1. 程式人生 > >tf.variable_scope和tf.name_scope的區別

tf.variable_scope和tf.name_scope的區別

f.variable_scope可以讓變數有相同的命名,包括tf.get_variable得到的變數,還有tf.Variable的變數

tf.name_scope可以讓變數有相同的命名,只是限於tf.Variable的變數

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:

換成下面的程式碼就可以執行:

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