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
About the Authors
About the Reviewer
Customer Feedback

Initializing and starting a new TensorFlow session

A big part of calculating the error metric such as mean square error (MSE) is initialization and starting a new TensorFlow session. Here is how we proceed with it.

Getting ready

This section provides the requirements for starting a new TensorFlow session used to compute the error metric.

  • mnist data is loaded in the environment
  • The TensorFlow graph for the RBM is loaded

How to do it...

This section provides the steps for optimizing the error using reconstruction from an RBM:

  1. Initialize the current and previous vector of biases and matrices of weights:
cur_w = tf$Variable(tf$zeros(shape = shape(num_input, num_hidden), dtype=tf$float32)) 
cur_vb = tf$Variable(tf$zeros(shape = shape(num_input), dtype=tf$float32)) 
cur_hb = tf$Variable(tf$zeros(shape = shape(num_hidden), dtype=tf$float32)) 
prv_w = tf$Variable(tf$random_normal(shape=shape(num_input, num_hidden), stddev=0.01, dtype=tf$float32)) 
prv_vb = tf$Variable(tf$zeros(shape = shape(num_input),...