目录

tf.control_dependencies 依赖关系配置

此函数指定某些操作执行的依赖关系

返回一个控制依赖的上下文管理器，使用 with 关键字可以让在这个上下文环境中的操作都在 control_inputs 执行 

with tf.control_dependencies([a, b]):
      c = ....
      d = ...
在执行完 a，b 操作之后，才能执行 c，d 操作。意思就是 c，d 操作依赖 a，b 操作 
with tf.control_dependencies([train_step, variable_averages_op]):
      train_op = tf.no_op(name='train')
tf.no_op()表示执行完 train_step, variable_averages_op 操作之后什么都不做

update_ops

global_norm

colocate_with

with ops.colocate_with(self.global_step):
    self.train = state_ops.assign_add(self.global_step, 1).op

It's a context manager to make sure that the operation or tensor you're about to create will be placed on the same device the reference operation is on. Consider this piece of code (tested): 

import tensorflow as tf

with tf.device("/cpu:0"):
  a = tf.constant(0.0, name="a")

with tf.device("/gpu:0"):
  b = tf.constant(0.0, name="b")
  with tf.colocate_with(a):
    c = tf.constant(0.0, name="c")
  d = tf.constant(0.0, name="d")
for operation in tf.get_default_graph().get_operations():
  print(operation.name, operation.device)

Outputs:

(u'a', u'/device:CPU:0')
(u'b', u'/device:GPU:0')
(u'c', u'/device:CPU:0')
(u'd', u'/device:GPU:0')
- 参考资料