model_CNN_BiGRU.py 文件源码

def forward(self, x):
        embed = self.embed(x)

        embed = self.dropout(embed)

        # CNN
        cnn_x = embed
        cnn_x = torch.transpose(cnn_x, 0, 1)
        cnn_x = cnn_x.unsqueeze(1)
        # cnn_x = [F.relu(conv(cnn_x)).squeeze(3) for conv in self.convs1]  # [(N,Co,W), ...]*len(Ks)
        cnn_x = [conv(cnn_x).squeeze(3) for conv in self.convs1]  # [(N,Co,W), ...]*len(Ks)
        # cnn_x = [F.max_pool1d(i, i.size(2)).squeeze(2) for i in cnn_x]  # [(N,Co), ...]*len(Ks)
        cnn_x = [F.tanh(F.max_pool1d(i, i.size(2)).squeeze(2)) for i in cnn_x]  # [(N,Co), ...]*len(Ks)
        cnn_x = torch.cat(cnn_x, 1)
        cnn_x = self.dropout(cnn_x)

        # BiGRU
        bigru_x = embed.view(len(x), embed.size(1), -1)
        bigru_x, self.hidden = self.bigru(bigru_x, self.hidden)
        bigru_x = torch.transpose(bigru_x, 0, 1)
        bigru_x = torch.transpose(bigru_x, 1, 2)
        # bilstm_out = F.tanh(bilstm_out)
        bigru_x = F.max_pool1d(bigru_x, bigru_x.size(2)).squeeze(2)
        bigru_x = F.tanh(bigru_x)

        # CNN and BiGRU CAT
        cnn_x = torch.transpose(cnn_x, 0, 1)
        bigru_x = torch.transpose(bigru_x, 0, 1)
        cnn_bigru_out = torch.cat((cnn_x, bigru_x), 0)
        cnn_bigru_out = torch.transpose(cnn_bigru_out, 0, 1)

        # linear
        cnn_bigru_out = self.hidden2label1(F.tanh(cnn_bigru_out))
        logit = self.hidden2label2(F.tanh(cnn_bigru_out))

        return logit