你可以写一个行注意力和列注意力的代码吗
时间: 2024-01-23 18:01:45 浏览: 19
当然可以,以下是一个简单的示例:
```
import torch
import torch.nn as nn
class SelfAttention(nn.Module):
def __init__(self, emb_size, heads):
super(SelfAttention, self).__init__()
self.emb_size = emb_size
self.heads = heads
# linear layers for projecting the input to keys, values, and queries
self.key_layer = nn.Linear(emb_size, emb_size)
self.value_layer = nn.Linear(emb_size, emb_size)
self.query_layer = nn.Linear(emb_size, emb_size)
# layer for combining the heads
self.combine_heads = nn.Linear(heads * emb_size, emb_size)
def forward(self, x):
batch_size, seq_len, emb_size = x.size()
heads = self.heads
# project the input to keys, values, and queries
keys = self.key_layer(x).view(batch_size, seq_len, heads, emb_size // heads)
values = self.value_layer(x).view(batch_size, seq_len, heads, emb_size // heads)
queries = self.query_layer(x).view(batch_size, seq_len, heads, emb_size // heads)
# calculate the attention scores and attention weights
scores = torch.matmul(queries, keys.transpose(-2, -1)) / (emb_size ** 0.5)
attention_weights = torch.softmax(scores, dim=-1)
# apply the attention weights to the values and combine the heads
attended_values = torch.matmul(attention_weights, values)
attended_values = attended_values.transpose(1, 2).contiguous().view(batch_size, seq_len, -1)
output = self.combine_heads(attended_values)
return output
```