tf.control_dependencies(control_inputs): control_dependencies(control_inputs)

ARGS：

• control_inputs：在運(yùn)行上下文中定義的操作之前必須執(zhí)行或計(jì)算的 Operation 列表或 Tensor 對(duì)象.也可以是不清除控件依賴項(xiàng).

返回：

指定上下文中構(gòu)建的所有操作的控制依賴關(guān)系的上下文管理器.

這個(gè)上下文就是with里邊的內(nèi)容

Use with the?with?keyword to specify that all operations constructed within the context should have control dependencies on?control_inputs. For example:

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

關(guān)于tf.GraphKeys.UPDATE_OPS，這是一個(gè)tensorflow的計(jì)算圖中內(nèi)置的一個(gè)集合，其中會(huì)保存一些需要在訓(xùn)練操作之前完成的操作，并配合tf.control_dependencies函數(shù)使用。

tf.get_collection():

get_collection(name,scope=None )

Args:

• name: The key for the collection. For example, the?GraphKeys?class contains many standard names for collections.
• scope: (Optional.) A string. If supplied, the resulting list is filtered to include only items whose?nameattribute matches?scope?using?re.match. Items without a?name?attribute are never returned if a scope is supplied. The choice of?re.match?means that a?scope?without special tokens filters by prefix.

# 在'My-TensorFlow-tutorials-master/02 CIFAR10/cifar10.py'代碼中variables = tf.get_collection(tf.GraphKeys.VARIABLES)for i in variables:print(i)>>> <tf.Variable 'conv1/weights:0' shape=(3, 3, 3, 96) dtype=float32_ref><tf.Variable 'conv1/biases:0' shape=(96,) dtype=float32_ref><tf.Variable 'conv2/weights:0' shape=(3, 3, 96, 64) dtype=float32_ref><tf.Variable 'conv2/biases:0' shape=(64,) dtype=float32_ref><tf.Variable 'local3/weights:0' shape=(16384, 384) dtype=float32_ref><tf.Variable 'local3/biases:0' shape=(384,) dtype=float32_ref><tf.Variable 'local4/weights:0' shape=(384, 192) dtype=float32_ref><tf.Variable 'local4/biases:0' shape=(192,) dtype=float32_ref><tf.Variable 'softmax_linear/softmax_linear:0' shape=(192, 10) dtype=float32_ref><tf.Variable 'softmax_linear/biases:0' shape=(10,) dtype=float32_ref>

?

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