bnlstm.py 文件源码

def forward(self, input_, hx):
        """
        Args:
            input_: A (batch, input_size) tensor containing input
                features.
            hx: A tuple (h_0, c_0), which contains the initial hidden
                and cell state, where the size of both states is
                (batch, hidden_size).
        Returns:
            h_1, c_1: Tensors containing the next hidden and cell state.
        """

        h_0, c_0 = hx
        batch_size = h_0.size(0)
        bias_batch = (self.bias.unsqueeze(0)
                      .expand(batch_size, *self.bias.size()))
        wh_b = torch.addmm(bias_batch, h_0, self.weight_hh)
        wi = torch.mm(input_, self.weight_ih)
        f, i, o, g = torch.split(wh_b + wi,
                                 split_size=self.hidden_size, dim=1)
        c_1 = torch.sigmoid(f)*c_0 + torch.sigmoid(i)*torch.tanh(g)
        h_1 = torch.sigmoid(o) * torch.tanh(c_1)
        return h_1, c_1