python人工智能tensorflow函数tf.get_collection使用方法
目录
参数数量及其作用
例子
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
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