text transformer模型
时间: 2023-11-23 22:57:59 浏览: 36
Transformer是一种基于自注意力机制的神经网络模型,广泛应用于自然语言处理领域中的各种任务,如机器翻译、文本摘要、问答系统等。相比于传统的循环神经网络和卷积神经网络,Transformer模型在处理长序列数据时具有更好的效果和更高的并行性。
Transformer模型的核心架构包括编码器和解码器两部分,其中编码器和解码器都由多个相同的层组成。每个层都包含了一个多头自注意力机制和一个全连接前馈神经网络,两者之间还有一个层标准化模块。编码器的输入是一个序列,输出是一个上下文向量,而解码器在编码器的基础上进一步引入了一个多头注意力机制,用于对编码器输出的上下文向量进行加权求和,以生成最终的输出序列。
下面是一个简单的Transformer模型的实现代码,用于将一个序列进行编码和解码:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class Transformer(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_layers, num_heads, dropout):
super().__init__()
self.embedding = nn.Embedding(input_dim, hidden_dim)
self.encoder_layers = nn.ModuleList([EncoderLayer(hidden_dim, num_heads, dropout) for _ in range(num_layers)])
self.decoder_layers = nn.ModuleList([DecoderLayer(hidden_dim, num_heads, dropout) for _ in range(num_layers)])
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, src, trg):
src_mask = self.make_src_mask(src)
trg_mask = self.make_trg_mask(trg)
src_embedded = self.embedding(src)
trg_embedded = self.embedding(trg)
enc_output = self.encode(src_embedded, src_mask)
dec_output = self.decode(trg_embedded, enc_output, src_mask, trg_mask)
output = self.fc(dec_output)
return output
def encode(self, src_embedded, src_mask):
enc_output = src_embedded
for layer in self.encoder_layers:
enc_output = layer(enc_output, src_mask)
return enc_output
def decode(self, trg_embedded, enc_output, src_mask, trg_mask):
dec_output = trg_embedded
for layer in self.decoder_layers:
dec_output = layer(dec_output, enc_output, src_mask, trg_mask)
return dec_output
def make_src_mask(self, src):
src_mask = (src != 0).unsqueeze(1).unsqueeze(2)
return src_mask
def make_trg_mask(self, trg):
trg_pad_mask = (trg != 0).unsqueeze(1).unsqueeze(2)
trg_len = trg.shape[1]
trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device=trg.device)).bool()
trg_mask = trg_pad_mask & trg_sub_mask
return trg_mask
class EncoderLayer(nn.Module):
def __init__(self, hidden_dim, num_heads, dropout):
super().__init__()
self.self_attn = nn.MultiheadAttention(hidden_dim, num_heads)
self.layer_norm1 = nn.LayerNorm(hidden_dim)
self.feed_forward = nn.Sequential(
nn.Linear(hidden_dim, 4 * hidden_dim),
nn.ReLU(),
nn.Linear(4 * hidden_dim, hidden_dim),
nn.Dropout(dropout)
)
self.layer_norm2 = nn.LayerNorm(hidden_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, src_embedded, src_mask):
attn_output, _ = self.self_attn(src_embedded, src_embedded, src_embedded, attn_mask=None, key_padding_mask=~src_mask)
attn_output = self.dropout(attn_output)
attn_output = self.layer_norm1(src_embedded + attn_output)
ff_output = self.feed_forward(attn_output)
ff_output = self.dropout(ff_output)
enc_output = self.layer_norm2(attn_output + ff_output)
return enc_output
class DecoderLayer(nn.Module):
def __init__(self, hidden_dim, num_heads, dropout):
super().__init__()
self.self_attn = nn.MultiheadAttention(hidden_dim, num_heads)
self.layer_norm1 = nn.LayerNorm(hidden_dim)
self.enc_attn = nn.MultiheadAttention(hidden_dim, num_heads)
self.layer_norm2 = nn.LayerNorm(hidden_dim)
self.feed_forward = nn.Sequential(
nn.Linear(hidden_dim, 4 * hidden_dim),
nn.ReLU(),
nn.Linear(4 * hidden_dim, hidden_dim),
nn.Dropout(dropout)
)
self.layer_norm3 = nn.LayerNorm(hidden_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, trg_embedded, enc_output, src_mask, trg_mask):
self_attn_output, _ = self.self_attn(trg_embedded, trg_embedded, trg_embedded, attn_mask=~trg_mask)
self_attn_output = self.dropout(self_attn_output)
self_attn_output = self.layer_norm1(trg_embedded + self_attn_output)
enc_attn_output, _ = self.enc_attn(self_attn_output, enc_output, enc_output, attn_mask=None, key_padding_mask=~src_mask)
enc_attn_output = self.dropout(enc_attn_output)
enc_attn_output = self.layer_norm2(self_attn_output + enc_attn_output)
ff_output = self.feed_forward(enc_attn_output)
ff_output = self.dropout(ff_output)
dec_output = self.layer_norm3(enc_attn_output + ff_output)
return dec_output
```