Learn R Programming

rnn

Implementation of a Recurrent Neural Network in R.

Demonstration

Installation

The stable version can be installed from CRAN using:

install.packages('rnn')

The development version, to be used at your peril, can be installed from GitHub using the remotes package.

if (!require('remotes')) install.packages('remotes')
remotes::install_github('bquast/rnn')

Usage

Following installation, the package can be loaded using:

library(rnn)

For general information on using the package, please refer to the help files.

help('trainr')
help('predictr')
help(package='rnn')

There is also a long form vignette available using:

vignette('rnn')

Additional Information

An overview of the changes is available in the NEWS file.

news(package='rnn')

There is a dedicated website with information hosted on my personal website.

https://qua.st/rnn/

Development

Development takes place on the GitHub page.

https://github.com/bquast/rnn/

Bugs can be filed on the issues page on GitHub.

https://github.com/bquast/rnn/issues

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Version

Install

install.packages('rnn')

Monthly Downloads

406

Version

1.9.0

License

GPL-3

Issues

Pull Requests

Stars

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Maintainer

Last Published

April 22nd, 2023

Functions in rnn (1.9.0)

predictr

Recurrent Neural Network
trainr

Recurrent Neural Network
predict_gru

gru prediction function
update_r

update_r
update_adagrad

update_adagrad
update_sgd

update_sgd
predict_lstm

gru prediction function
clean_r

init_r
backprop_gru

backprop_gru
epoch_annealing

epoch annealing
clean_lstm

clean_lstm
clean_rnn

clean_rnn
epoch_print

epoch printing for trainr
backprop_r

backprop_r
bin2int

Binary to Integer
int2bin

Integer to Binary
loss_L1

L1 loss
backprop_rnn

backprop_rnn
backprop_lstm

backprop_lstm
predict_rnn

Recurrent Neural Network
init_rnn

init_rnn
init_r

init_r
init_gru

init_gru
rnn

Recurrent Neural Network
init_lstm

init_lstm