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Python使用pytorch動手實(shí)現(xiàn)LSTM模塊

更新時(shí)間：2022年07月27日 08:53:21 作者：qyhyzard

這篇文章主要介紹了Python使用pytorch動手實(shí)現(xiàn)LSTM模塊，LSTM是RNN中一個(gè)較為流行的網(wǎng)絡(luò)模塊。主要包括輸入，輸入門，輸出門，遺忘門，激活函數(shù)，全連接層（Cell）和輸出

LSTM 簡介：

LSTM是RNN中一個(gè)較為流行的網(wǎng)絡(luò)模塊。主要包括輸入，輸入門，輸出門，遺忘門，激活函數(shù)，全連接層（Cell）和輸出。

其結(jié)構(gòu)如下：

上述公式不做解釋，我們只要大概記得以下幾個(gè)點(diǎn)就可以了：

當(dāng)前時(shí)刻LSTM模塊的輸入有來自當(dāng)前時(shí)刻的輸入值，上一時(shí)刻的輸出值，輸入值和隱含層輸出值，就是一共有四個(gè)輸入值，這意味著一個(gè)LSTM模塊的輸入量是原來普通全連接層的四倍左右，計(jì)算量多了許多。
所謂的門就是前一時(shí)刻的計(jì)算值輸入到sigmoid激活函數(shù)得到一個(gè)概率值，這個(gè)概率值決定了當(dāng)前輸入的強(qiáng)弱程度。 這個(gè)概率值和當(dāng)前輸入進(jìn)行矩陣乘法得到經(jīng)過門控處理后的實(shí)際值。
門控的激活函數(shù)都是sigmoid，范圍在（0，1），而輸出輸出單元的激活函數(shù)都是tanh，范圍在（-1，1）。

Pytorch實(shí)現(xiàn)如下：

import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.nn import init
from torch import Tensor
import math
class NaiveLSTM(nn.Module):
    """Naive LSTM like nn.LSTM"""
    def __init__(self, input_size: int, hidden_size: int):
        super(NaiveLSTM, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

        # input gate
        self.w_ii = Parameter(Tensor(hidden_size, input_size))
        self.w_hi = Parameter(Tensor(hidden_size, hidden_size))
        self.b_ii = Parameter(Tensor(hidden_size, 1))
        self.b_hi = Parameter(Tensor(hidden_size, 1))

        # forget gate
        self.w_if = Parameter(Tensor(hidden_size, input_size))
        self.w_hf = Parameter(Tensor(hidden_size, hidden_size))
        self.b_if = Parameter(Tensor(hidden_size, 1))
        self.b_hf = Parameter(Tensor(hidden_size, 1))

        # output gate
        self.w_io = Parameter(Tensor(hidden_size, input_size))
        self.w_ho = Parameter(Tensor(hidden_size, hidden_size))
        self.b_io = Parameter(Tensor(hidden_size, 1))
        self.b_ho = Parameter(Tensor(hidden_size, 1))

        # cell
        self.w_ig = Parameter(Tensor(hidden_size, input_size))
        self.w_hg = Parameter(Tensor(hidden_size, hidden_size))
        self.b_ig = Parameter(Tensor(hidden_size, 1))
        self.b_hg = Parameter(Tensor(hidden_size, 1))

        self.reset_weigths()

    def reset_weigths(self):
        """reset weights
        """
        stdv = 1.0 / math.sqrt(self.hidden_size)
        for weight in self.parameters():
            init.uniform_(weight, -stdv, stdv)

    def forward(self, inputs: Tensor, state: Tuple[Tensor]) \
        -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
        """Forward
        Args:
            inputs: [1, 1, input_size]
            state: ([1, 1, hidden_size], [1, 1, hidden_size])
        """
#         seq_size, batch_size, _ = inputs.size()

        if state is None:
            h_t = torch.zeros(1, self.hidden_size).t()
            c_t = torch.zeros(1, self.hidden_size).t()
        else:
            (h, c) = state
            h_t = h.squeeze(0).t()
            c_t = c.squeeze(0).t()

        hidden_seq = []

        seq_size = 1
        for t in range(seq_size):
            x = inputs[:, t, :].t()
            # input gate
            i = torch.sigmoid(self.w_ii @ x + self.b_ii + self.w_hi @ h_t +
                              self.b_hi)
            # forget gate
            f = torch.sigmoid(self.w_if @ x + self.b_if + self.w_hf @ h_t +
                              self.b_hf)
            # cell
            g = torch.tanh(self.w_ig @ x + self.b_ig + self.w_hg @ h_t
                           + self.b_hg)
            # output gate
            o = torch.sigmoid(self.w_io @ x + self.b_io + self.w_ho @ h_t +
                              self.b_ho)

            c_next = f * c_t + i * g
            h_next = o * torch.tanh(c_next)
            c_next_t = c_next.t().unsqueeze(0)
            h_next_t = h_next.t().unsqueeze(0)
            hidden_seq.append(h_next_t)

        hidden_seq = torch.cat(hidden_seq, dim=0)
        return hidden_seq, (h_next_t, c_next_t)

def reset_weigths(model):
    """reset weights
    """
    for weight in model.parameters():
        init.constant_(weight, 0.5)
### test 
inputs = torch.ones(1, 1, 10)
h0 = torch.ones(1, 1, 20)
c0 = torch.ones(1, 1, 20)
print(h0.shape, h0)
print(c0.shape, c0)
print(inputs.shape, inputs)
# test naive_lstm with input_size=10, hidden_size=20
naive_lstm = NaiveLSTM(10, 20)
reset_weigths(naive_lstm)
output1, (hn1, cn1) = naive_lstm(inputs, (h0, c0))
print(hn1.shape, cn1.shape, output1.shape)
print(hn1)
print(cn1)
print(output1)

對比官方實(shí)現(xiàn)：

# Use official lstm with input_size=10, hidden_size=20
lstm = nn.LSTM(10, 20)
reset_weigths(lstm)
output2, (hn2, cn2) = lstm(inputs, (h0, c0))
print(hn2.shape, cn2.shape, output2.shape)
print(hn2)
print(cn2)
print(output2)

可以看到與官方的實(shí)現(xiàn)有些許的不同，但是輸出的結(jié)果仍舊一致。