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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

Building an RBM model

In this recipe, we will build an RBM model as discussed (in detail) in Chapter 5, Generative Models in Deep Learning.

Getting ready

Let's set up our system for the model:

  1. In Piano, the lowest note is 24 and the highest is 102; hence, the range of notes is 78. Thus, the number of columns in the encoded matrix is 156 (that is, 78 for note-on and 78 for note-off):
lowest_note = 24L 
highest_note = 102L 
note_range = highest_note-lowest_note 
  1. We will create notes for 15 number of steps at a time with 2,340 nodes in the input layer and 50 nodes in the hidden layer:
num_timesteps  = 15L 
num_input      = 2L*note_range*num_timesteps 
num_hidden       = 50L 
 
  1. The learning rate (alpha) is 0.1:
alpha...
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