Hi Richard!
It looks like this is related to your previous post:
My guess is that you’re not going about your problem in a sensible way.
But unless you describe your actual use case, it’s hard to know.
.data is deprecated, and the forum experts will threaten you with
the specter of computation-graph gremlins if you use it.
If you really want to do this, something like:
with torch.no_grad():
self.classify.weight.copy_ (self.classify.weight.data.clamp(min=0))
might be better.
Is this the key to what you are trying to do? What would it mean to
build a classifier that “only looks for ‘positive’ features?”
Well, if you only look for positive features, I suppose that it would
be natural to only find positive features, and therefore only output
probabilities greater than 50%.
But yes, this does seem problematic to me.
I doubt anything I say where will be relevant to your actual problem,
but here are some observations:
Just because your last layer has only positive weights (as distinct
from biases) doesn’t mean that your output values (even if you had
no biases) would be positive. Negative inputs to a positive-weight
layer will produce negative outputs.
But yes, a negative bias could flip the value of what would have been
a positive output to negative.
However, as it stands, I don’t see how your optimizer would know that
you want positive weights. So even if your biases could “fix” the
problem, your optimizer could well just take steps that leave your
biases alone, and make your weights negative (even though you
then force them by hand to be positive).
Good luck.
K. Frank