tf.control_dependencies的一点理解

先看一段代码：来源：https://blog.csdn.net/*new/article/details/80611165

import tensorflow as tf

a = tf.Variable(2)
selfAdd = tf.Variable(0)
# selfAddition = tf.assign_add(selfAdd, 3)
selfSub = tf.Variable(0)
# selfSubtraction1 = tf.assign_sub(  selfSub , 2  )
# print('op 1：', selfSubtraction1 )

with  tf.control_dependencies([tf.assign_add(selfAdd, 3)]):
    tf.assign_sub(selfSub, 2)
    selfSubtraction = tf.no_op()#tf.assign_sub(selfSub, 2)
    print('op：', selfSubtraction)
with tf.Session() as sess:
    init = tf.global_variables_initializer()
    sess.run(init)
    for i in range(20):
        sess.run(selfSubtraction)
        print("selfAdd:", sess.run(selfAdd))
        print('selfSub:', sess.run(selfSub))
    ra = sess.run(selfAdd)
    print('@end selfAdd:', ra)
    rs = sess.run(selfSub)
    print('@end selfSub:', rs)

控制依赖关系主要用来解决某些操作在执行（sess.run()）的时候无法被执行的情况，比如assign操作，在没有返回值的情况下若有多条控制流则无法被正确执行。

-------------------------------------------------一点点感悟，到时候忘了可以来看看。