PyTorch中常见损失函数的使用详解

import torch
from torch.autograd import Variable
import torch.nn as nn
sample=Variable(torch.ones(2,2))
a=torch.Tensor(2,2)
a[0,0]=0
a[0,1]=1
a[1,0]=2
a[1,1]=3
target=Variable(a)
print(sample,target)

loss=FunLoss(sample,target)['L1Loss']
print(loss)

loss=FunLoss(sample,target)['SmoothL1Loss']
print(loss)

loss=FunLoss(sample,target)['MSELoss']
print(loss)

loss=FunLoss(sample,target)['CrossEntropyLoss']
print(loss)

loss=FunLoss(sample,target)['NLLLoss']
print(loss)

class FunLoss():
    def __init__(self, sample, target):
        self.sample = sample
        self.target = target
        self.loss = {
            'L1Loss': nn.L1Loss(),
            'SmoothL1Loss': nn.SmoothL1Loss(),
            'MSELoss': nn.MSELoss(),
            'CrossEntropyLoss': nn.CrossEntropyLoss(),
            'NLLLoss': nn.NLLLoss()
        }
    def __getitem__(self, loss_type):
        if loss_type in self.loss:
            loss_func = self.loss[loss_type]
            return loss_func(self.sample, self.target)
        else:
            raise KeyError(f"Invalid loss type '{loss_type}'")
if __name__=="__main__":
    loss=FunLoss(sample,target)['NLLLoss']
    print(loss)