如何在 Tensorflow 中创建当前范围之外的变量? [英] How to create variable outside of current scope in Tensorflow?

问题描述

例如我有这样的代码:

def test():
    v = tf.get_variable('test')  # => foo/test

with tf.variable_scope('foo'):
    test()

现在我想创建一个超出范围 'foo' 的变量:

Now I want to make a variable outside of scope 'foo':

def test():
    with tf.variable_scope('bar'):
        v = tf.get_variable('test')  # foo/bar/test

但它被放置为'foo/bar/test'.我应该在 test() 主体中做什么以将其放置为没有 'foo' 根的 'bar/test'?

But it is placed as 'foo/bar/test'. What should I do in test() body to place it as 'bar/test' without 'foo' root?

推荐答案

您可以通过提供现有范围的实例来清除当前变量范围.因此，为了实现这一点，只需引用顶级变量范围并使用它:

You can clear the current variable scope by providing an instance of an existing scope. So in order to pull this off, just make a reference to the top-level variable scope and use it:

top_scope = tf.get_variable_scope()   # top-level scope

def test():
  v = tf.get_variable('test', [1], dtype=tf.float32)
  print(v.name)

  with tf.variable_scope(top_scope):  # resets the current scope!
    # Can nest the scopes further, if needed
    w = tf.get_variable('test', [1], dtype=tf.float32)
    print(w.name)

with tf.variable_scope('foo'):
  test()

输出:

foo/test:0
test:0

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