损失函数与反向传播

nn.L1Loss()

import torch
from torch import nn

inputs = torch.tensor([1, 2, 3], dtype=torch.float32)
targets = torch.tensor([1, 2, 5], dtype=torch.float32)

inputs = torch.reshape(inputs, (1, 1, 1, 3))
targets = torch.reshape(targets, (1, 1, 1, 3))

loss = nn.L1Loss()
result = loss(inputs, targets)

print(result)  # tensor(0.6667)

reduction=‘sum’（默认是mean）

loss = nn.L1Loss(reduction='sum')

MSE

loss_mse = nn.MSELoss()
result_mse = loss_mse(inputs, targets)

CrossEntropyLoss_交叉熵损失

x = torch.tensor([0.1, 0.2, 0.3])
y = torch.tensor([1])
x = torch.reshape(x, (1,3))
loss_cross = nn.CrossEntropyLoss()
result_cross = loss_cross(x, y)
print(result_cross)

使用之前的网络

import torchvision.datasets
from torch import nn
from torch.utils.data import DataLoader

dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True,
                                       transform=torchvision.transforms.ToTensor())
dataloader = DataLoader(dataset, batch_size=64, shuffle=True)


class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.model1 = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=32, kernel_size=5, stride=1, padding=2),
            nn.MaxPool2d(kernel_size=2),
            nn.Conv2d(in_channels=32, out_channels=32, kernel_size=5, stride=1, padding=2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 5, padding=2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(1024, 64),
            nn.Linear(64, 10)
        )

    def forward(self, x):
        x = self.model1(x)
        return x
tudui = Tudui()
for data in dataloader:
    imgs, targets = data
    outputs = tudui(imgs)
    print(outputs)
    print(targets)

可以正确输出，且是分类问题，因此使用交叉熵

tudui = Tudui()
loss = nn.CrossEntropyLoss()
for data in dataloader:
    imgs, targets = data
    outputs = tudui(imgs)
    result_loss = loss(outputs, targets)
    print(result_loss)

1.计算实际输出和目标之间的差距
2.为我们更新输出提供一定的依据（反向传播）grad

查看梯度

执行反向传播后