pytorch不适用lstm()实现lstm

在 PyTorch 中，实现 LSTM 的方式有两种：使用 `nn.LSTM` 模块或手动实现。如果不想使用 `nn.LSTM` 模块实现 LSTM，可以手动编写 LSTM 的前向传播函数。下面是一个手动实现 LSTM 的示例代码：

import torch

class LSTM(torch.nn.Module):
    def __init__(self, input_size, hidden_size):
        super().__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

        self.W_i = torch.nn.Parameter(torch.Tensor(input_size, hidden_size))
        self.U_i = torch.nn.Parameter(torch.Tensor(hidden_size, hidden_size))
        self.b_i = torch.nn.Parameter(torch.Tensor(hidden_size))

        self.W_f = torch.nn.Parameter(torch.Tensor(input_size, hidden_size))
        self.U_f = torch.nn.Parameter(torch.Tensor(hidden_size, hidden_size))
        self.b_f = torch.nn.Parameter(torch.Tensor(hidden_size))

        self.W_c = torch.nn.Parameter(torch.Tensor(input_size, hidden_size))
        self.U_c = torch.nn.Parameter(torch.Tensor(hidden_size, hidden_size))
        self.b_c = torch.nn.Parameter(torch.Tensor(hidden_size))

        self.W_o = torch.nn.Parameter(torch.Tensor(input_size, hidden_size))
        self.U_o = torch.nn.Parameter(torch.Tensor(hidden_size, hidden_size))
        self.b_o = torch.nn.Parameter(torch.Tensor(hidden_size))

        self.reset_parameters()

    def reset_parameters(self):
        stdv = 1.0 / math.sqrt(self.hidden_size)
        for weight in self.parameters():
            weight.data.uniform_(-stdv, stdv)

    def forward(self, x, h0=None, c0=None):
        seq_len, batch_size, _ = x.size()
        if h0 is None:
            h0 = x.new_zeros(batch_size, self.hidden_size)
        if c0 is None:
            c0 = x.new_zeros(batch_size, self.hidden_size)

        hiddens = []
        for t in range(seq_len):
            xt = x[t]
            i_t = torch.sigmoid(xt @ self.W_i + h0 @ self.U_i + self.b_i)
            f_t = torch.sigmoid(xt @ self.W_f + h0 @ self.U_f + self.b_f)
            c_tilda_t = torch.tanh(xt @ self.W_c + h0 @ self.U_c + self.b_c)
            c_t = f_t * c0 + i_t * c_tilda_t
            o_t = torch.sigmoid(xt @ self.W_o + h0 @ self.U_o + self.b_o)
            h_t = o_t * torch.tanh(c_t)
            hiddens.append(h_t.unsqueeze(0))
            h0, c0 = h_t, c_t
        hiddens = torch.cat(hiddens, dim=0)
        return hiddens, (h_t, c_t)

这个示例代码中，我们手动实现了 LSTM 的前向传播过程，包括输入门、遗忘门、细胞状态和输出门。这个实现与使用 `nn.LSTM` 模块的实现方式相似，但是需要手动计算 LSTM 的参数。