public void propagateBackward(Matrix target) {
NeuralNetwork nn = getNeuralNetwork();
OutputErrorDerivative d = getProperties().getParameter(Constants.OUTPUT_ERROR_DERIVATIVE);
Matrix outputErrorDerivative =
d.getOutputErrorDerivative(activations.getValues(nn.getOutputLayer()), target);
backpropagation.addValues(nn.getOutputLayer(), outputErrorDerivative);
Set<Layer> calculatedLayers = new UniqueList<Layer>();
calculatedLayers.add(nn.getOutputLayer());
BackPropagationLayerCalculator blc = getBPLayerCalculator();
blc.backpropagate(nn, calculatedLayers, activations, backpropagation);
}
public void propagateForward(Matrix input) {
NeuralNetwork nn = getNeuralNetwork();
Set<Layer> calculatedLayers = new UniqueList<Layer>();
calculatedLayers.add(nn.getInputLayer());
activations.addValues(nn.getInputLayer(), input);
nn.getLayerCalculator().calculate(nn, nn.getOutputLayer(), calculatedLayers, activations);
}
@Override
protected ConnectionCalculator createInputFunction(
SortedMap<GraphConnections, Integer> inputConnections,
ValuesProvider valuesProvider,
Layer targetLayer) {
return new AparapiSigmoidFunction(inputConnections, valuesProvider.getColumns(), targetLayer);
}