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

Defining placeholder variables


In this recipe, let's define the placeholder variables that serve as input to the modules in a TensorFlow computational graph. These are typically multidimensional arrays or matrices in the form of tensors.

Getting ready

The data type of placeholder variables is set to float32 (tf$float32) and the shape is set to a two-dimensional tensor.

How to do it...

  1. Create an input placeholder variable:
x = tf$placeholder(tf$float32, shape=shape(NULL, img_size_flat), name='x')

The NULL value in the placeholder allows us to pass non-deterministic arrays size.

  1. Reshape the input placeholder x into a four-dimensional tensor:
x_image = tf$reshape(x, shape(-1L, img_size, img_size, num_channels))
  1. Create an output placeholder variable:
y_true = tf$placeholder(tf$float32, shape=shape(NULL, num_classes), name='y_true')
  1. Get the (true) classes of the output using argmax:
y_true_cls = tf$argmax(y_true, dimension=1L)

How it works...

In step 1, we define an input placeholder variable. The dimensions...