documentation/reference/error__utilities_8h_source.html

//   OpenNN: Open Neural Networks Library

//   www.opennn.net

//

//   E R R O R   U T I L I T I E S   H E A D E R

//

//   Artificial Intelligence Techniques SL

//   artelnics@artelnics.com


#pragma once


#include "tensor_utilities.h"


namespace opennn

{


void mean_squared_error(const TensorView& input, const TensorView& target, float& error, float* workspace_device);

void mean_squared_error_gradient(const TensorView& input, const TensorView& target, const TensorView& input_delta);


void normalized_squared_error(const TensorView& input, const TensorView& target, float coefficient, float& error, float* workspace_device);

void normalized_squared_error_gradient(const TensorView& input, const TensorView& target, float coefficient, const TensorView& input_delta);


void weighted_squared_error(const TensorView& input, const TensorView& target, float pos_w, float neg_w, float& error, float* workspace_device);

void weighted_squared_error_gradient(const TensorView& input, const TensorView& target, float pos_w, float neg_w, float coefficient, const TensorView& input_delta);


void binary_cross_entropy(const TensorView& input, const TensorView& target, float& error, float* workspace_device);

void categorical_cross_entropy(const TensorView& input, const TensorView& target, float& error, float* workspace_device);

void cross_entropy_gradient(const TensorView& input, const TensorView& target, const TensorView& input_delta);


void minkowski_error(const TensorView& input, const TensorView& target, float power, float& error, float* workspace_device);

void minkowski_error_gradient(const TensorView& input, const TensorView& target, float power, const TensorView& input_delta);


void cross_entropy_3d(const TensorView& input, const TensorView& target, float& error, Index& active_tokens_out, Index& correct_tokens_out, float* errors_device = nullptr);

void cross_entropy_3d_gradient(const TensorView& input, const TensorView& target, const TensorView& input_delta, Index active_tokens_count);

void cross_entropy_3d_gradient_device_count(const TensorView& input, const TensorView& target, const TensorView& input_delta, const float* active_tokens_count_device);


void l1_regularization(const TensorView& parameters, float lambda, float& penalty);

void l1_regularization_gradient(const TensorView& parameters, float lambda, const TensorView& gradient);


void l2_regularization(const TensorView& parameters, float lambda, float& penalty);

void l2_regularization_gradient(const TensorView& parameters, float lambda, const TensorView& gradient);


}


// OpenNN: Open Neural Networks Library.

// Copyright(C) 2005-2026 Artificial Intelligence Techniques, SL.

// Licensed under the GNU Lesser General Public License v2.1 or later.

opennn
Definition adaptive_moment_estimation.h:14

opennn::categorical_cross_entropy
void categorical_cross_entropy(const TensorView &input, const TensorView &target, float &error, float *workspace_device)
Computes the multi-class (categorical) cross-entropy between softmax probabilities and one-hot target...

opennn::minkowski_error
void minkowski_error(const TensorView &input, const TensorView &target, float power, float &error, float *workspace_device)
Computes the Minkowski error sum(|input - target|^power) for the given power exponent.

opennn::minkowski_error_gradient
void minkowski_error_gradient(const TensorView &input, const TensorView &target, float power, const TensorView &input_delta)
Writes the Minkowski-error gradient with respect to the predictions into input_delta.

opennn::mean_squared_error_gradient
void mean_squared_error_gradient(const TensorView &input, const TensorView &target, const TensorView &input_delta)
Writes the MSE gradient with respect to the predictions into input_delta.

opennn::mean_squared_error
void mean_squared_error(const TensorView &input, const TensorView &target, float &error, float *workspace_device)
Computes the mean squared error between predictions and targets.

opennn::normalized_squared_error_gradient
void normalized_squared_error_gradient(const TensorView &input, const TensorView &target, float coefficient, const TensorView &input_delta)
Writes the normalized-squared-error gradient with respect to the predictions into input_delta.

opennn::cross_entropy_3d
void cross_entropy_3d(const TensorView &input, const TensorView &target, float &error, Index &active_tokens_out, Index &correct_tokens_out, float *errors_device=nullptr)
Computes 3-D (sequence) cross-entropy used by transformer-style targets, ignoring padded positions.

opennn::l2_regularization_gradient
void l2_regularization_gradient(const TensorView &parameters, float lambda, const TensorView &gradient)
Adds the L2 regularization gradient 2 * lambda * parameters into the gradient tensor.

opennn::weighted_squared_error_gradient
void weighted_squared_error_gradient(const TensorView &input, const TensorView &target, float pos_w, float neg_w, float coefficient, const TensorView &input_delta)
Writes the gradient of the weighted squared error scaled by coefficient into input_delta.

opennn::weighted_squared_error
void weighted_squared_error(const TensorView &input, const TensorView &target, float pos_w, float neg_w, float &error, float *workspace_device)
Computes the binary squared error weighted asymmetrically for positive and negative classes.

opennn::cross_entropy_gradient
void cross_entropy_gradient(const TensorView &input, const TensorView &target, const TensorView &input_delta)
Writes the cross-entropy gradient with respect to the (pre-softmax/logit) predictions into input_delt...

opennn::binary_cross_entropy
void binary_cross_entropy(const TensorView &input, const TensorView &target, float &error, float *workspace_device)
Computes the binary cross-entropy between predicted probabilities and binary targets.

opennn::cross_entropy_3d_gradient_device_count
void cross_entropy_3d_gradient_device_count(const TensorView &input, const TensorView &target, const TensorView &input_delta, const float *active_tokens_count_device)
Variant of cross_entropy_3d_gradient that reads the active-token count from device memory.

opennn::l2_regularization
void l2_regularization(const TensorView &parameters, float lambda, float &penalty)
Computes the L2 regularization penalty lambda * sum(parameters^2).

opennn::l1_regularization_gradient
void l1_regularization_gradient(const TensorView &parameters, float lambda, const TensorView &gradient)
Adds the L1 regularization gradient lambda * sign(parameters) into the gradient tensor.

opennn::normalized_squared_error
void normalized_squared_error(const TensorView &input, const TensorView &target, float coefficient, float &error, float *workspace_device)
Computes the squared error normalized by a dataset-level coefficient.

opennn::cross_entropy_3d_gradient
void cross_entropy_3d_gradient(const TensorView &input, const TensorView &target, const TensorView &input_delta, Index active_tokens_count)
Writes the 3-D cross-entropy gradient into input_delta, normalizing by the host-side active-token cou...

opennn::l1_regularization
void l1_regularization(const TensorView &parameters, float lambda, float &penalty)
Computes the L1 regularization penalty lambda * sum(|parameters|).

opennn::TensorView
Non-owning view over a tensor: pointer, shape, and data type with rich reshape helpers.
Definition tensor_utilities.h:293

tensor_utilities.h