-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathexecute.py
162 lines (136 loc) · 7.8 KB
/
execute.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
# coding=utf-8
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import logging
import datetime
import time
import tensorflow as tf
import operator
from Med_data_helper import load_train_data, load_test_data, load_embedding, batch_iter
from polymerization import LSTM_QA
#------------------------- define parameter -----------------------------
# 第一个是参数名称，第二个参数是默认值，第三个是参数描述
tf.flags.DEFINE_string("train_file", "../train_file.csv", "train corpus file")
tf.flags.DEFINE_string("test_file", "../testFile.csv", "test corpus file")
tf.flags.DEFINE_string("valid_file", "../valid_File.csv", "test corpus file")
tf.flags.DEFINE_string("embedding_file", "../dic4.txt", "embedding file")
tf.flags.DEFINE_integer("embedding_size", 100, "embedding size")
tf.flags.DEFINE_float("dropout", 1, "the proportion of dropout")
tf.flags.DEFINE_float("lr", 0.1, "the proportion of dropout")
tf.flags.DEFINE_integer("batch_size", 100, "batch size of each batch") # 100调整到30
tf.flags.DEFINE_integer("epoches", 300, "epoches")
tf.flags.DEFINE_integer("rnn_size", 300, "embedding size")
tf.flags.DEFINE_integer("num_rnn_layers", 1, "embedding size")
tf.flags.DEFINE_integer("evaluate_every", 1000, "run evaluation")
tf.flags.DEFINE_integer("num_unroll_steps", 100, "embedding size") # 句子截取字的个数
tf.flags.DEFINE_integer("max_grad_norm", 5, "embedding size")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", True, "Log placement of ops on devices")
tf.flags.DEFINE_float("gpu_options", 0.9, "use memory rate")
FLAGS = tf.flags.FLAGS
#----------------------------- define parameter end ----------------------------------
#----------------------------- define a logger -------------------------------
logger = logging.getLogger("execute")
logger.setLevel(logging.INFO)
fh = logging.FileHandler("./run.log", mode="w")
fh.setLevel(logging.INFO)
fmt = "%(asctime)-15s %(levelname)s %(filename)s %(lineno)d %(process)d %(message)s"
datefmt = "%a %d %b %Y %H:%M:%S"
formatter = logging.Formatter(fmt, datefmt)
fh.setFormatter(formatter)
logger.addHandler(fh)
#----------------------------- define a logger end ----------------------------------
#------------------------------------load data -------------------------------
embedding, word2idx, idx2word = load_embedding(FLAGS.embedding_file, FLAGS.embedding_size)
ori_quests, cand_quests = load_train_data(FLAGS.train_file, word2idx, FLAGS.num_unroll_steps)
test_ori_quests, test_cand_quests, labels, results = load_test_data(FLAGS.test_file, word2idx, FLAGS.num_unroll_steps)
valid_ori_quests, valid_cand_quests, valid_labels, valid_results = load_test_data(FLAGS.valid_file, word2idx, FLAGS.num_unroll_steps)
#----------------------------------- load data end ----------------------
#----------------------------------- execute train model ---------------------------------
def run_step(sess, ori_batch, cand_batch, neg_batch, lstm, dropout=1.):
start_time = time.time()
feed_dict = {
lstm.ori_input_quests : ori_batch,
lstm.cand_input_quests : cand_batch,
lstm.neg_input_quests : neg_batch,
lstm.keep_prob : dropout
}
_, step, ori_cand_score, ori_neg_score, cur_loss, cur_acc = sess.run([train_op, global_step, lstm.ori_cand, lstm.ori_neg, lstm.loss, lstm.acc], feed_dict)
time_str = datetime.datetime.now().isoformat()
right, wrong, score = [0.0] * 3
for i in range(0 ,len(ori_batch)):
if ori_cand_score[i] > 0.55 and ori_neg_score[i] < 0.4:
