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tf22_scope.py
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# visit https://mofanpy.com/tutorials/ for more!
# 22 scope (name_scope/variable_scope)
from __future__ import print_function
import tensorflow as tf
with tf.name_scope("a_name_scope"):
initializer = tf.constant_initializer(value=1)
var1 = tf.get_variable(name='var1', shape=[1], dtype=tf.float32, initializer=initializer)
var2 = tf.Variable(name='var2', initial_value=[2], dtype=tf.float32)
var21 = tf.Variable(name='var2', initial_value=[2.1], dtype=tf.float32)
var22 = tf.Variable(name='var2', initial_value=[2.2], dtype=tf.float32)
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
print(var1.name) # var1:0
print(sess.run(var1)) # [ 1.]
print(var2.name) # a_name_scope/var2:0
print(sess.run(var2)) # [ 2.]
print(var21.name) # a_name_scope/var2_1:0
print(sess.run(var21)) # [ 2.0999999]
print(var22.name) # a_name_scope/var2_2:0
print(sess.run(var22)) # [ 2.20000005]
with tf.variable_scope("a_variable_scope") as scope:
initializer = tf.constant_initializer(value=3)
var3 = tf.get_variable(name='var3', shape=[1], dtype=tf.float32, initializer=initializer)
var4 = tf.Variable(name='var4', initial_value=[4], dtype=tf.float32)
var4_reuse = tf.Variable(name='var4', initial_value=[4], dtype=tf.float32)
scope.reuse_variables()
var3_reuse = tf.get_variable(name='var3',)
with tf.Session() as sess:
# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
init = tf.initialize_all_variables()
else:
init = tf.global_variables_initializer()
sess.run(init)
print(var3.name) # a_variable_scope/var3:0
print(sess.run(var3)) # [ 3.]
print(var4.name) # a_variable_scope/var4:0
print(sess.run(var4)) # [ 4.]
print(var4_reuse.name) # a_variable_scope/var4_1:0
print(sess.run(var4_reuse)) # [ 4.]
print(var3_reuse.name) # a_variable_scope/var3:0
print(sess.run(var3_reuse)) # [ 3.]