tensorflow中命名空间、变量命名的问题

时间：2024-04-21 01:18:21

1.简介

对比分析tf.Variable / tf.get_variable | tf.name_scope / tf.variable_scope的异同

2.说明

  • tf.Variable创建变量；tf.get_variable创建与获取变量
  • tf.Variable自动检测命名冲突并且处理；tf.get_variable在没有设置reuse时会报错
  • tf.name_scope没有reuse功能，tf.get_variable在变量冲突时报错；tf.variable_scope有reuse功能，可配合tf.get_variable实现变量共享
  • tf.get_variable变量命名不受tf.name_scope的影响；tf.Variable受两者的影响

3.代码示例

3.1 tf.Variable

tf.Variable在命名冲突时自动处理冲突问题

 import tensorflow as tf

 a1 = tf.Variable(tf.constant(1.0, shape=[1]),name="a")

 a2 = tf.Variable(tf.constant(1.0, shape=[1]),name="a")

 print(a1)

 print(a2)

 print(a1==a2)

 ###

10 <tf.Variable 'a:0' shape=(1,) dtype=float32_ref>

11 <tf.Variable 'a_1:0' shape=(1,) dtype=float32_ref>

12 False

3.2 tf.get_variable

tf.get_variable在没有设置命名空间reuse的情况下变量命名冲突时报错

 import tensorflow as tf

 a3 = tf.get_variable("a", shape=[1], initializer=tf.constant_initializer(1.0))

 a4 = tf.get_variable("a", shape=[1], initializer=tf.constant_initializer(1.0))

 ###

7 ValueError: Variable a already exists, disallowed.

8 Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope?

3.3 tf.name_scope

tf.name_scope没有reuse功能，tf.get_variable命名不受它影响，并且命名冲突时报错；tf.Variable命名受它影响

 import tensorflow as tf

 a = tf.Variable(tf.constant(1.0, shape=[1]),name="a")

 with tf.name_scope('layer2'):

     a1 = tf.Variable(tf.constant(1.0, shape=[1]),name="a")

     a2 = tf.Variable(tf.constant(1.0, shape=[1]),name="a")

     a3 = tf.get_variable("a", shape=[1], initializer=tf.constant_initializer(1.0))

 7 #     a4 = tf.get_variable("a", shape=[1], initializer=tf.constant_initializer(1.0))   该句会报错

     print(a)

     print(a1)

     print(a2)

     print(a3)

     print(a1==a2)

 ###

16 <tf.Variable 'a:0' shape=(1,) dtype=float32_ref>

17 <tf.Variable 'layer2/a:0' shape=(1,) dtype=float32_ref>

18 <tf.Variable 'layer2/a_1:0' shape=(1,) dtype=float32_ref>

19 <tf.Variable 'a_1:0' shape=(1,) dtype=float32_ref>

20 False

3.4 tf.variable_scope

tf.variable_scope可以配tf.get_variable实现变量共享；reuse默认为None，有False/True/tf.AUTO_REUSE可选：

  • 设置reuse = None/False时tf.get_variable创建新变量，变量存在则报错
  • 设置reuse = True时tf.get_variable只讲获取已存在的变量，变量不存在时报错
  • 设置reuse = tf.AUTO_REUSE时tf.get_variable在变量已存在则自动复用，不存在则创建
 import tensorflow as tf

 with tf.variable_scope('layer1',reuse=tf.AUTO_REUSE):

     a1 = tf.Variable(tf.constant(1.0, shape=[1]),name="a")

     a2 = tf.Variable(tf.constant(1.0, shape=[1]),name="a")

     a3 = tf.get_variable("a", shape=[1], initializer=tf.constant_initializer(1.0))

     a4 = tf.get_variable("a", shape=[1], initializer=tf.constant_initializer(1.0))

     print(a1)

     print(a2)

     print(a1==a2)

     print(a3)

     print(a4)

     print(a3==a4)

 ###

16 <tf.Variable 'layer1_1/a:0' shape=(1,) dtype=float32_ref>

17 <tf.Variable 'layer1_1/a_1:0' shape=(1,) dtype=float32_ref>

18 False

19 <tf.Variable 'layer1/a_2:0' shape=(1,) dtype=float32_ref>

20 <tf.Variable 'layer1/a_2:0' shape=(1,) dtype=float32_ref>

21 True

!!!

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