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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
Languages
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Toc

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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TensorFlow on mobile platforms

TensorFlow can be integrated into mobile apps for many use cases that involve one or more of the following machine learning tasks:

  • Speech recognition
  • Image recognition
  • Gesture recognition
  • Optical character recognition
  • Image or text classification
  • Image, text, or speech synthesis
  • Object identification

To run TensorFlow on mobile apps, we need two major ingredients:

  • A trained and saved model that can be used for predictions
  • A TensorFlow binary that can receive the inputs, apply the model, produce the predictions, and send the predictions as output

The high-level architecture looks like the following figure:

The mobile application code sends the inputs to the TensorFlow binary, which uses the trained model to compute predictions and send the predictions back.

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