// masked forward, top-k backward, collect activeness
public override Tensor StepTopK(Flow f, Tensor x, int k)
{
return(f.Mask(f.AddBias(f.MultiplyTopRecord(x, Wxh, k, _r), Bh), _r.Mask));
}
// normal forward, top-k backward, collect activeness
// inds control the neurons computed, originally for efficient dropout
// not used currently (you could use this to see how the activeness of the neurons change when top-k backprop)
public override Tensor StepTopK(Flow f, Tensor x, int[] inds, int k)
{
return(f.AddBias(f.MultiplyTopRecord(x, Wxh, k, _r, inds), Bh, inds));
}
// normal forward, normal backward
public override Tensor Step(Flow f, Tensor x)
{
return(f.Mask(f.AddBias(f.Multiply(x, Wxh), Bh), _r.Mask));
}
// normal forward, top-k backward
public virtual Tensor StepTopK(Flow f, Tensor x, int k)
{
return(f.AddBias(f.MultiplyTop(x, Wxh, k), Bh));
}
// normal forward, normal backward
// inds control the neurons computed, originally for efficient dropout
public virtual Tensor Step(Flow f, Tensor x, int[] inds)
{
return(f.AddBias(f.Multiply(x, Wxh, inds), Bh, inds));
}
// normal forward, normal backward
public virtual Tensor Step(Flow f, Tensor x)
{
return(f.AddBias(f.Multiply(x, Wxh), Bh));
}