ak.softmax ---------- .. py:module: ak.softmax Defined in `awkward.operations.ak_softmax `__ on `line 9 `__. .. py:function:: ak.softmax(x, axis=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: ``0`` is the outermost, ``1`` is the first level of nested lists, etc., and negative ``axis`` counts from the innermost: ``-1`` is the innermost, ``-2`` is the next level up, etc. :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 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.