基于pytorch写一段线性分类器的代码

以下是基于PyTorch实现的线性分类器代码：

import torch
import torch.nn as nn
import torch.optim as optim

# 定义线性分类器模型
class LinearClassifier(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LinearClassifier, self).__init__()
        self.linear = nn.Linear(input_dim, output_dim)
        
    def forward(self, x):
        out = self.linear(x)
        return out

# 定义训练函数
def train(model, train_loader, criterion, optimizer, epochs):
    for epoch in range(epochs):
        for i, (inputs, labels) in enumerate(train_loader):
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            if (i+1) % 100 == 0:
                print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, epochs, i+1, len(train_loader), loss.item()))

# 定义测试函数
def test(model, test_loader):
    with torch.no_grad():
        correct = 0
        total = 0
        for inputs, labels in test_loader:
            outputs = model(inputs)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
        accuracy = 100 * correct / total
        print('Accuracy of the model on the test images: {} %'.format(accuracy))

# 加载数据
train_data = # 加载训练数据
test_data = # 加载测试数据
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)

# 定义模型、损失函数和优化器
model = LinearClassifier(input_dim=784, output_dim=10)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 训练模型并测试
train(model, train_loader, criterion, optimizer, epochs=5)
test(model, test_loader)

其中，LinearClassifier类定义了一个简单的线性分类器模型，train函数用于训练模型，test函数用于测试模型。在主函数中，我们加载数据、定义模型、损失函数和优化器，然后训练模型并测试。注：该代码仅供参考，具体实现需要根据实际情况进行修改