tf.control_dependencies-优快云博客

本文解析了TensorFlow中tf.control_dependencies()的作用，如何通过with语句控制操作执行顺序，并介绍了嵌套使用和取消依赖的情况。重点讲解了控制依赖在代码中的实际应用和注意事项，帮助理解算法流程控制。

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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，这时可以使用tf.control_dependencies(control_inputs)来实现。该函数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 = ...

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'

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'

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：

def my_func(pred, tensor):  # WRONG
    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

def my_func(pred, tensor):  # RIGHT
    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)