import torch
from torch import nn
#搭建神经网络
class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        #在 Tudui 类的构造函数中调用其父类的构造函数，以确保执行父类的初始化操作
        #通过 super(Tudui, self).__init__()，我们获取了 Tudui 类的父类对象，
        # 然后调用了该父类的构造函数 __init__()，从而执行了父类的初始化逻辑
        self.model = nn.Sequential(
            nn.Conv2d(3,32,5,1,2),
            nn.MaxPool2d(2),
            nn.Conv2d(32,32,5,1,2),
            nn.MaxPool2d(2),
            nn.Conv2d(32,64,5,1,2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(64*4*4,64),
            nn.Linear(64,10)
        )
    def forward(self, x):
        x = self.model(x)
        return x

if __name__ == '__main__':
    tudui = Tudui()
    input = torch.ones((64,3,32,32))
    output = tudui(input)
    print(output.shape)

问题：构造函数的名称拼写错误，应该是 init 而不是 int。
一直报错说是没有正确初始化model属性

import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

from src.model import*
#准备数据集
train_data = torchvision.datasets.CIFAR10(root="../data",train=True,
                                          transform=torchvision.transforms.ToTensor(),
                                          download=True)
test_data = torchvision.datasets.CIFAR10(root="../data",train=False,
                                          transform=torchvision.transforms.ToTensor(),
                                          download=True)
#legth长度
train_data_size = len(train_data)
test_data_size = len(test_data)
#输出数据集的长度信息
print("训练数据集的长度为：{}".format(train_data_size))
print("测试数据集的长度为：{}".format(test_data_size))
#利用Dataloader来加载数据集
train_dataloader = DataLoader(train_data,batch_size=64)
test_dataloader= DataLoader(test_data,batch_size=64)

#创建网络模型
tudui = Tudui()
#损失函数
loss_fn = nn.CrossEntropyLoss()
#优化器
learning_rate = 0.01
optimizer = torch.optim.SGD(tudui.parameters(),lr=learning_rate)
#设置训练网络的一些参数
#记录训练的次数
total_train_step = 0
#记录测试的次数
total_test_step = 0
#训练的轮数
epoch = 2

# 写入board
writer = SummaryWriter("logs")

for i in range (epoch):
    print("----第{}轮训练开始----".format(i+1))

    #训练步骤开始
    tudui.train()
    for data in train_dataloader:
        imgs,targets = data
        outputs = tudui(imgs)
        loss = loss_fn(outputs,targets)
        #优化器优化模型
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_train_step = total_train_step+1
        # batch=64,训练集=5W，学习一边训练集就需要781.25次训练
        writer.add_scalar("train loss", loss.item(), total_train_step)
        if total_train_step%100 == 0:
            print("训练次数：{}，Loss：{}".format(total_train_step,loss.item()))

    #测试步骤开始
    tudui.eval()
    total_test_loss = 0
    total_accuracy = 0
    with torch.no_grad():
        for data in test_dataloader:
            imgs,targets = data
            print(targets)
            outputs = tudui(imgs)
            print(outputs)
            loss = loss_fn(outputs,targets)
            total_test_loss = total_test_loss + loss.item()
            accuracy = (outputs.argmax(1) == targets).sum()
            total_accuracy += accuracy

    print("整体测试集上的Loss：{}".format(total_test_loss))
    print("整体测试集上的正确率: {}".format(total_accuracy / test_data_size))
    writer.add_scalar("test loss", total_test_loss, total_test_step)
    writer.add_scalar("test accuracy", total_accuracy / test_data_size, total_test_step)
    total_test_step = total_test_step + 1
    torch.save(tudui, "tudui_{}.pth".format(i))
    #torch.save(tudui.state_dict(),"tudui_{}.ph".format(i))
    print("模型已保存")

writer.close()