regressoR (version 1.1.8)

nn_model: nn_model

Description

generates the code to create the neural network model.

Usage

nn_model(data = "datos.aprendizaje", variable.pred = NULL,
  model.var = "modelo.nn", mean.var = "mean.nn", sd.var = "sd.nn",
  threshold = 0.01, stepmax = 1000, cant.hidden = 2, ...)

Arguments

data

the name of the learning data.

variable.pred

the name of the variable to be predicted.

model.var

the name of the variable that stores the resulting model.

mean.var

the name of the variable that stores the mean of the columns.

sd.var

the name of the variable that stores the standard deviation of the columns.

threshold

the threshold parameter of the model.

stepmax

the stepmax parameter of the model.

cant.hidden

the quantity of hidden layers that are going to be used.

...

a vector with the number of nodes in each hidden layer.

See Also

neuralnet

Examples

# NOT RUN {
library(neuralnet)
library(dummies)

x <- nn_model('iris', 'Petal.Length','modelo.nn', 'mean.nn', 'sd.nn', 0.05, 2000, 3, 30, 50, 80)
exe(x)

print(modelo.nn)
print(mean.nn)
print(sd.nn)
# }