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]
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