TensorFlow的全局變數「global variables」

TensorFlow有一個colletion機制，http://blog.csdn.net/shenxiaolu1984/article/details/52815641 這裡描述得非常好：「零存整取」

tensorflow用集合colletion組織不同類別的對象。tf.GraphKeys中包含了所有默認集合的名稱。

collection提供了一種「零存整取」的思路：在任意位置，任意層次都可以創造對象，存入相應collection中；創造完成後，統一從一個collection中取出一類變數，施加相應操作。

例如，tf.Optimizer只優化tf.GraphKeys.TRAINABLE_VARIABLES中的變數。

global_variables也是一個collection。

TensorFlow維護的幾個collection：

【1】先看文檔

tf.global_variables

global_variables(scope=None)

Defined in tensorflow/python/ops/variables.py.n

See the guide: Variables > Variable helper functions

Returns global variables.

Global variables are variables that are shared across machines in a distributed environment.

這句話似乎並沒有什麼幫助。。。

The Variable()constructor or get_variable() automatically adds new variables to the graph collectionGraphKeys.GLOBAL_VARIABLES. 【Variable()和get_variable()會生成新的global variable】

This convenience function returns the contents of that collection.

An alternative to global variables are local variables. See tf.local_variables

Args:

• scope: (Optional.) A string. If supplied, the resulting list is filtered to include only items whose name attribute matches scope using re.match. 【name用正則匹配name】Items without a name attribute are never returned if a scope is supplied.【沒有name的不會被匹配】 The choice of re.match means that a scope without special tokens filters by prefix.

Returns:

A list of Variable objects.

【2】通過tf.global_variables()查看「全局變數」

restore_var = [v for v in tf.global_variables()]n

以下是PSPNet的全部「global variables」，可以看到基本上是各層的權重和各種參數：

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