1. 连续值损失函数 

总结：主要使用胡贝儿损失函数，应用于连续数值的预测之间的误差损失，参考地址

import torch
import torch.nn as nn

a = torch.tensor([[1, 2], [3, 4]], dtype=torch.float)
b = torch.tensor([[3, 5], [8, 6]], dtype=torch.float)

loss_fn1 = torch.nn.MSELoss(reduction='none')
loss1 = loss_fn1(a, b)           # loss1 是按照原始维度输出，即对应位置的元素相减然后求平方                
print(loss1)  # 输出结果：tensor([[4., 9.], [25., 4.]])

loss_fn2 = torch.nn.MSELoss(reduction='sum')
loss2 = loss_fn2(a, b)          # loss2 是所有位置的损失求和
print(loss2)  # 输出结果：tensor(42.)

loss_fn3 = torch.nn.MSELoss(reduction='mean')
loss3 = loss_fn3(a, b)          # loss3 是所有位置的损失求和后取平均
print(loss3)  # 输出结果：tensor(10.5000)

2. 二分类损失函数