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

Performing gradient descent cost optimization


In this recipe, let's define an optimizer that can minimize the cost. Post optimization, check for CNN performance.

Getting ready

The optimizer definition will require the cost recipe to be defined as it goes as input to the optimizer.

How to do it...

  1. Run an Adam optimizer with the objective of minimizing the cost for a given learning_rate:
optimizer = tf$train$AdamOptimizer(learning_rate=1e-4)$minimize(cost)
  1. Extract the number of correct_predictions and calculate the mean percentage accuracy:
correct_prediction = tf$equal(y_pred_cls, y_true_cls)
accuracy = tf$reduce_mean(tf$cast(correct_prediction, tf$float32))