alexnet pytorch 怎么使用

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from torchvision.models import alexnet

# 加载数据集
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
train_dataset = datasets.ImageFolder('path_to_train_data', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 初始化AlexNet模型
model = alexnet(pretrained=True)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 2000))
print('Finished Training')