stacked_rbm: Fit a Stack of Restricted Boltzmann Machines

In zachmayer/rbm: Restricted Boltzmann Machines

Description

Fit a Stack of Restricted Boltzmann Machines

Usage

stacked_rbm(x, layers = c(30, 100, 30), learning_rate = 0.1,
  verbose_stack = TRUE, use_gpu = FALSE, ...)

Arguments

x	a sparse matrix
layers	an integer vector of the number of neurons in each RBM
learning_rate	The learning rate
verbose_stack	Print messages while training the stack
use_gpu	use rbm_gpu instead of rbm to train the rbm on a gpu
...	passed to the rbm function

Value

a stacked_rbm object

References

• http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines

Examples

#Setup a dataset
set.seed(10)
data(movie_reviews)
data(george_reviews)
Stacked_RBM <- stacked_rbm(movie_reviews, layers = c(3, 3), max_epochs=25)
predict(Stacked_RBM, movie_reviews)

zachmayer/rbm documentation built on May 4, 2019, 9:07 p.m.