right += 1.0
else:
wrong += 1.0
score += ori_cand_score[i] - ori_neg_score[i]
time_elapsed = time.time() - start_time
logger.info("%s: step %s, loss %s, acc %s, score %s, wrong %s, %6.7f secs/batch"%(time_str, step, cur_loss, cur_acc, score, wrong, time_elapsed))
return cur_loss, ori_cand_score
def valid_run_step(sess, ori_batch, cand_batch, lstm, dropout=1.):
feed_dict = {
lstm.test_input_q: ori_batch,
lstm.test_input_a: cand_batch,
lstm.keep_prob : dropout
}
step, ori_cand_score = sess.run([global_step, lstm.test_q_a], feed_dict)
return ori_cand_score
#---------------------------------- execute train model end --------------------------------------
def cal_acc(labels, results, total_ori_cand):
if len(labels) == len(results) == len(total_ori_cand):
retdict = {}
for label, result, ori_cand in zip(labels, results, total_ori_cand):
if result not in retdict:
retdict[result] = []
retdict[result].append((ori_cand, label))
correct = 0
for key, value in retdict.items():
value.sort(key=operator.itemgetter(0), reverse=True)
score, flag = value[0]
if flag == 1:
correct += 1
return 1. * correct/len(retdict)
else:
logger.info("data error")
return 0
#---------------------------------- execute valid model ------------------------------------------
def valid_model(sess, lstm, valid_ori_quests, valid_cand_quests, labels, results):
logger.info("start to validate model")
total_ori_cand = []
for ori_valid, cand_valid, neg_valid in batch_iter(valid_ori_quests, valid_cand_quests, FLAGS.batch_size, 1, is_valid=True):
ori_cand = valid_run_step(sess, ori_valid, cand_valid, lstm)
total_ori_cand.extend(ori_cand)
data_len = len(total_ori_cand)
acc = cal_acc(labels[:data_len], results[:data_len], total_ori_cand)
timestr = datetime.datetime.now().isoformat()
logger.info("%s, evaluation acc:%s"%(timestr, acc))
#---------------------------------- execute valid model end --------------------------------------
#----------------------------------- begin to train -----------------------------------
with tf.Graph().as_default():
with tf.device("/gpu:2"):
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu_options)
session_conf = tf.ConfigProto(allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement, gpu_options=gpu_options)
with tf.Session(config=session_conf).as_default() as sess:
lstm = LSTM_QA(FLAGS.batch_size, FLAGS.num_unroll_steps, embedding, FLAGS.embedding_size, FLAGS.rnn_size, FLAGS.num_rnn_layers, FLAGS.max_grad_norm)
global_step = tf.Variable(0, name="globle_step",trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(lstm.loss, tvars),
FLAGS.max_grad_norm)
#optimizer = tf.train.GradientDescentOptimizer(lstm.lr)
optimizer = tf.train.GradientDescentOptimizer(1e-1)
optimizer.apply_gradients(zip(grads, tvars))
train_op=optimizer.apply_gradients(zip(grads, tvars), global_step=global_step)
sess.run(tf.initialize_all_variables())
for epoch in range(FLAGS.epoches):
# cur_lr = FLAGS.lr / (epoch + 1)
# lstm.assign_new_lr(sess, cur_lr)
# logger.info("current learning ratio:" + str(cur_lr))
for ori_train, cand_train, neg_train in batch_iter(ori_quests, cand_quests, FLAGS.batch_size,
epoches=1):
run_step(sess, ori_train, cand_train, neg_train, lstm)
cur_step = tf.train.global_step(sess, global_step)
if cur_step % FLAGS.evaluate_every == 0 and cur_step != 0:
valid_model(sess, lstm, valid_ori_quests, valid_cand_quests, valid_labels, valid_results)
valid_model(sess, lstm, test_ori_quests, test_cand_quests, labels, results)
#---------------------------------- end train -----------------------------------