基于transformer

好的，基于Transformer的时间序列分类代码如下所示（以PyTorch为例）：

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

class TransformerTimeSeriesClassifier(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes, num_layers, num_heads, dropout):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_classes = num_classes
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.dropout = dropout
        
        self.pos_encoder = PositionalEncoding(input_size, dropout)
        encoder_layer = nn.TransformerEncoderLayer(d_model=input_size, nhead=num_heads, dropout=dropout)
        self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        self.fc = nn.Linear(input_size, num_classes)
        
    def forward(self, x):
        # x.shape = (seq_len, batch_size, input_size)
        x = self.pos_encoder(x)
        # x.shape = (seq_len, batch_size, input_size)
        x = self.transformer_encoder(x)
        # x.shape = (seq_len, batch_size, input_size)
        x = torch.mean(x, dim=0)
        # x.shape = (batch_size, input_size)
        x = self.fc(x)
        # x.shape = (batch_size, num_classes)
        return x

class PositionalEncoding(nn.Module):
    def __init__(self, d_model, dropout=0.1, max_len=5000):
        super().__init__()
        self.dropout = nn.Dropout(p=dropout)

        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return self.dropout(x)

其中，`TransformerTimeSeriesClassifier`是我们定义的基于Transformer的时间序列分类器，`PositionalEncoding`是Transformer中的位置编码模块。对于每个时间序列样本，输入数据的维度为`(seq_len, batch_size, input_size)`，其中`seq_len`是序列长度，`batch_size`是批次大小，`input_size`是输入数据的特征维度。模型的超参数包括`input_size`、`hidden_size`、`num_classes`、`num_layers`、`num_heads`和`dropout`，分别表示输入数据的特征维度、Transformer的隐藏层维度、分类的类别数、Transformer的层数、注意力头数和dropout概率。在模型的前向传播中，我们首先对输入数据进行位置编码，然后通过多层Transformer编码器处理序列信息，最后通过全连接层将编码后的序列信息映射到分类结果。