public double TrainOne(double [] input, int y)
{
if (_rand == null)
{
_rand = new RNG(_seed);
}
var f = new Flow(true, _rand);
Timer.build.Start();
BuildOne(f, input, y, out var probs);
Timer.build.Stop();
Timer.forward.Start();
Forward(f);
Timer.forward.Stop();
Timer.backward.Start();
Backward(f);
Timer.backward.Stop();
Timer.update.Start();
_opt.Update(FixedParam);
Timer.update.Stop();
return(Evaluate(y, probs));
}
public float TrainOne(Sent sent)
{
G.Need = true;
var c = new ArcStandardConfig(sent, false, _rngChoice);
GetTokenRepr(sent, true);
var losses = 0f;
while (!c.IsTerminal())
{
var oracle = c.GetOracle(_conf.OraType, out float[] target, out string slabel);
GetTrans(c, out Tensor op, out Tensor label);
G.SoftmaxWithCrossEntropy(op, target, out float loss);
losses += loss;
if (slabel != null)
{
// in mapping 0 is unk, 1 is root
var labelid = _train.DepLabel[slabel] - 1;
G.SoftmaxWithCrossEntropy(label, labelid, out loss);
losses += loss;
}
c.Apply(oracle, slabel);
}
G.Backward();
_opt.Update(FixedParams);
_opt.Update(VariedParams);
VariedParams.Clear();
G.Clear();
return(losses / sent.Count);
}
// Update the parameters of the neural network
// Time it when training
public void Update()
{
Timer.Update.Start();
_opt.Update(FixedParam);
Timer.Update.Stop();
}