rbm.train: Training a RBM(restricted Boltzmann Machine)

In DimitriF/DLC: Deep Layer Chromatography

Description

Training a RBM(restricted Boltzmann Machine)

Usage

rbm.train(x, hidden, numepochs = 3, batchsize = 100,
  learningrate = 0.8, learningrate_scale = 1, momentum = 0.5,
  visible_type = "bin", hidden_type = "bin", cd = 1, verbose = F,
  keep.data = F, model = NULL)

Arguments

x	matrix of x values for examples
hidden	number of hidden units
numepochs	number of iteration for samples Default is 3.
batchsize	size of mini-batch. Default is 100.
learningrate	learning rate for gradient descent. Default is 0.8.
learningrate_scale	learning rate will be mutiplied by this scale after every iteration. Default is 1 .
momentum	momentum for gradient descent. Default is 0.5 .
visible_type	activation function of input unit.Only support "sigm" now
hidden_type	activation function of hidden unit.Only support "sigm" now
cd	number of iteration for Gibbs sample of CD algorithm.
verbose	print a string for each epoch done
keep.data	keep the weight for each epoch, usefull for DPE

Author(s)

Xiao Rong

Examples

Var1 <- c(rep(1,50),rep(0,50))
Var2 <- c(rep(0,50),rep(1,50))
x3 <- matrix(c(Var1,Var2),nrow=100,ncol=2)
r1 <- rbm.train(x3,10,numepochs=20,cd=10)

DimitriF/DLC documentation built on Oct. 14, 2020, 4:33 p.m.