帮我构建一个pytorch框架下的脉冲神经网络，用它来处理MNIST数据集，并输出准确率

在PyTorch框架下构建一个脉冲神经网络(PulseCNN)来处理MNIST数据集，首先需要安装必要的库，如torch、torchvision等。脉冲神经网络通常包含卷积层、激活函数（例如脉冲激活）、池化层以及全连接层。以下是一个简单的示例代码：

import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms

# 数据预处理
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)

train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

class PulseCNN(nn.Module):
    def __init__(self):
        super(PulseCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 8, kernel_size=3, stride=1, padding=1)  # 输入通道数为1（灰度图像）
        self.pulse_layer = nn.PReLU()  # 使用PReLU作为脉冲激活层
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)  
        self.fc1 = nn.Linear(8 * 10 * 10, 128)  # 全连接层
        self.fc2 = nn.Linear(128, 10)  # 输出层，对应MNIST的10个类别

    def forward(self, x):
        x = self.conv1(x)
        x = self.pulse_layer(x)
        x = self.pool(x)
        x = x.view(-1, 8 * 10 * 10)  # reshape以便于全连接层处理
        x = self.fc1(x)
        x = self.fc2(x)
        return x

model = PulseCNN()
criterion = nn.CrossEntropyLoss()  # 损失函数
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)  # 使用Adam优化器

def train(model, train_loader, criterion, optimizer, epoch):
    model.train()
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)
        labels = labels.to(device)
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        if (i+1) % 100 == 0:
            print(f"Epoch {epoch+1}, Step {i+1}/{len(train_loader)}, Loss: {loss.item():.4f}")

def test(model, test_loader):
    model.eval()
    correct = 0
    with torch.no_grad():
        for images, labels in test_loader:
            images = images.to(device)
            labels = labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, 1)
            correct += (predicted == labels).sum().item()
    accuracy = correct / len(test_loader.dataset)
    print(f"Test Accuracy: {accuracy:.4f}")

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)

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

在这个例子中，我们首先加载并预处理MNIST数据，然后定义了脉冲神经网络模型结构。训练部分通过反向传播更新权重，测试部分计算并输出模型在测试集上的准确率。