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 testWeightUpdateMatrixesFormedCorrectly()
{
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 weightUpdateMatrix2 = BackPropLearning.calculateWeightUpdates(
layer2Sensitivity, layer1.getLastActivationValues(), 0.1);
Assert.AreEqual(0.0809, weightUpdateMatrix2.get(0, 0), 0.001);
Assert.AreEqual(0.0928, weightUpdateMatrix2.get(0, 1), 0.001);
Matrix lastWeightUpdateMatrix2 = layer2.getLastWeightUpdateMatrix();
Assert.AreEqual(0.0809, lastWeightUpdateMatrix2.get(0, 0), 0.001);
Assert.AreEqual(0.0928, lastWeightUpdateMatrix2.get(0, 1), 0.001);
Matrix penultimateWeightUpdatematrix2 = layer2
.getPenultimateWeightUpdateMatrix();
Assert.AreEqual(0.0, penultimateWeightUpdatematrix2.get(0, 0),
0.001);
Assert.AreEqual(0.0, penultimateWeightUpdatematrix2.get(0, 1),
0.001);
Matrix weightUpdateMatrix1 = BackPropLearning.calculateWeightUpdates(
layer1Sensitivity, inputVector1, 0.1);
Assert.AreEqual(0.0049, weightUpdateMatrix1.get(0, 0), 0.001);
Assert.AreEqual(-0.00997, weightUpdateMatrix1.get(1, 0), 0.001);
Matrix lastWeightUpdateMatrix1 = layer1.getLastWeightUpdateMatrix();
Assert.AreEqual(0.0049, lastWeightUpdateMatrix1.get(0, 0), 0.001);
Assert.AreEqual(-0.00997, lastWeightUpdateMatrix1.get(1, 0), 0.001);
Matrix penultimateWeightUpdatematrix1 = layer1
.getPenultimateWeightUpdateMatrix();
Assert.AreEqual(0.0, penultimateWeightUpdatematrix1.get(0, 0),
0.001);
Assert.AreEqual(0.0, penultimateWeightUpdatematrix1.get(1, 0),
0.001);
}
