import tensorflow as tf
with tf.name_scope("hello") as name_scope:
    arr1 = tf.get_variable("arr1", shape=[2,10],dtype=tf.float32)

    print (name_scope)
    print (arr1.name)
    print ("scope_name:%s " % tf.get_variable_scope().original_name_scope)

运行后的结果如下：

hello/
arr1:0
scope_name: 

import tensorflow as tf
with tf.name_scope('hidden') as scope:
  a = tf.constant(5, name='alpha')
  W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0), name='weights')
  b = tf.Variable(tf.zeros([1]), name='biases')
  print (a.name)
  print (W.name)
  print (b.name)

运行的结果：

hidden/alpha:0
hidden/weights:0
hidden/biases:0

红色字体要强调的部分所以把字体改成了红色，理解name_scope 对 tf.get_variable()的作用和 tf.Variable()的不同
 

参考文献：http://blog.csdn.net/u012436149/article/details/53081454

tf.variable_scope的详细说明，我觉得讲的非常清楚明白

https://www.jianshu.com/p/ab0d38725f88