p31利用GPU训练2,更常用

 # 准备数据集 import torch.optim import torchvision from torch import nn from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter import time  # from model import *  train_data = torchvision.datasets.CIFAR10("./dataset", train=True, transform=torchvision.transforms.ToTensor(),                                           download=True) test_data = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor(),                                          download=True) # length train_data_size = len(train_data) test_data_siz = len(test_data) print(f"训练数据集的长度如下：{ 
          train_data_size}") print(f"测试数据集的长度如下：{ 
          test_data_siz}")  # 利用DataLoader加载 train_data_loader = DataLoader(train_data, batch_size=64) test_data_loader = DataLoader(test_data, batch_size=64)  # 定义训练的设备
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


# 创建网络模型
class Lixinyu(nn.Module):
    def __init__(self):
        super(Lixinyu, self).__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(1024, 64),
            nn.Linear(64, 10)
        )
    def forward(self, x):
        x = self.model(x)
        return x

lixinyu = Lixinyu()
lixinyu = lixinyu.to(device)  ##########直接写lixinyu.to(device)也可以，但对于imgs和targets必须重新赋值

# 损失函数
loss_fn = nn.CrossEntropyLoss()
loss_fn = loss_fn.to(device)  ########

# 优化器
# learning_rate = 0.01
learning_rate = 1e-2
optimizer = torch.optim.SGD(lixinyu.parameters(), learning_rate)

# 设置训练网络参数
# 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 = 10

# 添加tensorboard
writer = SummaryWriter("p28")
start_time = time.time()

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

    # 训练步骤开始
    lixinyu.train()   ####
    for data in train_data_loader:
        imgs, targets = data
        imgs = imgs.to(device)   ##############
        targets = targets.to(device)   ###########
        output = lixinyu(imgs)
        loss = loss_fn(output, targets)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        total_train_step += 1
        if total_train_step % 100 == 0:
            end_time = time.time()
            print(f"训练次数{ 
          total_train_step} 损失值{ 
          loss.item()}耗费时间{ 
          end_time-start_time}")  # loss.item()标准写法，这里直接写loss也行
            writer.add_scalar("train_loss", loss.item(), total_train_step)

    # 测试步骤开始
    lixinyu.eval()  #####
    total_test_loss = 0
    total_accuracy = 0
    with torch.no_grad():
        for data in test_data_loader:
            imgs, targets = data
            imgs = imgs.to(device)  ##############
            targets = targets.to(device)  ###########
            outputs = lixinyu(imgs)
            loss = loss_fn(outputs, targets)
            total_test_loss = total_test_loss + loss.item()
            accuracy = (outputs.argmax(1) == targets).sum()
            total_accuracy = total_accuracy + accuracy
    print(f'整体测试loss为：{ 
          total_test_loss}')
    print(f'整体测试数据集上的正确率：{ 
          total_accuracy/test_data_siz}')
    writer.add_scalar("total_test_loss", total_test_loss, total_test_step)
    writer.add_scalar("total_accuracy", total_accuracy/test_data_siz, total_test_step)
    total_test_step = total_test_step + 1
    torch.save(lixinyu, f"lixinyu_epoch{ 
          i}.pth")
    # torch.save(lixinyu.state_dict(), f"lixinyu_{i}.pth")
    print("模型已保存")

writer.close()