Hello Shaun!
In short, “class ‘1’” means whatever you trained you model for it
to mean.
To explain this, let me go back to one of your earlier posts:
You talk about x_test
and y_test
(and y_pred
). I assume
that y_test
is a vector of length the number of test samples,
with each value in y_test
being the number 0, meaning that
this sample is in class “0”, or the number 1, meaning class “1”.
(And x_test
is the input data whose classes you are trying
to predict.)
You don’t mention it, but I assume you also have an x_train
and y_train
whose meanings are analogous, and that you
used x_train
and y_train
to train your network. The point
is that the meaning of the output of your model (whether a
given value for y_pred = classifier(x_test)
means
class “0” or class “1”) depends on how you trained your model.
If you train your model with values of y_train
of 1 indicating
class “1” (and feed it into BCEWithLogitsLoss
) then larger
(more positive) values of y_pred
will mean that you are
predicting class “1” to be more likely and smaller (more negative)
values of y_pred
predict class “0” to be more likely (and
class “1” to be less likely), with the predicted probability of
class “'1” given by the sigmoid
of y_pred
, as discussed in
the earlier posts.
To summarize, the meaning of y_pred
depends in a
straightforward way on the meaning of the y_train
that you
used to train your classifier.
Best.
K. Frank