private static Dictionary <Node, double> UpdateOutputLayer(Layer outputLayer, double[] targetOutputs, ErrorFunctionType errorFunctionType, double learningRate, double momentumMagnitude)
{
var errorFunctionDifferential = ErrorFunctionResolver.ResolveErrorFunctionDifferential(errorFunctionType);
var deltas = new Dictionary <Node, double>();
for (var i = 0; i < outputLayer.Nodes.Count; i++)
{
var node = outputLayer.Nodes[i];
var delta = Math.MinMagnitude(errorFunctionDifferential.Invoke(targetOutputs[i], node.Output), 100)
* outputLayer.ActivationFunctionDifferential(node.Output)
* learningRate;
deltas.Add(node, delta);
foreach (var(prevNode, weightForPrevNode) in node.Weights)
{
UpdateNodeWeight(prevNode, weightForPrevNode, delta, momentumMagnitude);
}
foreach (var(_, weightForPrevLayer) in node.BiasWeights)
{
UpdateBiasNodeWeight(weightForPrevLayer, delta, momentumMagnitude);
}
}
return(deltas);
}
private static Dictionary <Node, double> NegativeSampleOutput(Layer outputLayer, double targetOutput, int outputIndex, ErrorFunctionType errorFunctionType, double learningRate, double momentumMagnitude)
{
var errorFunctionDifferential = ErrorFunctionResolver.ResolveErrorFunctionDifferential(errorFunctionType);
var outputNode = outputLayer.Nodes[outputIndex];
var delta = errorFunctionDifferential.Invoke(targetOutput, outputNode.Output)
* outputLayer.ActivationFunctionDifferential(outputNode.Output)
* learningRate;
foreach (var(prevNode, weightForPrevNode) in outputNode.Weights)
{
Backpropagation.UpdateNodeWeight(prevNode, weightForPrevNode, delta, momentumMagnitude);
}
foreach (var(_, weightForPrevLayer) in outputNode.BiasWeights)
{
Backpropagation.UpdateBiasNodeWeight(weightForPrevLayer, delta, momentumMagnitude);
}
return(new Dictionary <Node, double> {
{ outputNode, delta }
});
}
