-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathML_Model.py
153 lines (111 loc) · 5.65 KB
/
ML_Model.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
######################## 机器学习基类 ##############################
# Author: 杨玉奇
# email: yangyuqi@sina.com
# url: https://github.com/jerryyyq/tf_algorithm_example
# copyright yangyuqi
# 著作权归作者 杨玉奇 所有。商业转载请联系作者获得授权，非商业转载请注明出处。
# date: 2017-09-12
###################################################################
import os
import tensorflow as tf
from common import *
class ML_Model:
####################### 构造与析构函数 #######################
def __init__(self):
self._sess = None
self.__x = tf.Variable(0., name = "x")
self.__save_path = os.path.join( '/tmp', self.__class__.__name__ + '.vari' )
def __del__(self):
if(self._sess):
self._sess.close()
####################### 学习与评价启动执行函数 #######################
# training_steps 实际训练迭代次数
# file_name: ['train_1.csv', 'train_2.csv']
def do_train(self, training_steps = 1000, train_file_name = [], train_batch_size = 10):
print( '-------------- do_train: start -----------------' )
self._sess = tf.Session()
self._sess.run( tf.initialize_all_variables() )
feature_batch, label_batch = self.inputs( train_file_name, train_batch_size )
train_op = self.train( feature_batch, label_batch )
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners( sess = self._sess, coord = coord )
try:
# while not coord.should_stop():
for step in range(training_steps): # 实际训练闭环
if coord.should_stop():
print('---- coord should stop ----')
break
self._sess.run(train_op)
# 查看训练过程损失递减
if step % 10 == 0:
echo_tensor( self._sess, feature_batch, 'features_' + str(step) )
echo_tensor( self._sess, label_batch, 'label_' + str(step) )
echo_tensor( self._sess, self._total_loss, 'step_' + str(step) + ' loss: ' )
#print( str(training_steps) + " final loss: ", sess.run([total_loss]) )
echo_tensor( self._sess, self._total_loss, 'training end. step_' + str(step) + ' final loss: ' )
saver = tf.train.Saver()
save_path = saver.save( self._sess, self.__save_path )
print('save_path is: ', save_path)
# 模型评估
evaluate_result = self.evaluate( feature_batch, label_batch )
echo_tensor( self._sess, evaluate_result, 'evaluate_result' )
except tf.errors.OutOfRangeError:
print( 'Done training -- epoch limit reached' )
finally:
coord.request_stop()
coord.join( threads )
self._sess.close()
print( '----------------- do_train: finish -----------------' )
# file_name: ['test_1.csv', 'test_2.csv']
def do_evaluate(self, file_name = [], batch_size = 10):
print( '-------------- do_evaluate: start -----------------\n' )
self._sess = tf.Session()
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state( '/tmp' )
if ckpt and ckpt.model_checkpoint_path:
# saver.restore( self._sess, ckpt.model_checkpoint_path )
saver.restore( self._sess, self.__save_path )
else:
print('not find ckpt file!!!!!')
test_features, test_label = self.inputs( file_name, batch_size )
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners( sess = self._sess, coord = coord )
self._sess.run([test_features, test_label])
accuracy_rate = self.evaluate( test_features, test_label )
echo_tensor( self._sess, accuracy_rate, 'accuracy_rate' )
coord.request_stop()
coord.join( threads )
self._sess.close()
print( '----------------- do_evaluate: finish -----------------\n' )
####################### 主流程函数 #######################
# 计算返回推断模型输出
def inference(self, features):
return features + self.__x
# 计算损失(训练数据 features 及 label)
def loss(self, features, label):
label_predicted = self.inference( features )
return label - label_predicted
# 读取或生成训练数据
# file_name: ['1.csv', '2.csv']
def inputs(self, file_name = [], batch_size = 10):
features = tf.constant(0.)
label = tf.constant(0.)
return features, label
# 训练
def train(self, feature_batch, label_batch):
self._total_loss = self.loss( feature_batch, label_batch )
# loss also is cost
learning_rate = 0.0000001
# return tf.train.Optimizer(False, name = 'sample').minimize( loss ) # 会产生： NotImplementedError 异常
return tf.train.GradientDescentOptimizer( learning_rate ).minimize( self._total_loss )
# 完成学习后，进行效果评估
def evaluate(self, test_features, test_label):
echo_tensor( self._sess, self.__x, 'At evaluate, the __x' )
label_predicted = tf.to_float( self.inference(test_features) )
different = (label_predicted - test_label) / test_label
echo_tensor( self._sess, test_label, 'test_label' )
echo_tensor( self._sess, label_predicted, 'label_predicted' )
return tf.reduce_mean(different)
if __name__ == '__main__':
one_ml = ML_Model()
one_ml.do_train( 10 )