public void processError(FeedForwardNeuralNetwork network, Vector error)
{
// TODO calculate total error somewhere
// create Sensitivity Matrices
outputSensitivity.sensitivityMatrixFromErrorMatrix(error);
hiddenSensitivity
.sensitivityMatrixFromSucceedingLayer(outputSensitivity);
// calculate weight Updates
calculateWeightUpdates(outputSensitivity, hiddenLayer
.getLastActivationValues(), learningRate, momentum);
calculateWeightUpdates(hiddenSensitivity, hiddenLayer
.getLastInputValues(), learningRate, momentum);
// calculate Bias Updates
calculateBiasUpdates(outputSensitivity, learningRate, momentum);
calculateBiasUpdates(hiddenSensitivity, learningRate, momentum);
// update weightsAndBiases
outputLayer.updateWeights();
outputLayer.updateBiases();
hiddenLayer.updateWeights();
hiddenLayer.updateBiases();
}
public Vector processInput(FeedForwardNeuralNetwork network, Vector input)
{
hiddenLayer.feedForward(input);
outputLayer.feedForward(hiddenLayer.getLastActivationValues());
return outputLayer.getLastActivationValues();
}
public void processError(FeedForwardNeuralNetwork network, Vector error)
{
// TODO calculate total error somewhere
// create Sensitivity Matrices
outputSensitivity.sensitivityMatrixFromErrorMatrix(error);
hiddenSensitivity
.sensitivityMatrixFromSucceedingLayer(outputSensitivity);
// calculate weight Updates
calculateWeightUpdates(outputSensitivity, hiddenLayer
.getLastActivationValues(), learningRate, momentum);
calculateWeightUpdates(hiddenSensitivity, hiddenLayer
.getLastInputValues(), learningRate, momentum);
// calculate Bias Updates
calculateBiasUpdates(outputSensitivity, learningRate, momentum);
calculateBiasUpdates(hiddenSensitivity, learningRate, momentum);
// update weightsAndBiases
outputLayer.updateWeights();
outputLayer.updateBiases();
hiddenLayer.updateWeights();
hiddenLayer.updateBiases();
}
public void setNeuralNetwork(FunctionApproximator fapp)
{
FeedForwardNeuralNetwork ffnn = (FeedForwardNeuralNetwork)fapp;
this.hiddenLayer = ffnn.getHiddenLayer();
this.outputLayer = ffnn.getOutputLayer();
this.hiddenSensitivity = new LayerSensitivity(hiddenLayer);
this.outputSensitivity = new LayerSensitivity(outputLayer);
}
public void testFeedForwardAndBAckLoopWorks()
{
// example 11.14 of Neural Network Design by Hagan, Demuth and Beale
Matrix hiddenLayerWeightMatrix = new Matrix(2, 1);
hiddenLayerWeightMatrix.set(0, 0, -0.27);
hiddenLayerWeightMatrix.set(1, 0, -0.41);
Vector hiddenLayerBiasVector = new Vector(2);
hiddenLayerBiasVector.setValue(0, -0.48);
hiddenLayerBiasVector.setValue(1, -0.13);
Vector input = new Vector(1);
input.setValue(0, 1);
Matrix outputLayerWeightMatrix = new Matrix(1, 2);
outputLayerWeightMatrix.set(0, 0, 0.09);
outputLayerWeightMatrix.set(0, 1, -0.17);
Vector outputLayerBiasVector = new Vector(1);
outputLayerBiasVector.setValue(0, 0.48);
Vector error = new Vector(1);
error.setValue(0, 1.261);
double learningRate = 0.1;
double momentumFactor = 0.0;
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(
hiddenLayerWeightMatrix, hiddenLayerBiasVector,
outputLayerWeightMatrix, outputLayerBiasVector);
ffnn.setTrainingScheme(new BackPropLearning(learningRate,
momentumFactor));
ffnn.processInput(input);
ffnn.processError(error);
Matrix finalHiddenLayerWeights = ffnn.getHiddenLayerWeights();
Assert.AreEqual(-0.265, finalHiddenLayerWeights.get(0, 0), 0.001);
Assert.AreEqual(-0.419, finalHiddenLayerWeights.get(1, 0), 0.001);
Vector hiddenLayerBias = ffnn.getHiddenLayerBias();
Assert.AreEqual(-0.475, hiddenLayerBias.getValue(0), 0.001);
Assert.AreEqual(-0.1399, hiddenLayerBias.getValue(1), 0.001);
Matrix finalOutputLayerWeights = ffnn.getOutputLayerWeights();
Assert.AreEqual(0.171, finalOutputLayerWeights.get(0, 0), 0.001);
Assert.AreEqual(-0.0772, finalOutputLayerWeights.get(0, 1), 0.001);
Vector outputLayerBias = ffnn.getOutputLayerBias();
Assert.AreEqual(0.7322, outputLayerBias.getValue(0), 0.001);
}
public void testDataSetPopulation()
{
DataSet irisDataSet = DataSetFactory.getIrisDataSet();
Numerizer numerizer = new IrisDataSetNumerizer();
NNDataSet innds = new IrisNNDataSet();
innds.createExamplesFromDataSet(irisDataSet, numerizer);
NNConfig config = new NNConfig();
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_INPUTS, 4);
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_OUTPUTS, 3);
config.setConfig(FeedForwardNeuralNetwork.NUMBER_OF_HIDDEN_NEURONS, 6);
config.setConfig(FeedForwardNeuralNetwork.LOWER_LIMIT_WEIGHTS, -2.0);
config.setConfig(FeedForwardNeuralNetwork.UPPER_LIMIT_WEIGHTS, 2.0);
FeedForwardNeuralNetwork ffnn = new FeedForwardNeuralNetwork(config);
ffnn.setTrainingScheme(new BackPropLearning(0.1, 0.9));
ffnn.trainOn(innds, 10);
innds.refreshDataset();
ffnn.testOnDataSet(innds);
}
public Vector processInput(FeedForwardNeuralNetwork network, Vector input)
{
hiddenLayer.feedForward(input);
outputLayer.feedForward(hiddenLayer.getLastActivationValues());
return(outputLayer.getLastActivationValues());
}
