吴裕雄 python 神经网络——TensorFlow 变量管理

import tensorflow as tf

with tf.variable_scope("foo"):
    v = tf.get_variable("v", [1], initializer=tf.constant_initializer(1.0))

#with tf.variable_scope("foo"):
   # v = tf.get_variable("v", [1])

with tf.variable_scope("foo", reuse=True):
    v1 = tf.get_variable("v", [1])
print(v == v1)

#with tf.variable_scope("bar", reuse=True):
   # v = tf.get_variable("v", [1])

with tf.variable_scope("root"):
    print(tf.get_variable_scope().reuse)

    with tf.variable_scope("foo", reuse=True):
        print(tf.get_variable_scope().reuse)

        with tf.variable_scope("bar"):
            print(tf.get_variable_scope().reuse)

    print(tf.get_variable_scope().reuse)

v1 = tf.get_variable("v", [1])
print(v1.name)

with tf.variable_scope("foo",reuse=True):
    v2 = tf.get_variable("v", [1])
print(v2.name)

with tf.variable_scope("foo"):
    with tf.variable_scope("bar"):
        v3 = tf.get_variable("v", [1])
        print(v3.name)

v4 = tf.get_variable("v1", [1])
print(v4.name)

with tf.variable_scope("",reuse=True):
    v5 = tf.get_variable("foo/bar/v", [1])
    print(v5 == v3)
    v6 = tf.get_variable("v1", [1])
    print(v6 == v4)