tf.control_dependencies()

参考这里点击打开链接的信息我们可以知道，TF可以协调多个数据流，在存在依赖的节点下非常有用，例如节点B要读取模型参数值V更新后的值，而节点A负责更新参数V，所以节点B就要等节点A执行完成后再执行，不然读到的就是更新以前的数据。这时候就需要个运算控制器tf.control_dependencies。

参考官方说明文档

format：control_dependencies(self, control_inputs)

arguments：control_inputs: A list of `Operation` or `Tensor` objects which must be executed or computed before running the operations defined in the context. （注意这里control_inputs是list）

return：  A context manager that specifies control dependencies for all operations constructed within the context.（返回所有在环境中的控制依赖的上下文管理器）

其实用法很简单，只有在 control_inputs被执行以后，上下文管理器中的操作才会被执行。例如

with tf.control_dependencies([a, b, c]):
# `d` and `e` will only run after `a`, `b`, and `c` have executed.
d = ...
e = ...只有[a,b,c]都被执行了才会执行d和e操作，这样就实现了流的控制。
当然，官方文档里还介绍了嵌套多个流控制

with tf.control_dependencies([a, b]):
# Ops constructed here run after `a` and `b`.
with tf.control_dependencies([c, d]):
# Ops constructed here run after `a`, `b`, `c`, and `d`也能通过参数None清除控制依赖例如
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`.