public WeightMatrix Perform(WeightMatrix input, WeightMatrix state, ComputeGraph g)
{
WeightMatrix context;
List <WeightMatrix> atten = new List <WeightMatrix>();
var stateRepeat = g.RepeatRows(state, input.Rows);
var baiseInput = new WeightMatrix(input.Rows, 1, 1);
var inputb = g.concatColumns(input, baiseInput);
var uh = g.mul(inputb, Ua);
baiseInput = new WeightMatrix(stateRepeat.Rows, 1, 1);
stateRepeat = g.concatColumns(stateRepeat, baiseInput);
var wc = g.mul(stateRepeat, Wa);
var gg = g.tanh(g.add(uh, wc));
var aa = g.mul(gg, V);
var res = g.Softmax(aa);
var weighted = g.weightRows(input, res);;
context = g.sumColumns(weighted);
return(context);
}
public WeightMatrix Perform(List <WeightMatrix> input, WeightMatrix state, ComputeGraph g)
{
WeightMatrix context;
List <WeightMatrix> atten = new List <WeightMatrix>();
foreach (var h_j in input)
{
var uh = g.add(g.mul(h_j, Ua), bUa);
var wc = g.add(g.mul(state, Wa), bWa);
var gg = g.tanh(g.add(uh, wc));
var aa = g.mul(gg, V);
atten.Add(aa);
}
var res = g.Softmax(atten);
var cmax = res[0].Weight[0];
int maxAtt = 0;
for (int i = 1; i < res.Count; i++)
{
if (res[i].Weight[0] > cmax)
{
cmax = res[i].Weight[0];
maxAtt = i;
}
}
this.MaxIndex = maxAtt;
context = g.scalemul(input[0], res[0]);
for (int hj = 1; hj < input.Count; hj++)
{
context = g.add(context, g.scalemul(input[hj], res[hj]));
}
return(context);
}
