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nn.softmax

   fn softmax(tensor: @Tensor<T>, axis: usize) -> 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.
softmax(xi)=exi∑j=1nexj\text{softmax}(x_i) = \frac{e^{x_i}}{\sum_{j=1}^n e^{x_j}}softmax(xi​)=∑j=1n​exj​exi​​
Args
tensor(@Tensor<T>) - The input tensor.
axis(usize) - The axis along which to compute the softmax.
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 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, 1);
}
>>> [[2255697,6132911],[2255697,6132911]]
    // The fixed point representation of
    // [[0.2689, 0.7311],[0.2689, 0.7311]]

nn.sigmoid

nn.logsoftmax

Last modified 20d ago