Python tensor_shape.dimension_value方法代碼示例

# 需要導入模塊: from tensorflow.python.framework import tensor_shape [as 別名]
# 或者: from tensorflow.python.framework.tensor_shape import dimension_value [as 別名]
def build(self, input_shape):
        super().build(input_shape)
        self.build = False
        self.last_dim = tensor_shape.dimension_value(input_shape[-1])
        self.noisy_w = self.add_weight(
            'noise_kernel',
            shape=[self.last_dim, self.units],
            initializer=tf.random_normal_initializer(0.0, .1),
            regularizer=self.kernel_regularizer,
            constraint=self.kernel_constraint,
            dtype=self.dtype,
            trainable=True)
        if self.use_bias:
            self.noisy_b = self.add_weight(
                'noise_bias',
                shape=[self.units, ],
                initializer=tf.constant_initializer(self.noise_sigma / (self.units**0.5)),
                regularizer=self.bias_regularizer,
                constraint=self.bias_constraint,
                dtype=self.dtype,
                trainable=True)
        else:
            self.bias = None
        self.build = True 

# 需要導入模塊: from tensorflow.python.framework import tensor_shape [as 別名]
# 或者: from tensorflow.python.framework.tensor_shape import dimension_value [as 別名]
def build(self, input_shape):
        if self.proj_weights is None:
            input_dim = tensor_shape.dimension_value(input_shape[-1])
            self.layer_weights = self.add_weight(
                'output_layer_weights',
                shape=[input_dim, self.output_dim],
                initializer=self.kernel_initializer,
                trainable=True)
        super(OutputLayer, self).build(input_shape) 

# 需要導入模塊: from tensorflow.python.framework import tensor_shape [as 別名]
# 或者: from tensorflow.python.framework.tensor_shape import dimension_value [as 別名]
def convert_legacy_structure(output_types, output_shapes, output_classes):
  """Returns a `Structure` that represents the given legacy structure.
  This method provides a way to convert from the existing `Dataset` and
  `Iterator` structure-related properties to a `Structure` object. A "legacy"
  structure is represented by the `tf.data.Dataset.output_types`,
  `tf.data.Dataset.output_shapes`, and `tf.data.Dataset.output_classes`
  properties.
  TODO(b/110122868): Remove this function once `Structure` is used throughout
  `tf.data`.
  Args:
    output_types: A nested structure of `tf.DType` objects corresponding to
      each component of a structured value.
    output_shapes: A nested structure of `tf.TensorShape` objects
      corresponding to each component a structured value.
    output_classes: A nested structure of Python `type` objects corresponding
      to each component of a structured value.
  Returns:
    A `Structure`.
  Raises:
    TypeError: If a structure cannot be built from the arguments, because one of
      the component classes in `output_classes` is not supported.
  """
  flat_types = nest.flatten(output_types)
  flat_shapes = nest.flatten(output_shapes)
  flat_classes = nest.flatten(output_classes)
  flat_ret = []
  for flat_type, flat_shape, flat_class in zip(flat_types, flat_shapes,
                                               flat_classes):
    if isinstance(flat_class, Structure):
      flat_ret.append(flat_class)
    elif issubclass(flat_class, sparse_tensor_lib.SparseTensor):
      flat_ret.append(SparseTensorStructure(flat_type, flat_shape))
    elif issubclass(flat_class, ops.Tensor):
      flat_ret.append(TensorStructure(flat_type, flat_shape))
    elif issubclass(flat_class, tensor_array_ops.TensorArray):
      # We sneaked the dynamic_size and infer_shape into the legacy shape.
      flat_ret.append(
          TensorArrayStructure(
              flat_type, flat_shape[2:],
              dynamic_size=tensor_shape.dimension_value(flat_shape[0]),
              infer_shape=tensor_shape.dimension_value(flat_shape[1])))
    else:
      # NOTE(mrry): Since legacy structures produced by iterators only
      # comprise Tensors, SparseTensors, and nests, we do not need to
      # support all structure types here.
      raise TypeError(
          "Could not build a structure for output class %r" % (flat_class,))

  ret = nest.pack_sequence_as(output_classes, flat_ret)
  if isinstance(ret, Structure):
    return ret
  else:
    return NestedStructure(ret)


# NOTE(mrry): The following classes make extensive use of non-public methods of
# their base class, so we disable the protected-access lint warning once here.
# pylint: disable=protected-access
# @tf_export("data.experimental.NestedStructure")