private float GetWi_Xi(FullyLayer lastfullyLayer, int fullyneurnIndex, int Xindex)
{
float sum = 0;
for (int i = 0; i < lastfullyLayer.fullyneurns.Count; i++)
{
Fullyneuron fullyneuron = lastfullyLayer.fullyneurns[i];
sum += fullyneuron.Wi[fullyneurnIndex] * fullyneuron.Xout[Xindex];
}
return(sum);
}
public void caculate()
{
if (InputArray == null || NumberOfLayorNeuron == null)
{
Console.WriteLine("Error!位置：\n\rFullyNet->caculate()");
return;
}
//初始化神经元
if (fullyLayers == null)
{
NumberOfLayorNeuron[0] = InputArray[0].Length;
fullyLayers = new List <FullyLayer>(NumberOfLayorNeuron.Length);
for (int i = 0; i < NumberOfLayorNeuron.Length; i++)
{
fullyLayers.Add(new FullyLayer());
}
for (int i = 0; i < fullyLayers.Count; i++)
{
if (fullyLayers[i].fullyneurns == null)
{
fullyLayers[i].fullyneurns = new List <Fullyneuron>(NumberOfLayorNeuron[i]);
for (int j = 0; j < NumberOfLayorNeuron[i]; j++)
{
fullyLayers[i].fullyneurns.Add(new Fullyneuron());
}
}
}
//初始化权值
for (int i = 0; i < fullyLayers.Count - 1; i++)
{
for (int j = 0; j < fullyLayers[i].fullyneurns.Count; j++)
{
Fullyneuron fullyneuron = fullyLayers[i].fullyneurns[j];
if (fullyneuron.Wi == null)
{
fullyneuron.Wi = new float[NumberOfLayorNeuron[i + 1]];
for (int k = 0; k < NumberOfLayorNeuron[i + 1]; k++)
{
fullyneuron.Wi[k] = W_value_method(NumberOfLayorNeuron[i]);
//fullyneuron.BN.Gamma = W_value_method(NumberOfLayorNeuron[i]);
//fullyneuron.BN.Bata = W_value_method(NumberOfLayorNeuron[i]);
}
}
}
}
}
//第一层输入
FullyLayer firstLayer = fullyLayers[0];
for (int i = 0; i < firstLayer.fullyneurns.Count; i++)
{
float[] XiArray = new float[AgentClass.Mini_batchsize];
for (int j = 0; j < AgentClass.Mini_batchsize; j++)
{
XiArray[j] = InputArray[j][i];
}
firstLayer.fullyneurns[i].forward(XiArray);
}
//隐藏层
for (int i = 1; i < fullyLayers.Count; i++)
{
forwardcaculate(fullyLayers[i - 1], fullyLayers[i]);
}
//输出
if (outData == null)
{
int lastNetNumber = NumberOfLayorNeuron[NumberOfLayorNeuron.Length - 1];
outData = new List <float[]>(AgentClass.Mini_batchsize);
for (int i = 0; i < AgentClass.Mini_batchsize; i++)
{
outData.Add(new float[lastNetNumber]);
}
}
FullyLayer lastFullyLayer = fullyLayers[fullyLayers.Count - 1];
for (int i = 0; i < AgentClass.Mini_batchsize; i++)
{
for (int j = 0; j < NumberOfLayorNeuron[NumberOfLayorNeuron.Length - 1]; j++)
{
outData[i][j] = lastFullyLayer.fullyneurns[j].Xout[i];
}
//outData[i] = lastFullyLayer.fullyneurns[i].Xout;
}
}
public void updateWi()
{
//输出节点
FullyLayer lastFullyLayer = fullyLayers[fullyLayers.Count - 1];
for (int i = 0; i < lastFullyLayer.fullyneurns.Count; i++)
{
lastFullyLayer.fullyneurns[i].backpropagation(lossValue[i]);
}
//隐藏节点
for (int i = fullyLayers.Count - 1; i > 0; i--)
{
backward(fullyLayers[i - 1], fullyLayers[i]);
}
//输出敏感度
if (OutSensitive == null)
{
OutSensitive = new List <float[]>(AgentClass.Mini_batchsize);
for (int i = 0; i < AgentClass.Mini_batchsize; i++)
{
OutSensitive.Add(new float[NumberOfLayorNeuron[0]]);
}
}
for (int i = 0; i < AgentClass.Mini_batchsize; i++)
{
for (int j = 0; j < fullyLayers[0].fullyneurns.Count; j++)
{
OutSensitive[i][j] = fullyLayers[0].fullyneurns[j].SensitiveValue[i];
}
}
//更新权值
//初始化ADAM
if (isAdamInit == false)
{
for (int i = 0; i < fullyLayers.Count - 1; i++)
{
for (int j = 0; j < fullyLayers[i].fullyneurns.Count; j++)
{
Fullyneuron fullyneuron = fullyLayers[i].fullyneurns[j];
if (fullyneuron.S == null)
{
fullyneuron.S = new float[NumberOfLayorNeuron[i + 1]];
fullyneuron.V = new float[NumberOfLayorNeuron[i + 1]];
}
}
}
isAdamInit = true;
}
//用于保存各个dw
float[] dwArray = new float[AgentClass.Mini_batchsize];
//更新权值
for (int i = 0; i < fullyLayers.Count - 1; i++)
{
float dw = 0;
for (int j = 0; j < fullyLayers[i].fullyneurns.Count; j++)
{
Fullyneuron fullyneuron = fullyLayers[i].fullyneurns[j];
for (int k = 0; k < fullyneuron.Wi.Length; k++)
{
for (int mini = 0; mini < AgentClass.Mini_batchsize; mini++)
{
dwArray[mini] = fullyneuron.Xout[mini] * fullyLayers[i + 1].fullyneurns[k].SensitiveValue[mini];
}
dw = ArrayAction.average(dwArray);
fullyneuron.AdamUpdateWi(dw, k);
//float quanzhi = Adam.GetAdamNumber(fullyneuron.V[k], fullyneuron.S[k], dw,out fullyneuron.V[k],out fullyneuron.S[k]);
//fullyneuron.Wi[k] -= AgentClass.Study_rate * quanzhi;
}
}
}
}
