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 neural network using TensorFlow


In this section, we will cover an application of TensorFlow in setting up a two-layer neural network model.

Getting ready

To start modeling, load the tensorflow package in the environment. R loads the default tf environment variable and also the NumPy library from Python in the np variable:

library("tensorflow") # Load Tensorflow 
np <- import("numpy") # Load numpy library

How to do it...

  1. The data is imported using the standard function from R, as shown in the following code. The data is imported using the read.csv file and transformed into the matrix format followed by selecting the features used for the modeling as defined in xFeatures and yFeatures:
# Loading input and test data
xFeatures = c("Temperature", "Humidity", "Light", "CO2", "HumidityRatio")
yFeatures = "Occupancy"
occupancy_train <-as.matrix(read.csv("datatraining.txt",stringsAsFactors = T))
occupancy_test <- as.matrix(read.csv("datatest.txt",stringsAsFactors = T))

# subset features...