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

Generating new music notes


In this recipe, we will generate new sample music notes. New musical notes can be generated by altering parameter num_timesteps. However, one should keep in mind to increase the timesteps, as it can become computationally inefficient to handle increased dimensionality of vectors in the current setup of RBM. These RBMs can be made efficient in learning by creating their stacks (namely Deep Belief Networks). Readers can leverage the DBN codes of Chapter 5, Generative Models in Deep Learning, to generate new musical notes.

How to do it...

  1. Create new sample music:
hh0 = tf$nn$sigmoid(tf$matmul(X, W) + hb) 
vv1 = tf$nn$sigmoid(tf$matmul(hh0, tf$transpose(W)) + vb) 
feed = sess$run(hh0, feed_dict=dict( X= sample_image, W= prv_w, hb= prv_hb)) 
rec = sess$run(vv1, feed_dict=dict( hh0= feed, W= prv_w, vb= prv_vb)) 
S = np$reshape(rec[1,],newshape=shape(num_timesteps,2*note_range)) 
  1. Regenerate the MIDI file:
midi_manipulation$noteStateMatrixToMidi(S, name=paste0("generated_chord_1...