tensorflow-影子变量值
#!/usr/bin/env python2 # -*- coding: utf-8 -*- import tensorflow as tf my_var=tf.Variable(0.) step=tf.Variable(0,trainable=False) ema=tf.train.ExponentialMovingAverage(0.99,step) maintain_average_op=ema.apply([my_var]) with tf.Session() as sess: init_op=tf.global_variables_initializer() sess.run(init_op) decay=0.99 #影子变量值变化 for i in range(1,6): print sess.run([my_var,ema.average(my_var)]) sess.run(my_var.assign_add(i)) sess.run(maintain_average_op) print sess.run([my_var,ema.average(my_var)]) print "===" print "----------------" #num_updates即step变化 sess.run(my_var.assign(5.)) for i in range(1,20,3): print sess.run([my_var,ema.average(my_var)]) sess.run(step.assign_add(i)) sess.run(maintain_average_op) print sess.run([my_var,ema.average(my_var)]) print "==="
滑动平均模型 shadow_variable= decay shadow_variable + (1 - decay) variable Reasonable values for?decay?are close to 1.0,typically in themultiple-nines range: 0.999,0.9999,etc. The?apply()?methodadds shadow copies of trained variables and add ops that maintain a movingaverage of the trained variables in their shadow copies. It is used whenbuilding the training model.? The optional?num_updates?parameter allows one to tweak thedecay rate dynamically. It is typical to pass the count of training steps,usually kept in a variable that is incremented at each step,in which case thedecay rate is lower at the start of training. This makes moving averages movefaster. If passed,the actual decay rate used is: min(decay,(1 +num_updates) / (10 + num_updates)) （编辑：李大同） 【声明】本站内容均来自网络，其相关言论仅代表作者个人观点，不代表本站立场。若无意侵犯到您的权利，请及时与联系站长删除相关内容! |