//propell the computation that actually emulates learning
public void BackProp(Perceptron[] outputs)
{
int i;
//traverse the output layer for computing values
for (i = 0; i < outputPer.Length; i++)
{
Perceptron p = outputPer[i];
float state = p.state;
float error = state * (1f - state);
error *= outputs[i].state - state;
p.AdjustWeights(error);
}
//traverse the internal perceptron layers
for (i = 0; i < hiddenPer.Length; i++)
{
Perceptron p = outputPer[i];
float state = p.state;
float sum = 0f;
for (i = 0; i < outputs.Length; i++)
{
float incomingW = outputs[i].GetIncomingWeight();
sum += incomingW * outputs[i].error;
float error = state * (1f - state) * sum;
p.AdjustWeights(error);
}
}
}
public void BackProp(Perceptron[] outputs)
{
int i;
for (i = 0; i < outputPer.Length; i++)
{
Perceptron p = outputPer[i];
float state = p.state;
float error = state * (1f - state);
error *= outputs[i].state - state;
p.AdjustWeights(error);
}
for (i = 0; i < hiddenPer.Length; i++)
{
Perceptron p = outputPer[i];
float state = p.state;
float sum = 0f;
for (i = 0; i < outputs.Length; i++)
{
float incomingW = outputs[i].GetIncomingWeight();
sum += incomingW * outputs[i].error;
float error = state * (1f - state) * sum;
p.AdjustWeights(error);
}
}
}