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Commitc7dce73

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Merge remote-tracking branch 'origin/emr' into emr
2 parents145e2d7 +dfd4f58 commitc7dce73

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6 files changed

+412
-42
lines changed

6 files changed

+412
-42
lines changed

‎python/dnlp/core/re_cnn.py‎

Lines changed: 148 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -1,16 +1,158 @@
1-
#-*- coding: UTF-8 -*-
1+
#-*- coding: UTF-8 -*-
22
importtensorflowastf
3+
importnumpyasnp
4+
importpickle
35
fromdnlp.core.re_cnn_baseimportRECNNBase
46
fromdnlp.configimportRECNNConfig
7+
fromdnlp.utils.constantimportBATCH_PAD,BATCH_PAD_VAL
8+
59

610
classRECNN(RECNNBase):
7-
def__init__(self,config:RECNNConfig,dtype:type=tf.float32,dict_path:str='',mode:str='train'):
8-
RECNNBase.__init__(self,config)
11+
def__init__(self,config:RECNNConfig,dtype:type=tf.float32,dict_path:str='',mode:str='train',
12+
data_path:str='',relation_count:int=2):
13+
RECNNBase.__init__(self,config,dict_path)
914
self.dtype=dtype
1015
self.mode=mode
11-
self.dictionary=self.read_dictionary(dict_path)
16+
self.data_path=data_path
17+
self.relation_count=relation_count
18+
19+
self.concat_embed_size=self.word_embed_size+2*self.position_embed_size
20+
self.input_characters=tf.placeholder(tf.int32, [None,self.batch_length])
21+
self.input_position=tf.placeholder(tf.int32, [None,self.batch_length])
22+
self.input=tf.placeholder(self.dtype, [None,self.batch_length,self.concat_embed_size,1])
23+
self.input_relation=tf.placeholder(self.dtype, [None,self.relation_count])
24+
self.position_embedding=self.__weight_variable([2*self.batch_length-1,self.position_embed_size],
25+
name='position_embedding')
26+
self.word_embedding=self.__weight_variable([self.words_size,self.word_embed_size],name='word_embedding')
27+
self.conv_kernel=self.get_conv_kernel()
28+
self.bias= [self.__weight_variable([self.filter_size],name='conv_bias')]*len(self.window_size)
29+
self.full_connected_weight=self.__weight_variable([self.filter_size*len(self.window_size),self.relation_count],
30+
name='full_connected_weight')
31+
self.full_connected_bias=self.__weight_variable([self.relation_count],name='full_connected_bias')
32+
self.position_lookup=tf.nn.embedding_lookup(self.position_embedding,self.input_position)
33+
self.character_lookup=tf.nn.embedding_lookup(self.word_embedding,self.input_characters)
34+
self.character_embed_holder=tf.placeholder(self.dtype,
35+
[None,self.batch_length,self.word_embed_size])
36+
self.primary_embed_holder=tf.placeholder(self.dtype,
37+
[None,self.batch_length,self.position_embed_size])
38+
self.secondary_embed_holder=tf.placeholder(self.dtype,
39+
[None,self.batch_length,self.position_embed_size])
40+
self.emebd_concat=tf.expand_dims(
41+
tf.concat([self.character_embed_holder,self.primary_embed_holder,self.secondary_embed_holder],2),3)
42+
ifself.mode=='train':
43+
self.hidden_layer=tf.layers.dropout(self.get_hidden(),self.dropout_rate)
44+
self.words,self.primary,self.secondary,self.labels=self.load_data()
45+
self.start=0
46+
self.data_count=len(self.words)
47+
else:
48+
self.hidden_layer=tf.expand_dims(tf.layers.dropout(self.get_hidden(),self.dropout_rate),0)
49+
self.output_no_softmax=tf.matmul(self.hidden_layer,self.full_connected_weight)+self.full_connected_bias
50+
self.output=tf.nn.softmax(tf.matmul(self.hidden_layer,self.full_connected_weight)+self.full_connected_bias)
51+
self.params= [self.position_embedding,self.word_embedding,self.full_connected_weight,
52+
self.full_connected_bias]+self.conv_kernel+self.bias
53+
self.regularization=tf.contrib.layers.apply_regularization(tf.contrib.layers.l2_regularizer(self.lam),
54+
self.params)
55+
self.loss=tf.reduce_sum(tf.square(self.output-self.input_relation))/self.batch_size+self.regularization
56+
self.cross_entropy=tf.nn.softmax_cross_entropy_with_logits(labels=self.input_relation,
57+
logits=self.output_no_softmax)+self.regularization
58+
self.optimizer=tf.train.GradientDescentOptimizer(self.learning_rate)
59+
# self.optimizer = tf.train.AdagradOptimizer(self.learning_rate)
60+
self.train_model=self.optimizer.minimize(self.loss)
61+
self.train_cross_entropy_model=self.optimizer.minimize(self.cross_entropy)
62+
self.saver=tf.train.Saver(max_to_keep=100)
63+
64+
defget_conv_kernel(self):
65+
conv_kernel= []
66+
forwinself.window_size:
67+
conv_kernel.append(self.__weight_variable([w,self.concat_embed_size,1,self.filter_size],name='conv_kernel'))
68+
returnconv_kernel
69+
70+
defget_max_pooling(self,x):
71+
max_pooling= []
72+
forwinself.window_size:
73+
max_pooling.append(self.max_pooling(x,w))
74+
returnmax_pooling
75+
76+
defget_hidden(self):
77+
h=None
78+
forw,conv,biasinzip(self.window_size,self.conv_kernel,self.bias):
79+
ifhisNone:
80+
h=tf.squeeze(self.max_pooling(tf.nn.relu(self.conv(conv)+bias),w))
81+
else:
82+
hh=tf.squeeze(self.max_pooling(tf.nn.relu(self.conv(conv)+bias),w))
83+
ifself.mode=='train':
84+
h=tf.concat([h,hh],1)
85+
else:
86+
h=tf.concat([h,hh],0)
87+
returnh
88+
89+
defconv(self,conv_kernel):
90+
returntf.nn.conv2d(self.input,conv_kernel,strides=[1,1,1,1],padding='VALID')
91+
92+
defmax_pooling(self,x,window_size):
93+
returntf.nn.max_pool(x,ksize=[1,self.batch_length-window_size+1,1,1],
94+
strides=[1,1,1,1],padding='VALID')
95+
96+
deffit(self,epochs=100,interval=20):
97+
withtf.Session()assess:
98+
tf.global_variables_initializer().run()
99+
sess.graph.finalize()
100+
foriinrange(1,epochs+1):
101+
print('epoch:'+str(i))
102+
for_inrange(self.data_count//self.batch_size):
103+
words,primary,secondary,labels=self.load_batch()
104+
character_embeds,primary_embeds=sess.run([self.character_lookup,self.position_lookup],
105+
feed_dict={self.input_characters:words,
106+
self.input_position:primary})
107+
secondary_embeds=sess.run(self.position_lookup,feed_dict={self.input_position:secondary})
108+
input=sess.run(self.emebd_concat,feed_dict={self.character_embed_holder:character_embeds,
109+
self.primary_embed_holder:primary_embeds,
110+
self.secondary_embed_holder:secondary_embeds})
111+
# sess.run(self.train_model, feed_dict={self.input: input, self.input_relation: batch['label']})
112+
sess.run(self.train_cross_entropy_model,feed_dict={self.input:input,self.input_relation:labels})
113+
ifi%interval==0:
114+
model_name='../dnlp/models/re/{0}-{1}.ckpt'.format(i,'_'.join(map(str,self.window_size)))
115+
self.saver.save(sess,model_name)
116+
117+
defload_batch(self):
118+
ifself.start+self.batch_size>self.data_count:
119+
new_start=self.start+self.batch_size-self.data_count
120+
words=np.concatenate([self.words[self.start:],self.words[:new_start]])
121+
primary=np.concatenate([self.primary[self.start:],self.primary[:new_start]])
122+
secondary=np.concatenate([self.secondary[self.start:],self.secondary[:new_start]])
123+
labels=np.concatenate([self.labels[self.start:],self.labels[:new_start]])
124+
self.start=new_start
125+
else:
126+
new_start=self.start+self.batch_size
127+
words=self.words[self.start:new_start]
128+
primary=self.primary[self.start:new_start]
129+
secondary=self.secondary[self.start:new_start]
130+
labels=self.labels[self.start:new_start]
131+
self.start=new_start
132+
returnwords,primary,secondary,labels
12133

