Multi label classification in pytorch

Question

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 separate

Answer 1

You are looking for torch.nn.BCELoss. Here's example code:

import torch

batch_size = 2
num_classes = 11

loss_fn = torch.nn.BCELoss()

outputs_before_sigmoid = torch.randn(batch_size, num_classes)
sigmoid_outputs = torch.sigmoid(outputs_before_sigmoid)
target_classes = torch.randint(0, 2, (batch_size, num_classes))  # randints in [0, 2).

loss = loss_fn(sigmoid_outputs, target_classes)

# alternatively, use BCE with logits, on outputs before sigmoid.
loss_fn_2 = torch.nn.BCEWithLogitsLoss()
loss2 = loss_fn_2(outputs_before_sigmoid, target_classes)
assert loss == loss2