nn.train: Training Neural Network

In DimitriF/DLC: Deep Layer Chromatography

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 .
output	function of output unit, can be "sigm","linear" or "softmax". Default is "sigm".
numepochs	number of iteration for samples Default is 3.
batchsize	size of mini-batch. Default is 100.
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))

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