libdl/loss_8hpp_source.html

#ifndef DL_LEARNING_LOSS_HPP

#define DL_LEARNING_LOSS_HPP


#include "../tensor/math.hpp"


namespace dl::loss {

    TensorPtr mse(TensorPtr x, TensorPtr y) noexcept { return dl::mean(dl::pow(x - y, 2.0f)); }


    TensorPtr bce(TensorPtr x, TensorPtr y) noexcept {

        return -dl::mean(y * dl::log(x) + (1.0f - y) * dl::log(1.0f - x));

    }


} // namespace dl::loss


#endif

dl::TensorPtr
The Tensor is a managed pointer to a tensor. It can generally be thought of like an std::unique_ptr<T...
Definition tensorptr.hpp:45

dl::pow
TensorPtr pow(TensorPtr base, float exponent) noexcept
Computes the exponent -th power of each element in base and returns the resulting tensor.

dl::log
TensorPtr log(TensorPtr x) noexcept
Natural logarithm, .

dl::mean
TensorPtr mean(TensorPtr x) noexcept
Mean.

dl::loss::mse
TensorPtr mse(TensorPtr x, TensorPtr y) noexcept
Mean Square Error.
Definition loss.hpp:23

dl::loss::bce
TensorPtr bce(TensorPtr x, TensorPtr y) noexcept
Binary Cross Entropy Loss.
Definition loss.hpp:37