tf.name_scope()和tf.variable_scope()

周小董

发布于 2019-03-25 10:35:37

6810

发布于 2019-03-25 10:35:37

文章被收录于专栏：python前行者python前行者

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：