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tensorflow - `get_variable()` 中的 `tf.zeros_initializer` 有什么问题？

问题描述

我想使用 CW 算法训练一些对抗性示例，我使用了这里的示例和这里的CW 实现。但是我遇到了一个错误：tf.zeros_initializer

ValueError: The initializer passed is not valid. It should be a callable with no arguments and the shape should not be provided or an instance of 
'tf.keras.initializers.*' and `shape` should be fully defined.

编辑：似乎未完全定义的形状与使用初始化程序冲突。我该如何解决？

这是一段代码：

# ... omitted
with tf.variable_scope('model', reuse=tf.AUTO_REUSE):
    # CW
    _, env.adv_cw, _ = cw.cw(model, env.x)

这是env.x：

env.x = tf.placeholder(tf.float32, (None, width, height, channels), name='x')

当我运行代码时，我收到错误消息：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-39-712c8b007d37> in <module>()
      8 with tf.variable_scope('model', reuse=tf.AUTO_REUSE):
      9     # CW
---> 10     _, env.adv_cw, _ = cw.cw(model, env.x)

5 frames
/content/cw.py in cw(model, x, y, eps, ord_, T, optimizer, alpha, min_prob, clip)
     50     """
     51     xshape = x.get_shape().as_list()
---> 52     noise = tf.get_variable('noise', shape=xshape, dtype=tf.float32,
     53                             initializer=tf.zeros_initializer)
     54 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in get_variable(name, shape, dtype, initializer, regularizer, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)
   1494       constraint=constraint,
   1495       synchronization=synchronization,
-> 1496       aggregation=aggregation)
   1497 
   1498 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in get_variable(self, var_store, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)
   1237           constraint=constraint,
   1238           synchronization=synchronization,
-> 1239           aggregation=aggregation)
   1240 
   1241   def _get_partitioned_variable(self,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in get_variable(self, name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, custom_getter, constraint, synchronization, aggregation)
    560           constraint=constraint,
    561           synchronization=synchronization,
--> 562           aggregation=aggregation)
    563 
    564   def _get_partitioned_variable(self,

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in _true_getter(name, shape, dtype, initializer, regularizer, reuse, trainable, collections, caching_device, partitioner, validate_shape, use_resource, constraint, synchronization, aggregation)
    512           constraint=constraint,
    513           synchronization=synchronization,
--> 514           aggregation=aggregation)
    515 
    516     synchronization, aggregation, trainable = (

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/variable_scope.py in _get_single_variable(self, name, shape, dtype, initializer, regularizer, partition_info, reuse, trainable, collections, caching_device, validate_shape, use_resource, constraint, synchronization, aggregation)
    906           variable_dtype = None
    907         else:
--> 908           raise ValueError("The initializer passed is not valid. It should "
    909                            "be a callable with no arguments and the "
    910                            "shape should not be provided or an instance of "

ValueError: The initializer passed is not valid. It should be a callable with no arguments and the shape should not be provided or an instance of `tf.keras.initializers.*' and `shape` should be fully defined.

但是谷歌的 TensorFlow Guide给出了一个使用的例子get_variable：

my_int_variable = tf.get_variable("my_int_variable", [1, 2, 3], dtype=tf.int32,
  initializer=tf.zeros_initializer)

环境： Google Colab、TensorFlow 1.14.0-rc1、Python 3.6

标签： tensorflow

解决方案

只需根据您的占位符尺寸进行更改，让我以您的占位符变量为例。

** x = placeholder(t f. float 32, (None, width, height, channels), name='x')**.

它有 4 个维度：[无，宽度，高度，通道]，但是没有定义宽度，高度，通道，意味着对于图像宽度 = 6，高度 = 6，通道 = 3 已定义，因此张量维度为 [6 x 6 x 3]。

您可以做的是，您读取的图像，获取不同变量中的所有三个维度值并将其传递给您的占位符变量。前任。 Image A = 32 x 32 x 3 width = A.shape[0] height = A.shape[1] channels =A.shape[2]

或者您可以通过这种方式定义占位符，直接将值提供给宽度、高度、通道（如果您知道输入数据的形状）。

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