之前一直很困惑，tf.variable_scope和tf.name_scope都是管理上下文環境的，它們有什麼不同？

查閱資料時，發現了一段有意思的測試程式碼

import tensorflow as tf
def scoping(fn, scope1, scope2, vals):
    with fn(scope1):
        a = tf.Variable(vals[0], name='a')
        # 此處注意 b是get_variable
        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])
# 如果加上這一行，就會報錯，因為d3的變數b會和d2的變數b衝突
 

# d3 = scoping(tf.name_scope,     'scope_name2', 'res', [1, 2, 3])
# 但這一行就不會衝突，因為d3和d1的變數b各自有作用域
# d3 = scoping(tf.variable_scope,     'scope_vars2', '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()
 

執行後，得到如下輸出

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 

TensorBoard顯示

總而言之，tf.name_scope僅為非tf.get_variable建立的tensor新增字首；而tf.variable_scope為所有tensor新增字首