jax.nn.softmax#

jax.nn.softmax(x, axis=- 1, where=None, initial=None)[source]#

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 (Any) – input array

  • axis (Union[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 (Optional[Any]) – Elements to include in the softmax.

  • initial (Optional[Any]) – The minimum value used to shift the input array. Must be present when where is not None.

Return type

Any