SnnsRObject-train: Train a network and test it in every training iteration
In RSNNS: Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

SnnsRObject$train

R Documentation

Train a network and test it in every training iteration

Description

SnnsR low-level function to train a network and test it in every training iteration.

Usage

## S4 method for signature 'SnnsR'
train(inputsTrain, targetsTrain=NULL, 
    initFunc="Randomize_Weights", initFuncParams=c(1.0, -1.0), 
    learnFunc="Std_Backpropagation", learnFuncParams=c(0.2, 0),
    updateFunc="Topological_Order", updateFuncParams=c(0.0), 
    outputMethod="reg_class", maxit=100, shufflePatterns=TRUE, 
    computeError=TRUE, inputsTest=NULL, targetsTest=NULL,
    pruneFunc=NULL, pruneFuncParams=NULL)

Arguments

`inputsTrain`	a matrix with inputs for the network
`targetsTrain`	the corresponding targets
`initFunc`	the initialization function to use
`initFuncParams`	the parameters for the initialization function
`learnFunc`	the learning function to use
`learnFuncParams`	the parameters for the learning function
`updateFunc`	the update function to use
`updateFuncParams`	the parameters for the update function
`outputMethod`	the output method of the net
`maxit`	maximum of iterations to learn
`shufflePatterns`	should the patterns be shuffled?
`computeError`	should the error be computed in every iteration?
`inputsTest`	a matrix with inputs to test the network
`targetsTest`	the corresponding targets for the test input
`pruneFunc`	the pruning function to use
`pruneFuncParams`	the parameters for the pruning function. Unlike the other functions, these have to be given in a named list. See the pruning demos for further explanation.

Value

a list containing:

`fitValues`	the fitted values, i.e. outputs of the training inputs
`IterativeFitError`	The SSE in every iteration/epoch on the training set
`testValues`	the predicted values, i.e. outputs of the test inputs
`IterativeTestError`	The SSE in every iteration/epoch on the test set