/
vgg_train_val.py
173 lines (149 loc) · 7.87 KB
/
vgg_train_val.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
163
164
165
166
167
168
169
170
171
172
173
#coding=utf-8
import tensorflow as tf
import numpy as np
import pdb
import os
from datetime import datetime
import slim.nets.vgg as vgg
from create_tf_record import *
import tensorflow.contrib.slim as slim
labels_nums = 5 # 类别个数
batch_size = 16 #
resize_height = 224 # 指定存储图片高度
resize_width = 224 # 指定存储图片宽度
depths = 3
data_shape = [batch_size, resize_height, resize_width, depths]
# 定义input_images为图片数据
input_images = tf.placeholder(dtype=tf.float32, shape=[None, resize_height, resize_width, depths], name='input')
# 定义input_labels为labels数据
# input_labels = tf.placeholder(dtype=tf.int32, shape=[None], name='label')
input_labels = tf.placeholder(dtype=tf.int32, shape=[None, labels_nums], name='label')
keep_prob = tf.placeholder(tf.float32,name='keep_prob')
is_training = tf.placeholder(tf.bool, name='is_training')
def net_evaluation(sess,loss,accuracy,val_images_batch,val_labels_batch,val_nums):
val_max_steps = int(val_nums / batch_size)
val_losses = []
val_accs = []
for _ in xrange(val_max_steps):
val_x, val_y = sess.run([val_images_batch, val_labels_batch])
# print('labels:',val_y)
# val_loss = sess.run(loss, feed_dict={x: val_x, y: val_y, keep_prob: 1.0})
# val_acc = sess.run(accuracy,feed_dict={x: val_x, y: val_y, keep_prob: 1.0})
val_loss,val_acc = sess.run([loss,accuracy], feed_dict={input_images: val_x, input_labels: val_y, keep_prob:1.0, is_training: False})
val_losses.append(val_loss)
val_accs.append(val_acc)
mean_loss = np.array(val_losses, dtype=np.float32).mean()
mean_acc = np.array(val_accs, dtype=np.float32).mean()
return mean_loss, mean_acc
def train(train_record_file,
train_log_step,
train_param,
val_record_file,
val_log_step,
labels_nums,
data_shape,
snapshot,
snapshot_prefix):
'''
:param train_record_file: 训练的tfrecord文件
:param train_log_step: 显示训练过程log信息间隔
:param train_param: train参数
:param val_record_file: 验证的tfrecord文件
:param val_log_step: 显示验证过程log信息间隔
:param val_param: val参数
:param labels_nums: labels数
:param data_shape: 输入数据shape
:param snapshot: 保存模型间隔
:param snapshot_prefix: 保存模型文件的前缀名
:return:
'''
[base_lr,max_steps]=train_param
[batch_size,resize_height,resize_width,depths]=data_shape
# 获得训练和测试的样本数
train_nums=get_example_nums(train_record_file)
val_nums=get_example_nums(val_record_file)
print('train nums:%d,val nums:%d'%(train_nums,val_nums))
# 从record中读取图片和labels数据
# train数据,训练数据一般要求打乱顺序shuffle=True
train_images, train_labels = read_records(train_record_file, resize_height, resize_width, type='normalization')
train_images_batch, train_labels_batch = get_batch_images(train_images, train_labels,
batch_size=batch_size, labels_nums=labels_nums,
one_hot=True, shuffle=True)
# val数据,验证数据可以不需要打乱数据
val_images, val_labels = read_records(val_record_file, resize_height, resize_width, type='normalization')
val_images_batch, val_labels_batch = get_batch_images(val_images, val_labels,
batch_size=batch_size, labels_nums=labels_nums,
one_hot=True, shuffle=False)
# Define the model:
with slim.arg_scope(vgg.vgg_arg_scope()):
out, end_points = vgg.vgg_16(inputs=input_images, num_classes=labels_nums, dropout_keep_prob=keep_prob, is_training=is_training)
# Specify the loss function: tf.losses定义的loss函数都会自动添加到loss函数,不需要add_loss()了
tf.losses.softmax_cross_entropy(onehot_labels=input_labels, logits=out)#添加交叉熵损失loss=1.6
# slim.losses.add_loss(my_loss)
loss = tf.losses.get_total_loss(add_regularization_losses=True)#添加正则化损失loss=2.2
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(out, 1), tf.argmax(input_labels, 1)), tf.float32))
# Specify the optimization scheme:
optimizer = tf.train.GradientDescentOptimizer(learning_rate=base_lr)
# create_train_op that ensures that when we evaluate it to get the loss,
# the update_ops are done and the gradient updates are computed.
train_op = slim.learning.create_train_op(total_loss=loss,optimizer=optimizer)
# global_step = tf.Variable(0, trainable=False)
# learning_rate = tf.train.exponential_decay(0.05, global_step, 150, 0.9)
#
# optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9)
# # train_op = optimizer.minimize(loss, global_step)
# train_op = slim.learning.create_train_op(loss, optimizer,global_step=global_step)
saver = tf.train.Saver()
max_acc=0.0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
for i in range(max_steps+1):
batch_input_images, batch_input_labels = sess.run([train_images_batch, train_labels_batch])
_, train_loss = sess.run([train_op, loss], feed_dict={input_images:batch_input_images,
input_labels:batch_input_labels,
keep_prob:0.5, is_training:True})
# train测试(这里仅测试训练集的一个batch)
if i%train_log_step == 0:
train_acc = sess.run(accuracy, feed_dict={input_images:batch_input_images,
input_labels: batch_input_labels,
keep_prob:1.0, is_training: False})
print "%s: Step [%d] train Loss : %f, training accuracy : %g" % (datetime.now(), i, train_loss, train_acc)
# val测试(测试全部val数据)
if i%val_log_step == 0:
mean_loss, mean_acc=net_evaluation(sess, loss, accuracy, val_images_batch, val_labels_batch,val_nums)
print "%s: Step [%d] val Loss : %f, val accuracy : %g" % (datetime.now(), i, mean_loss, mean_acc)
# 模型保存:每迭代snapshot次或者最后一次保存模型
if (i %snapshot == 0 and i >0)or i == max_steps:
print('-----save:{}-{}'.format(snapshot_prefix,i))
saver.save(sess, snapshot_prefix, global_step=i)
# 保存val准确率最高的模型
if mean_acc>max_acc and mean_acc>0.5:
max_acc=mean_acc
path = os.path.dirname(snapshot_prefix)
best_models=os.path.join(path,'best_models_{}_{:.4f}.ckpt'.format(i,max_acc))
print('------save:{}'.format(best_models))
saver.save(sess, best_models)
coord.request_stop()
coord.join(threads)
if __name__ == '__main__':
train_record_file='dataset/record/train.tfrecords'
val_record_file='dataset/record/val.tfrecords'
train_log_step=100
base_lr = 0.01 # 学习率
max_steps = 10000 # 迭代次数
train_param=[base_lr,max_steps]
val_log_step=200
snapshot=2000#保存文件间隔
snapshot_prefix='models/model.ckpt'
train(train_record_file=train_record_file,
train_log_step=train_log_step,
train_param=train_param,
val_record_file=val_record_file,
val_log_step=val_log_step,
labels_nums=labels_nums,
data_shape=data_shape,
snapshot=snapshot,
snapshot_prefix=snapshot_prefix)