Hi,
I start saying that I know the BCELoss is generally exploited when there’s a classification problem, but I’m also quite new with ML.
I’m trying to implement a system that is explained in a paper, in which it is said they built a NN whose output layer is formed by a single neuron with Sigmoid activation function. Consequently, the output of the NN is a number comprised between zero and one. Based on the comparison of this latter and a threshold, they decide to accept or refuse a given parameter’s value (which was given as input to the NN). And that’s why they call this NN classifier, even if its output is a real value which doesn’t correspond to any class. Moreover, it is written that the loss function they use is the Binary Cross Entropy.
Summarizing, I should train a NN so that it outputs a certain value between 0 and 1 using the BCELoss. So the label for each input will be a real quantity - 0.02, 0.96, … .
So, what I would like to ask is: is it really possible to use the BCELoss function in such a case or maybe I’m missing something? I’m pretty sure to have well understood the paper, but maybe I should train the NN differently.
[ I say sorry if I do not report any code and/or if the type of question is anomalous, but I’m new also for these kind of forums. If there are problems with my topic, I will delete it asap ]