python人工智能tensorflow函数tf.get_collection使用方法_python

 def get_collection(key, scope=None)
 Wrapper for Graph.get_collection() using the default graph.
 See tf.Graph.get_collection for more details.
 Args:
   key: The key for the collection. For example, the `GraphKeys` class  
     contains many standard names for collections.  
   scope: (Optional.) If supplied, the resulting list is filtered to include  
     only items whose `name` attribute matches using `re.match`. Items  
     without a `name` attribute are never returned if a scope is supplied and  
     the choice or `re.match` means that a `scope` without special tokens  
     filters by prefix.  
 Returns:
   The list of values in the collection with the given `name`, or  
   an empty list if no value has been added to that collection. The  
   list contains the values in the order under which they were  
   collected.  

 import tensorflow as tf;  
 import numpy as np;  
 c1 = ['c1', tf.GraphKeys.GLOBAL_VARIABLES]
 v1 = tf.get_variable('v1', [1], initializer=tf.constant_initializer(1),collections=c1)
 v2 = tf.get_variable('v2', [1], initializer=tf.constant_initializer(2))
 tf.add_to_collection('c2', v2)
 with tf.Session() as sess:
     sess.run(tf.global_variables_initializer())
     print(tf.get_collection('c1'))
     print(tf.get_collection('c2'))

 [<tf.Variable 'v1:0' shape=(1,) dtype=float32_ref>]
 [<tf.Variable 'v2:0' shape=(1,) dtype=float32_ref>]