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

Product type Paperback

Published in Jan 2018

Publisher Packt

ISBN-13 9781788292061

Length 474 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Table of Contents (26) Chapters

Title Page

Packt Upsell

Foreword

Contributors

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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The tfruns package in R

Note

You can follow along with the code in the Jupyter R notebook ch-17d_TensorBoard_in_R.

The tfruns package is very useful tool provided in R that helps in tracking multiple runs for training the models. The run data is automatically captured by the tfruns package for models built using the keras and tfestimators packages in R. Using tfruns is pretty simple and easy. Just create your code in an R file and then execute the file using the training_run() function. For example, if you have a mnist_model.R file, then execute it using the training_run() function in the interactive R console as follows:

library(tfruns)
training_run('mnist_model.R')

Once the training is finished, the window displaying the summary of the run appears automatically. We get the following output in the window from the mnist_mlp.R we got from the tfruns GitHub repository (https://github.com/rstudio/tfruns/blob/master/inst/examples/mnist_mlp/mnist_mlp.R).