Function expand_dims

tensorflow/python/ops/array_ops.py:214–265

Inserts a dimension of 1 into a tensor's shape. Given a tensor `input`, this operation inserts a dimension of 1 at the dimension index `axis` of `input`'s shape. The dimension index `axis` starts at zero; if you specify a negative number for `axis` it is counted backward from the end. Th

(input, axis=None, name=None, dim=None)

@dispatch.add_dispatch_support
@deprecation.deprecated_args(None, "Use the `axis` argument instead", "dim")
def expand_dims(input, axis=None, name=None, dim=None):
  """Inserts a dimension of 1 into a tensor's shape.

  Given a tensor `input`, this operation inserts a dimension of 1 at the
  dimension index `axis` of `input`'s shape. The dimension index `axis` starts
  at zero; if you specify a negative number for `axis` it is counted backward
  from the end.

  This operation is useful if you want to add a batch dimension to a single
  element. For example, if you have a single image of shape `[height, width,
  channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
  which will make the shape `[1, height, width, channels]`.

  Other examples:

  ```python
  # 't' is a tensor of shape [2]
  tf.shape(tf.expand_dims(t, 0))  # [1, 2]
  tf.shape(tf.expand_dims(t, 1))  # [2, 1]
  tf.shape(tf.expand_dims(t, -1))  # [2, 1]

  # 't2' is a tensor of shape [2, 3, 5]
  tf.shape(tf.expand_dims(t2, 0))  # [1, 2, 3, 5]
  tf.shape(tf.expand_dims(t2, 2))  # [2, 3, 1, 5]
  tf.shape(tf.expand_dims(t2, 3))  # [2, 3, 5, 1]
  ```

  This operation requires that:

  `-1-input.dims() <= dim <= input.dims()`

  This operation is related to `squeeze()`, which removes dimensions of
  size 1.

  Args:
    input: A `Tensor`.
    axis: 0-D (scalar). Specifies the dimension index at which to expand the
      shape of `input`. Must be in the range `[-rank(input) - 1, rank(input)]`.
    name: The name of the output `Tensor` (optional).
    dim: 0-D (scalar). Equivalent to `axis`, to be deprecated.

  Returns:
    A `Tensor` with the same data as `input`, but its shape has an additional
    dimension of size 1 added.

  Raises:
    ValueError: if either both or neither of `dim` and `axis` are specified.
  """
  axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim)
  if axis is None:
    raise ValueError("Must specify an axis argument to tf.expand_dims()")
  return expand_dims_v2(input, axis, name)


@tf_export("expand_dims", v1=[])