函数调用后，Tensorflow变量的名称已更改

Question

我正在尝试编写自己的批处理规范代码。因此，我测试下面的代码。为了跟踪在线平均均值和方差，我将它们作为参数传递给getsta（）函数。但是，我发现“avg_mean”和“avg_variance”的名称已更改。虽然以后我可以手动强制更改名称，但似乎Tensorflow将每个名称都视为单独的变量。

def getsta(x,avg_mean,avg_variance): 
    print('getsta start...') 

    decay=0.9 

    mean = tf.get_variable(
      'mean', [1], tf.float32, 
      initializer=tf.constant_initializer(0.0, tf.float32)) 
    variance = tf.get_variable(
      'howvariance', [1], tf.float32, 
      initializer=tf.constant_initializer(1.0, tf.float32)) 
    ''' 
    if (avg_mean == 0.0): 
     avg_mean = tf.get_variable(
       'avg_mean', [1], tf.float32, 
       initializer=tf.constant_initializer(0.0, tf.float32)) 
    if (avg_variance == 0.0): 
     avg_variance = tf.get_variable(
       'avg_variance', [1], tf.float32, 
       initializer=tf.constant_initializer(0.0, tf.float32)) 
    ''' 
    mean, variance = tf.nn.moments(x, [0], name='moments') 

    avg_mean -= (1.0 - decay) * (avg_mean - mean) 
    avg_variance -= (1.0 - decay) * (avg_variance - variance) 

    return x, mean, variance, avg_mean, avg_variance 

def train(): 
    x1 = tf.constant([1,2,3,4], tf.float32) 
    x2 = tf.constant([5,6,7,8], tf.float32) 
    x3 = tf.constant([1,3,5,7], tf.float32) 
    x4 = tf.constant([4,8,12,16], tf.float32) 

    with tf.variable_scope("AAA") as scopeA: 
    avg_mean1 = tf.get_variable(
       'test1', [1], tf.float32, 
       initializer=tf.constant_initializer(0.0, tf.float32)) 
    avg_variance1 = tf.get_variable(
       'test2', [1], tf.float32, 
       initializer=tf.constant_initializer(0.0, tf.float32)) 
    print([avg_mean1.name, avg_variance1.name]) 
    y1, mean1, variance1, avg_mean1, avg_variance1 = getsta(x1,avg_mean1,avg_variance1) 
    print([avg_mean1.name, avg_variance1.name]) 
    scopeA.reuse_variables() 
    print([avg_mean1.name, avg_variance1.name]) 
    y1, mean1, variance1, avg_mean1, avg_variance1 = getsta(x2,avg_mean1,avg_variance1) 
    print([avg_mean1.name, avg_variance1.name]) 
    avg_mean1 = tf.identity(avg_mean1, name='avg_mean') 
    avg_variance1 = tf.identity(avg_variance1, name='avg_variance') 
    print([avg_mean1.name, avg_variance1.name]) 

    with tf.variable_scope("BBB") as scopeB: 
    avg_mean2 = tf.get_variable(
       'test1', [1], tf.float32, 
       initializer=tf.constant_initializer(0.0, tf.float32)) 
    avg_variance2 = tf.get_variable(
       'test2', [1], tf.float32, 
       initializer=tf.constant_initializer(0.0, tf.float32)) 
    print([avg_mean2.name, avg_variance2.name]) 
    y2, mean2, variance2, avg_mean2, avg_variance2 = getsta(x3, avg_mean2, avg_variance2) 
    print([avg_mean2.name, avg_variance2.name]) 
    scopeB.reuse_variables() 
    print([avg_mean2.name, avg_variance2.name]) 
    y2, mean2, variance2, avg_mean2, avg_variance2 = getsta(x4, avg_mean2, avg_variance2) 
    print([avg_mean2.name, avg_variance2.name]) 
    avg_mean2 = tf.identity(avg_mean2, name='avg_mean') 
    avg_variance2 = tf.identity(avg_variance2, name='avg_variance') 
    print([avg_mean2.name, avg_variance2.name]) 

    saver = tf.train.Saver() 
    sess = tf.InteractiveSession() 
    sess.run(tf.global_variables_initializer()) 
    print(sess.run([y1, mean1, variance1, avg_mean1, avg_variance1])) 
    print(sess.run([y2, mean2, variance2, avg_mean2, avg_variance2])) 

    allVars = tf.global_variables() 
    values = sess.run(allVars) 
    for var, val in zip(allVars, values): 
    print(var.name, val) 

    saver.save(sess, "/tmp/test_EMA/var_save.ckpt") 

