损失函数

L1损失函数

#默认情况下L1Loss的reduction是mean，表示计算均值
mean_loss = L1Loss()
#指定reduction为sum，表示计算误差和
sum_loss = L1Loss(reduction='sum')

mean_result = mean_loss(inputs, targets)
sum_result = sum_loss(inputs, targets)

均方误差

mse_loss = nn.MSELoss()
mse_result = mse_loss

交叉熵损失

loss_cross = nn.CrossEntropyLoss()
result_cross = loss_cross(inputs, targets)

完整代码

import torch
import torchvision
from torch import nn
from torch.nn import L1Loss
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear
from torch.utils.data import DataLoader

dataset = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(),download=True)
dataloader = DataLoader(dataset, batch_size=1)

class Test(nn.Module):
    def __init__(self):
        super(Test, self).__init__()
        self.model1 = Sequential(
            Conv2d(3, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self, x):
        x = self.model1(x)
        return x

loss = nn.CrossEntropyLoss()
test = Test()

for data in dataloader:
    imgs, targets = data
    outputs = test(imgs)
    print("outputs:", outputs)
    print("targets:", targets)
    result_loss = loss(outputs, targets)
    print("loss:",result_loss)