jax.nn.softmax

jax.nn.softmax(x, axis=-1, where=None, initial=None)

Softmax function.

Computes the function which rescales elements to the range \([0, 1]\) such that the elements along axis sum to \(1\).

\[\mathrm{softmax}(x) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}\]

Parameters:

• x (ArrayLike) – input array

• axis (int | tuple[int, …] | None) – the axis or axes along which the softmax should be computed. The softmax output summed across these dimensions should sum to \(1\). Either an integer or a tuple of integers.

• where (ArrayLike | None) – Elements to include in the softmax.

• initial (ArrayLike | None) – The minimum value used to shift the input array. Must be present when where is not None.

Return type:

Array

Returns:

An array.

See also

log_softmax()