Hello MngFrc!



 MngFrc:

I am looking for a NN that, given an input that represents the parameters of a system, outputs its error probability. The final aim is to look for the right parameter tuning through an iterative way: I start with an input, and if its error probability is above a threshold, I reduce the input until I find a a satisfactory error probability [below the threshold].
So my dataset consists on a set of measurements which couple system parameters and error probabilities.


It’s not at all clear to me what you are trying to do, so it’s hard
to judge whether your approach makes sense.
It might be a good idea for you to start a new thread.
If you do, could you make concrete what data you’re starting
with, and what the numbers mean?  What is the shape of the
input to your network?  Roughly – in words – what is the
architecture of your network?  Just a few Linear layers?
Lots of convolutions and poolings and dropouts, etc.?
You say that you have “input that represents the parameters of a
system,” and that  you want “to look for the right parameter tuning
through an iterative way.”  Does this mean that you want to tune
your input parameters, rather than the parameters (e.g., weights
and biases) in neural network?
You say “I reduce the input until I find a a satisfactory error
probability.”  Does that mean that you simply make the values
of your input parameters smaller?  Or do you adjust your input
parameters in some more complicated way?  Are you hoping to
use a pytorch optimizer to adjust your input parameters for you?
(Again, these questions aren’t specifically about BCELoss, so
it would make sense to start a new thread.)
Best.
K. Frank