网络模型训练完整代码

存个代码
具体看这位博主的网络模型训练完整套路 写的比较清晰

import torchvision, torch
from torch import nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWritertrain_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)train_data_size = len(train_data)
test_data_size = len(test_data)
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))train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)# 这一块替换为要训练的网络模型
''' 
class Mydata(nn.Module):def __init__(self):super().__init__()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
'''
mydata = Mydata()
loss_fn = nn.CrossEntropyLoss()
learning_rate = 1e-2
optimizer = torch.optim.SGD(mydata.parameters(), lr=learning_rate)  total_train_step = 0
total_test_step = 0
epoch = 10
writer = SummaryWriter("logs")for i in range(epoch):print("------------第 {} 轮训练开始------------".format(i + 1))mydata.train()  for data in train_dataloader:imgs, targets = dataoutputs = mydata(imgs)loss = loss_fn(outputs, targets)optimizer.zero_grad()  loss.backward()optimizer.step()total_train_step = total_train_step + 1if total_train_step % 100 == 0:print("训练次数：{}, Loss: {}".format(total_train_step, loss.item()))  writer.add_scalar("train_loss", loss.item(), total_train_step)  mydata.eval()  total_test_loss = 0total_accuracy = 0with torch.no_grad():  for data in test_dataloader:  imgs, targets = dataoutputs = mydata(imgs)  loss = loss_fn(outputs, targets)total_test_loss = total_test_loss + loss.item()  accuracy = (outputs.argmax(1) == targets).sum()  total_accuracy = total_accuracy + accuracyprint("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 + 1torch.save(mydata, "mydata_{}.pth".format(i)) print("模型已保存")writer.close()