public static Tensor[] Unstack(this Session session, Tensor x, int axis)
{
const string ActionName = "unstack";
if (axis < 0)
{
throw new ArgumentException(Properties.Resources.E_NegativeAxisIndex, nameof(axis));
}
return(session.RunOperation(
ActionName,
() =>
{
bool calculateGradient = session.CalculateGradients && x.CalculateGradient;
// allocate destination
Tensor[] ys = session.AllocateTensors(ActionName, x.Axes[axis], x.Shape.RemoveAxis(axis), calculateGradient);
ArrayOperations.Unstack(x, axis, ys, false);
#if !NOLEARNING
if (calculateGradient)
{
session.Push(ActionName, () => ArrayOperations.Stack(ys, axis, x, true));
// return copy of the array; calling method can replace its content; our closure keeps the array, not its items
return ys.ToArray();
}
#endif
return ys;
}));
}
public static Tensor Stack(this Session session, IList <Tensor> xs, int axis)
{
const string ActionName = "stack";
if (axis < 0)
{
throw new ArgumentException(Properties.Resources.E_NegativeAxisIndex, nameof(axis));
}
return(session.RunOperation(
ActionName,
() =>
{
bool calculateGradient = session.CalculateGradients && xs.Any(x => x.CalculateGradient);
// check source
int xdim = xs.Count;
if (xdim == 0)
{
throw new ArgumentException("There should be at least one source tensor.");
}
if (!Shape.AreSame(xs))
{
throw new ArgumentException("All source tensors must have the same rank and shape.");
}
Tensor y = session.AllocateTensor(ActionName, xs[0].Shape.InsertAxis(axis, xdim), calculateGradient);
ArrayOperations.Stack(xs, axis, y, false);
#if !NOLEARNING
if (calculateGradient)
{
session.Push(ActionName, () => ArrayOperations.Unstack(y, axis, xs, true));
}
#endif
y.Validate();
return y;
}));
}