resnet18训练mnist数据集

要使用ResNet-18来训练MNIST数据集，首先需要导入相应的库，并获取MNIST数据集。然后，需要调整ResNet-18模型的全连接层的输出，以适应MNIST数据集的10个类别。接下来，可以开启训练过程，并在训练过程中显示损失值。训练完成后，可以保存训练好的模型权重文件。最后，在测试集上测试训练后模型的准确率。

下面是一种可能的实现：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, models, transforms

# 导入MNIST数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)

# 创建ResNet-18模型
model = models.resnet18(pretrained=True)
in_features = model.fc.in_features
model.fc = nn.Linear(in_features, 10)

# 设置优化器和损失函数
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
criterion = nn.CrossEntropyLoss()

# 开始训练
num_epochs = 10
batch_size = 64
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

for epoch in range(num_epochs):
    running_loss = 0.0
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
    
    print("Epoch {} - Training loss: {:.4f}".format(epoch+1, running_loss / len(train_loader)))

# 保存训练好的模型权重文件
torch.save(model.state_dict(), 'resnet18_mnist.pth')

# 在测试集上测试模型准确率
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

correct = 0
total = 0

with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print("Test accuracy: {:.2f}%".format(accuracy))