tensor.reduce_log_sum

tensor.reduce_log_sum

   fn reduce_log_sum(self: @Tensor<T>, axis: usize, keepdims: bool) -> Tensor<T>;

Computes the log sum of the input tensor's elements along the provided axes.

Args

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

• axis(usize) - The dimension to reduce.

• keepdims(bool) - If true, retains reduced dimensions with length 1.

Panics

• Panics if axis is not in the range of the input tensor's dimensions.

Returns

A new Tensor<T> instance with the specified axis reduced by summing its elements.

Examples

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

use orion::operators::tensor::{TensorTrait, Tensor, FP16x16Tensor};
use orion::numbers::{FixedTrait, FP16x16};

fn reduce_log_sum() -> Tensor<FP16x16> {

   let mut sizes = ArrayTrait::new();
   sizes.append(2);
   sizes.append(2);
   sizes.append(2);

   let mut data = ArrayTrait::new();
   data.append(FixedTrait::new_unscaled(1, false));
   data.append(FixedTrait::new_unscaled(2, false));
   data.append(FixedTrait::new_unscaled(3, false));
   data.append(FixedTrait::new_unscaled(4, false));
   data.append(FixedTrait::new_unscaled(5, false));
   data.append(FixedTrait::new_unscaled(6, false));
   data.append(FixedTrait::new_unscaled(7, false));
   data.append(FixedTrait::new_unscaled(8, false));

   let tensor = TensorTrait::<FP16x16>::new(sizes.span(), data.span());

    We can call `reduce_log_sum` function as follows.
    return tensor.reduce_log_sum(axis: 2, keepdims: false);
}
>>> [[0x11938, 0x1f203], [0x265d9, 0x2b540]]