ak.softmax
----------

.. py:module: ak.softmax

Defined in `awkward.operations.ak_softmax <https://github.com/scikit-hep/awkward/blob/e9dd2694cf5c6ccb1717358fe926816d8ecbfb9c/src/awkward/operations/ak_softmax.py>`__ on `line 22 <https://github.com/scikit-hep/awkward/blob/e9dd2694cf5c6ccb1717358fe926816d8ecbfb9c/src/awkward/operations/ak_softmax.py#L22>`__.

.. py:function:: ak.softmax(x, axis=None, *, keepdims=False, mask_identity=False, highlevel=True, behavior=None, attrs=None)


    :param x: The data on which to compute the softmax (anything :py:obj:`ak.to_layout` recognizes).
    :param axis: If None, combine all values from the array into
             a single scalar result; if an int, group by that axis. Only ``axis``
             arguments equivalent to ``-1`` (softmax reduction along the innermost
             dimension) is supported.
    :type axis: None or int
    :param keepdims: If False, this function decreases the number of
                 dimensions by 1; if True, the output values are wrapped in a new
                 length-1 dimension so that the result of this operation may be
                 broadcasted with the original array.
    :type keepdims: bool
    :param mask_identity: If True, the application of this function on
                      empty lists results in None (an option type); otherwise, the
                      calculation is followed through with the reducers' identities,
                      usually resulting in floating-point ``nan``.
    :type mask_identity: bool
    :param highlevel: If True, return an :py:obj:`ak.Array`; otherwise, return
                  a low-level :py:obj:`ak.contents.Content` subclass.
    :type highlevel: bool
    :param behavior: Custom :py:obj:`ak.behavior` for the output array, if
                 high-level.
    :type behavior: None or dict
    :param attrs: Custom attributes for the output array, if
              high-level.
    :type attrs: None or dict

Computes the softmax in each group of elements from ``x`` (many
types supported, including all Awkward Arrays and Records). The grouping
is performed the same way as for reducers, though this operation is not a
reducer and has no identity.

This function has no NumPy equivalent.

Passing all arguments to the reducers, the softmax is calculated as

.. code-block:: python


    np.exp(x) / ak.sum(np.exp(x))

See :py:obj:`ak.sum` for a complete description of handling nested lists and
missing values (None) in reducers, and :py:obj:`ak.mean` for an example with another
non-reducer.