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 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 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 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 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; }