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

Setting up a Deep Restricted Boltzmann Machine


Unlike DBNs, Deep Restricted Boltzmann Machines (DRBM) are undirected networks of interconnected hidden layers with the capability to learn joint probabilities over these connections. In the current setup, centering is performed where visible and hidden variables are subtracted from offset bias vectors after every iteration. Research has shown that centering optimizes the performance of DRBMs and can reach higher log-likelihood values in comparison with traditional RBMs.

Getting ready

This section provides the requirements for setting up a DRBM:

  • The MNIST dataset is loaded and set up
  • The tensorflow package is set up and loaded

How to do it...

This section covers detailed the steps for setting up the DRBM model using TensorFlow in R:

  1. Define the parameters for the DRBM:
learning_rate = 0.005
momentum = 0.005
minbatch_size = 25
hidden_layers = c(400,100)
biases = list(-1,-1)
  1. Define a sigmoid function using a hyperbolic arc tangent [(log(1+x) -log(1-x))...