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Neural Network Programming with TensorFlow

You're reading from   Neural Network Programming with TensorFlow Unleash the power of TensorFlow to train efficient neural networks

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788390392
Length 274 pages
Edition 1st Edition
Languages
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Authors (2):
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Manpreet Singh Ghotra Manpreet Singh Ghotra
Author Profile Icon Manpreet Singh Ghotra
Manpreet Singh Ghotra
Rajdeep Dua Rajdeep Dua
Author Profile Icon Rajdeep Dua
Rajdeep Dua
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Toc

Table of Contents (11) Chapters Close

Preface 1. Maths for Neural Networks FREE CHAPTER 2. Deep Feedforward Networks 3. Optimization for Neural Networks 4. Convolutional Neural Networks 5. Recurrent Neural Networks 6. Generative Models 7. Deep Belief Networking 8. Autoencoders 9. Research in Neural Networks 10. Getting started with TensorFlow

TensorBoard

TensorFlow has a powerful built-in visualization tool called TensorBoard. It allows developers to interpret, visualize, and debug computational graphs. To visualize graph and metrics automatically in TensorBoard, TensorFlow writes events related to the execution of a computational graph to a particular folder.

This example shows a computational graph of the analysis done earlier:

To examine the graph, click on the graph tab on the top panel of TensorBoard. If the graph has several nodes, visualizing it in a single view can be hard. To make our visualization more accessible, we can organize the related operations into groups using tf.name_scope with specific names.

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