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gnn_evaluation.h File Reference

#include <gsl/gsl_matrix.h>
#include "gnn_node.h"
#include "gnn_criterion.h"
#include "gnn_dataset.h"
#include "gnn_output.h"

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Go to the source code of this file.

Data Structures

struct _gnn_eval

struct _gnn_grad

struct _gnn_line

Defines

#define GNN_EVAL_GET_NODE(eval)   ((eval)->node)

Returns a pointer to the associated gnn_node.

#define GNN_EVAL_GET_INPUT(eval)   ((eval)->in)

Returns a pointer to the associated gnn_input : Reading and handling of sets of vectors..

#define GNN_EVAL_GET_OUTPUT(eval)   ((eval)->out)

Returns a pointer to the associated gnn_output : Writing sets of vectors..

#define GNN_EVAL_INPUT_SIZE(eval)   ((eval)->n)

Returns the size of the input.

#define GNN_EVAL_OUTPUT_SIZE(eval)   ((eval)->m)

Returns the size of the output.

#define GNN_EVAL_PARAMETER_SIZE(eval)   ((eval)->l)

Returns the size of the parameter vector.

#define GNN_EVAL_AMOUNT(eval)   ((eval)->P)

Returns the amount of examples to be evaluated by the Evaluations..

#define GNN_GRAD_GET_NODE(grad)   ((grad)->node)

Returns a pointer to the associated gnn_node.

#define GNN_GRAD_GET_INPUT(grad)   ((grad)->in)

Returns a pointer to the associated gnn_input : Reading and handling of sets of vectors..

#define GNN_GRAD_GET_OUTPUT(grad)   ((grad)->out)

Returns a pointer to the associated gnn_output : Writing sets of vectors..

#define GNN_GRAD_INPUT_SIZE(grad)   ((grad)->n)

Returns the size of the input.

#define GNN_GRAD_OUTPUT_SIZE(grad)   ((grad)->m)

Returns the size of the output.

#define GNN_GRAD_PARAMETER_SIZE(grad)   ((grad)->l)

Returns the size of the parameter vector.

#define GNN_GRAD_AMOUNT(grad)   ((grad)->P)

Returns the amount of examples considered by the Evaluations..

#define GNN_GRAD_SUMP(grad)   ((grad)->mp)

Returns the last pattern weight sum.

#define GNN_GRAD_SUMP(grad)   ((grad)->mp)

Returns the last pattern weight sum.

#define GNN_GRAD_SUME(grad)   ((grad)->mp * (grad)->me)

Returns the sum of the last batch's error.

#define GNN_GRAD_E(grad)   ((grad)->me)

Returns the last batch's mean error sum.

#define GNN_GRAD_DX(grad)   ((grad)->mdx)

Returns the last batch's mean error sum.

#define GNN_GRAD_DW(grad)   ((grad)->mdw)

Returns the last batch's mean error sum.

#define GNN_LINE_W(line)   ((line)->w)

Returns the origin of the line evaluations.

#define GNN_LINE_DIR(line)   ((line)->d)

Returns the direction of the line evaluations.

#define GNN_LINE_E(line)   ((line)->error)

Returns the last computed error along the line evaluation.

#define GNN_LINE_DE(line)   ((line)->derivative)

Returns the last computed derivative along the line evaluation.

Typedefs

typedef _gnn_eval gnn_eval

Evaluation buffer structure.

typedef _gnn_grad gnn_grad

Error and Gradients Evaluation buffer structure.

typedef enum _gnn_grad_eval gnn_grad_eval

Gradient evaluation flags.

typedef _gnn_line gnn_line

Directional Error and Gradients Evaluation buffer structure.

typedef enum _gnn_line_eval gnn_line_eval

Line evaluation flags.

