pytorch的损失函数
1.nn.L1Loss
Examples::
>>> loss = nn.L1Loss(reduction='sum')
>>> input = torch.tensor([1., 2, 3, 4])
>>> target = torch.tensor([4., 5, 6, 7])
>>> output = loss(input, target)
>>> print(output)
两个输入类型必须一致，reduction是损失函数一个参数，有三个值：‘none’，返回的是一个向量
(batch_size, )
。‘sum’，返回的是求和，‘elementwise_mean’，返回的是求均值。上面例子用不同的参数的话返回分别为：tensor([3., 3., 3., 3.])，tensor(3.)，tensor(12.)。
2.nn.SmoothL1Loss
求导
import torch
import torch.nn as nn
import torch.nn.functional as F
a = torch.tensor([1., 2, 3, 4])
b = torch.tensor([1.1, 5, 6, 7])
loss_fn = nn.SmoothL1Loss(reduction='none')
loss = loss_fn(a, b)
print(loss)
#out
tensor([0.0050, 2.5000, 2.5000, 2.5000])
3.nn.MSELoss
loss(
x
i,
y
i)=(
x
i−
y
i)2
两个输入类型必须一致,
a = torch.tensor([1., 2, 3, 4])
b = torch.tensor([4., 5, 6, 7])
loss_fn = nn.MSEL