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utest.py
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#! /user/bin/evn python
# -*- coding:utf8 -*-
"""
@Author : Lau James
@Contact : LauJames2017@whu.edu.cn
@Project : Tagging
@File : utest.py
@Time : 2018/3/2 16:25
@Software : PyCharm
@Copyright: "Copyright (c) 2018 Lau James. All Rights Reserved"
"""
import re
import time
import datetime
import pickle
import numpy as np
import tensorflow as tf
from model.BiLSTM import BiLSTM
def get_time_dif(start_time):
"""获取已使用时间"""
end_time = time.time()
time_dif = end_time - start_time
return datetime.timedelta(seconds=int(round(time_dif)))
def x_padding(ids, max_len=32):
"""
:param words:
:return: ids
"""
if len(ids) >= max_len: # if it is longer , cut it off
return ids[:max_len]
ids.extend([0] * (max_len-len(ids))) # if it is short, filled it
return ids
def word2id(word):
with open('./data/data.pkl', 'rb') as inp:
x = pickle.load(inp)
y = pickle.load(inp)
word2id = pickle.load(inp)
id2word = pickle.load(inp)
tag2id = pickle.load(inp)
id2tag = pickle.load(inp)
return word2id[word]
def id2tag(id):
with open('./data/data.pkl', 'rb') as inp:
x = pickle.load(inp)
y = pickle.load(inp)
word2id = pickle.load(inp)
id2word = pickle.load(inp)
tag2id = pickle.load(inp)
id2tag = pickle.load(inp)
return id2tag[id]
def reorganized_data(sentence):
sentences = re.split(u'[，。！？、‘’“”]', sentence)
words = list()
for sentence in sentences:
print(sentence + '\n')
return sentences
if __name__ == "__main__":
"""
tags = ['s', 'b', 'e', 's', 's', 'b', 'm', 'e']
words = ['他', '今', '天', '吃', '了', '马', '铃', '薯']
rss = ''
for i in range(len(tags)):
if tags[i] in ['s', 'e']:
rss = rss + words[i] + ' '
else:
rss = rss + words[i]
print(rss)
"""
start_time = time.time()
model = BiLSTM(
timestep_size=32,
tag_class=5,
vocab_size=5159,
embedding_dim=256,
num_layers=2,
hidden_dim=128,
learning_rate=0.001
)
session = tf.Session()
session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(session, save_path='checkpoints/BiLSTM/best_validation')
test = '人们常说，生活是一部教科书。'
x_test_raw = reorganized_data(test)
textID = list()
for text in x_test_raw:
if text:
text_len = len(text)
words = list()
for word in text:
textID.append(word2id(word))
words.append(word)
textID = np.asarray(x_padding(textID)).reshape(-1, 32)
predict = session.run(model.y_pred, feed_dict={
model.input_x: textID,
model.dropout_keep_prob: 1.0
})
textID = list()
predict_tags = np.argmax(predict, axis=1)[:text_len] # padding部分直接丢弃
tags = list()
for id in predict_tags:
tags.append(id2tag(id))
rss = ''
for i in range(len(tags)):
if tags[i] in ['s', 'e']:
rss = rss + words[i] + ' '
else:
rss = rss + words[i]
print(rss)