写个小代码，理解深度学习

import torch
import torch.nn as nn


class AlexNet(nn.Module):
    q = 5

    def __init__(self):
        super(AlexNet, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 8, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(8, 1, kernel_size=3, padding=1),
            nn.ReLU(inplace=True),
        )

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


class My_loss(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x, y):
        return torch.mean(torch.pow((x - y), 2))


if __name__ == '__main__':
    criterion = My_loss()
    model = AlexNet()
    optimizer = torch.optim.SGD(params=model.parameters(), lr=0.0001, weight_decay=0.0001)

    for i in range(1000000):
        inputs = torch.randn((4, 3, 8, 8))
        outputs = model(inputs)
        targets = torch.ones((4, 1, 8, 8))
        loss = criterion(outputs, targets)
        loss.backward()  # 计算梯度
        optimizer.step()  # 更新网络参数
        optimizer.zero_grad()

        if i % 10 == 0:
            test = torch.randn((4, 3, 8, 8))
            re = model(test)
            print((torch.mean(torch.abs(re)).item()))