What is the difference between BCEWithLogitsLoss and MultiLabelSoftMarginLoss

1

I think there is no difference between BCEWithLogitsLoss and MultiLabelSoftMarginLoss.

BCEWithLogitsLoss = One Sigmoid Layer + BCELoss (solved numerically unstable problem)

MultiLabelSoftMargin’s fomula is also same with BCEWithLogitsLoss.

One difference is BCEWithLogitsLoss has a ‘weight’ parameter, MultiLabelSoftMarginLoss no has)

BCEWithLogitsLoss :
image

MultiLabelSoftMarginLoss :
image

The two formula is exactly the same except for the weight value.

2

You are right. Both loss functions seem to return the same loss values:

x = Variable(torch.randn(10, 3))
y = Variable(torch.FloatTensor(10, 3).random_(2))

# double the loss for class 1
class_weight = torch.FloatTensor([1.0, 2.0, 1.0])
# double the loss for last sample
element_weight = torch.FloatTensor([1.0]*9 + [2.0]).view(-1, 1)
element_weight = element_weight.repeat(1, 3)

bce_criterion = nn.BCEWithLogitsLoss(weight=None, reduce=False)
multi_criterion = nn.MultiLabelSoftMarginLoss(weight=None, reduce=False)

bce_criterion_class = nn.BCEWithLogitsLoss(weight=class_weight, reduce=False)
multi_criterion_class = nn.MultiLabelSoftMarginLoss(weight=class_weight, reduce=False)

bce_criterion_element = nn.BCEWithLogitsLoss(weight=element_weight, reduce=False)
multi_criterion_element = nn.MultiLabelSoftMarginLoss(weight=element_weight, reduce=False)

bce_loss = bce_criterion(x, y)
multi_loss = multi_criterion(x, y)

bce_loss_class = bce_criterion_class(x, y)
multi_loss_class = multi_criterion_class(x, y)

bce_loss_element = bce_criterion_element(x, y)
multi_loss_element = multi_criterion_element(x, y)

print(bce_loss - multi_loss)
print(bce_loss_class - multi_loss_class)
print(bce_loss_element - multi_loss_element)
3

Thank you for your reply, And I confirmed that there was no difference.

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But, I wonder why the same functions are separated.

P.S.
what version of pytorch do you use?
my pytorch version is ‘0.3.0 post4’, this version doesn’t have a ‘reduce’ parameter in BCEWithLogitsLoss and MultiLabelSoftMarginLoss.

Thank you for your reply again!

4

I’m using '0.4.0a0+5eefe87' (compiled a while ago from master).
You could update to 0.3.1, although the reduce argument will still be missing (doc) or compile from master.
You can find the build instructions here.

5

What’s the plan with these two functions? Will one of them be deleted?

7

Can you better explain your argument? I do not understand why one of them should not be deleted.

9

I was referring to BCEWithLogitsLoss and MultiLabelSoftMarginLoss.

10

up… Can anyone explain which to use?

11

@albanD @smth @apaszke can we please have some clarifications on this? I can verify that both the losses indeed give the same values for the same input.

12

I have the same confusion about the two loss functions.

13

As far as I understand, BCEWithLogitsLoss is used for Binary Cross Entropy loss and MultiLabelSoftMarginLoss for Multi-Label Cross Entropy loss.

Sure, when you have a binary case both of them will give you the same result. But using BCEWithLogitsLoss for binary classification will make your code more readable and easier to comprehend.