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

Using functions to flatten the densely connected layer


The CNN generally ends with a fully connected multilayered perceptron using softmax activation in the output layer. Here, each neuron in the previous convoluted-flattened layer is connected to every neuron in the next (fully connected) layer.

Note

The key purpose of the fully convoluted layer is to use the features generated in the convolution and pooling stage to classify the given input image into various outcome classes (here, 10L). It also helps in learning the non-linear combinations of these features to define the outcome classes.

In this chapter, we use two fully connected layers for optimization. This function is primarily used to define the mathematical formulas in the TensorFlow graph, which is later used in actual computation during optimization.

Getting ready

The (create_fc_layer) function takes four input parameters, which are as follows:

  • Input: This is similar to the input of the new convolution layer function
  • Num_inputs: This...