Comments (3)
Hi @guzy0324,
as I said, you can do so inside the module computing the scale, i.e. the param_map
.
Best regards,
Vincent
from normalizing-flows.
Hi @guzy0324,
thanks for this interesting suggestion. You are welcome to implement your own flow layers for normflows
like this and try whether it works in practice. However, I will not implement it, since this breaks the invertibility of the flow. Instead, I suggest shrinking the scale parameter, i.e. by applying a tanh
output function in the MLP
module, which will also prevent z
from becoming too large. Using "sigmoid"
or "sigmoid_inv"
as scale_map
, depending on in which direction you use the flow during training, should also help.
Best regards,
Vincent
from normalizing-flows.
Oh, I didn't notice that applying clamp
on z2
would break the invertibility. How about applying clamp
on scale
? It seems to prevent z
from becoming too large and preserve the invertibility.
from normalizing-flows.
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from normalizing-flows.