attention预测时序数据代码

注意力机制（Attention Mechanism）常用于处理时序数据，特别是在自然语言处理任务中，如Transformer模型就是基于自注意力设计的。以下是使用Python（例如PyTorch库）实现一个简单的基于自注意力的时序数据预测模型的简要概述：

import torch
from torch import nn

class AttentionBlock(nn.Module):
    def __init__(self, d_model, heads=8):
        super(AttentionBlock, self).__init__()
        self.d_model = d_model
        self.heads = 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.dropout = nn.Dropout(p=0.1)

    def forward(self, query, key, value, mask=None):
        batch_size, seq_len, _ = query.size()
        # 分割成头部的注意力计算
        query_heads = self.query_linear(query).view(batch_size, seq_len, self.heads, -1)
        key_heads = self.key_linear(key).view(batch_size, seq_len, self.heads, -1)
        value_heads = self.value_linear(value).view(batch_size, seq_len, self.heads, -1)

        # 形状转换并计算注意力权重
        energy = torch.matmul(query_heads, key_heads.permute(0, 1, 3, 2)) / math.sqrt(self.d_model / self.heads)
        if mask is not None:
            energy = energy.masked_fill(mask == 0, float('-inf'))
        weights = nn.functional.softmax(energy, dim=-1)
        
        # 加权求和得到上下文向量
        context = torch.matmul(weights, value_heads).permute(0, 1, 2, 4).contiguous()
        context = context.view(batch_size, seq_len, self.d_model)

        # 合并头部后通过线性层
        out = self.dropout(nn.Linear(self.d_model, self.d_model)(context))
        return out

# 示例应用
model = AttentionBlock(d_model=512)  # 假设d_model设置为512
query = ...  # 输入的查询序列
key = ...  # 输入的关键值序列
value = ...  # 输入的值序列
mask = ...  # 可选的掩码 (如果输入序列有依赖关系)
output = model(query, key, value, mask)

在这个例子中，`AttentionBlock`是一个模块，接受查询、键和值作为输入，并返回经过注意力机制处理后的输出。你需要提供实际的输入数据和相应的掩码（如果存在依赖关系）。