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cifar10_Vgg16_Cons-Def_train.py
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cifar10_Vgg16_Cons-Def_train.py
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"""
This file used to train a model using augmented images with TensorFlow.
The augmentations are processed in the function file data_pertprep.py and they are loaded in memory
Revised from the work of PanJinquan: https://github.com/PanJinquan/tensorflow_models_learning
Xintao Ding
School of Computer and Information, Anhui Normal University
xintaoding@163.com
"""#coding=utf-8
import tensorflow as tf
import numpy as np
import os
from datetime import datetime
import slim.nets.vgg160 as vgg
from cifar10_create_tf_record import get_example_nums, read_records, get_batch_images
import tensorflow.contrib.slim as slim
labels_nums = 10 # the number of labels
batch_size = 90 #
resize_height = 32 # Cifar10 size
resize_width = 32
net_height= 160#the size must be suitable with layer in vgg (net, 4096, [5, 5], padding=fc_conv_padding, scope='fc6'), 224*224 corresponds to [7, 7], 160*160 correpsonds to [5, 5],
net_width = 160#vgg16 size
depths = 3
data_shape = [batch_size, resize_height, resize_width, depths]
#input_images = tf.placeholder(dtype=tf.float32, shape=[None, resize_height, resize_width, depths], name='input')
input_images = tf.placeholder(dtype=tf.float32, shape=[None, net_height, net_width, depths], name='input')
# 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 = 0.5
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 range(val_max_steps):
val_x, val_y = sess.run([val_images_batch, val_labels_batch])
# print('labels:',val_y)
# val_loss,val_acc = sess.run([loss,accuracy], feed_dict={input_images: val_x, input_labels: val_y, keep_prob:keep_prob, is_training: False})
val_loss,val_acc = sess.run([loss,accuracy], feed_dict={input_images: val_x, input_labels: val_y, 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 = 0
for i in range(len(train_record_file)):
train_nums = train_nums+get_example_nums(train_record_file[i])
for i in range(len(val_record_file)):
val_nums=get_example_nums(val_record_file[i])
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, depths,
type='normalization', crop_flip=True)
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)
train_images_batch = tf.image.resize_images(train_images_batch,size=(net_height, net_width))
# 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, depths, 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)
val_images_batch = tf.image.resize_images(val_images_batch,size=(net_height, net_width))
# Define the model:
with slim.arg_scope(vgg.vgg_arg_scope()):
# out, end_points = vgg.vgg_16(inputs=input_images, num_classes=labels_nums, keep_prob=keep_prob, is_training=is_training)
out, end_points = vgg.vgg_16(inputs=input_images, num_classes=labels_nums, 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)
train_op = tf.train.AdadeltaOptimizer(learning_rate=base_lr).minimize(loss)#revised from GradientDescentOptimizer######
# global_step = tf.Variable(0, trainable=False)
# learning_rate = tf.train.exponential_decay(0.045, global_step, int(train_nums/batch_size*2),0.94, staircase=True)
# add_global=global_step.assign_add(1)
#
# optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
# # train_op = optimizer.minimize(loss, global_step)
# update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
# with tf.control_dependencies(update_ops):
# train_op = slim.learning.create_train_op(total_loss=loss, optimizer=optimizer,global_step=global_step,clip_gradient_norm=2.)
# boundaries = [200, 400]
# lrs = [0.01, 0.001, 0.0001]
# global_step = tf.Variable(0, trainable=False)
# add_global=global_step.assign_add(1)
# learning_rate=tf.train.piecewise_constant(global_step, boundaries=boundaries, values=lrs)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
# train_op = slim.learning.create_train_op(total_loss=loss, optimizer=optimizer,global_step=global_step,clip_gradient_norm=2.)
saver = tf.train.Saver(max_to_keep=100)
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,
is_training:True})
# keep_prob:keep_prob, 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:keep_prob, is_training: False})
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>0.8:
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=['cifar10_extensions/cifar10_train_aug64.tfrecords_seg0',
'cifar10_extensions/cifar10_train_aug64.tfrecords_seg1',
'cifar10_extensions/cifar10_train_aug64.tfrecords_seg2',
'cifar10_extensions/cifar10_train_aug64.tfrecords_seg3',
'cifar10_extensions/cifar10_train_aug64.tfrecords_seg4',
'cifar10_extensions/cifar10_train_aug64.tfrecords_seg5',
'cifar10_extensions/cifar10_train_aug64.tfrecords_seg6',
'cifar10_extensions/cifar10_train_aug64.tfrecords_seg7',
'cifar10_extensions/cifar10_train_aug64.tfrecords_seg8',
'cifar10_extensions/cifar10_train_aug64.tfrecords_seg9']
# train_record_file=['cifar10_extensions/cifar10_train.tfrecords_seg']
val_record_file=['cifar10_extensions/cifar10_test.tfrecords_seg']
train_log_step=200
base_lr = 0.1 # 学习率
max_steps = 2540000 # 迭代次数
train_param=[base_lr,max_steps]
val_log_step=500
snapshot=20000#保存文件间隔
snapshot_prefix='cifar10_extensions/vgg16/cifar_vgg16_aug160'
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)