我想使用CW算法训练一些对抗性示例，我使用了here中的示例和here中的CW实现。但是我遇到关于tf.zeros_initializer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)
这里是env.x：
env.x = tf.placeholder(tf.float32, (None, width, height, channels), name='x')
运行代码时，出现错误消息：
---------------------------------------------------------------------------
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Google的TensorFlow Guide给出了get_variable用法的示例：
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
答案 0 :(得分：-1)
只需根据您的占位符维度进行更改，让我以您的占位符变量为例。
** x = placeholder(t f. float 32, (None, width, height, channels), name='x')**.
它具有4个维度：[无，宽度，高度，通道]，但是未定义宽度，高度，通道，这意味着对于图像宽度= 6，高度= 6，通道= 3定义了张量尺寸是[6 x 6 x 3]。
您可以做的是，读取图像，将所有三个维度的值获取到不同的变量中，并将其传递给占位符变量。
例如
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直接将宽度，高度，通道的值（如果您知道输入数据的形状）提供给占位符。