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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

412

Version

1.8.0

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Bastiaan Quast

Last Published

July 12th, 2022

Functions in rnn (1.8.0)

clean_lstm

clean_lstm
backprop_r

backprop_r
loss_L1

L1 loss
backprop_rnn

backprop_rnn
init_gru

init_gru
bin2int

Binary to Integer
init_lstm

init_lstm
trainr

Recurrent Neural Network
backprop_gru

backprop_gru
update_sgd

update_sgd
predict_gru

gru prediction function
rnn

Recurrent Neural Network
backprop_lstm

backprop_lstm
int2bin

Integer to Binary
predict_rnn

Recurrent Neural Network
clean_r

init_r
clean_rnn

clean_rnn
update_adagrad

update_adagrad
update_r

update_r
predictr

Recurrent Neural Network
predict_lstm

gru prediction function
epoch_print

epoch printing for trainr
init_r

init_r
epoch_annealing

epoch annealing
init_rnn

init_rnn