ak.softmax

Defined in awkward.operations.reducers on line 1394.

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 descreases 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.