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 softmax activation to obtain a predicted class


In this recipe, we will normalize the outputs of the second fully connected layer using softmax activation such that each class has a (probability) value restricted between 0 and 1, and all the values across 10 classes add up to 1.

Getting ready

The activation function is applied at the end of the pipeline on predictions generated by the deep learning model. Before executing this step, all steps in the pipeline need to be executed. The recipe requires the TensorFlow library.

How to do it...

  1. Run the softmax activation function on the output of the second fully connected layer:
y_pred = tf$nn$softmax(layer_fc2_drop)
  1. Use the argmax function to determine the class number of the label. It is the index of the class with the largest (probability) value:
y_pred_cls = tf$argmax(y_pred, dimension=1L)