorFlow 3. Keras 101 4. Classical Machine Learning with TensorFlow 5. Neural Networks and MLP with TensorFlow and Keras 6. RNN with TensorFlow and Keras 7. RNN for Time Series Data with TensorFlow and Keras 8. RNN for Text Data with TensorFlow and Keras 9. CNN with TensorFlow and Keras 10. Autoencoder with TensorFlow and Keras 11. TensorFlow Models in Production with TF Serving 12. Transfer Learning and Pre-Trained Models 13. Deep Reinforcement Learning 14. Generative Adversarial Networks 15. Distributed Models with TensorFlow Clusters 16. TensorFlow Models on Mobile and Embedded Platforms 17. TensorFlow and Keras in R 18. Debugging TensorFlow Models 19. Tensor Processing Units
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Sonnet

Sonnet is an object-oriented library written in Python. It was released by DeepMind in 2017. Sonnet intends to cleanly separate the following two aspects of building computation graphs from objects:

  • The configuration of objects called modules
  • The connection of objects to computation graphs

Sonnet can be installed in Python 3 with the following command:

pip3 install dm-sonnet
Sonnet can be installed from the source by following directions from the following link: https://github.com/deepmind/sonnet/blob/master/docs/INSTALL.md.

The modules are defined as sub-classes of the abstract class sonnet.AbstractModule. At the time of writing this book, the following modules are available in Sonnet:

Basic modules AddBias, BatchApply, BatchFlatten, BatchReshape, FlattenTrailingDimensions, Linear, MergeDims, SelectInput, SliceByDim, TileByDim, and TrainableVariable
Recurrent modules...
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