Class TensorShape

tensorboard/compat/tensorflow_stub/tensor_shape.py:493–979

Represents the shape of a `Tensor`. A `TensorShape` represents a possibly-partial shape specification for a `Tensor`. It may be one of the following: * *Fully-known shape:* has a known number of dimensions and a known size for each dimension. e.g. `TensorShape([16, 256])` * *

# @tf_export("TensorShape")
class TensorShape:
    """Represents the shape of a `Tensor`.

    A `TensorShape` represents a possibly-partial shape specification for a
    `Tensor`. It may be one of the following:

    * *Fully-known shape:* has a known number of dimensions and a known size
      for each dimension. e.g. `TensorShape([16, 256])`
    * *Partially-known shape:* has a known number of dimensions, and an unknown
      size for one or more dimension. e.g. `TensorShape([None, 256])`
    * *Unknown shape:* has an unknown number of dimensions, and an unknown
      size in all dimensions. e.g. `TensorShape(None)`

    If a tensor is produced by an operation of type `"Foo"`, its shape
    may be inferred if there is a registered shape function for
    `"Foo"`. See @{$adding_an_op#shape-functions-in-c$`Shape functions in C++`}
    for details of shape functions and how to register them. Alternatively,
    the shape may be set explicitly using @{tf.Tensor.set_shape}.
    """

    def __init__(self, dims):
        """Creates a new TensorShape with the given dimensions.

        Args:
          dims: A list of Dimensions, or None if the shape is unspecified.
            DEPRECATED: A single integer is treated as a singleton list.

        Raises:
          TypeError: If dims cannot be converted to a list of dimensions.
        """
        # TODO(irving): Eliminate the single integer special case.
        if dims is None:
            self._dims = None
        elif isinstance(dims, compat.bytes_or_text_types):
            raise TypeError(
                "A string has ambiguous TensorShape, please wrap in a "
                "list or convert to an int: %s" % dims
            )
        elif isinstance(dims, tensor_shape_pb2.TensorShapeProto):
            if dims.unknown_rank:
                self._dims = None
            else:
                self._dims = [
                    # Protos store variable-size dimensions as -1
                    as_dimension(dim.size if dim.size != -1 else None)
                    for dim in dims.dim
                ]
        elif isinstance(dims, TensorShape):
            self._dims = dims.dims
        else:
            try:
                dims_iter = iter(dims)
            except TypeError:
                # Treat as a singleton dimension
                self._dims = [as_dimension(dims)]
            else:
                # Got a list of dimensions
                self._dims = [as_dimension(d) for d in dims_iter]