Book Image

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

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...