tensorflow学习笔记（二十二）：Supervisor

variables
...
ops
...
summary_op
...
merge_all_summarie
saver
init_op

with tf.Session() as sess:
  writer = tf.tf.train.SummaryWriter()
  sess.run(init)
  saver.restore()
  for ...:
    train
    merged_summary = sess.run(merge_all_summarie)
    writer.add_summary(merged_summary,i)
  saver.save

import tensorflow as tf
a = tf.Variable(1)
b = tf.Variable(2)
c = tf.add(a,b)
update = tf.assign(a,c)
tf.scalar_summary("a",a)
init_op = tf.initialize_all_variables()
merged_summary_op = tf.merge_all_summaries()
sv = tf.train.Supervisor(logdir="/home/keith/tmp/",init_op=init_op) #logdir用来保存checkpoint和summary
saver=sv.saver #创建saver
with sv.managed_session() as sess: #会自动去logdir中去找checkpoint，如果没有的话，自动执行初始化
    for i in xrange(1000):
        update_ = sess.run(update)
        print update_
        if i % 10 == 0:
            merged_summary = sess.run(merged_summary_op)
            sv.summary_computed(sess, merged_summary,global_step=i)
        if i%100 == 0:
            saver.save(sess,logdir="/home/keith/tmp/",global_step=i)