class BiLSTM_CRF(nn.Module):
def __init__(self, data):
super(BiLSTM_CRF, self).__init__()
print("build batched lstmcrf...")
self.gpu = data.HP_gpu
# For CRF, we need to add extra two label START and END for downlayer lstm, use original label size for CRF
label_size = data.label_alphabet_size
data.label_alphabet_size += 2
self.lstm = BiLSTM(data)
self.crf = CRF(label_size, self.gpu)
def neg_log_likelihood_loss(self, gaz_list, char_inputs, bichar_inputs,
char_seq_lengths, batch_label, mask):
outs = self.lstm.get_output_score(gaz_list, char_inputs, bichar_inputs,
char_seq_lengths)
total_loss = self.crf.neg_log_likelihood_loss(outs, mask, batch_label)
scores, tag_seq = self.crf._viterbi_decode(outs, mask)
return total_loss, tag_seq
def forward(self, gaz_list, char_inputs, bichar_inputs, char_seq_lengths,
mask):
outs = self.lstm.get_output_score(gaz_list, char_inputs, bichar_inputs,
char_seq_lengths)
scores, tag_seq = self.crf._viterbi_decode(outs, mask)
return tag_seq
def get_lstm_features(self, gaz_list, char_inputs, bichar_inputs,
char_seq_lengths):
return self.lstm.get_lstm_features(gaz_list, char_inputs,
bichar_inputs, char_seq_lengths)
class BiLSTM_CRF(nn.Module):
def __init__(self, data):
super(BiLSTM_CRF, self).__init__()
print ("build batched lstmcrf...")
self.gpu = data.HP_gpu
## add two more label for downlayer lstm, use original label size for CRF
label_size = data.label_alphabet_size
data.label_alphabet_size += 2
self.lstm = BiLSTM(data)
self.crf = CRF(label_size, self.gpu)
def neg_log_likelihood_loss(self, gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover, batch_label, mask):
outs = self.lstm.get_output_score(gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover)
total_loss = self.crf.neg_log_likelihood_loss(outs, mask, batch_label)
scores, tag_seq = self.crf._viterbi_decode(outs, mask)
return total_loss, tag_seq
def forward(self, gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover, mask):
outs = self.lstm.get_output_score(gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover)
batch_size = word_inputs.size(0)
seq_len = word_inputs.size(1)
scores, tag_seq = self.crf._viterbi_decode(outs, mask)
return tag_seq
def get_lstm_features(self, gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover):
return self.lstm.get_lstm_features(gaz_list, word_inputs, biword_inputs, word_seq_lengths, char_inputs, char_seq_lengths, char_seq_recover)
class BiLSTM_CRF(nn.Module):
def __init__(self, data):
super(BiLSTM_CRF, self).__init__()
print("build batched lstmcrf...")
self.gpu = data.HP_gpu
## add two more label for downlayer lstm, use original label size for CRF
self.lstm = BiLSTM(data)
self.softmax = nn.LogSoftmax()
self.loss_op = nn.CrossEntropyLoss()
self.negid = data.label_alphabet.get_index("NEGATIVE")
# self.crf = CRF(label_size, self.gpu)
def count_weight_loss(self, output, targets):
#计算loss,排除negtive这一类
ONLY_POSITIVE = False
targets_tensor = targets #torch.from_numpy(targets).type(torch.LongTensor)
softmax_result = Fun.log_softmax(output)
log_loss = softmax_result[
torch.arange(targets_tensor.shape[0]).type(torch.LongTensor),
targets_tensor]
if ONLY_POSITIVE:
classify_loss = -torch.mean(log_loss)
else:
if (targets_tensor !=
self.negid).nonzero().shape[0] == 0: #如果只有negid
classify_loss = -torch.mean(
1 * log_loss[(targets_tensor == self.negid).nonzero()])
else:
classify_loss = -torch.mean(log_loss[
(targets_tensor != self.negid).nonzero()]) - torch.mean(
1 * log_loss[(targets_tensor == self.negid).nonzero()]
