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nerApp.py
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nerApp.py
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# coding: utf-8
import tensorflow as tf
from tensorflow.contrib import crf
import random
from nerUtils import *
import logging
import datetime
from BiLSTM_CRF import BiLSTM_CRF
debug = False
batch_size =100
class nerAppication:
#参数
def __init__(self,sess,device='/gpu:1'):
with sess.as_default():
with sess.graph.as_default():
self.dataGen = DATAPROCESS(train_data_path="./data_ai/nerData/train_cutword_data.txt",
train_label_path="./data_ai/nerData/label_cutword_data.txt",
test_data_path="./data_ai/nerData/test_data.txt",
test_label_path="./data_ai/nerData/test_label.txt",
word_embedings_path="./data_ai/cbowData/document.txt.ebd.npy",
vocb_path="./data_ai/cbowData/document.txt.vab",
batch_size=batch_size
)
self.dataGen.load_wordebedding()
self.tag_nums =13 # 标签数目
self.hidden_nums = 650 # bi-lstm的隐藏层单元数目
self.sentence_len = self.dataGen.sentence_length # 句子长度,输入到网络的序列长度
self.model_checkpoint_path ="./data_ai/nerModel/"
self.model = BiLSTM_CRF(
batch_size = batch_size,
tag_nums=self.tag_nums,
hidden_nums = self.hidden_nums,
sentence_len = self.sentence_len,
word_embeddings=self.dataGen.word_embeddings,
device = device
)
self.saver = tf.train.Saver(max_to_keep=1)
ckpt = tf.train.get_checkpoint_state(self.model_checkpoint_path)
if ckpt and ckpt.model_checkpoint_path:
self.saver.restore(sess,ckpt.model_checkpoint_path)
logging.info("model loading successful")
def nerApp(self,sess):
with sess.as_default():
with sess.graph.as_default():
text = "application"
while(text!="" and text!=" "):
text=input("请输入一句话：")
if text == "quit" or text=="" or text == " ":break
data_line,data_x,efficient_sequence_length=self.dataGen.handleInputData(text)
if debug:
print(np.array(data_x).shape)
print(data_x)
print(np.array(efficient_sequence_length).shape)
feed_dict={self.model.input_x:data_x,
self.model.sequence_lengths:efficient_sequence_length,
self.model.dropout_keep_prob:1
}
predict_labels = sess.run([self.model.crf_labels],feed_dict)#predict_labels是三维的[1,1,25]，第1维包含了一个矩阵
lable_line =[]
if debug:
print(type(predict_labels))
print(predict_labels)
print(np.array(predict_labels).shape)
for idx in range(len(predict_labels[0])):
_label = predict_labels[0][idx].reshape(1,-1)
lable_line.append( list(_label[0]))
for idx in range(len(data_line)):
for each in range(efficient_sequence_length[idx]):
print("%s:%s"%(data_line[idx][each],lable_line[idx][each]),end=" ")
print('\n')
def questionNer(self,sess,text):
with sess.as_default():
with sess.graph.as_default():
if text== " ":
print("文本为空，错误")
return
data_line,data_x,efficient_sequence_length=self.dataGen.handleInputData(text)
feed_dict={self.model.input_x:data_x,
self.model.sequence_lengths:efficient_sequence_length,
self.model.dropout_keep_prob:1 }
predict_labels = sess.run([self.model.crf_labels],feed_dict)#predict_labels是三维的[1,1,25]，第1维包含了一个矩阵
lable_line =[]
for idx in range(len(predict_labels[0])):
_label = predict_labels[0][idx].reshape(1,-1)
lable_line.append( list(_label[0]))
return data_line,lable_line,efficient_sequence_length
if __name__=="__main__":
graph = tf.Graph()
log_device_placement = True # 是否打印设备分配日志
allow_soft_placement = True # 如果你指定的设备不存在，允许TF自动分配设备
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
session_conf = tf.ConfigProto(gpu_options=gpu_options,allow_soft_placement= allow_soft_placement,log_device_placement= log_device_placement)
sess = tf.Session(graph=graph,config=session_conf)
app = nerAppication(sess)
text="我发烧流鼻涕怎么办"
while(text!="" and text!=" "):
text=input("请输入一句话：")
if text == "quit" or text=="" or text == " ":break
data_line,lable_line,efficient_sequence_length = app.questionNer(sess,text)
for idx in range(len(data_line)):
for each in range(efficient_sequence_length[idx]):
print("%s:%s"%(data_line[idx][each],lable_line[idx][each]),end=" ")
print('\n')