float SumDOW(NeuronLayer nextLayer)
{
float sum = 0.0f;
for (int i = 0; i < nextLayer.Neurons.Count; i++)
{
sum += Weights[i] * nextLayer.Neurons[i].Gradient;
}
return(sum);
}
public void BackPropagate(float[] targetValues, float learningRate = 0.3f, float momentum = 0.5f)
{
if (NeuronLayers.Count == 0)
{
Debug.LogError("BackPropagate failed because no layers exist in the neural network");
return;
}
NeuronLayer outputLayer = NeuronLayers[NeuronLayers.Count - 1];
if (targetValues.Length != outputLayer.Neurons.Count)
{
Debug.Log("BackPropagate passed " + targetValues.Length.ToString() + " elements but needed " + outputLayer.Neurons.Count.ToString());
return;
}
error = 0.0f;
for (int i = 0; i < outputLayer.Neurons.Count; i++)
{
float delta = targetValues[i] - outputLayer.Neurons[i].Output;
error += delta * delta;
}
error /= outputLayer.Neurons.Count;
error = Mathf.Sqrt(error);
for (int i = 0; i < outputLayer.Neurons.Count; i++)
{
outputLayer.Neurons[i].CalculateOutputGradients(targetValues[i]);
}
for (int i = NeuronLayers.Count - 2; i > 0; i--)
{
NeuronLayer hiddenLayer = NeuronLayers[i];
NeuronLayer nextLayer = NeuronLayers[i + 1];
for (int j = 0; j < hiddenLayer.Neurons.Count; j++)
{
hiddenLayer.Neurons[j].CalculateHiddenGradients(nextLayer);
}
}
for (int i = NeuronLayers.Count - 1; i > 0; i--)
{
NeuronLayer layer = NeuronLayers[i];
NeuronLayer previousLayer = NeuronLayers[i - 1];
for (int j = 0; j < layer.Neurons.Count; j++)
{
layer.Neurons[j].UpdateWeights(previousLayer);
}
}
}
public void FeedForward(NeuronLayer inputLayer)
{
float sum = 0.0f;
foreach (Neuron neuron in inputLayer.Neurons)
{
sum += neuron.Weights[index] * neuron.Output;
}
sum += inputLayer.Bias;
Output = Transfer(sum);
}
public void UpdateWeights(NeuronLayer previousLayer, float learningRate = 0.3f, float momentum = 0.5f)
{
for (int i = 0; i < previousLayer.Neurons.Count; i++)
{
Neuron neuron = previousLayer.Neurons[i];
float oldDeltaWeight = neuron.DeltaWeights[index];
float newDeltaWeight = learningRate * neuron.Output * Gradient + momentum * oldDeltaWeight;
neuron.DeltaWeights[index] = newDeltaWeight;
neuron.Weights[index] += newDeltaWeight;
}
}
public void CalculateHiddenGradients(NeuronLayer nextLayer)
{
float dow = SumDOW(nextLayer);
Gradient = dow * TransferDerivative(Output);
}