Function expand_dims

tensorflow/contrib/labeled_tensor/python/ops/core.py:725–771

Insert dimensions of size 1. See tf.expand_dims. Args: labeled_tensor: The input tensor. axes: The desired axis names as strings or tuples of (name, label), where `label` is the coordinate name for the new dimension `name`. These must include the existing axis names, and th

(labeled_tensor, axes, name=None)

                         tc.Tuple(string_types, collections_abc.Hashable))),
            tc.Optional(string_types))
def expand_dims(labeled_tensor, axes, name=None):
  """Insert dimensions of size 1.

  See tf.expand_dims.

  Args:
    labeled_tensor: The input tensor.
    axes: The desired axis names as strings or tuples of (name, label), where
      `label` is the coordinate name for the new dimension `name`. These must
      include the existing axis names, and the existing names must appear in the
      same order in this list as they do in the input tensor.
    name: Optional op name.

  Returns:
    A tensor with an axis for each axis in axes.
    New axes are created with size 1 and do not have labeled coordinates.

  Raises:
    AxisOrderError: If axis names don't appear in the same order in axes
      and the labeled tensor.
  """
  with ops.name_scope(name, 'lt_expand_dims', [labeled_tensor]) as scope:
    labeled_tensor = convert_to_labeled_tensor(labeled_tensor)

    axis_names = [a if isinstance(a, string_types) else a[0] for a in axes]
    check_axis_order(labeled_tensor, axis_names)

    reshaped_axes = []
    shape = []
    for axis_spec in axes:
      if axis_spec in labeled_tensor.axes:
        axis = labeled_tensor.axes[axis_spec]
        reshaped_axes.append(axis)
        shape.append(-1 if axis.size is None else axis.size)
      else:
        if isinstance(axis_spec, string_types):
          reshaped_axes.append((axis_spec, 1))
        else:
          (name, label) = axis_spec
          reshaped_axes.append((name, (label,)))

        shape.append(1)

    reshaped_tensor = array_ops.reshape(
        labeled_tensor.tensor, shape, name=scope)

    return LabeledTensor(reshaped_tensor, reshaped_axes)


# This should only be added to a graph collection once.