private string GetNeuronInfo(int layerID, int positionID)
{
AnnNeuron neuron = Net.NetLayers[layerID][positionID];
string message = "Neuron" + "\n";
message += "Layer / NeuronID: " + layerID + " / " + positionID + "\n";
message += "Input: " + neuron.Input + "\n";
message += "Output: " + neuron.Output + "\n";
message += "Activation Function: " + neuron.ActivationFunction.ActivationFunctionType + "\n";
return(message);
}
private double BackPropSingleDataSet(double[] sampleDataSet, double[] targetDataSet)
{
AnnFeedForward feedForwardModel = new AnnFeedForward(net);
feedForwardModel.FeedForward(sampleDataSet);
int outputLayerID = net.NetLayers.Length - 1;
double[] errors = new double[net.NetLayers[outputLayerID].Length];
// calculate output deltas
for (int outNeuronID = 0; outNeuronID < net.NetLayers[outputLayerID].Length; outNeuronID++)
{
AnnNeuron neuron = net.NetLayers[outputLayerID][outNeuronID];
double derivAFOutput = neuron.ActivationFunction.GetAFDerivValue(neuron.Input);
double outputScalar = neuron.ActivationFunction.Scalar;
double derivError = -(targetDataSet[outNeuronID] / outputScalar - neuron.Output / outputScalar);
if (targetDataSet[outNeuronID] != 0)
{
errors[outNeuronID] += (targetDataSet[outNeuronID] - neuron.Output) / targetDataSet[outNeuronID];
}
else
{
errors[outNeuronID] += 0;
}
neuron.BackPropDelta = derivAFOutput * derivError;
}
// calculate remaining deltas
for (int layerID = outputLayerID - 1; layerID > 0; layerID--)
{
for (int neuronID = 0; neuronID < net.NetLayers[layerID].Length; neuronID++)
{
net.NetLayers[layerID][neuronID].BackPropDeltaUpdate();
}
}
// add delta * input neuron output to the synapse deltainput list
for (int synapseID = 0; synapseID < net.SynapsesAll.Count; synapseID++)
{
AnnSynapse synapse = net.SynapsesAll[synapseID];
synapse.DeltaInputAdd(synapse.OutputNeuron.BackPropDelta * synapse.InputNeuron.Output);
}
return(MaxPercentageError(errors));
}