transformer代码python
时间: 2023-08-17 10:04:49 浏览: 242
以下是一个简单的 Transformer 模型的代码示例(使用 PyTorch):
```python
import torch
import torch.nn as nn
import torch.optim as optim
class TransformerModel(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers):
super(TransformerModel, self).__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.transformer = nn.Transformer(
d_model=embedding_dim,
nhead=2,
num_encoder_layers=num_layers,
num_decoder_layers=num_layers,
dim_feedforward=hidden_dim,
)
self.fc = nn.Linear(embedding_dim, vocab_size)
def forward(self, src, tgt):
src_embed = self.embedding(src)
tgt_embed = self.embedding(tgt)
src_pad_mask = self._generate_padding_mask(src)
tgt_pad_mask = self._generate_padding_mask(tgt)
tgt_subseq_mask = self._generate_subsequent_mask(tgt)
tgt_mask = tgt_pad_mask & tgt_subseq_mask
src_encoded = self.transformer.encoder(src_embed)
tgt_decoded = self.transformer.decoder(tgt_embed, src_encoded, tgt_mask=tgt_mask, src_key_padding_mask=src_pad_mask, tgt_key_padding_mask=tgt_pad_mask)
output = self.fc(tgt_decoded)
return output
def _generate_padding_mask(self, seq):
mask = (seq == 0) # Pad token index is assumed to be 0
return mask
def _generate_subsequent_mask(self, seq):
seq_len = seq.shape[1]
mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1).bool() # Upper triangular matrix
return mask
# 示例用法
vocab_size = 10000
embedding_dim = 256
hidden_dim = 512
num_layers = 4
model = TransformerModel(vocab_size, embedding_dim, hidden_dim, num_layers)
src = torch.tensor([[1, 2, 3, 4, 5]])
tgt = torch.tensor([[6, 7, 8, 9, 10]])
output = model(src, tgt)
print(output.shape) # 输出: torch.Size([1, 5, 10000])
# 损失函数和优化器的定义和训练过程略过
```
这是一个简单的 Transformer 模型,用于序列到序列的任务。它使用了 Embedding 层、Transformer 层和全连接层来实现。你可以根据自己的需求进行修改和扩展。
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