public Matrix calculateWeightUpdates(LayerSensitivity layerSensitivity,
Vector previousLayerActivationOrInput, double alpha, double momentum)
{
Layer layer = layerSensitivity.getLayer();
Matrix activationTranspose = previousLayerActivationOrInput.transpose();
Matrix momentumLessUpdate = layerSensitivity.getSensitivityMatrix()
.times(activationTranspose).times(alpha).times(-1.0);
Matrix updateWithMomentum = layer.getLastWeightUpdateMatrix().times(
momentum).plus(momentumLessUpdate.times(1.0 - momentum));
layer.acceptNewWeightUpdate(updateWithMomentum.copy());
return updateWithMomentum;
}
public Matrix sensitivityMatrixFromSucceedingLayer(
LayerSensitivity nextLayerSensitivity) {
Layer nextLayer = nextLayerSensitivity.getLayer();
Matrix derivativeMatrix = createDerivativeMatrix(layer
.getLastInducedField());
Matrix weightTranspose = nextLayer.getWeightMatrix().transpose();
Matrix calculatedSensitivityMatrix = derivativeMatrix.times(
weightTranspose).times(
nextLayerSensitivity.getSensitivityMatrix());
sensitivityMatrix = calculatedSensitivityMatrix.copy();
return sensitivityMatrix;
}
public static Matrix calculateWeightUpdates(
LayerSensitivity layerSensitivity,
Vector previousLayerActivationOrInput, double alpha)
{
Layer layer = layerSensitivity.getLayer();
Matrix activationTranspose = previousLayerActivationOrInput.transpose();
Matrix weightUpdateMatrix = layerSensitivity.getSensitivityMatrix()
.times(activationTranspose).times(alpha).times(-1.0);
layer.acceptNewWeightUpdate(weightUpdateMatrix.copy());
return(weightUpdateMatrix);
}
public Matrix calculateWeightUpdates(LayerSensitivity layerSensitivity,
Vector previousLayerActivationOrInput, double alpha, double momentum)
{
Layer layer = layerSensitivity.getLayer();
Matrix activationTranspose = previousLayerActivationOrInput.transpose();
Matrix momentumLessUpdate = layerSensitivity.getSensitivityMatrix()
.times(activationTranspose).times(alpha).times(-1.0);
Matrix updateWithMomentum = layer.getLastWeightUpdateMatrix().times(
momentum).plus(momentumLessUpdate.times(1.0 - momentum));
layer.acceptNewWeightUpdate(updateWithMomentum.copy());
return(updateWithMomentum);
}
public Matrix sensitivityMatrixFromSucceedingLayer(
LayerSensitivity nextLayerSensitivity)
{
Layer nextLayer = nextLayerSensitivity.getLayer();
Matrix derivativeMatrix = createDerivativeMatrix(layer
.getLastInducedField());
Matrix weightTranspose = nextLayer.getWeightMatrix().transpose();
Matrix calculatedSensitivityMatrix = derivativeMatrix.times(
weightTranspose).times(
nextLayerSensitivity.getSensitivityMatrix());
sensitivityMatrix = calculatedSensitivityMatrix.copy();
return(sensitivityMatrix);
}
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);
}
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 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 Matrix calculateWeightUpdates(
LayerSensitivity layerSensitivity,
Vector previousLayerActivationOrInput, double alpha)
{
Layer layer = layerSensitivity.getLayer();
Matrix activationTranspose = previousLayerActivationOrInput.transpose();
Matrix weightUpdateMatrix = layerSensitivity.getSensitivityMatrix()
.times(activationTranspose).times(alpha).times(-1.0);
layer.acceptNewWeightUpdate(weightUpdateMatrix.copy());
return weightUpdateMatrix;
}
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;
}
