tensorflow of variable scope

First, the origin of

Depth learning requires significant set of variables, in the past we only need to write code to do a global limit on it, but in tensorflow in doing so is neither easy to manage a set of variables, there are not easy to package, thus providing a variable management tensorflow methods: variable scoping mechanism

 

Second, the two important API

tf.get_variable (name, shape = None) # according to the given name or create a variable return

tf.variable_scope (name_or_scope, reuse = None) # of all the variables in the composition of a namespace name_or_scope　　

 

Third, the interpretation

Let me talk first API

tf.get_variable (name, shape = None) This method is tf.Variable () exactly the same as when creating a variable, except that it will search if there is a variable with the same name;

1 import tensorflow as tf
2 
3 
4 with tf.variable_scope('const'):
5     a = tf.get_variable('a', [1], initializer=tf.constant_initializer(1.))

 

 

Besides the second API

When the most important parameters reuse this method, there are three values: None, True, tf.AUTO_REUSE

reuse sign inherit the parent class: reuse = None

reuse = True: only multiplexing, can not create

 1 import tensorflow as tf
 2 
 3 
 4 with tf.variable_scope('const'):
 5     a = tf.get_variable('a', [1])
 6 
 7 with tf.variable_scope('const', reuse=tf.AUTO_REUSE):
 8     b = tf.get_variable('a', [1])
 9 
10 print(a==b)     # True

reuse = tf.AUTO_REUSE: is not created, has multiplexing, which is the most secure usage

. 1  Import tensorflow TF AS
 2  
. 3  
. 4  DEF Test ():
 . 5      with tf.variable_scope ( ' const ' , Reuse = tf.AUTO_REUSE):
 . 6          A = tf.get_variable ( ' A ' , [. 1 ])
 . 7  
. 8      return A
 . 9  
10 the X-= the Test ()   # is not created 
11 the y-= the Test ()   # have to multiplex 
12  Print (the y-the X-==)      # True