tf.variable_scope() and tf.name_scope()

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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本文探讨了TensorFlow中tf.variable_scope与tf.name_scope的区别，通过实例演示如何使用这两个作用域来管理变量命名，以及它们在tf.get_variable与tf.Variable之间的适用性差异。

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https://blog.youkuaiyun.com/UESTC_C2_403/article/details/72328815

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