loss函数之MarginRankingLoss

MarginRankingLoss

排序损失函数

对于包含个样本的batch数据 , , 是给定的待排序的两个输入，代表真实的标签，属于。当是，应该排在之前，是，应该排在之后。第个样本对应的计算如下:

若, 排序正确且, 则loss为0；其他情况下是loss为

class MarginRankingLoss(_Loss):
    __constants__ = ['margin', 'reduction']
    def __init__(self, margin=0., size_average=None, reduce=None, reduction='mean'):
        super(MarginRankingLoss, self).__init__(size_average, reduce, reduction)
        self.margin = margin
    def forward(self, input1, input2, target):
        return F.margin_ranking_loss(input1, input2, target, margin=self.margin, reduction=self.reduction)

pytorch中通过torch.nn.MarginRankingLoss类实现，也可以直接调用F.margin_ranking_loss 函数，代码中的size_average与reduce已经弃用。reduction有三种取值mean, sum, none，对应不同的返回。 默认为mean，对应于上述的计算

默认取值0

例子：

import torch
import torch.nn.functional as F
import torch.nn as nn
import math


def validate_MarginRankingLoss(input1, input2, target, margin):
    val = 0
    for x1, x2, y in zip(input1, input2, target):
        loss_val = max(0, -y * (x1 - x2) + margin)
        val += loss_val
    return val / input1.nelement()


torch.manual_seed(10)
margin = 0
loss = nn.MarginRankingLoss()
input1 = torch.randn([3], requires_grad=True)
input2 = torch.randn([3], requires_grad=True)
target = torch.tensor([1, -1, -1])
print(target)
output = loss(input1, input2, target)
print(output.item())

output = validate_MarginRankingLoss(input1, input2, target, margin)
print(output.item())

loss = nn.MarginRankingLoss(reduction="none")
output = loss(input1, input2, target)
print(output)

输出：

tensor([ 1, -1, -1])
0.015400052070617676
0.015400052070617676
tensor([0.0000, 0.0000, 0.0462], grad_fn=<ClampMinBackward>)