Enumerations

enum _gnn_grad_eval { gnnGradE, gnnGradDx, gnnGradDw }

enum _gnn_line_eval { gnnLineE, gnnLineDE }

Functions

gnn_eval * gnn_eval_new (gnn_node *node, gnn_input *in, gnn_output *out)

Builds a new buffer for output evaluation.

void gnn_eval_destroy (gnn_eval *eval)

Destroys an evaluation buffer.

int gnn_eval_pats (gnn_eval *eval, size_t s, size_t n)

Computes the outputs for a given input set.

int gnn_eval_all (gnn_eval *eval)

Computes the outputs for a given input set.

gnn_grad * gnn_grad_new (gnn_node *node, gnn_criterion *crit, gnn_dataset *data)

Builds a new buffer for gradient evaluation.

void gnn_grad_destroy (gnn_grad *grad)

Destroys a gradient buffer.

int gnn_grad_pats (gnn_grad *grad, gnn_grad_eval flag, size_t s, size_t n)

Compute the mean cost and gradients.

int gnn_grad_all (gnn_grad *grad, gnn_grad_eval flag)

Compute the mean cost and gradients.

gnn_line * gnn_line_new (gnn_grad *grad, gsl_vector *direction)

Returns a new buffer structure for line evaluations.

void gnn_line_destroy (gnn_line *line)

Destroys a given line evaluation buffer.

int gnn_line_set_direction (gnn_line *line, const gsl_vector *dir)

Sets a new direction for the Evaluations. structure.

const gsl_vector * gnn_line_get_direction (gnn_line *line)

Gets the installed directional vector .

int gnn_line_set_origin (gnn_line *line, const gsl_vector *origin)

Sets a new origin for the Evaluations. structure.

const gsl_vector * gnn_line_get_origin (gnn_line *line)

Gets the installed origin vector .

gnn_grad * gnn_line_get_grad (gnn_line *line)

Gets the installed Evaluations. evaluation buffer.

int gnn_line_pats (gnn_line *line, double alpha, gnn_line_eval flag, size_t s, size_t n)

Compute mean cost and gradients along a direction for a minibatch.

int gnn_line_all (gnn_line *line, double alpha, gnn_line_eval flag)

Compute mean cost and gradients along a direction for a minibatch.

Define Documentation

#define GNN_GRAD_SUMP ( grad ) ((grad)->mp)

This macro returns the sum

of the last processed minibatch in the given Evaluations. buffer.
Definition at line 176 of file gnn_evaluation.h.

Enumeration Type Documentation

enum _gnn_grad_eval

Enumeration values:

gnnGradE

gnnGradDx

gnnGradDw

Definition at line 347 of file gnn_evaluation.h.

enum _gnn_line_eval

Enumeration values:

gnnLineE

gnnLineDE

Definition at line 390 of file gnn_evaluation.h.

Function Documentation

int gnn_eval_all ( gnn_eval * eval )

This function computes, given a set of input samples, all associated outputs, storing them into the output device provided by the Evaluations. structure. The patterns that are evaluated are those between and .
Parameters:

eval A pointer to a Evaluations..

s The index of the first input sample to be evaluated.

n The amount of samples to be evaluated.

Returns:
Returns 0 if suceeded.

Definition at line 418 of file gnn_evaluation.c.

void gnn_eval_destroy ( gnn_eval * eval )

Parameters:

eval A pointer a Evaluations. buffer.

Definition at line 355 of file gnn_evaluation.c.

gnn_eval* gnn_eval_new ( gnn_node * node,

gnn_input * in,

gnn_output * out

)

This function builds a new Evaluations. buffer structure for computing outputs.
Parameters:

node A pointer to a gnn_node.

in A pointer to a gnn_input : Reading and handling of sets of vectors. input sampler.

out A pointer to a gnn_output : Writing sets of vectors. output device.

Returns:
Returns a pointer to a new buffer structure of NULL if failed.

