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;
}
}
}
}
