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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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Table of Contents (21) Chapters Close

Preface 1. TensorFlow 101 2. High-Level Libraries for TensorFlow FREE CHAPTER 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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High-Level Libraries for TensorFlow

There are several high-level libraries and interfaces (API) for TensorFlow that allow us to build and train models easily and with less amount of code such as TF Learn, TF Slim, Sonnet, PrettyTensor, Keras and recently released TensorFlow Estimators.

We will cover the following high-level libraries in this chapter while dedicating the next chapter to Keras:

  • TF Estimator - previously TF Learn
  • TF Slim
  • TFLearn
  • PrettyTensor
  • Sonnet

We shall provide examples of building the models for MNIST dataset using all of the five libraries. Do not worry about understanding the details of the models yet as we cover the details of models from chapter 4 onwards.

You can follow the code examples in this chapter with the Jupyter Notebook ch-02_TF_High_Level_Libraries included in the code bundle. Try modifying the examples in the notebook to experiment and play...
You have been reading a chapter from
Mastering TensorFlow 1.x
Published in: Jan 2018
Publisher: Packt
ISBN-13: 9781788292061
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