TensorFlow变量管理详解

import tensorflow as tf 
 
# 在名字为foo的命名空间内创建名字为v的变量 
with tf.variable_scope("foo"): 
  v = tf.get_variable("v", [1], initializer=tf.constant_initializer(1.0)) 
 
''''' 
# 因为命名空间foo内已经存在变量v，再次创建则报错 
with tf.variable_scope("foo"): 
  v = tf.get_variable("v", [1]) 
# ValueError: Variable foo/v already exists, disallowed. 
# Did you mean to set reuse=True in VarScope&＃63; 
''' 
# 将参数reuse参数设置为True，则tf.get_variable可直接获取已声明的变量 
with tf.variable_scope("foo", reuse=True): 
  v1 = tf.get_variable("v", [1]) 
  print(v == v1) # True 
 
''''' 
# 当reuse=True时，tf.get_variable只能获取指定命名空间内的已创建的变量 
with tf.variable_scope("bar", reuse=True): 
  v2 = tf.get_variable("v", [1]) 
# ValueError: Variable bar/v does not exist, or was not created with 
# tf.get_variable(). Did you mean to set reuse=None in VarScope&＃63; 
''' 
 
with tf.variable_scope("root"): 
  # 通过tf.get_variable_scope().reuse函数获取当前上下文管理器内的reuse参数取值 
  print(tf.get_variable_scope().reuse) # False 
 
  with tf.variable_scope("foo1", reuse=True): 
    print(tf.get_variable_scope().reuse) # True 
 
    with tf.variable_scope("bar1"): 
      # 嵌套在上下文管理器foo1内的bar1内未指定reuse参数，则保持与外层一致 
      print(tf.get_variable_scope().reuse) # True 
 
  print(tf.get_variable_scope().reuse) # False 
 
# tf.variable_scope函数提供了一个管理变量命名空间的方式 
u1 = tf.get_variable("u", [1]) 
print(u1.name) # u:0 
with tf.variable_scope("foou"): 
  u2 = tf.get_variable("u", [1]) 
  print(u2.name) # foou/u:0 
 
with tf.variable_scope("foou"): 
  with tf.variable_scope("baru"): 
    u3 = tf.get_variable("u", [1]) 
    print(u3.name) # foou/baru/u:0 
 
  u4 = tf.get_variable("u1", [1]) 
  print(u4.name) # foou/u1:0 
 
# 可直接通过带命名空间名称的变量名来获取其命名空间下的变量 
with tf.variable_scope("", reuse=True): 
  u5 = tf.get_variable("foou/baru/u", [1]) 
  print(u5.name) # foou/baru/u:0 
  print(u5 == u3) # True 
  u6 = tf.get_variable("foou/u1", [1]) 
  print(u6.name) # foou/u1:0 
  print(u6 == u4) # True