子网重量在张量流中的不同范围上共享

Question

与TensorFlow一起工作，我正在尝试在不同的可变范围中共享相同的网络权重以节省内存。但是，似乎没有简单的方法可以做到这一点。我已经准备了一个小型代码样本，以在较小的规模上说明我想用较大的子网做什么：

import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
    with tf.variable_scope("super_scope_one"):
        scope1 = tf.variable_scope("sub_scope_one")
        with scope1:
            number_one = tf.get_variable("number_one", shape=[1],
                                         initializer=tf.ones_initializer)
    with tf.variable_scope("super_scope_two"):
        with tf.variable_scope("sub_scope_one", reuse=True) as scope2:
            # Here is the problem.
            # scope1.reuse_variables() # this crashes too if reuse=None.
            number_one = tf.get_variable("number_one", shape=[1])
        with tf.variable_scope("sub_scope_two"):
            number_two = tf.get_variable("number_two", shape=[1],
                                         initializer=tf.ones_initializer)
        number_three = number_one + number_two
    init_op = tf.global_variables_initializer()
with tf.Session(graph=graph):
    init_op.run()
    print(number_three.eval())

是否可以在两个子范围中共享变量，而无需删除上面的范围？如果没有，这是一个很好的理由，为什么这将是一个坏主意？

Answer 1

您可以简单地在"super_scope_one"中仅定义number_one并在"super_scope_two"中使用它。

不同范围中的两个变量可以一起使用。请参阅下面：

import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
    with tf.variable_scope("super_scope_one"):
        scope1 = tf.variable_scope("sub_scope_one")
        with scope1:
            number_one = tf.get_variable("number_one", shape=[1],
                                         initializer=tf.ones_initializer)
    with tf.variable_scope("super_scope_two"):
        with tf.variable_scope("sub_scope_two"):
            number_two = tf.get_variable("number_two", shape=[1],
                                         initializer=tf.ones_initializer)
        number_three = number_one + number_two
    init_op = tf.global_variables_initializer()
    with tf.Session(graph=graph):
        init_op.run()
        print(number_three.eval())

返回[2]