public Matrix Activate_1(Matrix input, ForwdBackwdProp g)
{
Matrix sum = g.Add(g.Mul(_w, input), _b);
Matrix returnObj = g.tanhNonlin(sum);
return(returnObj);
}
public Matrix Activate(Matrix input, ForwdBackwdProp g)
{
Matrix sum = g.Add(g.Mul(_w, input), _b);
Matrix returnObj = sum;
return(returnObj);
}
public List <Matrix> activate(List <Matrix> x, ForwdBackwdProp g)
{
// List<int> input = x.inputs;
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 < x.Count; i++)
{
//input gate
Matrix sum0 = g.Mul(_wix, x[i]);
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, x[i]);
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, x[i]);
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, x[i]);
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);
}
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(List <Matrix> input, ForwdBackwdProp g)
{
List <Matrix> temp = new List <Matrix>();
for (int i = 1; i < input.Count; i++)
{
Matrix concanate = g.ConcatVectors(input[i - 1], input[i]);
//Matrix rl = g.sigNonlin(g.Add(g.Mul(rl1w, input[i - 1]), g.Mul(rl2w, input[i])));
Matrix rl = g.sigNonlin(g.Mul(_gl, concanate));
Matrix rr = g.sigNonlin(g.Mul(_gr, concanate));
//Matrix rr = g.sigNonlin(g.Add(g.Mul(rr1w, input[i - 1]), g.Mul(rr2w, input[i])));
//Matrix hh = g.tanhNonlin(g.Add(g.Mul(wi, input[i - 1]), g.Mul(wx, input[i])));
Matrix hh = g.tanhNonlin(g.Mul(hh1w, g.ConcatVectors(g.Elmul(rl, input[i - 1]), g.Elmul(rr, input[i]))));
//Matrix hhh = g.ConcatVectors(g.ConcatVectors(hh, input[i - 1]), input[i]);
//Matrix zh1 = g.sigNonlin(g.Mul(hh2w, hhh));
//Matrix zh2 = g.sigNonlin(g.Mul(hh3w, hhh));
//Matrix zh3 = g.tanhNonlin(g.Mul(hh4w, hhh));
//Matrix z1=g.Exp()
Matrix concanate1 = g.ConcatVectors(g.ConcatVectors(hh, input[i - 1]), input[i]);
Matrix z1 = g.sigNonlin(g.Mul(_u1, concanate1));
Matrix z2 = g.sigNonlin(g.Mul(_u2, concanate1));
Matrix z3 = g.sigNonlin(g.Mul(_u3, concanate1));
//Matrix zl = g.SumDivid1(zh1, zh2, zh3);
// Matrix zm = g.SumDivid2(zh1, zh2, zh3);
//Matrix zr = g.SumDivid3(zh1, zh2, zh3);
Matrix output = g.Add(g.Add(g.Elmul(z1, hh), g.Elmul(z2, input[i - 1])), g.Elmul(z3, input[i]));
temp.Add(output);
}
return(temp);
}
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]);
}
}
