pytorch实现svm特征10分类

以下是使用PyTorch实现SVM特征10分类的代码示例：

首先，我们导入必要的库和数据集：

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

# 加载MNIST数据集
train_data = datasets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())
test_data = datasets.MNIST(root='./data', train=False, download=True, transform=transforms.ToTensor())

接下来，我们定义SVM模型：

class SVM(nn.Module):
    def __init__(self):
        super(SVM, self).__init__()
        self.linear = nn.Linear(784, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        return self.linear(x)

然后，我们定义训练函数和测试函数：

def train(model, train_loader, optimizer, criterion):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

def test(model, test_loader, criterion):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)

    print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
        test_loss, correct, len(test_loader.dataset), accuracy))

最后，我们定义数据加载器、优化器和损失函数，并开始训练：

# 定义数据加载器
train_loader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=64, shuffle=False)

# 定义优化器和损失函数
model = SVM()
optimizer = optim.SGD(model.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()

# 开始训练
for epoch in range(10):
    train(model, train_loader, optimizer, criterion)
    test(model, test_loader, criterion)

这样，我们就可以使用PyTorch实现SVM特征10分类了。