code/cnn_dropout/train.py

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms
from lenet import LeNet5
import tqdm


# 指定日志目录
writer = SummaryWriter(log_dir='D:/gitcode/cnn_dropout/logs')

# 1. 判断是否使用CUDA
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Using device: {device}')
# 2. 准备数据
train_set = datasets.CIFAR10(root='D:/gitcode/cnn_dropout/data', train=True, download=True, transform=transforms.ToTensor())
test_set = datasets.CIFAR10(root='D:/gitcode/cnn_dropout/data', train=False, download=True, transform=transforms.ToTensor())
train_loader = DataLoader(dataset=train_set, batch_size=100, shuffle=True)
test_loader = DataLoader(dataset=test_set, batch_size=100, shuffle=False)
# 3. 创建模型
model = LeNet5()
model = model.to(device)
# 4. 确定损失函数
loss_fn = nn.CrossEntropyLoss()
# 5. 创建优化器 使用梯度下降算法 参数的更新
opt = torch.optim.Adam(model.parameters())

for epoch in range(100):
    # 6. 训练模型
    model.train()
    train_total_loss = 0
    for images, labels in tqdm.tqdm(train_loader, desc="train", total=len(train_loader)):
        # 将数据移动到设备
        images, labels = images.to(device), labels.to(device)
        outputs = model(images) # 前向传播
        loss = loss_fn(outputs, labels) # 计算损失

        opt.zero_grad() # 清空梯度
        loss.backward() # 反向传播 计算梯度
        opt.step() # 更新参数
        train_total_loss += loss.item()
    train_avg_loss = train_total_loss / len(train_loader)
    print(f'Epoch {epoch+1}, Loss: {train_avg_loss:.4f}')
    # 7. 测试模型
    model.eval()
    test_total_loss = 0
    test_total_acc = 0
    with torch.inference_mode(): 
        for images, labels in tqdm.tqdm(test_loader, desc="test", total=len(test_loader)):
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            # 计算损失
            loss = loss_fn(outputs, labels)
            test_total_loss += loss.item()

            pred = torch.argmax(outputs, dim=1)
            acc = torch.eq(pred, labels).float().mean()
            test_total_acc += acc.item()

    test_avg_acc = test_total_acc / len(test_loader)
    print(f'Epoch {epoch+1} Test Accuracy: {test_avg_acc:.4f}')

    test_avg_loss = test_total_loss / len(test_loader)
    print(f'Epoch {epoch+1} Test Loss: {test_avg_loss:.4f}')
    # 记录数据
    writer.add_scalars('Loss/train', {'train_avg_loss': train_avg_loss,
    'test_avg_loss': test_avg_loss}, epoch)
    writer.add_scalar('Accuracy/test', test_avg_acc, epoch)
    # 8. 保存模型
    torch.save(model.state_dict(), 'D:/gitcode/cnn_dropout/model/lenet5.pth')