tf.variable_scope与tf.name_scope的用法辨析

tf.variable_scope与tf.name_scope的用法辨析

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

代码示例：

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')

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    print(a1.name)
    print(a2.name)
    print(a3.name)
    print(a4.name)

output:

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

换成下面的代码则不能运行

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')

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    print(a1.name)
    print(a2.name)
    print(a3.name)
    print(a4.name)

output:

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

需要改成如下：

代码片

下面展示一些 内联代码片。

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')

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    #print(a1.name)
    print(a2.name)
    #print(a3.name)
    print(a4.name)

output:

V1/a2:0
V2/a2:0