ak.softmax — Awkward Array documentation

# ak.softmax

Defined in awkward.operations.reducers on line 1470.

ak.softmax(x, axis=None, keepdims=False, mask_identity=False)
  Parameters:

    - x – the data on which to compute the softmax.

    - weight – data that can be broadcasted to `x` to give each value a weight. Weighting values equally is the same as no weights; weighting some values higher increases the significance of those values. Weights can be zero or negative.

    - axis (None or int) – 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.

    - keepdims (bool) – 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.

    - mask_identity (bool) – 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`.

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

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

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