 | |
| Original author(s) | Bastiaan Quast |
|---|---|
| Initial release | 30 November 2015 |
| Stable release | 1.9.0
/ 22 April 2023 |
| Preview release | 1.9.0.9000
/ 22 April 2023 |
| Repository | github |
| Written in | R |
| Operating system | macOS, Linux, Windows |
| Size | 564.2 kB (v. 1.9.0) |
| License | GPL v3 |
| Website | cran |
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.
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
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]
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]
