Currently if we see an array as a kwarg to @compact we put it in trainable mode.
But consider an UDE as a motivating example:
@compact(; model, p_true) do x, p
dudt(u, p, t) = <nn part> .+ <use p_true>
...
end
We could in-principle construct the model and then use freeze, but freeze is known to not work well with non-NamedTuple inputs so it becomes a problem for sciml use cases.
Proposed solution(s):
Add a @const or @fixed "pseudo"-macro that doesn't really exist but if we find that symbol during parsing, we make that parameter fixed.
Add a reserved kwarg that allows passing a list of symbols that should be considered non-trainable. This is a bit more clunky and not something I prefer to do.
Currently if we see an array as a kwarg to
@compactwe put it in trainable mode.But consider an UDE as a motivating example:
We could in-principle construct the model and then use
freeze, butfreezeis known to not work well with non-NamedTuple inputs so it becomes a problem for sciml use cases.Proposed solution(s):
@constor@fixed"pseudo"-macro that doesn't really exist but if we find that symbol during parsing, we make that parameter fixed.