private GameObject CreateSynapseObject(AnnSynapse aNN_Synapse, int synapseID)
{
int inputLayerID = aNN_Synapse.InputNeuron.LayerID;
int inputPositionID = aNN_Synapse.InputNeuron.PositionID;
int outputLayerID = aNN_Synapse.OutputNeuron.LayerID;
int outputPositionID = aNN_Synapse.OutputNeuron.PositionID;
GameObject synapseGO = new GameObject
{
name = "Synapse_" + synapseID
};
AddTextObject(synapseGO);
Vector3 dir = (neuronObjects[outputLayerID][outputPositionID].transform.position - neuronObjects[inputLayerID][inputPositionID].transform.position).normalized;
synapseGO.transform.position = neuronObjects[inputLayerID][inputPositionID].transform.position + dir * horizontalSpacing * weightPos;
GameObject go = GameObject.CreatePrimitive(PrimitiveType.Cylinder);
go.name = "-2_" + synapseID;
float diameter = 0.05f;
float length = Vector3.Distance(neuronObjects[inputLayerID][inputPositionID].transform.position, neuronObjects[outputLayerID][outputPositionID].transform.position) / 2;
go.transform.localScale = new Vector3(diameter, length, diameter);
go.transform.position = neuronObjects[inputLayerID][inputPositionID].transform.position;
go.transform.LookAt(neuronObjects[outputLayerID][outputPositionID].transform);
go.transform.rotation *= Quaternion.Euler(90, 0, 0);
go.transform.position = (neuronObjects[outputLayerID][outputPositionID].transform.position + neuronObjects[inputLayerID][inputPositionID].transform.position) / 2.0f;
go.transform.SetParent(synapseGO.transform);
MakeClickable(go);
return(synapseGO);
}
private string GetSynapseInfo(int positionID)
{
AnnSynapse synapse = Net.SynapsesAll[positionID];
string message = "Synapse" + "\n";
message += "ID: " + positionID + "\n";
message += "Input Neuron: " + synapse.InputNeuron.LayerID + " / " + synapse.InputNeuron.PositionID + "\n";
message += "Input Neuron [Output]: " + synapse.InputNeuron.Output + "\n";
message += "Output Neuron: " + synapse.OutputNeuron.LayerID + " / " + synapse.OutputNeuron.PositionID + "\n";
message += "Output Neuron [Total Input]: " + synapse.OutputNeuron.Input + "\n";
message += "Weight: " + synapse.Weight + "\n";
message += "Synapse output: " + synapse.Weight * synapse.InputNeuron.Output + "\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));
}