dnnet: Multilayer Perceptron Model for Regression or Classification
In SkadiEye/dnnet: Deep Neural Nets (Multilayer Perceptrons)

Description Usage Arguments Value See Also

View source: R/2-2-dnnet.R

Fit a Multilayer Perceptron Model for Regression or Classification

dnnet(train, validate = NULL, norm.x = TRUE,
  norm.y = ifelse(is.factor(train@y), FALSE, TRUE), activate = "elu",
  n.hidden = c(10, 10), learning.rate = ifelse(learning.rate.adaptive %in%
  c("adam"), 0.001, 0.01), l1.reg = 0, l2.reg = 0, n.batch = 100,
  n.epoch = 100, early.stop = ifelse(is.null(validate), FALSE, TRUE),
  early.stop.det = 5, plot = FALSE, accel = c("rcpp", "gpu", "none")[3],
  learning.rate.adaptive = c("constant", "adadelta", "adagrad", "momentum",
  "adam")[2], rho = c(0.9, 0.95, 0.99, 0.999)[ifelse(learning.rate.adaptive ==
  "momentum", 1, 3)], epsilon = c(10^-10, 10^-8, 10^-6, 10^-4)[2],
  beta1 = 0.9, beta2 = 0.999, loss.f = ifelse(is.factor(train@y), "logit",
  "mse"), ...)

`train`	A `dnnetInput` object, the training set.
`validate`	A `dnnetInput` object, the validation set, optional.
`norm.x`	A boolean variable indicating whether to normalize the input matrix.
`norm.y`	A boolean variable indicating whether to normalize the response (if continuous).
`activate`	Activation Function. One of the following, "sigmoid", "tanh", "relu", "prelu", "elu", "celu".
`learning.rate`	Initial learning rate, 0.001 by default; If "adam" is chosen as an adaptive learning rate adjustment method, 0.1 by defalut.
`l1.reg`	weight for l1 regularization, optional.
`l2.reg`	weight for l2 regularization, optional.
`n.batch`	Batch size for batch gradient descent.
`n.epoch`	Maximum number of epochs.
`early.stop`	Indicate whether early stop is used (only if there exists a validation set).
`early.stop.det`	Number of epochs of increasing loss to determine the early stop.
`plot`	Indicate whether to plot the loss.
`accel`	"rcpp" to use the Rcpp version and "none" (default) to use the R version for back propagation.
`learning.rate.adaptive`	Adaptive learning rate adjustment methods, one of the following, "constant", "adadelta", "adagrad", "momentum", "adam".
`epsilon`	A parameter used in Adagrad and Adam.
`beta1`	A parameter used in Adam.
`beta2`	A parameter used in Adam.
`loss.f`	Loss function of choice.