sequence_labeling.py 文件源码

def _get_rnn_output(self, input_word, input_char, mask=None, length=None, hx=None):
        # [batch, length, word_dim]
        word = self.word_embedd(input_word)

        # [batch, length, char_length, char_dim]
        char = self.char_embedd(input_char)
        char_size = char.size()
        # first transform to [batch *length, char_length, char_dim]
        # then transpose to [batch * length, char_dim, char_length]
        char = char.view(char_size[0] * char_size[1], char_size[2], char_size[3]).transpose(1, 2)
        # put into cnn [batch*length, char_filters, char_length]
        # then put into maxpooling [batch * length, char_filters]
        char, _ = self.conv1d(char).max(dim=2)
        # reshape to [batch, length, char_filters]
        char = torch.tanh(char).view(char_size[0], char_size[1], -1)

        # concatenate word and char [batch, length, word_dim+char_filter]
        input = torch.cat([word, char], dim=2)
        # output from rnn [batch, length, hidden_size]
        output, hn = self.rnn(input, mask, hx=hx)
        # apply dropout for the output of rnn
        output = self.dropout_rnn(output.transpose(1, 2)).transpose(1, 2)

        if self.dense is not None:
            # [batch, length, tag_space]
            output = F.elu(self.dense(output))

        return output, hn, mask, length