Hello  Pytorch Community ,
I’m a machine learning student recently involved into PyTorch  and i have a question regarding multi-label classification
I have trained my neural network based on BCEWithLogitsLoss for a 27-classes classification problem . However ,  as soon as I started trying to evaluate my model , i struggled …
For the sake of clarity let s limit our example to 6 classes
I’m using One hot encoding …  so as a result for instance  :
for a sample Xi  i ll get
y_true  = [ 0 , 1 , 0 , 1 , 0  , 0 ]
y_pred = [ 0 , 1 , 0 , 0 , 0  , 1 ]
how to evaluate my test_dataset  using pytorch  ?  i was thinking about F1 measure  ?
What about the predicted zeros ? are there significant in our sparse Y to evaluate our model ?