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 create a new convolution layer


Creating a convolution layer is the primary step in a CNN TensorFlow computational graph. 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 input dataset is defined and loaded. The create_conv_layer function presented in the recipe takes the following five input parameters and needs to be defined while setting-up a convolution layer:

  1. Input: This is a four-dimensional tensor (or a list) that comprises a number of (input) images, the height of each image (here 32L), the width of each image (here 32L), and the number of channels of each image (here 3L : red, blue, and green).
  2. Num_input_channels: This is defined as the number of color channels in the case of the first convolution layer or the number of filter channels in the case of subsequent convolution layers.
  3. Filter_size: This is defined as the width and height of each filter...