forked from NanGC/CNN
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathTrain.py
108 lines (90 loc) · 3.63 KB
/
Train.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
'''
Train.py
对搭建好的网络进行训练，并保存训练参数，以便下次使用
2018/7/19实现文件调用
-------copyright@GCN-------
'''
# 导入文件
import os
import numpy as np
import tensorflow as tf
from PreWork import get_file, get_batch
from Model import inference, losses, trainning, evaluation
# 变量声明
N_CLASSES = 6
IMG_W = 28 # resize图像，太大的话训练时间久
IMG_H = 28
BATCH_SIZE = 20 # 每个batch要放多少张图片
CAPACITY = 200 # 一个队列最大多少
MAX_STEP = 10000 # 一般大于10K
learning_rate = 0.0001 # 一般小于0.0001
# 获取批次batch
train_dir = 'F:/Python/PycharmProjects/DeepLearning/CK+_part' # 训练样本的读入路径
logs_train_dir = 'F:/Python/PycharmProjects/DeepLearning/CK+_part' #logs存储路径
train, train_label = get_file(train_dir)
# 训练数据及标签
train_batch, train_label_batch = get_batch(train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
# 训练操作定义
train_logits = inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = losses(train_logits, train_label_batch)
train_op = trainning(train_loss, learning_rate)
train_acc = evaluation(train_logits, train_label_batch)
# 这个是log汇总记录
summary_op = tf.summary.merge_all()
# 产生一个会话
sess = tf.Session()
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
# 产生一个saver来存储训练好的模型
saver = tf.train.Saver()
# 所有节点初始化
sess.run(tf.global_variables_initializer())
# 队列监控
coord = tf.train.Coordinator() # 设置多线程协调器
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# 进行batch的训练
try:
# 执行MAX_STEP步的训练，一步一个batch
for step in np.arange(MAX_STEP):
if coord.should_stop():
break
# 启动以下操作节点，有个疑问，为什么train_logits在这里没有开启？
_, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc])
# 每隔50步打印一次当前的loss以及acc，同时记录log，写入writer
if step % 100 == 0:
print('Step %d, train loss = %.2f, train accuracy = %.2f%%' % (step, tra_loss, tra_acc * 100.0))
summary_str = sess.run(summary_op)
train_writer.add_summary(summary_str, step)
checkpoint_path = os.path.join(logs_train_dir, 'thing.ckpt')
saver.save(sess, checkpoint_path)
'''
# 每隔100步，保存一次训练好的模型
if (step + 1) == MAX_STEP:
checkpoint_path = os.path.join(logs_train_dir, 'thing.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
'''
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
coord.request_stop()
coord.join(threads)
sess.close()
'''
y_conv = deep_CNN(train_batch)
# 定义损失函数
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=train_label_batch, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# 训练验证
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(train_label_batch, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) # 数据类型转换
print("start train")
sess = tf.Session()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
_, train_loss, train_accuracy = sess.run([train_step, correct_prediction, accuracy])
for i in range(1000):
if i % 100 == 0:
print('step:%d, training accuracy %g' % (i, train_accuracy))
saver.save(sess, "input_data/some")
'''