【tensorflow控制流】tf.control_dependencies()作用及用法

谁践踏了优雅 2022-05-18 06:15 133阅读 0赞

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 = ...
     
      
       1
       2
       3
       4

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`.
     
      
       1
       2
       3
       4

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`.
     
      
       1
       2
       3
       4
       5
       6
       7

**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)
     
      
       1
       2
       3
       4
       5
       6
       7
       8
       9
       10
       11
       12
       13
       14

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)
     
      
       1
       2
       3

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)
     
      
       1
       2