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python人工智能tensorflow函數(shù)tf.get_collection使用方法

 更新時(shí)間:2022年05月05日 10:59:04   作者:Bubbliiiing  
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參數(shù)數(shù)量及其作用

該函數(shù)共有兩個(gè)參數(shù),分別是key和scope。

 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.  

該函數(shù)的作用是從一個(gè)collection中取出全部變量,形成列個(gè)列表,key參數(shù)中輸入的是collection的名稱。

該函數(shù)常常與tf.get_variable和tf.add_to_collection配合使用。

例子

該例子將分別舉例tf.get_collection與tf.get_variable和tf.add_to_collection的配合使用方法。

 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'))

其輸出為:

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

tf.get_variable的用法可以參照我的另一篇博文:

python人工智能tensorflow函數(shù)tf.get_variable使用方法

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