/// <summary>
/// backward action
/// </summary>
/// <param name="lr"> lerning rate</param>
/// <param name="gradTns"> grad error tensor</param>
/// <returns> true - ok</returns>
public bool backward(float lr, Tensor gradTns)
{
if ((net_ == null) && !createNet())
{
return(false);
}
return(snBackward(net_, lr, gradTns.size(), gradTns.data()));
}
/// <summary>
/// forward action
/// </summary>
/// <param name="isLern"> is lerning ?</param>
/// <param name="inTns"> in tensor</param>
/// <param name="outTns"> out result tensor</param>
/// <returns> true - ok</returns>
public bool forward(bool isLern, Tensor inTns, Tensor outTns)
{
if ((net_ == null) && !createNet())
{
return(false);
}
return(snForward(net_, isLern, inTns.size(), inTns.data(), outTns.size(), outTns.data()));
}
/// <summary>
/// set weight of node
/// </summary>
/// <param name="name"> name node in architecture of net</param>
/// <param name="weight"> set weight tensor</param>
/// <returns> true - ok</returns>
public bool setWeightNode(string name, Tensor weight)
{
if (net_ == null)
{
return(false);
}
IntPtr cname = Marshal.StringToHGlobalAnsi(name);
bool ok = snSetWeightNode(net_, cname, weight.size(), weight.data());
Marshal.FreeHGlobal(cname);
return(ok);
}
/// <summary>
/// cycle forward-backward
/// </summary>
/// <param name="lr"> lerning rate</param>
/// <param name="inTns"> in tensor</param>
/// <param name="outTns"> out tensor</param>
/// <param name="targetTns"> target tensor</param>
/// <param name="outAccurate"> accurate error</param>
/// <returns> true - ok</returns>
public bool training(float lr, Tensor inTns, Tensor outTns, Tensor targetTns, ref float outAccurate)
{
if ((net_ == null) && !createNet())
{
return(false);
}
float accurate = 0;
bool ok = snTraining(net_, lr, inTns.size(), inTns.data(),
outTns.size(), outTns.data(), targetTns.data(), &accurate);
outAccurate = accurate;
return(ok);
}
