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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
20. Other Books You May Enjoy

Saving and Restoring models in TensorFlow

You can save and restore the models and the variables in TensorFlow by one of the following two methods:

  • A saver object created from the tf.train.Saver class
  • A SavedModel format based object created from the tf.saved_model_builder.SavedModelBuilder class

Let us see both the methods in action.

You can follow along with the code in the Jupyter notebook ch-11a_Saving_and_Restoring_TF_Models.

Saving and restoring all graph variables with the saver class

We proceed as follows:

  1. To use the saver class, first an object of this class is created:
saver = tf.train.Saver()
  1. The simplest way to save all the variables in a graph is to call the save() method with the following two parameters...
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