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Make torch.nn.functional.multilabel_soft_margin_loss more stable #9141

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vadimkantorov opened this issue Jul 3, 2018 · 0 comments
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Make torch.nn.functional.multilabel_soft_margin_loss more stable #9141

vadimkantorov opened this issue Jul 3, 2018 · 0 comments
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todo Not as important as medium or high priority tasks, but we will work on these.

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vadimkantorov commented Jul 3, 2018 •
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Currently it calls sigmoid and then binary_cross_entropy (which does log internally):
https://github.com/pytorch/pytorch/blob/master/torch/nn/functional.py#L1796-L1797

Is it not possible to compute it as: torch.mean(-(y * F.logsigmoid(x) + (1 - y) * F.logsigmoid(-x))) ?

y * log(1 / (1 + exp(-x)) + (1 - y) * log(1 - 1 / (1 + exp(-x))) =
y * logsigmoid(x) + (1 - y) * log((1 + exp(-x) - 1) / (1 + exp(-x))) = 
y * logsigmoid(x) + (1 - y) * log(1 / (1 + exp(x))) = 
y * logsigmoid(x) + (1 - y) * logsigmoid(-x)
@zou3519 zou3519 added the todo Not as important as medium or high priority tasks, but we will work on these. label Jul 9, 2018
@weiyangfb weiyangfb self-assigned this Jul 26, 2018
@weiyangfb weiyangfb mentioned this issue Jul 27, 2018
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goodlux pushed a commit to goodlux/pytorch that referenced this issue Aug 15, 2018
@weiyangfb
use logsigmoid at multilabel_soft_margin_loss, and change output from…
f6b1259 
… shape=(N, C)to (N,) (pytorch#9965)

Summary:
- fixes pytorch#9141, pytorch#9301
- use logsigmoid at multilabel_soft_margin_loss to make it more stable (NOT fixing legacy MultiLabelSoftMarginCriterion)
- return (N) instead of (N, C) to match the same behavior as MultiMarginLoss
- Note that with this PR, the following behavior is expected:
```
loss = F.multilabel_soft_margin_loss(outputs, labels, reduction='none')
loss_mean = F.multilabel_soft_margin_loss(outputs, labels, reduction='elementwise_mean')
loss_sum = F.multilabel_soft_margin_loss(outputs, labels, reduction='sum')

loss.sum() == loss_sum  # True
loss.mean() == loss_mean  # True
```
Pull Request resolved: pytorch#9965

Differential Revision: D9038402

Pulled By: weiyangfb

fbshipit-source-id: 0fa94c7b3cd370ea62bd6333f1a0e9bd0b8ccbb9
@vadimkantorov vadimkantorov mentioned this issue Dec 11, 2023
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