model_GRU.py 文件源码

def forward(self, input):
        self.hidden = self.init_hidden(self.num_layers, input.size(1))
        embed = self.embed(input)
        input = embed.view(len(input), embed.size(1), -1)
        # lstm
        # print(input)
        # print("a", self.hidden)
        lstm_out, hidden = self.gru(input, self.hidden)
        lstm_out = torch.transpose(lstm_out, 0, 1)
        lstm_out = torch.transpose(lstm_out, 1, 2)
        # pooling
        lstm_out = F.max_pool1d(lstm_out, lstm_out.size(2)).squeeze(2)
        lstm_out = F.tanh(lstm_out)
        # linear
        y = self.hidden2label(lstm_out)
        logit = y
        return logit