(TensorFlow)——tf.variable_scope和tf.name_scope详解

最新推荐文章于 2022-10-12 14:40:03 发布

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最新推荐文章于 2022-10-12 14:40:03 发布

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作者在学习TensorFlow时，对tf.variable_scope和tf.name_scope的作用未深入了解，遂转载博客记录二者区别。tf.variable_scope可让tf.get_variable和tf.Variable得到的变量有相同命名，tf.name_scope仅对tf.Variable的变量有效，并给出了相关示例。

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这几天学习tensorflow，看到关于tf.variable_scope和tf.name_scop，一直没有深入了解其中的作用。转载一篇博客：

Tensorflow中tf.name_scope() 和 tf.variable_scope() 的区别记录一下作用，一面以后忘记。

在这里简单点说下：

tf.variable_scope可以让变量有相同的命名，包括tf.get_variable得到的变量，还有tf.Variable的变量

tf.name_scope可以让变量有相同的命名，只是限于tf.Variable的变量

例如：

import tensorflow as tf;  
import numpy as np;  
import matplotlib.pyplot as plt;  
 
with tf.variable_scope('V1'):
	a1 = tf.get_variable(name='a1', shape=[1], initializer=tf.constant_initializer(1))
	a2 = tf.Variable(tf.random_normal(shape=[2,3], mean=0, stddev=1), name='a2')
with tf.variable_scope('V2'):
	a3 = tf.get_variable(name='a1', shape=[1], initializer=tf.constant_initializer(1))
	a4 = tf.Variable(tf.random_normal(shape=[2,3], mean=0, stddev=1), name='a2')
  
with tf.Session() as sess:
	sess.run(tf.initialize_all_variables())
	print a1.name
	print a2.name
	print a3.name
	print a4.name

输出：

V1/a1:0
V1/a2:0
V2/a1:0
V2/a2:0

例子2：

import tensorflow as tf;  
import numpy as np;  
import matplotlib.pyplot as plt;  
 
with tf.name_scope('V1'):
	a1 = tf.get_variable(name='a1', shape=[1], initializer=tf.constant_initializer(1))
	a2 = tf.Variable(tf.random_normal(shape=[2,3], mean=0, stddev=1), name='a2')
with tf.name_scope('V2'):
	a3 = tf.get_variable(name='a1', shape=[1], initializer=tf.constant_initializer(1))
	a4 = tf.Variable(tf.random_normal(shape=[2,3], mean=0, stddev=1), name='a2')
  
with tf.Session() as sess:
	sess.run(tf.initialize_all_variables())
	print a1.name
	print a2.name
	print a3.name
	print a4.name

报错：Variable a1 already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:

换成下面的代码就可以执行

import tensorflow as tf;  
import numpy as np;  
import matplotlib.pyplot as plt;  
 
with tf.name_scope('V1'):
	# a1 = tf.get_variable(name='a1', shape=[1], initializer=tf.constant_initializer(1))
	a2 = tf.Variable(tf.random_normal(shape=[2,3], mean=0, stddev=1), name='a2')
with tf.name_scope('V2'):
	# a3 = tf.get_variable(name='a1', shape=[1], initializer=tf.constant_initializer(1))
	a4 = tf.Variable(tf.random_normal(shape=[2,3], mean=0, stddev=1), name='a2')
  
with tf.Session() as sess:
	sess.run(tf.initialize_all_variables())
	# print a1.name
	print a2.name
	# print a3.name
	print a4.name

输出：

V1/a2:0
V2/a2:0