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

Tuning hyper-parameters using grid searches in H2O


H2O packages also allow you to perform hyper-parameter tuning using grid search (h2o.grid).

Getting ready

We first load and initialize the H2O package with the following code:

# Load the required packages
require(h2o)

# Initialize H2O instance (single node)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE,min_mem_size = "20G",nthreads = 8)

The occupancy dataset is loaded, converted to hex format, and named occupancy_train.hex.

How to do it...

The section will focus on optimizing hyper parameters in H2O using grid searches.

  1. In our case, we will optimize for the activation function, the number of hidden layers (along with the number of neurons in each layer), epochs, and regularization lambda (l1 and l2):
# Perform hyper parameter tuning
activation_opt <- c("Rectifier","RectifierWithDropout", "Maxout","MaxoutWithDropout")
hidden_opt <- list(5, c(5,5))
epoch_opt <- c(10,50,100)
l1_opt <- c(0,1e-3,1e-4)
l2_opt <- c...