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

参数数量及其作用

该函数共有两个参数，分别是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.  

该函数的作用是从一个collection中取出全部变量，形成列个列表，key参数中输入的是collection的名称。

该函数常常与tf.get_variable和tf.add_to_collection配合使用。

例子

该例子将分别举例tf.get_collection与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'))

其输出为：

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

tf.get_variable的用法可以参照我的另一篇博文：

python人工智能tensorflow函数tf.get_variable使用方法

来源：https://blog.csdn.net/weixin_44791964/article/details/96132749