what does tf.no_op do and tf.control_dependencies work？

- 控制依赖

with tf.control_dependencies([train_step, variables_averages_op]):
    train_op = tf.no_op(name='train') #train_op does nothin

或者

train_op = tf.group(train_step, variables_averages_op)

贴上stackflow上大神对train_op的回答：

As the documentation says, tf.no_op() does nothing. However, when you create a tf.no_op() inside a with tf.control_dependencies([x, y, z]): block, the op will gain control dependencies on ops x, y, and z. Therefore it can be used to group together a set of side effecting ops, and give you a single op to pass to sess.run() in order to run all of them in a single step.

- tf.control_dependencies

tf.control_dependencies(control_inputs)
control_inputs: A list of Operation or Tensor objects, which must be executed or computed before running the operations defined in the context.
returns：A context manager that specifies control dependencies for all operations constructed within the context

该函数的参数是一个包含Op或者tensor的列表，列表里的操作的执行顺序先于context里面定义的Op和tensor；
该函数返回一个上下文管理器

 

代码示例

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

#也可以嵌套使用
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`.
          ...

#传入None消除依赖关系
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`.

 

另外：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)

参考

https://stackoverflow.com/questions/46801154/what-does-tf-no-op-do
https://blog.csdn.net/PKU_Jade/article/details/73498753
https://blog.csdn.net/u012436149/article/details/72084744

记录时间

2018/9/13 12:07 第一次