def neg_log_likelihood_loss(self, gaz_list, reverse_gaz_list, word_inputs,
word_seq_lengths, batch_label, mask):
"""
get the neg_log_likelihood_loss
Args:
gaz_list: the batch data's gaz, for every chinese char
reverse_gaz_list: the reverse list
word_inputs: word input ids, [batch_size, seq_len]
word_seq_lengths: [batch_size]
batch_label: [batch_size, seq_len]
mask: [batch_size, seq_len]
"""
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
lengths = list(map(int, word_seq_lengths))
# print('one ', reverse_gaz_list[0][:10])
## get batch gaz ids
batch_gaz_ids, batch_gaz_length, batch_gaz_mask = get_batch_gaz(
reverse_gaz_list, batch_size, seq_len, self.gpu, type=self.type)
# print('two ', batch_gaz_ids[0][:10])
reverse_batch_gaz_ids, reverse_batch_gaz_length, reverse_batch_gaz_mask = get_batch_gaz(
gaz_list, batch_size, seq_len, self.gpu, type=self.type)
reverse_batch_gaz_ids = reverse_padded_sequence(
reverse_batch_gaz_ids, lengths)
reverse_batch_gaz_length = reverse_padded_sequence(
reverse_batch_gaz_length, lengths)
reverse_batch_gaz_mask = reverse_padded_sequence(
reverse_batch_gaz_mask, lengths)
## word embedding
word_embs = self.word_embedding(word_inputs)
reverse_word_embs = reverse_padded_sequence(word_embs, lengths)
## gaz embedding
gaz_embs = self.gaz_embed(
(batch_gaz_ids, batch_gaz_length, batch_gaz_mask))
reverse_gaz_embs = self.gaz_embed(
(reverse_batch_gaz_ids, reverse_batch_gaz_length,
reverse_batch_gaz_mask))
## lstm
forward_inputs = torch.cat((word_embs, gaz_embs), dim=-1)
backward_inputs = torch.cat((reverse_word_embs, reverse_gaz_embs),
dim=-1)
lstm_outs, _ = self.lstm((forward_inputs, backward_inputs),
word_seq_lengths)
## hidden2tag
outs = self.hidden2tag(lstm_outs)
## crf and loss
loss = self.crf.neg_log_likelihood_loss(outs, mask, batch_label)
_, tag_seq = self.crf._viterbi_decode(outs, mask)
return loss, tag_seq
def forward(self, gaz_list, reverse_gaz_list, word_inputs,
word_seq_lengths, mask):
"""
Args:
gaz_list: the forward gaz_list
reverse_gaz_list: the backward gaz list
word_inputs: word ids
word_seq_lengths: each sentence length
mask: sentence mask
"""
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
lengths = list(map(int, word_seq_lengths))
## get batch gaz ids
batch_gaz_ids, batch_gaz_length, batch_gaz_mask = get_batch_gaz(
reverse_gaz_list, batch_size, seq_len, self.gpu, type=self.type)
reverse_batch_gaz_ids, reverse_batch_gaz_length, reverse_batch_gaz_mask = get_batch_gaz(
gaz_list, batch_size, seq_len, self.gpu, type=self.type)
reverse_batch_gaz_ids = reverse_padded_sequence(
reverse_batch_gaz_ids, lengths)
reverse_batch_gaz_length = reverse_padded_sequence(
reverse_batch_gaz_length, lengths)
reverse_batch_gaz_mask = reverse_padded_sequence(
reverse_batch_gaz_mask, lengths)
## word embedding
word_embs = self.word_embedding(word_inputs)
reverse_word_embs = reverse_padded_sequence(word_embs, lengths)
## gaz embedding
gaz_embs = self.gaz_embed(
(batch_gaz_ids, batch_gaz_length, batch_gaz_mask))
reverse_gaz_embs = self.gaz_embed(
(reverse_batch_gaz_ids, reverse_batch_gaz_length,
reverse_batch_gaz_mask))
## lstm
forward_inputs = torch.cat((word_embs, gaz_embs), dim=-1)
backward_inputs = torch.cat((reverse_word_embs, reverse_gaz_embs),
dim=-1)
lstm_outs, _ = self.lstm((forward_inputs, backward_inputs),
word_seq_lengths)
## hidden2tag
outs = self.hidden2tag(lstm_outs)
## crf and loss
_, tag_seq = self.crf._viterbi_decode(outs, mask)
return tag_seq