public Vector feedForward(Vector inputVector) {
lastInput = inputVector;
Matrix inducedField = weightMatrix.times(inputVector).plus(biasVector);
Vector inducedFieldVector = new Vector(numberOfNeurons());
for (int i = 0; i < numberOfNeurons(); i++) {
inducedFieldVector.setValue(i, inducedField.get(i, 0));
}
lastInducedField = inducedFieldVector.copyVector();
Vector resultVector = new Vector(numberOfNeurons());
for (int i = 0; i < numberOfNeurons(); i++) {
resultVector.setValue(i, activationFunction
.activation(inducedFieldVector.getValue(i)));
}
// set the result as the last activation value
lastActivationValues = resultVector.copyVector();
return resultVector;
}
public Vector calculateBiasUpdates(LayerSensitivity layerSensitivity,
double alpha, double momentum)
{
Layer layer = layerSensitivity.getLayer();
Matrix biasUpdateMatrixWithoutMomentum = layerSensitivity
.getSensitivityMatrix().times(alpha).times(-1.0);
Matrix biasUpdateMatrixWithMomentum = layer.getLastBiasUpdateVector()
.times(momentum).plus(
biasUpdateMatrixWithoutMomentum.times(1.0 - momentum));
Vector result = new Vector(biasUpdateMatrixWithMomentum
.getRowDimension());
for (int i = 0; i < biasUpdateMatrixWithMomentum.getRowDimension(); i++)
{
result.setValue(i, biasUpdateMatrixWithMomentum.get(i, 0));
}
layer.acceptNewBiasUpdate(result.copyVector());
return result;
}
public static Vector calculateBiasUpdates(
LayerSensitivity layerSensitivity, double alpha)
{
Layer layer = layerSensitivity.getLayer();
Matrix biasUpdateMatrix = layerSensitivity.getSensitivityMatrix()
.times(alpha).times(-1.0);
Vector result = new Vector(biasUpdateMatrix.getRowDimension());
for (int i = 0; i < biasUpdateMatrix.getRowDimension(); i++)
{
result.setValue(i, biasUpdateMatrix.get(i, 0));
}
layer.acceptNewBiasUpdate(result.copyVector());
return result;
}
