用pytorch写一个迁移学习代码

好的，以下是一个用 PyTorch 实现的迁移学习代码示例：

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

# 加载预训练的 ResNet50 模型
model = models.resnet50(pretrained=True)

# 冻结所有层的参数
for param in model.parameters():
    param.requires_grad = False

# 替换最后一层全连接层
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, 10)

# 定义数据增强和预处理的转换
transform = transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载数据集
train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = Adam(model.fc.parameters(), lr=0.001)

# 训练模型
for epoch in range(10):
    for i, (images, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, i+1, len(train_loader), loss.item()))

希望这个示例能够帮助你理解如何用 PyTorch 实现迁移学习。