automl_train

The multi deep neural network automatic train function (several deep neural networks are trained with automatic hyperparameters tuning, best model is kept)
This function launches the automl_train_manual function by passing it parameters for each particle at each converging step

automl_train(Xref, Yref, autopar = list(), hpar = list(), mdlref = NULL)

Arguments

Xref	inputs matrix or data.frame (containing numerical values only)
Yref	target matrix or data.frame (containing numerical values only)
autopar	list of parameters for hyperparameters optimization, see autopar section Not mandatory (the list is preset and all arguments are initialized with default value) but it is advisable to adjust some important arguments for performance reasons (including processing time)
hpar	list of parameters and hyperparameters for Deep Neural Network, see hpar section Not mandatory (the list is preset and all arguments are initialized with default value) but it is advisable to adjust some important arguments for performance reasons (including processing time)
mdlref	model trained with automl_train to start training with saved hpar and autopar (not the model) nb: manually entered parameters above override loaded ones

Examples

if (FALSE) {
##REGRESSION (predict Sepal.Length given other Iris parameters)
data(iris)
xmat <- cbind(iris[,2:4], as.numeric(iris$Species))
ymat <- iris[,1]
amlmodel <- automl_train(Xref = xmat, Yref = ymat)
}
##CLASSIFICATION (predict Species given other Iris parameters)
data(iris)
xmat = iris[,1:4]
lab2pred <- levels(iris$Species)
lghlab <- length(lab2pred)
iris$Species <- as.numeric(iris$Species)
ymat <- matrix(seq(from = 1, to = lghlab, by = 1), nrow(xmat), lghlab, byrow = TRUE)
ymat <- (ymat == as.numeric(iris$Species)) + 0
#with gradient descent and random hyperparameters sets
amlmodel <- automl_train(Xref = xmat, Yref = ymat,
                          autopar = list(numiterations = 1, psopartpopsize = 1, seed = 11),
                          hpar = list(numiterations = 10))
#> (cost: crossentropy)
#> iteration 1 particle 1 weighted err: 2.08682 (train: 2.06276 cvalid: 2.00259 ) BEST MODEL KEPT

Arguments

Examples

Contents