pytorch中故障诊断完整的1DCNN代码
时间: 2023-11-15 22:06:28 浏览: 354
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。
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