Description Usage Arguments Examples
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
1 |
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 |
hpar |
list of parameters and hyperparameters for Deep Neural Network, see hpar section |
mdlref |
model trained with automl_train to start training with saved hpar and autopar
(not the model) |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ## Not run:
##REGRESSION (predict Sepal.Length given other Iris parameters)
data(iris)
xmat <- cbind(iris[,2:4], as.numeric(iris$Species))
ymat <- iris[,1]
amlmodel <- automl_train(Xref = xmat, Yref = ymat)
## End(Not run)
##CLASSIFICATION (predict Species given other Iris parameters)
data(iris)
xmat = iris[,1:4]
lab2pred <- levels(iris$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))
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