) #(targets_tensor != label_dict["NEGATIVE"])
l2_reg = Variable(torch.FloatTensor([0]), requires_grad=True)
loss = Variable(torch.FloatTensor([0]), requires_grad=True)
if args.cuda:
l2_reg = l2_reg.cuda()
loss = loss.cuda()
for W in filter(lambda p: p.requires_grad, model.parameters()):
l2_reg += W.norm(2)
loss = classify_loss + 1e-5 * l2_reg #args.l2_weight
loss = loss.squeeze()
return loss
def neg_log_likelihood_loss(self, gaz_list, batch_word, batch_entity,
batch_gloss, batch_label, mask):
outs = self.lstm.get_output_score(gaz_list, batch_word, batch_entity,
batch_gloss, batch_label, mask)
outs = outs.view([-1, outs.shape[2]])
batch_label = batch_label.view([-1])
# batch_label = torch.zeros(batch_label.shape[0], batch_label.shape[1], outs.shape[2]).scatter_(2, batch_label.unsqueeze(-1).type(torch.LongTensor), 1)
loss = self.loss_op(outs, batch_label).sum()
# loss = self.count_weight_loss(outs,batch_label).sum()
return loss, torch.max(outs, 1)[1]
class BiLSTM_CRF(nn.Module):
def __init__(self, data):
super(BiLSTM_CRF, self).__init__()
print("build batched lstmcrf...")
self.gpu = data.HP_gpu
## add two more label for downlayer lstm, use original label size for CRF
label_size = data.label_alphabet_size
data.label_alphabet_size += 2
self.crf = CRF(label_size, self.gpu)
label_size_ner = data.label_alphabet_size_ner
data.label_alphabet_size_ner += 2
self.crf_ner = CRF(label_size_ner, self.gpu)
label_size_general = data.label_alphabet_size_general
data.label_alphabet_size_general += 2
self.crf_general = CRF(label_size_general, self.gpu)
self.lstm = BiLSTM(data)
def neg_log_likelihood_loss(self, gaz_list, word_inputs, biword_inputs,
word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover,
batch_label, mask):
outs = self.lstm.get_output_score(gaz_list, word_inputs, biword_inputs,
word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover)
total_loss = self.crf.neg_log_likelihood_loss(outs, mask, batch_label)
scores, tag_seq = self.crf._viterbi_decode(outs, mask)
return total_loss, tag_seq
def neg_log_likelihood_loss_ner(self, gaz_list, word_inputs, biword_inputs,
word_seq_lengths, char_inputs,
char_seq_lengths, char_seq_recover,
batch_label, mask):
outs = self.lstm.get_output_score_ner(gaz_list, word_inputs,
biword_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover)
total_loss = self.crf_ner.neg_log_likelihood_loss(
outs, mask, batch_label)
scores, tag_seq = self.crf_ner._viterbi_decode(outs, mask)
return total_loss, tag_seq
def neg_log_likelihood_loss_general(self, gaz_list, word_inputs,
biword_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover, batch_label, mask):
outs = self.lstm.get_output_score_general(
gaz_list, word_inputs, biword_inputs, word_seq_lengths,
char_inputs, char_seq_lengths, char_seq_recover)
total_loss = self.crf_general.neg_log_likelihood_loss(
outs, mask, batch_label)
scores, tag_seq = self.crf_general._viterbi_decode(outs, mask)
return total_loss, tag_seq
def forward(self, is_ner, gaz_list, word_inputs, biword_inputs,
word_seq_lengths, char_inputs, char_seq_lengths,
char_seq_recover, mask):
if not is_ner:
outs = self.lstm.get_output_score(gaz_list, word_inputs,
biword_inputs, word_seq_lengths,
char_inputs, char_seq_lengths,
char_seq_recover)
scores, tag_seq = self.crf._viterbi_decode(outs, mask)
else:
outs = self.lstm.get_output_score_ner(
gaz_list, word_inputs, biword_inputs, word_seq_lengths,
char_inputs, char_seq_lengths, char_seq_recover)
scores, tag_seq = self.crf_ner._viterbi_decode(outs, mask)
return tag_seq