public void run(object param)
{
param pa = (param)param;
List <DataStep> x1 = pa.datastep;
List <Matrix> xpro = new List <Matrix>();
ForwdBackwdProp g = new ForwdBackwdProp(_train);
for (int i = 0; i < x1.Count; i++)
{
DataStep x = x1[i];
List <Matrix> add = new List <Matrix>();
for (int k = 0; k < 5; k++)
{
add.Add(Global.wordEmbedding[x.inputs[k]]);
}
List <Matrix> returnObj2 = Global.GRNNLayer1.activate(add, g);
List <Matrix> returnObj3 = Global.GRNNLayer2.activate(returnObj2, g);
List <Matrix> returnObj4 = Global.GRNNLayer3.activate(returnObj3, g);
List <Matrix> returnObj5 = Global.GRNNLayer4.activate(returnObj4, g);
xpro.Add(returnObj5[0]);
}
List <Matrix> returnObj6 = Global.upLSTMLayer.activate(xpro, g);
List <Matrix> returnObj7 = Global.upLSTMLayerr.activate(reverse(xpro), g);
List <Matrix> sum = new List <Matrix>();
for (int inde = 0; inde < returnObj6.Count(); inde++)
{
sum.Add(g.Add(returnObj6[inde], returnObj7[returnObj7.Count - inde - 1]));
}
for (int i = 0; i < returnObj6.Count; i++)
{
Matrix returnObj9 = Global.feedForwardLayer.Activate(sum[i], g);
double loss = LossSoftmax.getLoss(returnObj9, x1[i].goldOutput);
if (double.IsNaN(loss) || double.IsInfinity(loss))
{
Console.WriteLine("WARNING!!!");
Global.swLog.WriteLine("WARNING!!!");
pa.mre.Set();
return;
}
LossSoftmax.getGrad(returnObj9, x1[i].goldOutput);
}
g.backwardProp();
pa.mre.Set();
}
public void runtest(object param)
{
param pa = (param)param;
List <DataStep> x1 = pa.datastep;
int[] ires4, igold4, wordindeis;
string str = "", str1 = "";
igold4 = new int[x1.Count];
ires4 = new int[x1.Count];
wordindeis = new int[x1.Count];
int index = 0, arraynum = 0;
ForwdBackwdProp g = new ForwdBackwdProp(_train);
int dim = 0;
dim = x1.Count;
Matrix[] xpro = new Matrix[dim];
List <int> ires_model = new List <int>();
//Parallel.For(0, temp.Count, i =>
for (int i = 0; i < x1.Count; i++)
{
List <Matrix> add = new List <Matrix>();
for (int k = 0; k < 5; k++)
{
add.Add(Global.wordEmbedding[x1[i].inputs[k]]);
}
List <Matrix> returnObj2 = Global.GRNNLayer1.activate(add, g);
List <Matrix> returnObj3 = Global.GRNNLayer2.activate(returnObj2, g);
List <Matrix> returnObj4 = Global.GRNNLayer3.activate(returnObj3, g);
List <Matrix> returnObj5 = Global.GRNNLayer4.activate(returnObj4, g);
xpro[i] = returnObj5[0];
}//);
List <Matrix> returnObj6 = Global.upLSTMLayer.activate(xpro.ToList(), g);
List <Matrix> returnObj7 = Global.upLSTMLayerr.activate(reverse(xpro.ToList()), g);
List <Matrix> sum = new List <Matrix>();
for (int inde = 0; inde < returnObj6.Count(); inde++)
{
sum.Add(g.Add(returnObj6[inde], returnObj7[returnObj7.Count - inde - 1]));
}
for (int i = 0; i < xpro.Length; i++)
{
Matrix returnObj9 = Global.feedForwardLayer.Activate(sum[i], g);
igold4[i] = LossSoftmax.getMax(x1[i].goldOutput);
ires4[i] = LossSoftmax.getMax(returnObj9);
}
//);
//fscore.backprocess(wordindeis, ires4);
pa.seq.write_string = "BOS O O" + "\n";
//pa.sw.WriteLine("BOS O O");
for (int i = 0; i < ires4.Count(); i++)
{
pa.seq.write_string += (Global.word[x1[i].wordindex] + " " + ires4[i] + " " + igold4[i] + "\n");
}
pa.seq.write_string += ("EOS O O" + "\n");
pa.seq.write_string += "\n";
List <string> res = fscore.getChunks4(ires4);
str1 = fscore.calcorrect(fscore.getChunks4(igold4), res);
lock (thislock)
{
string[] strs1 = str1.Split();
_total4 += Int32.Parse(strs1[0]);
_prTotal4 += Int32.Parse(strs1[1]);
_correct4 += Int32.Parse(strs1[2]);
}
}
