public ModifierCell(RecurrentCell base_cell)
: base(base_cell.Prefix + base_cell.Alias(), base_cell.Params)
{
BaseCell = base_cell;
}
public override Batch <double[]> Forward(Batch <double[]> inputs, bool isTraining)
{
var length1 = this.inputs * this.outputs;
var length2 = this.outputs * this.outputs;
var length3 = this.timesteps * this.outputs;
var xWeights = new double[length1];
var hWeights = new double[length2];
var outputs = new double[inputs.Size][];
for (int i = 0; i < length1; i++)
{
xWeights[i] = this.weights[i];
}
for (int i = 0, j = length1; i < length2; i++, j++)
{
hWeights[i] = this.weights[j];
}
for (int i = 0; i < inputs.Size; i++)
{
outputs[i] = new double[length3];
}
this.layerList = new List <RecurrentCell>();
if (!this.stateful || this.h == null)
{
this.h = new Batch <double[]>(new double[inputs.Size][]);
for (int i = 0; i < inputs.Size; i++)
{
this.h[i] = new double[this.outputs];
for (int j = 0; j < this.outputs; j++)
{
this.h[i][j] = 0.0;
}
}
}
else if (this.h.Size < inputs.Size)
{
var batch = new Batch <double[]>(new double[inputs.Size][]);
for (int i = 0; i < this.h.Size; i++)
{
batch[i] = this.h[i];
}
for (int i = this.h.Size; i < inputs.Size; i++)
{
batch[i] = new double[this.outputs];
for (int j = 0; j < this.outputs; j++)
{
batch[i][j] = 0.0;
}
}
this.h = batch;
}
for (int t = 0; t < this.timesteps; t++)
{
var layer = new RecurrentCell(this.inputs, this.outputs, xWeights, hWeights, this.biases, this.tanhActivationFunction);
var x = new Batch <double[]>(new double[inputs.Size][]);
for (int i = 0; i < inputs.Size; i++)
{
var vector = new double[this.inputs];
for (int j = 0, k = this.inputs * t; j < this.inputs; j++, k++)
{
vector[j] = inputs[i][k];
}
x[i] = vector;
}
this.h = layer.Forward(x, this.h);
for (int i = 0; i < inputs.Size; i++)
{
for (int j = 0, k = this.outputs * t; j < this.outputs; j++, k++)
{
outputs[i][k] = this.h[i][j];
}
}
this.layerList.Add(layer);
}
return(new Batch <double[]>(outputs));
}
