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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 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 101

TensorFlow is one of the popular libraries for solving problems with machine learning and deep learning. After being developed for internal use by Google, it was released for public use and development as open source. Let us understand the three models of the TensorFlow: data model, programming model, and execution model.

TensorFlow data model consists of tensors, and the programming model consists of data flow graphs or computation graphs. TensorFlow execution model consists of firing the nodes in a sequence based on the dependence conditions, starting from the initial nodes that depend on inputs.

In this chapter, we will review the elements of TensorFlow that make up these three models, also known as the core TensorFlow.

We will cover the following topics in this chapter:

  • TensorFlow core
    • Tensors
    • Constants
    • Placeholders
    • Operations
    • Creating tensors from Python objects
    • Variables
    • Tensors generated from library functions
  • Data flow graph or computation graph
    • Order of execution and lazy loading
    • Executing graphs across compute devices - CPU and GPGPU
    • Multiple graphs
  • TensorBoard overview
This book is written with a practical focus in mind, hence you can clone the code from the book's GitHub repository or download it from Packt Publishing. You can follow the code examples in this chapter with the Jupyter Notebook ch-01_TensorFlow_101 included in the code bundle.
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