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

Creating the first convolution layer


In this recipe, let's create the first convolution layer.

Getting ready

The following are the inputs to the function create_conv_layer defined in the recipe Using functions to create a new convolution layer.

  • Input: This is a four-dimensional reshaped input placeholder variable: x_image
  • Num_input_channels: This is the number of color channels, namely num_channels
  • Filter_size: This is the height and width of the filter layer filter_size1
  • Num_filters: This is the depth of the filter layer, namely num_filters1
  • Use_pooling: This is the binary flag set to TRUE

How to do it...

  1. Run the create_conv_layer function with the preceding input parameters:
# Convolutional Layer 1
conv1 <- create_conv_layer(input=x_image,
num_input_channels=num_channels,
filter_size=filter_size1,
num_filters=num_filters1,
use_pooling=TRUE)
  1. Extract the layers of the first convolution layer:
layer_conv1 <- conv1$layer
conv1_images <- conv1$layer$eval(feed_dict = dict(x = train_data$images...