Hello Hannes!



 Hannes_Kickhop:

If I understood you correctly, both target and input should be either 0 and 1,


Just to clarify, binary_cross_entropy() does not require the input
and target values to be either 0 or 1.  Rather, it requires these
values to be probabilities, that is, values in the range [0.0, 1.0]
(inclusive).
In practice, it is often the case that the target values are 0 or 1
(and can be understood as binary class labels), but forcing your
input values to be 0 or 1 will break differentiablity, and hence
break back-propagation.
Two more general questions:  Why are you reparameterizing your
data?  Conceptually, why are you using binary_cross_entropy(),
and how is this supposed to interact with your reparameterization?
Best.
K. Frank