损失函数和反向传播

import torch
from torch.nn import L1Loss, MSELoss, CrossEntropyLoss

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 = L1Loss()
result = loss(inputs, targets)
print(result)

loss_mse = MSELoss()
result_mse = loss_mse(inputs, targets)
print(result_mse)

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

import torch
from torch import nn
import torchvision
from torch.nn import Conv2d,  MaxPool2d, ReLU, Linear, Flatten
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

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

class MyModule(nn.Module):
    def __init__(self):
        super(MyModule, self).__init__()
        self.module1 = nn.Sequential(
            Conv2d(in_channels=3, out_channels=32, kernel_size=5, padding=2),
            MaxPool2d(kernel_size=2),
            Conv2d(in_channels=32, out_channels=32, kernel_size=5, padding=2),
            MaxPool2d(kernel_size=2),
            Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2),
            MaxPool2d(kernel_size=2),
            Flatten(),
            Linear(in_features=1024, out_features=64),
            Linear(in_features=64, out_features=10)
        )

    def forward(self, x):
        x = self.module1(x)
        return x

module = MyModule()
# print(module)

loss = nn.CrossEntropyLoss()
for data in dataloader:
    imgs, targets = data
    outputs = module(imgs)
    # print(outputs)
    # print(targets)
    result_loss = loss(outputs, targets)
    # print(result_loss)
    result_loss.backward()

参考地址：https://www.bilibili.com/video/BV1hE411t7RN?p=23