/// <summary>
///
/// </summary>
/// <param name="input"></param>
/// <param name="innerGraph"></param>
/// <returns></returns>
public WeightMatrix Step(WeightMatrix input, ComputeGraph innerGraph)
{
var hidden_prev = ht;
var cell_prev = ct;
var cell = this;
var h0 = innerGraph.mul(input, cell.Wix);
var h1 = innerGraph.mul(hidden_prev, cell.Wih);
var input_gate = innerGraph.sigmoid(
innerGraph.add(
innerGraph.add(h0, h1),
cell.bi
)
);
var h2 = innerGraph.mul(input, cell.Wfx);
var h3 = innerGraph.mul(hidden_prev, cell.Wfh);
var forget_gate = innerGraph.sigmoid(
innerGraph.add(
innerGraph.add(h2, h3),
cell.bf
)
);
var h4 = innerGraph.mul(input, cell.Wox);
var h5 = innerGraph.mul(hidden_prev, cell.Woh);
var output_gate = innerGraph.sigmoid(
innerGraph.add(
innerGraph.add(h4, h5),
cell.bo
)
);
var h6 = innerGraph.mul(input, cell.Wcx);
var h7 = innerGraph.mul(hidden_prev, cell.Wch);
var cell_write = innerGraph.tanh(
innerGraph.add(
innerGraph.add(h6, h7),
cell.bc
)
);
// compute new cell activation
var retain_cell = innerGraph.eltmul(forget_gate, cell_prev); // what do we keep from cell
var write_cell = innerGraph.eltmul(input_gate, cell_write); // what do we write to cell
var cell_d = innerGraph.add(retain_cell, write_cell); // new cell contents
// compute hidden state as gated, saturated cell activations
var hidden_d = innerGraph.eltmul(output_gate, innerGraph.tanh(cell_d));
this.ht = hidden_d;
this.ct = cell_d;
return(ht);
}
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);
}