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

Applying dropout to the first fully connected layer


In this recipe, let's apply dropout to the output of the fully connected layer to reduce the chance of overfitting. The dropout step involves removing some neurons randomly during the learning process.

Getting ready

The dropout is connected to the output of the layer. Thus, model initial structure is set up and loaded. For example, in dropout current layer layer_fc1 is defined, on which dropout is applied.

How to do it...

  1. Create a placeholder for dropout that can take probability as an input:
keep_prob <- tf$placeholder(tf$float32)
  1. Use TensorFlow's dropout function to handle the scaling and masking of neuron outputs:
layer_fc1_drop <- tf$nn$dropout(layer_fc1, keep_prob)

How it works...

In steps 1 and 2, we can drop (or mask) out the output neurons based on the input probability (or percentage). The dropout is generally allowed during training and can be turned off (by assigning probability as 1 or NULL) during testing.