View source: R/sae_dnn_train.R
sae.dnn.train | R Documentation |
Training a Deep neural network with weights initialized by Stacked AutoEncoder
sae.dnn.train(x, y, hidden = c(1), activationfun = "sigm", learningrate = 0.8, momentum = 0.5, learningrate_scale = 1, output = "sigm", sae_output = "linear", numepochs = 3, batchsize = 100, hidden_dropout = 0, visible_dropout = 0)
x |
matrix of x values for examples |
y |
vector or matrix of target values for examples |
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". |
sae_output |
function of autoencoder output unit, can be "sigm","linear" or "softmax". Default is "linear". |
hidden_dropout |
drop out fraction for hidden layer. Default is 0. |
visible_dropout |
drop out fraction for input layer Default is 0. |
Xiao Rong
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)) dnn <- sae.dnn.train(x, y, hidden = c(5, 5)) ## predict by dnn test_Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2)) test_Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1)) test_x <- matrix(c(test_Var1, test_Var2), nrow = 100, ncol = 2) nn.test(dnn, test_x, y)
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