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R Deep Learning Cookbook

You're reading from R Deep Learning Cookbook Solve complex neural net problems with TensorFlow, H2O and MXNet

Product type Paperback

Published in Aug 2017

Publisher Packt

ISBN-13 9781787121089

Length 288 pages

Edition 1st Edition

Languages

R

Tools

H2O

Concepts

Deep Learning

Authors (2):

Achyutuni Sri Krishna Rao

PKS Prakash

View More author details

Table of Contents (11) Chapters

Preface

1. Getting Started FREE CHAPTER

2. Deep Learning with R

3. Convolution Neural Network

4. Data Representation Using Autoencoders

5. Generative Models in Deep Learning

6. Recurrent Neural Networks

7. Reinforcement Learning

8. Application of Deep Learning in Text Mining

9. Application of Deep Learning to Signal processing

10. Transfer Learning

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...

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.

Reshape the input placeholder x into a four-dimensional tensor:

x_image = tf$reshape(x, shape(-1L, img_size, img_size, num_channels))

Create an output placeholder variable:

y_true = tf$placeholder(tf$float32, shape=shape(NULL, num_classes), name='y_true')

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...

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Authors (2)

PKS Prakash

PKS Prakash

Dr. PKS Prakash is a data scientist and author. He has spent the last 12 years in developing many data science solutions in several practical areas in healthcare, manufacturing, pharmaceuticals, and e-commerce. He currently works as the data science manager at ZS Associates. He is the co-founder of Warwick Analytics, a spin-off from University of Warwick, UK. Prakash has published articles widely in research areas of operational research and management, soft computing tools, and advanced algorithms in leading journals such as IEEE-Trans, EJOR, and IJPR, among others. He has edited an article on Intelligent Approaches to Complex Systems and contributed to books such as Evolutionary Computing in Advanced Manufacturing published by WILEY and Algorithms and Data Structures using R and R Deep Learning Cookbook, published by PACKT.

See other products by PKS Prakash

Sri Krishna Rao

Sri Krishna Rao

Achyutuni Sri Krishna Rao is a Data Scientist, a Civil Engineer and an Author. He has spent last 4 years in developing many data science solutions to solve problems from leading companies in healthcare, pharmaceutical and manufacturing domain. He is working as Data Science Consultant at ZS Associates. Sri Krishnas background involves a masters in Enterprise Business Analytics and Machine Learning from National University of Singapore, Singapore. His other educational background involves a Bachelors from National Institute of Technology Warangal, India. Sri Krishna has published widely in research areas of civil engineering. He has contributed in a book titled Algorithms and Data Structures using R published by PACKT.

See other products by Sri Krishna Rao

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