transformer 故障诊断代码
时间: 2023-09-10 07:07:26 浏览: 72
对于 Transformer 故障诊断,以下是一个示例代码:
```python
import torch
import torch.nn as nn
class Transformer(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_layers, num_heads):
super(Transformer, self).__init__()
self.embedding = nn.Linear(input_dim, hidden_dim)
self.encoder_layer = nn.TransformerEncoderLayer(hidden_dim, num_heads)
self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers)
self.decoder = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = self.embedding(x)
x = x.permute(1, 0, 2) # shape: (seq_len, batch_size, input_dim)
x = self.transformer_encoder(x)
x = x.permute(1, 0, 2) # shape: (batch_size, seq_len, hidden_dim)
x = self.decoder(x[:, -1, :]) # shape: (batch_size, output_dim)
return x
# 使用示例
input_dim = 100
hidden_dim = 256
output_dim = 10
num_layers = 4
num_heads = 8
model = Transformer(input_dim, hidden_dim, output_dim, num_layers, num_heads)
input_data = torch.randn(32, 10, input_dim) # shape: (batch_size, seq_len, input_dim)
output_data = model(input_data) # shape: (batch_size, output_dim)
```
这是一个基本的 Transformer 模型实现,可用于故障诊断等任务。根据具体需求,你可能需要调整模型的参数和结构。