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

Flattening the second convolution layer


In this recipe, let's flatten the second convolution layer that we created.

Getting ready

The following is the input to the function defined in the recipe Creating the second convolution layer, flatten_conv_layer:

  • Layer: This is the output of the second convolution layer, layer_conv2

How to do it...

  1. Run the flatten_conv_layer function with the preceding input parameter:
flatten_lay <- flatten_conv_layer(layer_conv2)
  1. Extract the flattened layer:
layer_flat <- flatten_lay$layer_flat
  1. Extract the number of (input) features generated for each image:
num_features <- flatten_lay$num_features

How it works...

Prior to connecting the output of the (second) convolution layer with a fully connected network, in step 1, we reshape the four-dimensional convolution layer into a two-dimensional tensor. The first dimension (?) represents any number of input images (as rows) and the second dimension represents the flattened vector of features generated for each image of...