请用pytorch写出用于故障诊断分类任务的cnn-attention模型的示例代码

好的，以下是一个用于故障诊断分类任务的cnn-attention模型的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class CNNAttention(nn.Module):
    def __init__(self, num_classes, num_filters=32, filter_sizes=[3,4,5], dropout_rate=0.5):
        super(CNNAttention, self).__init__()
        self.num_filters = num_filters
        self.filter_sizes = filter_sizes
        self.num_classes = num_classes
        
        # CNN layers
        self.convs = nn.ModuleList([
            nn.Conv2d(1, num_filters, (k, 300)) for k in filter_sizes
        ])
        
        # Attention mechanism
        self.attention_weight = nn.Parameter(torch.Tensor(num_filters, num_filters))
        nn.init.xavier_uniform_(self.attention_weight)
        
        # Fully connected layer
        self.fc = nn.Linear(num_filters, num_classes)
        
        # Dropout
        self.dropout = nn.Dropout(p=dropout_rate)
        
    def forward(self, x):
        # x shape: (batch_size, seq_len, embedding_dim)
        x = x.unsqueeze(1)  # x shape: (batch_size, 1, seq_len, embedding_dim)
        
        # Convolutional layers
        conv_outputs = []
        for conv in self.convs:
            conv_output = F.relu(conv(x)).squeeze(3)  # conv_output shape: (batch_size, num_filters, seq_len - filter_size + 1)
            conv_outputs.append(conv_output)
        
        # Attention mechanism
        conv_outputs = torch.stack(conv_outputs, dim=1)  # conv_outputs shape: (batch_size, num_filters, num_conv_filter_sizes, seq_len - max(filter_sizes) + 1)
        attention_scores = torch.bmm(conv_outputs.transpose(1,2), self.attention_weight.unsqueeze(0).repeat(conv_outputs.shape[0],1,1))  # attention_scores shape: (batch_size, num_conv_filter_sizes, num_filters)
        attention_scores = F.softmax(attention_scores, dim=-1)  # attention_scores shape: (batch_size, num_conv_filter_sizes, num_filters)
        conv_outputs = torch.bmm(attention_scores, conv_outputs)  # conv_outputs shape: (batch_size, num_filters, seq_len - max(filter_sizes) + 1)
        
        # Max pooling
        pooled = F.max_pool1d(conv_outputs, conv_outputs.size(2)).squeeze(2)  # pooled shape: (batch_size, num_filters)
        
        # Dropout
        pooled = self.dropout(pooled)
        
        # Fully connected layer
        logits = self.fc(pooled)  # logits shape: (batch_size, num_classes)
        
        return logits

这个模型使用了卷积神经网络（CNN）和注意力机制（Attention）来提取文本特征，然后通过全连接层进行分类。其中，CNN用于提取局部特征，Attention用于加强重要特征的权重，从而更好地捕捉文本中的关键信息。