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 second fully connected layer with dropout


In this recipe, let's create the second fully connected layer along with dropout.

Getting ready

The following are the inputs to the function defined in the recipe Using functions to flatten the densely connected layer, create_fc_layer:

  • Input: This is the output of the first fully connected layer; that is, layer_fc1
  • Num_inputs: This is the number of features in the output of the first fully connected layer, fc_size
  • Num_outputs: This is the number of the fully connected neurons output (equal to the number of labels, num_classes )
  • Use_relu: This is the binary flag set to FALSE

How to do it...

  1. Run the create_fc_layer function with the preceding input parameters:
layer_fc2 = create_fc_layer(input=layer_fc1_drop,
num_inputs=fc_size,
num_outputs=num_classes,
use_relu=FALSE)
  1. Use TensorFlow's dropout function to handle the scaling and masking of neuron outputs:
layer_fc2_drop <- tf$nn$dropout(layer_fc2, keep_prob)

How it works...

In step 1, we create a...