tensorflow學習筆記(十五): variable scope
variable scope
tensorflow 為了更好的管理變數,提供了variable scope機制
官方解釋:
Variable scope object to carry defaults to provide to get_variable.
Many of the arguments we need for get_variable in a variable store are most easily handled with a context. This object is used for the defaults.
Attributes:
- name: name of the current scope, used as prefix in get_variable.
- initializer: 傳給get_variable的預設initializer.如果get_variable的時候指定了initializer,那麼將覆蓋這個預設的initializer.
- regularizer: 傳給get_variable的預設regulizer.
- reuse: Boolean or None, setting the reuse in get_variable.
- caching_device: string, callable, or None: the caching device passed to get_variable.
- partitioner: callable or None: the partitioner passed to get_variable.
- custom_getter: default custom getter passed to get_variable.
- name_scope: The name passed to tf.name_scope.
- dtype: default type passed to get_variable (defaults to DT_FLOAT).
regularizer
引數的作用是給在本variable_scope
下建立的weights
加上正則項.這樣我們就可以不同variable_scope
下的引數加不同的正則項了.
可以看出,用variable scope管理get_varibale是很方便的
如何確定 get_variable 的 prefixed name
首先, variable scope是可以巢狀的:
with variable_scope.variable_scope("tet1"):
var3 = tf.get_variable("var3",shape=[2],dtype=tf.float32)
print var3.name
with variable_scope.variable_scope("tet2"):
var4 = tf.get_variable("var4",shape=[2],dtype=tf.float32)
print var4.name
#輸出為****************
#tet1/var3:0
#tet1/tet2/var4:0
#*********************
get_varibale.name 以建立變數的 scope
作為名字的prefix
def te2():
with variable_scope.variable_scope("te2"):
var2 = tf.get_variable("var2",shape=[2], dtype=tf.float32)
print var2.name
def te1():
with variable_scope.variable_scope("te1"):
var1 = tf.get_variable("var1", shape=[2], dtype=tf.float32)
return var1
return te1() #在scope te2 內呼叫的
res = te2()
print res.name
#輸出*********************
#te2/var2:0
#te2/te1/var1:0
#************************
觀察和上個程式的不同
def te2():
with variable_scope.variable_scope("te2"):
var2 = tf.get_variable("var2",shape=[2], dtype=tf.float32)
print var2.name
def te1():
with variable_scope.variable_scope("te1"):
var1 = tf.get_variable("var1", shape=[2], dtype=tf.float32)
return var1
return te1() #在scope te2外面呼叫的
res = te2()
print res.name
#輸出*********************
#te2/var2:0
#te1/var1:0
#************************
還有需要注意一點的是tf.variable_scope("name")
與 tf.variable_scope(scope)
的區別,看下面程式碼
程式碼1
import tensorflow as tf
with tf.variable_scope("scope"):
tf.get_variable("w",shape=[1])#這個變數的name是 scope/w
with tf.variable_scope("scope"):
tf.get_variable("w", shape=[1]) #這個變數的name是 scope/scope/w
# 這兩個變數的名字是不一樣的,所以不會產生衝突
程式碼2
import tensorflow as tf
with tf.variable_scope("yin"):
tf.get_variable("w",shape=[1])
scope = tf.get_variable_scope()#這個變數的name是 scope/w
with tf.variable_scope(scope):#這種方式設定的scope,是用的外部的scope
tf.get_variable("w", shape=[1])#這個變數的name也是 scope/w
# 兩個變數的名字一樣,會報錯
共享變數
共享變數的前提是,變數的名字是一樣的,變數的名字是由變數名
和其scope
字首一起構成, tf.get_variable_scope().reuse_variables()
是允許共享當前scope
下的所有變數。reused variables
可以看同一個節點
with tf.variable_scope("level1"):
tf.get_variable("w",shape=[1])
scope = tf.get_variable_scope()
with tf.variable_scope("level2"):
tf.get_variable("w", shape=[1])
with tf.variable_scope("level1", reuse=True): #即使巢狀的variable_scope也會被reuse
tf.get_variable("w",shape=[1])
scope = tf.get_variable_scope()
with tf.variable_scope("level2"):
tf.get_variable("w", shape=[1])
其它
tf.get_variable_scope()
:獲取當前scope
tf.get_variable_scope().reuse_variables()
共享變數