Python使用pytorch動手實現LSTM模塊

LSTM 簡介：

LSTM是RNN中一個較為流行得網絡模塊。主要包括輸入，輸入門，輸出門，遺忘門，激活函數，全連接層（Cell）和輸出。

其結構如下：

上述公式不做解釋，我們只要大概記得以下幾個點就可以了：

• 當前時刻LSTM模塊得輸入有來自當前時刻得輸入值，上一時刻得輸出值，輸入值和隱含層輸出值，就是一共有四個輸入值，這意味著一個LSTM模塊得輸入量是原來普通全連接層得四倍左右，計算量多了許多。
• 所謂得門就是前一時刻得計算值輸入到sigmoid激活函數得到一個概率值，這個概率值決定了當前輸入得強弱程度。 這個概率值和當前輸入進行矩陣乘法得到經過門控處理后得實際值。
• 門控得激活函數都是sigmoid，范圍在（0，1），而輸出輸出單元得激活函數都是tanh，范圍在（-1，1）。

Pytorch實現如下：

import torchimport torch.nn as nnfrom torch.nn import Parameterfrom torch.nn import initfrom torch import Tensorimport mathclass 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=20naive_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)

對比官方實現：

# Use official lstm with input_size=10, hidden_size=20lstm = 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)

可以看到與官方得實現有些許得不同，但是輸出得結果仍舊一致。

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