mlp: Create and train a multi-layer perceptron (MLP)

In RSNNS: Neural Networks using the Stuttgart Neural Network Simulator (SNNS)

Create and train a multi-layer perceptron (MLP)

Description

This function creates a multilayer perceptron (MLP) and trains it. MLPs are fully connected feedforward networks, and probably the most common network architecture in use. Training is usually performed by error backpropagation or a related procedure.

There are a lot of different learning functions present in SNNS that can be used together with this function, e.g., Std_Backpropagation, BackpropBatch, BackpropChunk, BackpropMomentum, BackpropWeightDecay, Rprop, Quickprop, SCG (scaled conjugate gradient), ...

Usage

mlp(x, ...)

## Default S3 method:
mlp(
  x,
  y,
  size = c(5),
  maxit = 100,
  initFunc = "Randomize_Weights",
  initFuncParams = c(-0.3, 0.3),
  learnFunc = "Std_Backpropagation",
  learnFuncParams = c(0.2, 0),
  updateFunc = "Topological_Order",
  updateFuncParams = c(0),
  hiddenActFunc = "Act_Logistic",
  shufflePatterns = TRUE,
  linOut = FALSE,
  outputActFunc = if (linOut) "Act_Identity" else "Act_Logistic",
  inputsTest = NULL,
  targetsTest = NULL,
  pruneFunc = NULL,
  pruneFuncParams = NULL,
  ...
)

Arguments

x	a matrix with training inputs for the network
...	additional function parameters (currently not used)
y	the corresponding targets values
size	number of units in the hidden layer(s)
maxit	maximum of iterations to learn
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
hiddenActFunc	the activation function of all hidden units
shufflePatterns	should the patterns be shuffled?
linOut	sets the activation function of the output units to linear or logistic (ignored if outputActFunc is given)
outputActFunc	the activation function of all output units
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.

Details

Std_Backpropagation, BackpropBatch, e.g., have two parameters, the learning rate and the maximum output difference. The learning rate is usually a value between 0.1 and 1. It specifies the gradient descent step width. The maximum difference defines, how much difference between output and target value is treated as zero error, and not backpropagated. This parameter is used to prevent overtraining. For a complete list of the parameters of all the learning functions, see the SNNS User Manual, pp. 67.

The defaults that are set for initialization and update functions usually don't have to be changed.

Value

an rsnns object.

References

Rosenblatt, F. (1958), 'The perceptron: A probabilistic model for information storage and organization in the brain', Psychological Review 65(6), 386–408.

Rumelhart, D. E.; Clelland, J. L. M. & Group, P. R. (1986), Parallel distributed processing :explorations in the microstructure of cognition, Mit, Cambridge, MA etc.

Zell, A. et al. (1998), 'SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2', IPVR, University of Stuttgart and WSI, University of Tübingen. https://www.ra.cs.uni-tuebingen.de/SNNS/welcome.html

Zell, A. (1994), Simulation Neuronaler Netze, Addison-Wesley. (in German)

Examples

## Not run: demo(iris)
## Not run: demo(laser)
## Not run: demo(encoderSnnsCLib)


data(iris)

#shuffle the vector
iris <- iris[sample(1:nrow(iris),length(1:nrow(iris))),1:ncol(iris)]

irisValues <- iris[,1:4]
irisTargets <- decodeClassLabels(iris[,5])
#irisTargets <- decodeClassLabels(iris[,5], valTrue=0.9, valFalse=0.1)

iris <- splitForTrainingAndTest(irisValues, irisTargets, ratio=0.15)
iris <- normTrainingAndTestSet(iris)

model <- mlp(iris$inputsTrain, iris$targetsTrain, size=5, learnFuncParams=c(0.1), 
              maxit=50, inputsTest=iris$inputsTest, targetsTest=iris$targetsTest)

summary(model)
model
weightMatrix(model)
extractNetInfo(model)

par(mfrow=c(2,2))
plotIterativeError(model)

predictions <- predict(model,iris$inputsTest)

plotRegressionError(predictions[,2], iris$targetsTest[,2])

confusionMatrix(iris$targetsTrain,fitted.values(model))
confusionMatrix(iris$targetsTest,predictions)

plotROC(fitted.values(model)[,2], iris$targetsTrain[,2])
plotROC(predictions[,2], iris$targetsTest[,2])

#confusion matrix with 402040-method
confusionMatrix(iris$targetsTrain, encodeClassLabels(fitted.values(model),
                                                       method="402040", l=0.4, h=0.6))

RSNNS documentation built on May 29, 2024, 4:37 a.m.