写一个torch版本的双层、带有attention机制的LSTM模型代码

首先, 您需要安装 PyTorch 库并导入它:

!pip install torch
import torch

然后, 您可以使用 `nn.LSTM` 类定义一个双层 LSTM 模型:

class LSTM(torch.nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = torch.nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = torch.nn.Linear(hidden_size, num_classes)
    
    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

其中, `input_size` 是输入序列中每个时间步的特征数量, `hidden_size` 是 LSTM 单元的隐藏状态大小, `num_layers` 是 LSTM 层数, `num_classes` 是输出类别数.

要添加 attention 机制, 您可以使用 `torch.nn.Linear` 层和 `torch.nn.functional.softmax` 函数来计算 attention 权重, 然后将 attention 权重乘以 LSTM 输出, 并使用 `torch.sum` 函数将它们相加.

下面是一个带有 attention 机制的双层 LSTM 模型的例子:

class AttentionLSTM(torch.nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(AttentionLSTM, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = torch.nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.attention = torch.