帮我写一段transformer的代码
时间: 2023-07-29 10:10:10 浏览: 36
当然可以!以下是一个简单的Transformer模型的代码示例:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Transformer(nn.Module):
def __init__(self, input_dim, output_dim, hidden_dim, num_layers, num_heads, dropout):
super(Transformer, self).__init__()
self.embedding = nn.Embedding(input_dim, hidden_dim)
self.positional_encoding = PositionalEncoding(hidden_dim, dropout)
encoder_layer = nn.TransformerEncoderLayer(hidden_dim, num_heads)
self.encoder = nn.TransformerEncoder(encoder_layer, num_layers)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, src):
embedded_src = self.embedding(src)
encoded_src = self.positional_encoding(embedded_src)
output = self.encoder(encoded_src)
output = self.fc(output.mean(dim=0))
return F.log_softmax(output, dim=-1)
class PositionalEncoding(nn.Module):
def __init__(self, hidden_dim, dropout):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(hidden_dim, hidden_dim)
position = torch.arange(0, hidden_dim, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, hidden_dim, 2).float() * (-math.log(10000.0) / hidden_dim))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
```
这个代码定义了一个简单的Transformer模型。它包含一个嵌入层(embedding layer)、位置编码层(positional encoding layer)、多层Transformer编码器(Transformer encoder)和一个全连接层(fully connected layer)用于输出预测结果。你可以根据自己的任务需求进行修改和扩展。