Tensorflow 什么'；“get_variable（）”中的“tf.zeros_初始值设定项”有什么问题吗？

我想用CW算法训练一些对抗性的例子，我使用了来自的例子和来自的CW实现。但是我遇到了一个关于

tf.zeros\u初始值设定项的错误：

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值设定项冲突。我怎样才能修好它


下面是一段代码：

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

以下是
env.x
：

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

运行代码时，我会收到错误消息：

---------------------------------------------------------------------------
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.

环境：Google Colab、TensorFlow 1.14.0-rc1、Python 3.6
只需根据占位符维度进行更改，让我以占位符变量为例

** 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]，但未定义宽度，高度，通道，这意味着，对于图像宽度=6，高度=6，通道=3，因此定义了张量维度[6 x 6 x 3]

您可以做的是，读取图像，获取不同变量中的所有三维值，并将其传递给占位符变量。
前任。
图像A=32 x 32 x 3
宽度=A.形状[0]
高度=A.形状[1]
通道=A.shape[2]

或者，您可以通过这种方式直接向宽度、高度、通道（如果您知道输入数据形状）提供值，从而定义占位符。
为什么要初始化占位符？你应该向它输入数据。添加
（）
，这样它就可以调用这样的函数
…，initializer=tf.zeros.initializer（）
@ImperishableNight我不想初始化占位符。该错误是由初始化变量
noise
引起的。
tf.placeholder
，
shape
的第二个参数没有完全定义（
（无，…）
），因此我收到了错误消息。@yolo如果
初始值设定项
是一个类，TensorFlow似乎会首先创建一个实例。
** x = placeholder(t f. float 32, (None, width, height, channels), name='x')**.