用pytorch编写基于TCN-CBAM的轴承寿命预测代码

当然可以！以下是使用PyTorch编写基于TCN-CBAM的轴承寿命预测代码的示例：

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

class TemporalConvNet(nn.Module):
    def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
        super(TemporalConvNet, self).__init__()
        layers = []
        num_levels = len(num_channels)
        
        for i in range(num_levels):
            dilation_size = 2 ** i
            in_channels = num_inputs if i == 0 else num_channels[i-1]
            out_channels = num_channels[i]
            padding = (kernel_size - 1) * dilation_size
            layers.append(nn.Conv1d(in_channels, out_channels, kernel_size,
                                    padding=padding, dilation=dilation_size))
            layers.append(nn.BatchNorm1d(out_channels))
            layers.append(nn.ReLU())
            layers.append(nn.Dropout(dropout))
        
        self.network = nn.Sequential(*layers)
        
    def forward(self, x):
        return self.network(x)
    

class CBAM(nn.Module):
    def __init__(self, channels, reduction_ratio=16):
        super(CBAM, self).__init__()
        self.channels = channels
        self.reduction_ratio = reduction_ratio
        
        self.avg_pool = nn.AdaptiveAvgPool1d(1)
        self.max_pool = nn.AdaptiveMaxPool1d(1)
        
        self.fc1 = nn.Linear(channels, channels // reduction_ratio)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(channels // reduction_ratio, channels)
        
        self.sigmoid_channel = nn.Sigmoid()
        
    def forward(self, x):
        avg_out = self.avg_pool(x).squeeze(-1)
        max_out = self.max_pool(x).squeeze(-1)
        
        avg_out = self.fc2(self.relu(self.fc1(avg_out)))
        max_out = self.fc2(self.relu(self.fc1(max_out)))
        
        channel_attention = self.sigmoid_channel(avg_out + max_out).unsqueeze(-1)
        
        return x * channel_attention
    
class TCN_CBAM(nn.Module):
    def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
        super(TCN_CBAM, self).__init__()
        self.tcn = TemporalConvNet(num_inputs, num_channels, kernel_size, dropout)
        self.cbam = CBAM(num_channels[-1])
        self.fc = nn.Linear(num_channels[-1], 1)
        
    def forward(self, x):
        x = self.tcn(x)
        x = self.cbam(x)
        x = torch.mean(x, dim=2)
        x = self.fc(x)
        return x.squeeze(-1)


# 示例用法
num_inputs = 10
num_channels = [64, 128, 256]
kernel_size = 3
dropout = 0.2

model = TCN_CBAM(num_inputs, num_channels, kernel_size, dropout)

# 将输入数据转换为PyTorch张量
input_data = torch.randn(32, num_inputs, 100)  # 假设输入样本数为32，时间步长为100

# 前向传播
output = model(input_data)

print(output.shape)  # 输出形状应为(32,)

这段代码实现了一个基于TCN-CBAM模型的轴承寿命预测模型。模型结构包括TemporalConvNet(TCN)和Channel Attention Module(CBAM)两部分。TCN用于捕捉时间序列数据的时序特征，CBAM用于对TCN的输出进行通道注意力加权。最后通过全连接层将输出映射为轴承寿命的预测值。

请根据实际需求调整模型的超参数和输入数据的形状。希望这对你有帮助！