Simple multi-laber classification example with Pytorch and MultiLabelSoftMarginLoss (https://en.wikipedia.org/wiki/Multi-label_classification)

multilabel_example.py

import torch
import torch.nn as nn
import numpy as np
import torch.optim as optim
from torch.autograd import Variable

# (1, 0) => target labels 0+2
# (0, 1) => target labels 1
# (1, 1) => target labels 3
train = []
labels = []
for i in range(10000):
    category = (np.random.choice([0, 1]), np.random.choice([0, 1]))
    if category == (1, 0):
        train.append([np.random.uniform(0.1, 1), 0])
        labels.append([1, 0, 1])
    if category == (0, 1):
        train.append([0, np.random.uniform(0.1, 1)])
        labels.append([0, 1, 0])
    if category == (0, 0):
        train.append([np.random.uniform(0.1, 1), np.random.uniform(0.1, 1)])
        labels.append([0, 0, 1])

class _classifier(nn.Module):
    def __init__(self, nlabel):
        super(_classifier, self).__init__()
        self.main = nn.Sequential(
            nn.Linear(2, 64),
            nn.ReLU(),
            nn.Linear(64, nlabel),
        )

    def forward(self, input):
        return self.main(input)

nlabel = len(labels[0]) # => 3
classifier = _classifier(nlabel)

optimizer = optim.Adam(classifier.parameters())
criterion = nn.MultiLabelSoftMarginLoss()

epochs = 5
for epoch in range(epochs):
    losses = []
    for i, sample in enumerate(train):
        inputv = Variable(torch.FloatTensor(sample)).view(1, -1)
        labelsv = Variable(torch.FloatTensor(labels[i])).view(1, -1)
        
        output = classifier(inputv)
        loss = criterion(output, labelsv)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        losses.append(loss.data.mean())
    print('[%d/%d] Loss: %.3f' % (epoch+1, epochs, np.mean(losses)))

output.txt

$ python multilabel.py
[1/5] Loss: 0.092
[2/5] Loss: 0.005
[3/5] Loss: 0.001
[4/5] Loss: 0.000
[5/5] Loss: 0.000

@jcfgonc No. You likely confused nn.MultiLabelSoftMarginLoss with nn.MultiLabelMarginLoss (note the Soft in the name). Despite the similar names, they require totally different label formats.

Loss Function	Use Case	Expected Label Format	Example Label
nn.MultiLabelSoftMarginLoss	Multi-Label Classification	Multi-hot Vector (0s and 1s)	[1, 0, 1] (Class 0 and 2 are present)
nn.MultiLabelMarginLoss	Multi-Label Classification	Vector of Class Indices	[0, 2, -1] (Indices of active classes; padded with -1)
nn.CrossEntropyLoss	Single-Class Classification	Single Class Index (Scalar)	2 (Only Class 2 is present)