134+
defload_data(self):
135+
primary= []
136+
secondary= []
137+
words= []
138+
labels= []
139+
withopen(self.data_path,'rb')asf:
140+
data=pickle.load(f)
141+
forsentenceindata:
142+
sentence_words=sentence['words']
143+
iflen(sentence_words)<self.batch_length:
144+
sentence_words+= [self.dictionary[BATCH_PAD]]* (self.batch_length-len(sentence_words))
145+
else:
146+
sentence_words=sentence_words[:self.batch_length]
147+
words.append(sentence_words)
148+
primary.append(np.arange(self.batch_length)-sentence['primary']+self.batch_length-1)
149+
secondary.append(np.arange(self.batch_length)-sentence['secondary']+self.batch_length-1)
150+
sentence_labels=np.zeros([self.relation_count])
151+
sentence_labels[sentence['type']]=1
152+
labels.append(sentence_labels)
153+
returnnp.array(words,np.int32),np.array(primary,np.int32),np.array(secondary,np.int32),np.array(labels,
154+
np.float32)
13155

14-
def__weight_variable(self,shape,name):
156+
def__weight_variable(self,shape,name):
15157
initial=tf.truncated_normal(shape,stddev=0.1,dtype=self.dtype)
16-
returntf.Variable(initial,name=name)
158+
returntf.Variable(initial,name=name)

‎python/dnlp/core/re_cnn_base.py‎

Lines changed: 3 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,7 +1,7 @@
11
# -*- coding:utf-8 -*-
22
fromdnlp.configimportRECNNConfig
33
classRECNNBase(object):
4-
def__init__(self,config:RECNNConfig):
4+
def__init__(self,config:RECNNConfig,dict_path:str):
55
self.window_size=config.window_size
66
self.filter_size=config.filter_size
77
self.learning_rate=config.learning_rate
@@ -11,6 +11,8 @@ def __init__(self, config:RECNNConfig):
1111
self.position_embed_size=config.position_embed_size
1212
self.batch_length=config.batch_length
1313
self.batch_size=config.batch_size
14+
self.dictionary=self.read_dictionary(dict_path)
15+
self.words_size=len(self.dictionary)
1416

1517
defread_dictionary(self,dict_path):
1618
withopen(dict_path,encoding='utf-8')asf:

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