Definition at line 284 of file gnn_evaluation.c.

int gnn_eval_pats ( gnn_eval * eval,

size_t s,

size_t n

)

This function computes, given a set of input samples, all associated outputs, storing them into the output device provided by the Evaluations. structure. The patterns that are evaluated are those between and .
Parameters:

eval A pointer to a Evaluations..

s The index of the first input sample to be evaluated.

n The amount of samples to be evaluated.

Returns:
Returns 0 if suceeded.

Definition at line 379 of file gnn_evaluation.c.

int gnn_grad_all ( gnn_grad * grad,

gnn_grad_eval flag

)

This function behaves like the gnn_grad_pats function, but it computes the gradients and the error over the whole dataset. That is, it computes:

and

where is the -th pattern's weight, is the gradient with respect to the inputs evaluated at the -th pattern, is the gradient with respect to the parameters evaluated at the -th pattern, and is the cost/error for the -th output.
Recall that is the number of patterns contained in the dataset.
The evaluation flag can be:

gnnGradE : only compute mean error .
gnnGradDx : compute and .
gnnGradDw : compute and and
The computed values can be retrieved directly from the Evaluations. buffer:
// compute all gnn_grad_all (g, gnnGradDw); // now, get them e = GNN_GRAD_E (g); // e is a double dx = GNN_GRAD_DX (g); // dx is a gsl_vector * dw = GNN_GRAD_DW (g); // dw is a gsl_vector * sp = GNN_GRAD_SUMP (g); // sp is a double se = GNN_GRAD_SUME (g); // se is a double
Parameters:

grad A pointer to a Evaluations..

flag The evaluation flag.

Returns:
Returns 0 if suceeded.

Definition at line 720 of file gnn_evaluation.c.

void gnn_grad_destroy ( gnn_grad * grad )

Parameters:

grad A pointer a Evaluations. buffer.

Definition at line 527 of file gnn_evaluation.c.

gnn_grad* gnn_grad_new ( gnn_node * node,

gnn_criterion * crit,

gnn_dataset * data

)

This function builds a new Evaluations. buffer structure for computing gradients.
Parameters:

node A pointer to a gnn_node.

crit A pointer to a gnn_criterion : Basic Criterion Function..

data A pointer to a gnn_dataset : Datasets for Training..

Returns:
Returns a pointer to a new buffer structure of NULL if failed.

Definition at line 442 of file gnn_evaluation.c.

int gnn_grad_pats ( gnn_grad * grad,

gnn_grad_eval flag,

size_t s,

size_t n

)

This function computes many things simultaneously in one pass. It builds estimations of the gradients and and the cost by averaging over the n patterns starting at s. That is, this function computes:

and

where is the -th pattern's weight, is the gradient with respect to the inputs evaluated at the -th pattern, is the gradient with respect to the parameters evaluated at the -th pattern, and is the cost/error for the -th output.
In order to obtain the results, you should tell the function how far it should process the patterns, using a special flag. The evaluation flag can be:

gnnGradE : only compute mean error .
gnnGradDx : compute and .
gnnGradDw : compute and and
The computed values can be retrieved directly from the Evaluations. buffer:
// compute all gnn_grad_pats (g, gnnGradDw, 10, 20); // now, get them e = GNN_GRAD_E (g); // e is a double dx = GNN_GRAD_DX (g); // dx is a gsl_vector * dw = GNN_GRAD_DW (g); // dw is a gsl_vector * sp = GNN_GRAD_SUMP (g); // sp is a double se = GNN_GRAD_SUME (g); // se is a double

The starting index s should be within the valid bounds. The size n of the batch will be adapted if s + n exceeds the valid bounds. That is, e.g. if there are 10 patterns (and so the index of the last pattern to be considered is 9), and s=8 and n=4, then the last pattern in the averaging will be 9, although 8+4=12.
Parameters:

grad A pointer to a Evaluations..

flag The evaluation flag.

s The index of the first pattern in the dataset to be considered in the averaging.

n The amount of patterns to be considered.

