pytorch中故障诊断完整的1DCNN代码

以下是一个简单的1D卷积神经网络的PyTorch代码：

import torch
import torch.nn as nn

class CNN1D(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(CNN1D, self).__init__()
        self.conv1 = nn.Conv1d(in_channels=input_dim, out_channels=hidden_dim, kernel_size=3, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv1d(in_channels=hidden_dim, out_channels=hidden_dim, kernel_size=3, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(hidden_dim*8, output_dim)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = x.view(-1, self.num_flat_features(x))
        x = self.fc1(x)
        return x
    
    def num_flat_features(self, x):
        size = x.size()[1:] 
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

这是一个包含两个卷积层和一个全连接层的1D CNN。在这个例子中，输入是一个1D张量，输入维度为input_dim，输出维度为output_dim。中间的隐藏层维度为hidden_dim。这个模型使用ReLU激活函数和MaxPool1D池化层来增强非线性特征。