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

Learning the architecture of a CNN classifier


The CNN classifier covered in this chapter has two convolution layers followed by two fully connected layers in the end, in which the last layer acts as a classifier using the softmax activation function.

Getting ready

The recipe requires the CIFAR-10 dataset. Thus, the CIFAR-10 dataset should be downloaded and loaded into the R environment. Also, images are of size 32 x 32 pixels.

How to do it...

Let's define the configuration of the CNN classifier as follows:

  1. Each input image (CIFAR-10) is of size 32 x 32 pixels and can be labeled one among 10 classes:
# CIFAR images are 32 x 32 pixels.
img_width  = 32L
img_height = 32L

# Tuple with height and width of images used to reshape arrays.
img_shape = c(img_width, img_height)
# Number of classes, one class for each of 10 images
num_classes = 10L
  1. The images of the CIFAR-10 dataset have three channels (red, green, and blue):
# Number of color channels for the images: 3 channel for red, blue, green scales....