nn.softmax

   fn softmax(tensor: @Tensor<T>, axis: Option<i32>) -> Tensor<T>;

Applies the Softmax function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0,1] and sum to 1.

$$\text{softmax}(x_i) = \frac{e^{x_i}}{\sum_{j=1}^n e^{x_j}}$$

Args

• tensor(@Tensor<T>) - The input tensor.

• axis(Option<i32>) - Describes the dimension Softmax will be performed on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input).

Returns

A Tensor of fixed point numbers with the same shape than the input Tensor.

Type Constraints

Constrain input and output types to fixed point tensors.

Examples

use core::array::{ArrayTrait, SpanTrait};

use orion::operators::tensor::{TensorTrait, Tensor, FP8x23Tensor};
use orion::operators::nn::{NNTrait, FP8x23NN};
use orion::numbers::{FP8x23, FixedTrait};

fn softmax_example() -> Tensor<FP8x23> {
    let tensor = TensorTrait::<FP8x23>::new(
        shape: array![2, 2].span(),
        data: array![
            FixedTrait::new(0, false),
            FixedTrait::new(1, false),
            FixedTrait::new(2, false),
            FixedTrait::new(3, false),
        ]
            .span(),
    );

    return NNTrait::softmax(@tensor, Option::Some(1));
}
>>> [[2255697,6132911],[2255697,6132911]]
    // The fixed point representation of
    // [[0.2689, 0.7311],[0.2689, 0.7311]]