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R Deep Learning Cookbook

You're reading from   R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781787121089
Length 288 pages
Edition 1st Edition
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Authors (2):
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Achyutuni Sri Krishna Rao Achyutuni Sri Krishna Rao
Author Profile Icon Achyutuni Sri Krishna Rao
Achyutuni Sri Krishna Rao
PKS Prakash PKS Prakash
Author Profile Icon PKS Prakash
PKS Prakash
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Deep Learning with R 3. Convolution Neural Network 4. Data Representation Using Autoencoders 5. Generative Models in Deep Learning 6. Recurrent Neural Networks 7. Reinforcement Learning 8. Application of Deep Learning in Text Mining 9. Application of Deep Learning to Signal processing 10. Transfer Learning

Visualizing TensorFlow graphs


TensorFlow graphs can be visualized using TensorBoard. It is a service that utilizes TensorFlow event files to visualize TensorFlow models as graphs. Graph model visualization in TensorBoard is also used to debug TensorFlow models.

Getting ready

TensorBoard can be started using the following command in the terminal:

$ tensorboard --logdir home/log --port 6006 

The following are the major parameters for TensorBoard:

  • --logdir : To map to the directory to load TensorFlow events
  • --debug: To increase log verbosity
  • --host: To define the host to listen to its localhost (127.0.0.1) by default
  • --port: To define the port to which TensorBoard will serve

The preceding command will launch the TensorFlow service on localhost at port 6006, as shown in the following screenshot:

TensorBoard

The tabs on the TensorBoard capture relevant data generated during graph execution.

How to do it...

The section covers how to visualize TensorFlow models and output in TernsorBoard.

  1. To visualize summaries...
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