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

Evaluating the performance on test data


In this recipe, we will look into the performance of the trained CNN on test images using a confusion matrix and plots.

Getting ready

The prerequisite packages for plots are imager and ggplot2.

How to do it...

  1. Get the actual or true class labels of test images:
test_true_class <- c(unlist(images.lab.test))
  1. Get the predicted class labels of test images. Remember to add 1 to each class label, as the starting index of TensorFlow (the same as Python) is 0 and that of R is 1:
test_pred_class <- y_pred_cls$eval(feed_dict = dict(
x = test_data$images, y_true = test_data$labels, keep_prob = 1.0))
test_pred_class <- test_pred_class + 1
  1. Generate the confusion matrix with rows as true labels and columns as predicted labels:
table(actual = test_true_class, predicted = test_pred_class)
  1. Generate a plot of the confusion matrix:
confusion <- as.data.frame(table(actual = test_true_class, predicted = test_pred_class))
plot <- ggplot(confusion)
plot + geom_tile(aes...