/// <summary>
/// 初始化神经网络
/// </summary>
/// <param name="inputLayerNodes">输入神经网络层的节点个数</param>
/// <param name="outputLayerNodes">输出神经网络层的节点个数</param>
/// <param name="hiddenLayerNodes">隐藏神经网络层的节点个数</param>
/// <param name="hiddenLayers">隐藏神经网络层的数量</param>
public void Init(int inputLayerNodes, int outputLayerNodes, int hiddenLayerNodes, int hiddenLayers)
{
// 清空
_allLayer.Clear();
_inputLayer = null;
_outputLayer = null;
int outputLayerInputs = inputLayerNodes;
// 输入层
_inputLayer = AddLayer(inputLayerNodes, 1, ENeuralLayerType.Input);
// 隐藏层
if (hiddenLayers > 0)
{
outputLayerInputs = hiddenLayerNodes;
// first hidden layer connect back to inputs
AddLayer(hiddenLayerNodes, inputLayerNodes, ENeuralLayerType.Hidden);
for (int i = 0; i < hiddenLayers - 1; i++)
{
AddLayer(hiddenLayerNodes, hiddenLayerNodes, ENeuralLayerType.Hidden);
}
}
// 输出层
_outputLayer = AddLayer(outputLayerNodes, outputLayerInputs, ENeuralLayerType.Output);
}
/// <summary>
/// 添加一个神经网络层到神经网络
/// </summary>
private MoNeuralLayer AddLayer(int neurons, int inputs, ENeuralLayerType layerType)
{
MoNeuralLayer layer = new MoNeuralLayer(neurons, inputs, layerType);
_allLayer.Add(layer);
return(layer);
}
/// <summary>
/// 正向传播
/// </summary>
public void Propagate(ENeuralAct neuralACT, MoNeuralLayer 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>
/// 修正权重值
/// </summary>
/// <param name="inputLayer">输入神经网络层</param>
/// <param name="learningRate">默认值为0.1f</param>
/// <param name="momentum">默认值为0.9f</param>
public void AdjustWeights(MoNeuralLayer inputLayer, float learningRate, float momentum)
{
for (int i = 0; i < Neurons.Count; i++)
{
int numWeights = Neurons[i].Weights.Count;
for (int j = 0; j < numWeights; ++j)
{
// bias weight always uses -1 output value
float output = (j == (numWeights - 1)) ? -1.0f : inputLayer.Neurons[j].Output;
float error = Neurons[i].Error;
float delta = momentum * Neurons[i].LastDelta[j] + (1.0f - momentum) * learningRate * error * output;
Neurons[i].Weights[j] += delta;
Neurons[i].LastDelta[j] = delta;
}
}
}
private void ActSoftmax(MoNeuralLayer outputs)
{
float total = 0.0f;
for (int i = 0; i < Neurons.Count; ++i)
{
#if UNITY_ENGINE
total = UnityEngine.Mathf.Exp(outputs.Neurons[i].Output);
#else
total = MathF.Exp(outputs.Neurons[i].Output);
#endif
}
for (int i = 0; i < Neurons.Count; ++i)
{
#if UNITY_ENGINE
outputs.Neurons[i].Output = UnityEngine.Mathf.Exp(outputs.Neurons[i].Output) / total;
#else
outputs.Neurons[i].Output = MathF.Exp(outputs.Neurons[i].Output) / total;
#endif
}
}
/// <summary>
/// 反向传播
/// </summary>
public void BackPropagate(ENeuralAct neuralACT, MoNeuralLayer 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();
}
}
}
}