transformer算法python

Transformer算法的Python实现可以参考以下代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
import math

class Generator(nn.Module):
    def __init__(self, d_model, vocab_size):
        super().__init__()
        self.project = nn.Linear(d_model, vocab_size)
        
    def forward(self, x):
        return F.softmax(self.project(x), dim=-1)

def attention(query, key, value, mask=None, dropout=None):
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
    
    if mask is not None:
        scores = scores.masked_fill(mask == 0, 1e-9)
        
    p_attn = F.softmax(scores, dim=-1)
    attn = torch.matmul(p_attn, value)
    
    return attn, p_attn

# 示例输入
x = de_result
d_model = 512
vocab_size = 10000

gen = Generator(d_model, vocab_size)
gen_result = gen(x)

print(gen_result)
print(gen_result.shape)

请注意，这只是一个Transformer算法的简单示例，具体实现可能会有所不同。