写一个利用多头注意力机制处理多维时序数据的代码

当然，我可以为您提供一个基于Transformer的多头注意力机制处理多维时序数据的代码示例：

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

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super(MultiHeadAttention, self).__init__()
        self.num_heads = num_heads
        self.d_model = d_model

        assert d_model % num_heads == 0

        self.depth = d_model // num_heads

        self.query_linear = nn.Linear(d_model, d_model)
        self.key_linear = nn.Linear(d_model, d_model)
        self.value_linear = nn.Linear(d_model, d_model)

        self.output_linear = nn.Linear(d_model, d_model)

    def split_heads(self, x, batch_size):
        x = x.view(batch_size, -1, self.num_heads, self.depth)
        return x.permute(0, 2, 1, 3)

    def forward(self, query, key, value, mask=None):
        batch_size = query.size(0)

        query = self.query_linear(query)
        key = self.key_linear(key)
        value = self.value_linear(value)

        query = self.split_heads(query, batch_size)
        key = self.split_heads(key, batch_size)
        value = self.split_heads(value, batch_size)

        scaled_attention_logits = torch.matmul(query, key.permute(0, 1, 3, 2)) / torch.sqrt(torch.tensor(self.depth, dtype=torch.float32))

        if mask is not None:
            scaled_attention_logits += (mask * -1e9)

        attention_weights = F.softmax(scaled_attention_logits, dim=-1)

        output = torch.matmul(attention_weights, value)
        output = output.permute(0, 2, 1, 3).contiguous()
        output = output.view(batch_size, -1, self.d_model)

        output = self.output_linear(output)

        return output

这个代码实现了一个多头注意力机制，可以用于处理多维时序数据。其中，d_model表示输入数据的维度，num_heads表示注意力头的数量。在forward函数中，输入的query、key、value都是形状为(batch_size, seq_len, d_model)的张量，其中seq_len表示时序数据的长度。如果需要进行mask操作，可以传入一个形状为(batch_size, num_heads, seq_len, seq_len)的张量作为mask参数。