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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

Backward or reconstruction phase of RBM


In the reconstruction phase, the data from the hidden layer is passed back to the visible layer. The hidden layer vector of probabilities h0 is multiplied by the transpose of the weight matrix W and added to a visible layer bias vb, which is then passed through a sigmoid function to generate a reconstructed input vector prob_v1.

A sample input vector is created using the reconstructed input vector, which is then multiplied by the weight matrix W and added to the hidden bias vector hb to generate an updated hidden vector of probabilities h1.

This is also called Gibbs sampling. In some scenarios, the sample input vector is not generated and the reconstructed input vector prob_v1 is directly used to update the hi

Getting ready

This section provides the requirements for image reconstruction using the input probability vector.

  • mnist data is loaded in the environment
  • The RBM model is trained using the recipe Training a Restricted Boltzmann machine

How to do it...

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