title: Multi label classification in pytorch
body: <p>I have a multi-label classification problem. I have 11 classes, around 4k examples. Each example can have from 1 to 4-5 label. At the moment, i'm training a classifier separately for each class with log_loss. As you can expect, it is taking quite some time to train 11 classifier, and i would like to try another approach and to train only 1 classifier. The idea is that the last layer of this classifer would have 11 nodes, and would output a real number by classes which would be converted to a proba by a sigmoid. The loss I want to optimize is the mean of the log_loss on all classes. </p>
  
  <p>Unfortunately, i'm some kind of noob with pytorch, and even by reading the source code of the losses, i can't figure out if one of the already existing losses does exactly what i want, or if I should create a new loss, and if that's the case, i don't really know how to do it.</p>
  
  <p>To be very specific, i want to give for each element of the batch one vector of size 11(which contains a real number for each label (the closer to infinity, the closer this class is predicted to be 1), and 1 vector of size 11 (which contains a 1 at every true label), and be able to compute the mean log_loss on all 11 labels, and optimize my classifier based on that loss.</p>
  
  <p>Any help would be greatly appreciated :) </p>