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 bidirectional RNN model


Recurrent Neural Networks focus on capturing the sequential information at time t by using historical states only. However, bidirectional RNN train the model from both directions using two RNN layers with one moving forwards from start to end and another RNN layer moving backwards from end to start of sequence.

Thus, the model is dependent on historical and future data. The bidirectional RNN models are useful where causal structure exists such as in text and speech. The unfolded structure of bidirectional RNN is shown in the following figure:

Unfolded bidirectional RNN architecture

Getting ready

Install and set up TensorFlow:

  1. Load required packages:
library(tensorflow) 
  1. Load MNIST dataset.
  2. The image from MNIST dataset is reduced to 16 x 16 pixels and normalized (Details are discussed in the Setting-up RNN model section).

How to do it...

This section covers the steps to set-up a bidirectional RNN model.

  1. Reset the graph and start an interactive session:
# Reset the...