tf.control_dependencies()作用及用法


在有些机器学习程序中我们想要指定某些操作执行的依赖关系，这时我们可以使用

tf.control_dependencies()

来实现。 

control_dependencies(control_inputs)

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 g.control_dependencies([a, b, c]):
# `d` and `e` will only run after `a`, `b`, and `c` have executed.
d = ...
e = ...

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可以嵌套

control_dependencies

 使用

with g.control_dependencies([a, b]):
# Ops constructed here run after `a` and `b`.
with g.control_dependencies([c, d]):
# Ops constructed here run after `a`, `b`, `c`, and `d`.

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可以传入

None

 来消除依赖：

with g.control_dependencies([a, b]):
# Ops constructed here run after `a` and `b`.
with g.control_dependencies(None):
# Ops constructed here run normally, not waiting for either `a` or `b`.
with g.control_dependencies([c, d]):
# Ops constructed here run after `c` and `d`, also not waiting
# for either `a` or `b`.

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注意： 

控制依赖只对那些在上下文环境中建立的操作有效，仅仅在context中使用一个操作或张量是没用的

# WRONG
def my_func(pred, tensor):
t = tf.matmul(tensor, tensor)
with tf.control_dependencies([pred]):
# The matmul op is created outside the context, so no control
# dependency will be added.
return t

# RIGHT
def my_func(pred, tensor):
with tf.control_dependencies([pred]):
# The matmul op is created in the context, so a control dependency
# will be added.
return tf.matmul(tensor, tensor)

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例子： 

在训练模型时我们每步训练可能要执行两种操作，

op a, b

 这时我们就可以使用如下代码：

with tf.control_dependencies([a, b]):
c= tf.no_op(name='train')#tf.no_op；什么也不做
sess.run(c)

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在这样简单的要求下，可以将上面代码替换为：

c= tf.group([a, b])
sess.run(c)