写一段基于pytorch用卷积神经网络来实现故障预测的代码

以下是一个基于PyTorch的卷积神经网络用于故障预测的示例代码：

import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3)
        self.fc1 = nn.Linear(32 * 10 * 10, 128)
        self.fc2 = nn.Linear(128, 2)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 32 * 10 * 10)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x
        
model = CNN()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)

# 训练模型
for epoch in range(10):
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        loss = F.cross_entropy(output, target)
        loss.backward()
        optimizer.step()

# 测试模型
test_loss = 0
correct = 0
for data, target in test_loader:
    data, target = Variable(data, volatile=True), Variable(target)
    output = model(data)
    test_loss += F.cross_entropy(output, target, size_average=False).data.item()
    pred = output.data.max(1, keepdim=True)[1]
    correct += pred.eq(target.data.view_as(pred)).cpu().sum()

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

这个示例代码中，我们定义了一个卷积神经网络模型CNN，它有两个卷积层和两个全连接层。我们使用SGD优化器进行训练，并使用交叉熵损失函数进行模型评估。在训练过程中，我们使用PyTorch的自动微分功能来计算梯度并更新模型参数。在测试过程中，我们计算模型的平均损失和准确率。