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 List <Matrix> activate(DataStep x, ForwdBackwdProp g)
{
List <int> input = x.inputs;
List <int> bigram = x.bigram;
Matrix final = new Matrix(Global.hiddenDim, 1);
List <Matrix> outputs = new List <Matrix>();
Matrix _h_tm1 = Matrix.newMatrix_0(_hiddenDim, 1);
Matrix _s_tm1 = Matrix.newMatrix_0(_hiddenDim, 1);
for (int i = 0; i < input.Count; i++)
{
//input gate
//Matrix con = g.ConcatVectors(Global.wordEmbedding[input[i]], Global.BigramwordEmbedding[bigram[i]]);
//Matrix inputt = g.tanhNonlin(g.Mul(_hw, con));
Matrix inputt = Global.wordEmbedding[input[i]];
Matrix sum0 = g.Mul(_wix, inputt);
Matrix sum1 = g.Mul(_wih, _h_tm1);
Matrix inputGate = g.sigNonlin(g.Add(g.Add(sum0, sum1), _iBias));
//forget gate
Matrix sum2 = g.Mul(_wfx, inputt);
Matrix sum3 = g.Mul(_wfh, _h_tm1);
Matrix forgetGate = g.sigNonlin(g.Add(g.Add(sum2, sum3), _fBias));
//output gate
Matrix sum4 = g.Mul(_wox, inputt);
Matrix sum5 = g.Mul(_woh, _h_tm1);
Matrix outputGate = g.sigNonlin(g.Add(g.Add(sum4, sum5), _Bias));
//write operation on cells
Matrix sum6 = g.Mul(_wcx, inputt);
Matrix sum7 = g.Mul(_wch, _h_tm1);
Matrix cellInput = g.tanhNonlin(g.Add(g.Add(sum6, sum7), _cBias));
//compute new cell activation
Matrix retainCell = g.Elmul(forgetGate, _s_tm1);
Matrix writeCell = g.Elmul(inputGate, cellInput);
Matrix cellAct = g.Add(retainCell, writeCell);
//compute hidden state as gated, saturated cell activations
Matrix output = g.Elmul(outputGate, g.tanhNonlin(cellAct));
//if (i == 0)
//{
// final = output;
//}
//else
//{
// final = g.ConcatVectors(final, output);
//}
//final = g.Add(final, output);
outputs.Add(output);
//rollover activations for next iteration
_h_tm1 = output;
_s_tm1 = cellAct;
//_h = g.Add(output, _h);
}
return(outputs);
}
