https://blog.csdn.net/UESTC_C2_403/article/details/72328815

tf.variable_scope可以讓變量有相同的命名，包括tf.get_variable得到的變量，還有tf.Variable的變量

tf.name_scope可以讓變量有相同的命名，只是限于tf.Variable的變量

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

換成下面的代碼就可以執行：

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
輸出：
V1/a2:0
V2/a2:0
?

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