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);
BackPropagationLearning.CalculateWeightUpdates(layer2Sensitivity,
layer1.GetLastActivationValues(), 0.1);
BackPropagationLearning.CalculateBiasUpdates(layer2Sensitivity, 0.1);
BackPropagationLearning.CalculateWeightUpdates(layer1Sensitivity,
inputVector1, 0.1);
BackPropagationLearning.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 = BackPropagationLearning.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 = BackPropagationLearning.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);
}