error_utilities.h File Reference

#include "tensor_utilities.h"

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Namespaces
namespace	opennn

Functions
void	opennn::mean_squared_error (const TensorView &input, const TensorView &target, float &error, float *workspace_device)
	Computes the mean squared error between predictions and targets.
void	opennn::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.
void	opennn::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.
void	opennn::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.
void	opennn::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.
void	opennn::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.
void	opennn::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.
void	opennn::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 targets.
void	opennn::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_delta.
void	opennn::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.
void	opennn::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.
void	opennn::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.
void	opennn::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 count.
void	opennn::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.
void	opennn::l1_regularization (const TensorView &parameters, float lambda, float &penalty)
	Computes the L1 regularization penalty lambda * sum(|parameters|).
void	opennn::l1_regularization_gradient (const TensorView &parameters, float lambda, const TensorView &gradient)
	Adds the L1 regularization gradient lambda * sign(parameters) into the gradient tensor.
void	opennn::l2_regularization (const TensorView &parameters, float lambda, float &penalty)
	Computes the L2 regularization penalty lambda * sum(parameters^2).
void	opennn::l2_regularization_gradient (const TensorView &parameters, float lambda, const TensorView &gradient)
	Adds the L2 regularization gradient 2 * lambda * parameters into the gradient tensor.