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 ?