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fix a bug of NER. #473

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merged 1 commit into from
Nov 16, 2017
Merged

fix a bug of NER. #473

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2 changes: 1 addition & 1 deletion sequence_tagging_for_ner/data/vocab.txt
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
<UNK>
UUUNKKK
the
,
.
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25 changes: 17 additions & 8 deletions sequence_tagging_for_ner/network_conf.py
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Expand Up @@ -11,16 +11,16 @@ def ner_net(word_dict_len, label_dict_len, stack_num=2, is_train=True):
hidden_dim = 128

word = paddle.layer.data(
name='word',
name="word",
type=paddle.data_type.integer_value_sequence(word_dict_len))
word_embedding = paddle.layer.embedding(
input=word,
size=word_dim,
param_attr=paddle.attr.Param(
name='emb', initial_std=math.sqrt(1. / word_dim), is_static=True))
name="emb", initial_std=math.sqrt(1. / word_dim), is_static=True))

mark = paddle.layer.data(
name='mark',
name="mark",
type=paddle.data_type.integer_value_sequence(mark_dict_len))
mark_embedding = paddle.layer.embedding(
input=mark,
Expand All @@ -35,7 +35,8 @@ def ner_net(word_dict_len, label_dict_len, stack_num=2, is_train=True):
hidden_para_attr = paddle.attr.Param(
initial_std=1 / math.sqrt(hidden_dim), learning_rate=mix_hidden_lr)

# the first rnn layer shares the input-to-hidden mappings.
# the first forward and backward rnn layer share the
# input-to-hidden mappings.
hidden = paddle.layer.fc(
name="__hidden00__",
size=hidden_dim,
Expand Down Expand Up @@ -72,32 +73,40 @@ def ner_net(word_dict_len, label_dict_len, stack_num=2, is_train=True):
input=fea,
param_attr=[hidden_para_attr, rnn_para_attr] * 2)

# NOTE: This fully connected layer calculates the emission feature for
# the CRF layer. Because the paddle.layer.crf performs global normalization
# over all possible sequences internally, it expects UNSCALED emission
# feature weights.
# Please do not add any nonlinear activation to this fully connected layer.
# The default activation for paddle.layer.fc is the tanh, here needs to set
# it to linear explictly.
emission = paddle.layer.fc(
size=label_dict_len,
bias_attr=False,
input=rnn_fea,
act=paddle.activation.Linear(),
param_attr=rnn_para_attr)

if is_train:
target = paddle.layer.data(
name='target',
name="target",
type=paddle.data_type.integer_value_sequence(label_dict_len))

crf = paddle.layer.crf(
size=label_dict_len,
input=emission,
label=target,
param_attr=paddle.attr.Param(name='crfw', initial_std=1e-3))
param_attr=paddle.attr.Param(name="crfw", initial_std=1e-3))

crf_dec = paddle.layer.crf_decoding(
size=label_dict_len,
input=emission,
label=target,
param_attr=paddle.attr.Param(name='crfw'))
param_attr=paddle.attr.Param(name="crfw"))
return crf, crf_dec, target
else:
predict = paddle.layer.crf_decoding(
size=label_dict_len,
input=emission,
param_attr=paddle.attr.Param(name='crfw'))
param_attr=paddle.attr.Param(name="crfw"))
return predict
1 change: 1 addition & 0 deletions sequence_tagging_for_ner/train.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import os
import gzip
import numpy as np

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