DRSN34网络结构实现pytorch

DRSN34网络结构是Deep Residual Shrinkage Networks（DRSN）的一个变种，用于故障诊断。下面是使用PyTorch实现DRSN34网络结构的示例代码：

import torch
import torch.nn as nn

class DRSN34(nn.Module):
    def __init__(self, num_classes=10):
        super(DRSN34, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 64, 3)
        self.layer2 = self._make_layer(64, 128, 4, stride=2)
        self.layer3 = self._make_layer(128, 256, 6, stride=2)
        self.layer4 = self._make_layer(256, 512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, in_channels, out_channels, blocks, stride=1):
        layers = []
        layers.append(nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False))
        layers.append(nn.BatchNorm2d(out_channels))
        layers.append(self.relu)
        for _ in range(1, blocks):
            layers.append(nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False))
            layers.append(nn.BatchNorm2d(out_channels))
            layers.append(self.relu)
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)
        return x

# 创建DRSN34网络实例
model = DRSN34()

请注意，上述代码只是DRSN34网络结构的一个示例实现，具体的训练和使用方法可能需要根据具体的任务和数据进行调整。