rnn (software)

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rnn
Original author(s)Bastiaan Quast
Initial release30 November 2015 (2015-11-30)
Stable release
1.9.0 / 22 April 2023; 11 months ago (2023-04-22)
Preview release
1.9.0.9000 / 22 April 2023; 11 months ago (2023-04-22)
Repositorygithub.com/bquast/rnn
Written inR
Operating systemmacOS, Linux, Windows
Size564.2 kB (v. 1.9.0)
LicenseGPL v3
Websitecran.r-project.org/web/packages/rnn/

rnn is an open-source machine learning framework that implements recurrent neural network architectures, such as LSTM and GRU, natively in the R programming language, that has been downloaded over 100,000 times (from the RStudio servers alone).[1]

The rnn package is distributed through the Comprehensive R Archive Network[2] under the open-source GPL v3 license.

Workflow[edit]

Demonstration of RNN package

The below example from the rnn documentation show how to train a recurrent neural network to solve the problem of bit-by-bit binary addition. 

> # install the rnn package, including the dependency sigmoid
> install.packages('rnn')

> # load the rnn package
> library(rnn)

> # create input data 
> X1 = sample(0:127, 10000, replace=TRUE)
> X2 = sample(0:127, 10000, replace=TRUE)

> # create output data
> Y <- X1 + X2

> # convert from decimal to binary notation 
> X1 <- int2bin(X1, length=8)
> X2 <- int2bin(X2, length=8)
> Y  <- int2bin(Y,  length=8)

> # move input data into single tensor
> X <- array( c(X1,X2), dim=c(dim(X1),2) )

> # train the model
> model <- trainr(Y=Y,
+                 X=X,
+                 learningrate   =  1,
+                 hidden_dim     = 16  )
Trained epoch: 1 - Learning rate: 1
Epoch error: 0.839787019539748

sigmoid[edit]

The sigmoid functions and derivatives used in the package were originally included in the package, from version 0.8.0 onwards, these were released in a separate R package sigmoid, with the intention to enable more general use. The sigmoid package is a dependency of the rnn package and therefore automatically installed with it.[3]

Reception[edit]

With the release of version 0.3.0 in April 2016[4] the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.",[5] which further increased usage.[6]

The book Neural Networks in R by Balaji Venkateswaran and Giuseppe Ciaburro uses rnn to demonstrate recurrent neural networks to R users.[7][8] It is also used in the r-exercises.com course "Neural network exercises".[9][10]

The RStudio CRAN mirror download logs [11] show that the package is downloaded on average about 2,000 per month from those servers ,[12] with a total of over 100,000 downloads since the first release,[13] according to RDocumentation.org, this puts the package in the 15th percentile of most popular R packages .[14]

References[edit]

  1. ^ Quast, Bastiaan (2019-08-30), GitHub - bquast/rnn: Recurrent Neural Networks in R., retrieved 2019-09-19
  2. ^ Quast, Bastiaan; Fichou, Dimitri (2019-05-27), rnn: Recurrent Neural Network, archived from the original on 2020-01-05, retrieved 2020-01-05
  3. ^ Quast, Bastiaan (2018-06-21), sigmoid: Sigmoid Functions for Machine Learning, archived from the original on 2020-01-05, retrieved 2020-01-05
  4. ^ Quast, Bastiaan (2020-01-03), RNN: Recurrent Neural Networks in R releases, retrieved 2020-01-05
  5. ^ Mic (2016-08-05). "The Beginner Programmer: Plain vanilla recurrent neural networks in R: waves prediction". The Beginner Programmer. Archived from the original on 2020-01-05. Retrieved 2020-01-05.
  6. ^ "LSTM or other RNN package for R". Data Science Stack Exchange. Retrieved 2018-07-05.
  7. ^ "Neural Networks with R". O'Reilly. September 2017. ISBN 9781788397872. Archived from the original on 2018-10-02. Retrieved 2018-10-02.
  8. ^ Ciaburro, Giuseppe; Venkateswaran, Balaji (2017-09-27). Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles. Packt Publishing Ltd. ISBN 978-1-78839-941-8.
  9. ^ Touzin, Guillaume (2017-06-21). "R-exercises – Neural networks Exercises (Part-3)". www.r-exercises.com. Archived from the original on 2020-01-05. Retrieved 2020-01-05.
  10. ^ Touzin, Guillaume (2017-06-21). "Neural networks Exercises (Part-3)". R-bloggers. Archived from the original on 2020-01-05. Retrieved 2020-01-05.
  11. ^ "RStudio CRAN logs".
  12. ^ "CRANlogs rnn package".
  13. ^ "CRANlogs rnn package".
  14. ^ "RDocumentation rnn".

External links[edit]

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