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.
## 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)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. |
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 |
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