04_Variable_Scope.ipynb_uploaded_180414_16_19.html
tf.get_variable(), tf.variable_scope(), tf.name_scope()
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# Tensorflow provides 2 different scope methods,
# to group and specify "variables,operations and constants"
# To understand it,it is better to first check,
# how tensorflow to name variables and operations
import tensorflow as tf
import numpy as np
# @
# Tensorflow naming method
# You can create variable with 'get_variable_scope'
# tf.get_variable(
# name,
# shape=None,
# dtype=None,
# initializer=None,
# regularizer=None,
# trainable=True,
# collections=None,
# caching_device=None,
# partitioner=None,
# validate_shape=True,
# use_resource=None,
# custom_getter=None,
# constraint=None
# )
# You can either get existing variable with these parameters,
# or create new one
a=tf.get_variable("a",[])
# print a.name
# a:0
# It means name of a node is 'a:0'
# You will try to create variable which has same name with "a:0"
# But, it will generate errors
# @
# Variable Scope
with tf.variable_scope("A"):
b=tf.get_variable("b",[])
print b.name
# A/b:0
# It means A is scope name,
# b is variable name
# With variable_scope,
# you can create many variables with same names
with tf.variable_scope("C"):
b2=tf.get_variable("b",[])
print b2.name
# C/b:0
# It means C is scope name,
# b is variable name
# from graph_visualizer import show_graph
# show_graph(tf.get_default_graph().as_graph_def())
# You can create nested variable scope
# You can nest variable scope many times
with tf.variable_scope("C"):
with tf.variable_scope("D"):
c=tf.get_variable("b",[])
print c
print c.name # --> C/D/b:0 C,D=scope name,b=variable name
# <tensorflow.python.ops.variables.Variable object at 0x7fb5afb2e710>
# C/D/b:0
# You can reuse existing variable,
# with get_variable_scope and 'reuse=True'
with tf.variable_scope("C"):
with tf.variable_scope("D",reuse=True): # <--- Notice,'reuse'
alias_c=tf.get_variable("b")
print alias_c
print alias_c.name
# <tensorflow.python.ops.variables.Variable object at 0x7fb5afb2e710>
# C/D/b:0
# Or, you can set all variables in scope will be 'reused' inside of the scope as follows:
with tf.variable_scope("C"):
with tf.variable_scope("D"): # <-- note! no 'reuse' flag here!
m=tf.get_variable("m",[])
print m
print m.name
print "current variable scope :",tf.get_variable_scope().name
tf.get_variable_scope().reuse_variables()
m2=tf.get_variable("m")
print m2
print m2.name
# <tensorflow.python.ops.variables.Variable object at 0x7fb5afb13750>
# C/D/m:0
# current variable scope : C/D
# <tensorflow.python.ops.variables.Variable object at 0x7fb5afb13750>
# C/D/m:0
# Namescope
# tf.name_scope(name,default_name=None,values=None)
# It returns context manager for use when defining Python op.
# This context manager validates,
# that given values are from same graph,
# makes that graph default graph,
# and pushes name scope in that graph,
# (see Graph.name_scope() for more details on that)
with tf.name_scope("custom_scope"):
x=tf.get_variable("x",[])
y=tf.Variable(0.0,"x")
op=x+y
# print x.name, " <--- tf.get_variable ignore name_scope"
# print y.name, " <--- name_scope() affects tf.Variable()"
# print op.name," <-- name_scope() affects operations"
# x:0 <--- tf.get_variable ignore name_scope
# custom_scope/Variable:0 <--- name_scope() affects tf.Variable()
# custom_scope/add:0 <-- name_scope() affects operations
with tf.variable_scope("custom_scope2"):
x=tf.get_variable("x",[])
y=tf.Variable(0.0,"y")
op=x+y
# print x.name, " <--- variable_scope() affects tf.get_variable()"
# print y.name, " <--- variable_scope() affects tf.Variable()"
# print op.name," <-- variable_scope() affects operations"
# custom_scope2/x:0 <--- variable_scope() affects tf.get_variable()
# custom_scope2/Variable:0 <--- variable_scope() affects tf.Variable()
# custom_scope2/add:0 <-- variable_scope() affects operations
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