运行上面的火车（）函数后，我得到了输出

['AAA/test1:0', 'AAA/test2:0'] 
getsta start... 
['AAA/sub_1:0', 'AAA/sub_3:0'] 
['AAA/sub_1:0', 'AAA/sub_3:0'] 
getsta start... 
['AAA/sub_5:0', 'AAA/sub_7:0'] 
['AAA/avg_mean:0', 'AAA/avg_variance:0'] 
['BBB/test1:0', 'BBB/test2:0'] 
getsta start... 
['BBB/sub_1:0', 'BBB/sub_3:0'] 
['BBB/sub_1:0', 'BBB/sub_3:0'] 
getsta start... 
['BBB/sub_5:0', 'BBB/sub_7:0'] 
['BBB/avg_mean:0', 'BBB/avg_variance:0'] 
[array([ 5., 6., 7., 8.], dtype=float32), 6.5, 1.25, array([ 0.875], dtype=float32), array([ 0.23750001], dtype=float32)] 
[array([ 4., 8., 12., 16.], dtype=float32), 10.0, 20.0, array([ 1.36000001], dtype=float32), array([ 2.45000005], dtype=float32)] 
AAA/test1:0 [ 0.] 
AAA/test2:0 [ 0.] 
AAA/mean:0 [ 0.] 
AAA/howvariance:0 [ 1.] 
BBB/test1:0 [ 0.] 
BBB/test2:0 [ 0.] 
BBB/mean:0 [ 0.] 
BBB/howvariance:0 [ 1.] 

我有几个问题：

1. 有什么办法来记录 “avg_mean” 不改变其名称？我想在加载模型时加载最终的更新值。
2. 是否有可能定义“avg_mean”的getsta（）函数内部，同时仍保持它像一个静态变量，即，它的初始值是从上一次的呼吁，但并不总是从0

3. 当启动我打印出所有变量，为什么名称和值都不正确，因为输出中的最后八行？我期望输出

   AAA/avg_mean:0 [0.875] 
   AAA/avg_variance:0 [0.23750001] 
   BBB/avg_mean:0 [1.36000001] 
   BBB/avg_variance:0 [2.45000005] 
   

感谢您的帮助提前！

-------------------- Update --------------------

I修改我的代码如下

def train(): 
    x1 = tf.constant([1,2,3,4], tf.float32) 
    x2 = tf.constant([5,6,7,8], tf.float32) 
    x3 = tf.constant([1,3,5,7], tf.float32) 
    x4 = tf.constant([4,8,12,16], tf.float32) 

    with tf.variable_scope("AAA") as scopeA: 
    avg_mean1 = tf.get_variable(
       'avg_mean', [1], tf.float32, 
       initializer=tf.constant_initializer(0.0, tf.float32)) 
    avg_variance1 = tf.get_variable(
       'avg_variance', [1], tf.float32, 
       initializer=tf.constant_initializer(0.0, tf.float32)) 
    y1, mean1, variance1 = getsta(x1,avg_mean1,avg_variance1) 
    scopeA.reuse_variables() 
    y1, mean1, variance1 = getsta(x2,avg_mean1,avg_variance1) 
    print([avg_mean1.name, avg_variance1.name]) 

    with tf.variable_scope("BBB") as scopeB: 
    avg_mean2 = tf.get_variable(
       'avg_mean', [1], tf.float32, 
       initializer=tf.constant_initializer(0.0, tf.float32)) 
    avg_variance2 = tf.get_variable(
       'avg_variance', [1], tf.float32, 
       initializer=tf.constant_initializer(0.0, tf.float32)) 
    y2, mean2, variance2 = getsta(x3, avg_mean2, avg_variance2) 
    scopeB.reuse_variables() 
    y2, mean2, variance2 = getsta(x4, avg_mean2, avg_variance2) 
    print([avg_mean2.name, avg_variance2.name]) 

    saver = tf.train.Saver() 
    sess = tf.InteractiveSession() 
    sess.run(tf.global_variables_initializer()) 
    print(sess.run([y1, mean1, variance1, avg_mean1, avg_variance1])) 
    print(sess.run([y2, mean2, variance2, avg_mean2, avg_variance2])) 

    allVars = tf.global_variables() 
    values = sess.run(allVars) 
    for var, val in zip(allVars, values): 
    print(var.name, val) 

    saver.save(sess, "/tmp/test_EMA/var_save.ckpt") 

现在变量的名称是固定的。但是，输出（变量的值）似乎不正确。输出是

[array([ 5., 6., 7., 8.], dtype=float32), 6.5, 1.25, array([ 0.], dtype=float32), array([ 0.], dtype=float32)] 
[array([ 4., 8., 12., 16.], dtype=float32), 10.0, 20.0, array([ 0.], dtype=float32), array([ 0.], dtype=float32)] 
AAA/avg_mean:0 [ 0.] 
AAA/avg_variance:0 [ 0.] 
AAA/mean:0 [ 0.] 
AAA/variance:0 [ 1.] 
BBB/avg_mean:0 [ 0.] 
BBB/avg_variance:0 [ 0.] 
BBB/mean:0 [ 0.] 

我该如何修改我的代码才能得到正确的结果？谢谢。

Answer 1

这里是在你的代码需要被修复的问题：

1在你与getsta返回operation更换avg_variance1以下行。你不能这样做。当您创建变量avg_variance1并将其传递给getsta时，该变量将在计算图中的任何位置进行更新，并且TensorFlow将处理此问题，您不需要明确地执行此操作。只要你指的是variable的name就没问题。 TensorFlow变量不像常规的Python变量。

y1, mean1, variance1, avg_mean1, avg_variance1 = getsta(x1,avg_mean1,avg_variance1) 

（你需要解决这个问题的avg_mean1，avg_mean2等）

2- avg_mean2，avg_variance2，avg_mean2和avg_variance2是Tensor，而不是variable。所以他们不在你打印的variables列表中。