/// <summary>
/// 正向传播
/// </summary>
private void Propagate()
{
for (int i = 0; i < _allLayer.Count - 1; i++)
{
ENeuralAct act = (_allLayer[i + 1].LayerType == ENeuralLayerType.Output) ? _outputNeuralAct : _generalNeuralAct;
_allLayer[i].Propagate(act, _allLayer[i + 1]);
}
}
/// <summary>
/// 正向传播
/// </summary>
public void Propagate(ENeuralAct neuralACT, NeuralLayer nextLayer)
{
int numNeurons = nextLayer.Neurons.Count;
for (int i = 0; i < numNeurons; ++i)
{
float value = 0.0f;
int numWeights = Neurons.Count;
for (int j = 0; j < numWeights; ++j)
{
// sum the (weights * inputs), the inputs are the outputs of the prop layer
value += nextLayer.Neurons[i].Weights[j] * Neurons[j].Output;
}
// add in the bias (always has an input of -1)
value += nextLayer.Neurons[i].Weights[numWeights] * -1.0f;
// store the outputs, but run activation first
switch (neuralACT)
{
case ENeuralAct.Step:
nextLayer.Neurons[i].Output = ActStep(value);
break;
case ENeuralAct.Tanh:
nextLayer.Neurons[i].Output = ActTanh(value);
break;
case ENeuralAct.Logistic:
nextLayer.Neurons[i].Output = ActLogistic(value);
break;
case ENeuralAct.BipolarSigmoid:
nextLayer.Neurons[i].Output = ActBipolarSigmoid(value);
break;
case ENeuralAct.Linear:
nextLayer.Neurons[i].Output = value;
break;
default:
throw new Exception("Should never get here.");
}
}
//if you wanted to run the Softmax activation function, you
//would do it here, since it needs all the output values
//if you pushed all the outputs into a vector, you could...
//outputs = ActSoftmax(outputs);
//and then put the outputs back into the correct slots
}
/// <summary>
/// 反向传播
/// </summary>
public void BackPropagate(ENeuralAct neuralACT, NeuralLayer nextLayer)
{
int numNeurons = nextLayer.Neurons.Count;
for (int i = 0; i < numNeurons; ++i)
{
float outputVal = nextLayer.Neurons[i].Output;
float error = 0;
for (int j = 0; j < Neurons.Count; ++j)
{
error += Neurons[j].Weights[i] * Neurons[j].Error;
}
switch (neuralACT)
{
case ENeuralAct.Tanh:
nextLayer.Neurons[i].Error = DerTanh(outputVal) * error;
break;
case ENeuralAct.Logistic:
nextLayer.Neurons[i].Error = DerLogistic(outputVal) * error;
break;
case ENeuralAct.BipolarSigmoid:
nextLayer.Neurons[i].Error = DerBipolarSigmoid(outputVal) * error;
break;
case ENeuralAct.Linear:
nextLayer.Neurons[i].Error = outputVal * error;
break;
default:
{
throw new NotImplementedException();
}
}
}
}