opennn::BackPropagationLM Struct Reference

#include <opennn/training/levenberg_marquardt.h>

Public Member Functions
	BackPropagationLM (const Index=0, Loss *=nullptr)

Public Attributes
float	error = 0.0f
float	regularization = 0.0f
float	loss = 0.0f
VectorR	errors
MatrixR	squared_errors_jacobian
VectorR	gradient
MatrixR	hessian
vector< Index >	dense_indices
vector< Index >	parameter_offsets
vector< MatrixR >	deltas
vector< MatrixR >	activation_derivatives

Constructor & Destructor Documentation

BackPropagationLM()

opennn::BackPropagationLM::BackPropagationLM ( const Index = 0, Loss * = nullptr )

explicit

Member Data Documentation

activation_derivatives

vector<MatrixR> opennn::BackPropagationLM::activation_derivatives

deltas

vector<MatrixR> opennn::BackPropagationLM::deltas

dense_indices

vector<Index> opennn::BackPropagationLM::dense_indices

error

float opennn::BackPropagationLM::error = 0.0f

errors

VectorR opennn::BackPropagationLM::errors

gradient

VectorR opennn::BackPropagationLM::gradient

hessian

MatrixR opennn::BackPropagationLM::hessian

loss

float opennn::BackPropagationLM::loss = 0.0f

parameter_offsets

vector<Index> opennn::BackPropagationLM::parameter_offsets

regularization

float opennn::BackPropagationLM::regularization = 0.0f

squared_errors_jacobian

MatrixR opennn::BackPropagationLM::squared_errors_jacobian