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Neural Networks with R

You're reading from   Neural Networks with R Build smart systems by implementing popular deep learning models in R

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781788397872
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Balaji Venkateswaran Balaji Venkateswaran
Author Profile Icon Balaji Venkateswaran
Balaji Venkateswaran
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Toc

Table of Contents (8) Chapters Close

Preface 1. Neural Network and Artificial Intelligence Concepts FREE CHAPTER 2. Learning Process in Neural Networks 3. Deep Learning Using Multilayer Neural Networks 4. Perceptron Neural Network Modeling – Basic Models 5. Training and Visualizing a Neural Network in R 6. Recurrent and Convolutional Neural Networks 7. Use Cases of Neural Networks – Advanced Topics

Keras integration with R


Keras is a set of open source neural network libraries coded in Python. It is capable of running on top of MxNet, TensorFlow, or Theano. The steps to install Keras in RStudio are very simple. The following code snippet gives the steps for installation and we can check whether Keras is working by checking the load of the MNIST dataset.

By default, RStudio loads the CPU version of TensorFlow. Once Keras is loaded, we have a powerful set of deep learning libraries that can be utilized by R programmers to execute neural networks and deep learning. To install Keras for R, use this code:

install.packages("devtools")
devtools::install_github("rstudio/keras")

At this point, we load the keras library:

library(keras)

Finally, we check whether keras is installed correctly by loading the MNIST dataset:

> data=dataset_mnist()
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