Update F.multi_label_soft_margin_loss/nn.MultiLabelSoftMarginLoss docs 易用性提升
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zhwesky2010
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Dec 23, 2024
| @@ -3449,13 +3449,11 @@ def multi_label_soft_margin_loss( | |||
| For each sample in the mini-batch: | |||
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| .. math:: | |||
| \text{loss}(x, y) = \sum_{ij}\frac{\max(0, 1 - (x[y[j]] - x[i]))}{\text{x.size}(0)} | |||
| \text{loss}(x, y) = - \frac{1}{C} * \sum_i y[i] * \log((1 + \exp(-x[i]))^{-1}) | |||
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是文档写成了MultiLabelMarginLoss，但是实现是MultiLabelSoftMarginLoss 对吧？MultiLabelSoftMarginLoss 没有问题吧
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是的，实现是MultiLabelSoftMarginLoss的实现
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Description
原文档公式是MultiLabelMarginLoss的公式 (参考 https://pytorch.org/docs/stable/generated/torch.nn.MultiLabelMarginLoss.html)
改为MultiLabelSoftMarginLoss对应公式 (参考https://pytorch.org/docs/stable/generated/torch.nn.MultiLabelSoftMarginLoss.html)