rbm.train {deepnet}

Training a RBM(restricted Boltzmann Machine)

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)

Arguments

x

matrix of x values for examples

hidden

number of hidden units

visible_type

activation function of input unit.Only support "sigm" now

hidden_type

activation function of hidden unit.Only support "sigm" now

learningrate

learning rate for gradient descent. Default is 0.8.

momentum

momentum for gradient descent. Default is 0.5 .

learningrate_scale

learning rate will be mutiplied by this scale after every iteration. Default is 1 .

numepochs

number of iteration for samples Default is 3.

batchsize

size of mini-batch. Default is 100.

cd

number of iteration for Gibbs sample of CD algorithm.

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)