类型错误：不支持 / 的操作数类型：TensorFlow 中的“维度”和“浮点数”

Question

我有一个在深度学习中实现的代码Tensorflow。我使用Keras模块

self.n_clusters = 10
self.alpha = 0.01
clustering_layer = ClusteringLayer(self.n_clusters, alpha=self.alpha, name='clustering')(hidden)

我的错误主要来自上面，所以我附上了它。

它给了我以下错误：

--> 118         clustering_layer = ClusteringLayer(self.n_clusters, alpha=self.alpha, name='clustering')(hidden)
    119         self.model = Model(inputs=self.autoencoder.input, outputs=clustering_layer)
    120 

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/keras/engine/base_layer.pyc in __call__(self, inputs, *args, **kwargs)
    694         if all(hasattr(x, 'get_shape') for x in input_list):
    695           input_shapes = nest.map_structure(lambda x: x.get_shape(), inputs)
--> 696         self.build(input_shapes)
    697 
    698       # Check input assumptions set after layer building, e.g. input shape.

<ipython-input-13-8890754cc8a3> in build(self, input_shape)
     73         input_dim = input_shape[1]
     74         self.input_spec = InputSpec(dtype=K.floatx(), shape=(None, input_dim))
---> 75         self.clusters = self.add_weight(shape=(self.n_clusters, input_dim), initializer='glorot_uniform', name='clusters')
     76         if self.initial_weights is not None:
     77             self.set_weights(self.initial_weights)

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/keras/engine/base_layer.pyc in add_weight(self, name, shape, dtype, initializer, regularizer, trainable, constraint, partitioner, use_resource, getter)
    532         trainable=trainable and self.trainable,
    533         partitioner=partitioner,
--> 534         use_resource=use_resource)
    535 
    536     if regularizer is not None:

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/training/checkpointable/base.pyc in _add_variable_with_custom_getter(self, name, shape, dtype, initializer, getter, overwrite, **kwargs_for_getter)
    495     new_variable = getter(
    496         name=name, shape=shape, dtype=dtype, initializer=initializer,
--> 497         **kwargs_for_getter)
    498 
    499     # If we set an initializer and the variable processed it, tracking will not

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/keras/engine/base_layer.pyc in make_variable(name, shape, dtype, initializer, partition_info, trainable, caching_device, validate_shape, constraint, use_resource, partitioner)
   1871       validate_shape=validate_shape,
   1872       constraint=constraint,
-> 1873       use_resource=use_resource)
   1874   return v

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in variable(initial_value, trainable, collections, validate_shape, caching_device, name, dtype, constraint, use_resource)
   2232                          name=name, dtype=dtype,
   2233                          constraint=constraint,
-> 2234                          use_resource=use_resource)
   2235 
   2236 

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in <lambda>(**kwargs)
   2222              constraint=None,
   2223              use_resource=None):
-> 2224   previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs)
   2225   for getter in ops.get_default_graph()._variable_creator_stack:  # pylint: disable=protected-access
   2226     previous_getter = _make_getter(getter, previous_getter)

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/ops/variable_scope.pyc in default_variable_creator(next_creator, **kwargs)
   2194         collections=collections, validate_shape=validate_shape,
   2195         caching_device=caching_device, name=name, dtype=dtype,
-> 2196         constraint=constraint)
   2197   elif not use_resource and context.executing_eagerly():
   2198     raise RuntimeError(

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/ops/resource_variable_ops.pyc in __init__(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, variable_def, import_scope, constraint)
    310           name=name,
    311           dtype=dtype,
--> 312           constraint=constraint)
    313 
    314   # pylint: disable=unused-argument

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/ops/resource_variable_ops.pyc in _init_from_args(self, initial_value, trainable, collections, validate_shape, caching_device, name, dtype, constraint)
    415               with ops.name_scope("Initializer"), ops.device(None):
    416                 initial_value = ops.convert_to_tensor(
--> 417                     initial_value(), name="initial_value", dtype=dtype)
    418               self._handle = _eager_safe_variable_handle(
    419                   shape=initial_value.get_shape(),

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/keras/engine/base_layer.pyc in <lambda>()
   1858         initializer = initializer(dtype=dtype)
   1859       init_val = lambda: initializer(  # pylint: disable=g-long-lambda
-> 1860           shape, dtype=dtype, partition_info=partition_info)
   1861       variable_dtype = dtype.base_dtype
   1862   if use_resource is None:

/usr/local/python/2.7-conda5.2/lib/python2.7/site-packages/tensorflow/python/ops/init_ops.pyc in __call__(self, shape, dtype, partition_info)
    466       scale /= max(1., fan_out)
    467     else:
--> 468       scale /= max(1., (fan_in + fan_out) / 2.)
    469     if self.distribution == "normal":
    470       stddev = math.sqrt(scale)

TypeError: unsupported operand type(s) for /: 'Dimension' and 'float'

第 118 行是我的代码位置。该错误似乎发生在 tensorflow 包中。它给了我TypeError: unsupported operand type(s) for /: 'Dimension' and 'float'. 我同时尝试python 2.7和python 3.6但有同样的问题。

如何处理这种情况？

一个非常相似的情况在github 中，它的错误可以在其代码中解决，但我的错误似乎发生在 init_ops.pyc

Answer 1

这里的问题似乎是 ClusteringLayer 传递了一个 tf.Dimension 对象作为簇数，用于初始化权重。改为执行 int(self.n_clusters) 以绕过维度对象问题。