SnnsRObject$train

0th

Percentile

Train a network and test it in every training iteration

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

Usage
# S4 method for 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

Aliases
  • SnnsRObject$train
  • SnnsR__train
  • train,SnnsR-method
Documentation reproduced from package RSNNS, version 0.4-12, License: LGPL (>= 2) | file LICENSE

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