class LSTM_Atten(nn.Module): """搭建Decoder结构""" def init(self, look_back, pre_len): super(LSTM_Atten, self).init() self.lstm = nn.LSTM(input_size=1, # 1个输入特征 hidden_size=128, # 隐状态h扩展为为128维 num_layers=1, # 1层LSTM batch_first=True, # 输入结构为(batch_size, seq_len, feature_size). Default: False ) self.lstmcell = nn.LSTMCell(input_size=128, hidden_size=128) self.drop = nn.Dropout(0.2) # 丢弃率 self.fc1 = nn.Linear(256, 128) self.fc2 = nn.Linear(128, 1) self.look_back = look_back self.pre_len = pre_len self.Softmax = nn.Softmax(dim=1) def forward(self, x): H, (h, c) = self.lstm(x.float(), None) # 编码 h = h.squeeze(0) c = c.squeeze(0) H_pre = torch.empty((h.shape[0], self.pre_len, 128 * 2)).to(device) for i in range(self.pre_len): # 解码 h_t, c_t = self.lstmcell(h, (h, c)) # 预测 H = torch.cat((H, h_t.unsqueeze(1)), 1) h_atten = self.Atten(H) # 获取结合了注意力的隐状态 H_pre[:, i, :] = h_atten # 记录解码器每一步的隐状态 h, c = h_t, c_t # 将当前的隐状态与细胞状态记录用于下一个时间步 return self.fc2(self.fc1(H_pre)).squeeze(2) def Atten(self, H): h = H[:, -1, :].unsqueeze(1) # [batch_size,1,128] H = H[:, -1 - self.look_back:-1, :] # [batch_size,look_back,128] atten = torch.matmul(h, H.transpose(1, 2)).transpose(1, 2) # 注意力矩阵 atten = self.Softmax(atten) atten_H = atten * H # 带有注意力的历史隐状态 atten_H = torch.sum(atten_H, dim=1).unsqueeze(1) # 按时间维度降维 return torch.cat((atten_H, h), 2).squeeze(1) 这段代码如何改能实现多特征的输入

首先需要将`input_size`修改为特征的数量，例如`input_size=2`表示有两个特征。

然后需要修改`forward`函数中的输入参数`x`的形状，将其变为(batch_size, seq_len, feature_size)。这可以通过在输入时进行reshape来实现。

最后需要修改`nn.Linear`的输入和输出维度，例如`self.fc1 = nn.Linear(256, 128)`可以改为`self.fc1 = nn.Linear(256 * feature_size, 128)`，其中256为上一层的输出维度，feature_size为特征的数量。

修改后的代码如下：

class LSTM_Atten(nn.Module):
    """搭建Decoder结构"""

    def __init__(self, look_back, pre_len, feature_size):
        super(LSTM_Atten, self).__init__()
        self.lstm = nn.LSTM(input_size=feature_size, # 特征的数量
                            hidden_size=128, # 隐状态h扩展为为128维
                            num_layers=1, # 1层LSTM
                            batch_first=True, # 输入结构为(batch_size, seq_len, feature_size). Default: False
                            )
        self.lstmcell = nn.LSTMCell(input_size=128, hidden_size=128)
        self.drop = nn.Dropout(0.2) # 丢弃率
        self.fc1 = nn.Linear(256 * feature_size, 128) # 修改输入和输出维度
        self.fc2 = nn.Linear(128, 1)
        self.look_back = look_back
        self.pre_len = pre_len
        self.Softmax = nn.Softmax(dim=1)

    def forward(self, x):
        batch_size, seq_len, feature_size = x.shape
        x = x.reshape(batch_size, seq_len, feature_size) # reshape输入数据
        H, (h, c) = self.lstm(x.float(), None) # 编码
        h = h.squeeze(0)
        c = c.squeeze(0)
        H_pre = torch.empty((h.shape[0], self.pre_len, 128 * 2)).to(device)
        for i in range(self.pre_len): # 解码
            h_t, c_t = self.lstmcell(h, (h, c)) # 预测
            H = torch.cat((H, h_t.unsqueeze(1)), 1)
            h_atten = self.Atten(H) # 获取结合了注意力的隐状态
            H_pre[:, i, :] = h_atten # 记录解码器每一步的隐状态
            h, c = h_t, c_t # 将当前的隐状态与细胞状态记录用于下一个时间步
        return self.fc2(self.fc1(H_pre.reshape(batch_size, -1))).squeeze(1) # reshape后进行线性变换

    def Atten(self, H):
        h = H[:, -1, :].unsqueeze(1) # [batch_size,1,128]
        H = H[:, -1 - self.look_back:-1, :] # [batch_size,look_back,128]
        atten = torch.matmul(h, H.transpose(1, 2)).transpose(1, 2) # 注意力矩阵
        atten = self.Softmax(atten)
        atten_H = atten * H # 带有注意力的历史隐状态
        atten_H = torch.sum(atten_H, dim=1).unsqueeze(1) # 按时间维度降维
        return torch.cat((atten_H, h), 2).squeeze(1)