|
public sensitivityMatrixFromErrorMatrix ( |
||
| errorVector | ||
| Résultat | Matrix | |
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 testSensitivityMatrixCalculationFromSucceedingLayer()
{
Matrix weightMatrix1 = new Matrix(2, 1);
weightMatrix1.set(0, 0, -0.27);
weightMatrix1.set(1, 0, -0.41);
Vector biasVector1 = new Vector(2);
biasVector1.setValue(0, -0.48);
biasVector1.setValue(1, -0.13);
Layer layer1 = new Layer(weightMatrix1, biasVector1,
new LogSigActivationFunction());
LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);
Vector inputVector1 = new Vector(1);
inputVector1.setValue(0, 1);
layer1.feedForward(inputVector1);
Matrix weightMatrix2 = new Matrix(1, 2);
weightMatrix2.set(0, 0, 0.09);
weightMatrix2.set(0, 1, -0.17);
Vector biasVector2 = new Vector(1);
biasVector2.setValue(0, 0.48);
Layer layer2 = new Layer(weightMatrix2, biasVector2,
new PureLinearActivationFunction());
Vector inputVector2 = layer1.getLastActivationValues();
layer2.feedForward(inputVector2);
Vector errorVector = new Vector(1);
errorVector.setValue(0, 1.261);
LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);
layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);
layer1Sensitivity
.sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);
Matrix sensitivityMatrix = layer1Sensitivity.getSensitivityMatrix();
Assert.AreEqual(2, sensitivityMatrix.getRowDimension());
Assert.AreEqual(1, sensitivityMatrix.getColumnDimension());
Assert.AreEqual(-0.0495, sensitivityMatrix.get(0, 0), 0.001);
Assert.AreEqual(0.0997, sensitivityMatrix.get(1, 0), 0.001);
}
public void testWeightsAndBiasesUpdatedCorrectly()
{
Matrix weightMatrix1 = new Matrix(2, 1);
weightMatrix1.set(0, 0, -0.27);
weightMatrix1.set(1, 0, -0.41);
Vector biasVector1 = new Vector(2);
biasVector1.setValue(0, -0.48);
biasVector1.setValue(1, -0.13);
Layer layer1 = new Layer(weightMatrix1, biasVector1,
new LogSigActivationFunction());
LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);
Vector inputVector1 = new Vector(1);
inputVector1.setValue(0, 1);
layer1.feedForward(inputVector1);
Matrix weightMatrix2 = new Matrix(1, 2);
weightMatrix2.set(0, 0, 0.09);
weightMatrix2.set(0, 1, -0.17);
Vector biasVector2 = new Vector(1);
biasVector2.setValue(0, 0.48);
Layer layer2 = new Layer(weightMatrix2, biasVector2,
new PureLinearActivationFunction());
Vector inputVector2 = layer1.getLastActivationValues();
layer2.feedForward(inputVector2);
Vector errorVector = new Vector(1);
errorVector.setValue(0, 1.261);
LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);
layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);
layer1Sensitivity
.sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);
BackPropLearning.calculateWeightUpdates(layer2Sensitivity, layer1
.getLastActivationValues(), 0.1);
BackPropLearning.calculateBiasUpdates(layer2Sensitivity, 0.1);
BackPropLearning.calculateWeightUpdates(layer1Sensitivity,
inputVector1, 0.1);
BackPropLearning.calculateBiasUpdates(layer1Sensitivity, 0.1);
layer2.updateWeights();
Matrix newWeightMatrix2 = layer2.getWeightMatrix();
Assert.AreEqual(0.171, newWeightMatrix2.get(0, 0), 0.001);
Assert.AreEqual(-0.0772, newWeightMatrix2.get(0, 1), 0.001);
layer2.updateBiases();
Vector newBiasVector2 = layer2.getBiasVector();
Assert.AreEqual(0.7322, newBiasVector2.getValue(0), 0.00001);
layer1.updateWeights();
Matrix newWeightMatrix1 = layer1.getWeightMatrix();
Assert.AreEqual(-0.265, newWeightMatrix1.get(0, 0), 0.001);
Assert.AreEqual(-0.419, newWeightMatrix1.get(1, 0), 0.001);
layer1.updateBiases();
Vector newBiasVector1 = layer1.getBiasVector();
Assert.AreEqual(-0.475, newBiasVector1.getValue(0), 0.001);
Assert.AreEqual(-0.139, newBiasVector1.getValue(1), 0.001);
}
public void testBiasUpdateMatrixesFormedCorrectly()
{
Matrix weightMatrix1 = new Matrix(2, 1);
weightMatrix1.set(0, 0, -0.27);
weightMatrix1.set(1, 0, -0.41);
Vector biasVector1 = new Vector(2);
biasVector1.setValue(0, -0.48);
biasVector1.setValue(1, -0.13);
Layer layer1 = new Layer(weightMatrix1, biasVector1,
new LogSigActivationFunction());
LayerSensitivity layer1Sensitivity = new LayerSensitivity(layer1);
Vector inputVector1 = new Vector(1);
inputVector1.setValue(0, 1);
layer1.feedForward(inputVector1);
Matrix weightMatrix2 = new Matrix(1, 2);
weightMatrix2.set(0, 0, 0.09);
weightMatrix2.set(0, 1, -0.17);
Vector biasVector2 = new Vector(1);
biasVector2.setValue(0, 0.48);
Layer layer2 = new Layer(weightMatrix2, biasVector2,
new PureLinearActivationFunction());
LayerSensitivity layer2Sensitivity = new LayerSensitivity(layer2);
Vector inputVector2 = layer1.getLastActivationValues();
layer2.feedForward(inputVector2);
Vector errorVector = new Vector(1);
errorVector.setValue(0, 1.261);
layer2Sensitivity.sensitivityMatrixFromErrorMatrix(errorVector);
layer1Sensitivity
.sensitivityMatrixFromSucceedingLayer(layer2Sensitivity);
Vector biasUpdateVector2 = BackPropLearning.calculateBiasUpdates(
layer2Sensitivity, 0.1);
Assert.AreEqual(0.2522, biasUpdateVector2.getValue(0), 0.001);
Vector lastBiasUpdateVector2 = layer2.getLastBiasUpdateVector();
Assert.AreEqual(0.2522, lastBiasUpdateVector2.getValue(0), 0.001);
Vector penultimateBiasUpdateVector2 = layer2
.getPenultimateBiasUpdateVector();
Assert.AreEqual(0.0, penultimateBiasUpdateVector2.getValue(0),
0.001);
Vector biasUpdateVector1 = BackPropLearning.calculateBiasUpdates(
layer1Sensitivity, 0.1);
Assert.AreEqual(0.00495, biasUpdateVector1.getValue(0), 0.001);
Assert.AreEqual(-0.00997, biasUpdateVector1.getValue(1), 0.001);
Vector lastBiasUpdateVector1 = layer1.getLastBiasUpdateVector();
Assert.AreEqual(0.00495, lastBiasUpdateVector1.getValue(0), 0.001);
Assert.AreEqual(-0.00997, lastBiasUpdateVector1.getValue(1), 0.001);
Vector penultimateBiasUpdateVector1 = layer1
.getPenultimateBiasUpdateVector();
Assert.AreEqual(0.0, penultimateBiasUpdateVector1.getValue(0),
0.001);
Assert.AreEqual(0.0, penultimateBiasUpdateVector1.getValue(1),
0.001);
}
