nn.train {deepnet}

Training Neural Network

Description

Training single or mutiple hidden layers neural network by BP

Usage

nn.train(x, y, initW = NULL, initB = NULL, hidden = c(10), activationfun = "sigm", 
    learningrate = 0.8, momentum = 0.5, learningrate_scale = 1, output = "sigm", 
    numepochs = 3, batchsize = 100, hidden_dropout = 0, visible_dropout = 0)

Arguments

x

matrix of x values for examples

y

vector or matrix of target values for examples

initW

initial weights. If missing chosen at random

initB

initial bias. If missing chosen at random

hidden

vector for number of units of hidden layers.Default is c(10).

activationfun

activation function of hidden unit.Can be "sigm","linear" or "tanh".Default is "sigm" for logistic function

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.

output

function of output unit, can be "sigm","linear" or "softmax". Default is "sigm".

hidden_dropout

drop out fraction for hidden layer. Default is 0.

visible_dropout

drop out fraction for input layer Default is 0.

Author(s)

Xiao Rong

Examples

Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2))
Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1))
x <- matrix(c(Var1, Var2), nrow = 100, ncol = 2)
y <- c(rep(1, 50), rep(0, 50))
nn <- nn.train(x, y, hidden = c(5))