variable_scope 和 name_scope 有什么区别? [英] What is the difference between variable_scope and name_scope?

问题描述

variable_scope 和 name_scope 有什么区别?

<小时>

这给了你答案:

现在您看到 tf.variable_scope() 为所有变量(无论您如何创建它们)、操作、常量的名称添加了前缀.另一方面，tf.name_scope() 忽略使用 tf.get_variable() 创建的变量，因为它假定您知道要使用哪个变量以及在哪个范围内使用.

关于共享变量的一份很好的文档告诉您

<块引用>

tf.variable_scope():管理传递给 tf.get_variable() 的名称的命名空间.

相同的文档提供了更多详细信息 Variable Scope 如何工作以及何时有用.

What is the difference between variable_scope and name_scope? The variable scope tutorial talks about variable_scope implicitly opening name_scope. I also noticed that creating a variable in a name_scope automatically expands its name with the scope name as well. So, what is the difference?

解决方案

I had problems understanding the difference between variable_scope and name_scope (they looked almost the same) before I tried to visualize everything by creating a simple example:

import tensorflow as tf
def scoping(fn, scope1, scope2, vals):
    with fn(scope1):
        a = tf.Variable(vals[0], name='a')
        b = tf.get_variable('b', initializer=vals[1])
        c = tf.constant(vals[2], name='c')
        with fn(scope2):
            d = tf.add(a * b, c, name='res')

        print '\n  '.join([scope1, a.name, b.name, c.name, d.name]), '\n'
    return d

d1 = scoping(tf.variable_scope, 'scope_vars', 'res', [1, 2, 3])
d2 = scoping(tf.name_scope,     'scope_name', 'res', [1, 2, 3])

with tf.Session() as sess:
    writer = tf.summary.FileWriter('logs', sess.graph)
    sess.run(tf.global_variables_initializer())
    print sess.run([d1, d2])
    writer.close()

Here I create a function that creates some variables and constants and groups them in scopes (depending by the type I provided). In this function I also print the names of all the variables. After that I executes the graph to get values of the resulting values and save event-files to investigate them in tensorboard. If you run this, you will get the following:

scope_vars
  scope_vars/a:0
  scope_vars/b:0
  scope_vars/c:0
  scope_vars/res/res:0 

scope_name
  scope_name/a:0
  b:0
  scope_name/c:0
  scope_name/res/res:0 

You see the similar pattern if you open TB (as you see b is outside of scope_name rectangular):

---

This gives you the answer:

Now you see that tf.variable_scope() adds a prefix to the names of all variables (no matter how you create them), ops, constants. On the other hand tf.name_scope() ignores variables created with tf.get_variable() because it assumes that you know which variable and in which scope you wanted to use.

A good documentation on Sharing variables tells you that

tf.variable_scope(): Manages namespaces for names passed to tf.get_variable().

The same documentation provides a more details how does Variable Scope work and when it is useful.

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