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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 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
20. Other Books You May Enjoy

Installing TensorFlow and Keras packages in R

To install the three R packages that support TensorFlow and Keras in R, execute the following commands in R.

  1. First, install devtools:
install.packages("devtools")
  1. Install the tensorflow and tfestimators package:
devtools::install_github("rstudio/tensorflow")
devtools::install_github("rstudio/tfestimators")
  1. Load the tensorflow library and install the required functions:
library(tensorflow)
install_tensorflow()
  1. By default, the install function creates a virtual environment and installs the tensorflow package in the virtual environment.

There are four installation methods available, which can be specified using the method parameter:

auto Automatically choose a default for the current platform
virtualenv Install into the virtual environment located at ~/.virtualenvs/r-tensorflow
conda Install...
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