Returns:
Returns 0 if suceeded.

Definition at line 600 of file gnn_evaluation.c.

int gnn_line_all ( gnn_line * line,

double alpha,

gnn_line_eval flag

)

This function behaves like the gnn_grad_pats function, but it computes the error and its derivative along a given line. That is, it performs all evaluations at

where are the node's current parameters, is a scalar and is a given direction, treating the function as it where just a one-dimensional function. All patterns are used for this evaluation.
The flag flag tells the function if it should compute only the error (gnnLineE) or the error with its derivative along the line (gnnLineDE). The approximated derivative is apropiately scaled in order to match the real derivative:

The results can be recovered by the macros Evaluations. and Evaluations.:

gnn_grad *grad; gnn_line *line; double error; // build grad and line buffers ... // evaluate all patterns at 1.2 gnn_line_all (line, 1.2, gnnLineE); // get results error = GNN_LINE_E (line);

Warning:
A line evaluation modifies the gnn_node's internal parameters. In order to recover the original vector, call this function with .
Parameters:

line A pointer to a Evaluations..

alpha The scalar .

flag The evaluation flag.

Returns:
Returns 0 if suceeded.

Definition at line 1072 of file gnn_evaluation.c.

void gnn_line_destroy ( gnn_line * line )

This function frees the Evaluations.'s internal buffers and deallocates the structure. The installed Evaluations. error and gradient evaluation buffer won't be destroyed.
Parameters:

line A pointer to a Evaluations..

Definition at line 806 of file gnn_evaluation.c.

const gsl_vector* gnn_line_get_direction ( gnn_line * line )

This function returns a pointer to the currently used directional buffer used by the Evaluations. buffer for its line evaluations.
Parameters:

line A pointer to a Evaluations..

Returns:
Returns a pointer to the directional vector.

Definition at line 860 of file gnn_evaluation.c.

gnn_grad* gnn_line_get_grad ( gnn_line * line )

This function returns a buffer for the installed Evaluations. error and gradients evaluation buffer used by the current Evaluations. structure for its line evaluation.
Parameters:

line A pointer to a Evaluations..

Returns:
Returns a pointer to the internal Evaluations. structure.

Definition at line 925 of file gnn_evaluation.c.

const gsl_vector* gnn_line_get_origin ( gnn_line * line )

This function returns a pointer to the currently used origin vector by the Evaluations. for its line evaluations.
Parameters:

line A pointer to a Evaluations..

Returns:
Returns a pointer to the origin vector.

Definition at line 907 of file gnn_evaluation.c.

gnn_line* gnn_line_new ( gnn_grad * grad,

gsl_vector * direction

)

This function builds a new buffer structure for line evaluations. The direction is given by the direction vector, and the origin is set at the gnn_node's current parameter vector. If the direction is omitted (NULL is given), then it will be a null vector.
Parameters:

grad A pointer to a Evaluations..

direction A pointer to a gsl_vector.

Returns:
Returns a pointer to the new Evaluations. buffer or NULL if failed.

Definition at line 747 of file gnn_evaluation.c.

int gnn_line_pats ( gnn_line * line,

double alpha,

gnn_line_eval flag,

size_t s,

size_t n

)

This function behaves like the gnn_grad_pats function, but it computes the error and its derivative along a given line. That is, it performs all evaluations at

where are the node's current parameters, is a scalar and is a given direction, treating the function as it where just a one-dimensional function. The patterns starting at s and ending at s+n are used for the averaging.
The flag flag tells the function if it should compute only the error (gnnLineE) or the error with its derivative along the line (gnnLineDE). The approximated derivative is apropiately scaled in order to match the real derivative:

The results can be recovered by the macros Evaluations. and Evaluations.:

gnn_grad *grad; gnn_line *line; double error; double derivative; // build grad and line buffers ... // evaluate the patterns 11-30 at 0.5 gnn_line_pats (line, 0.5, gnnLineDE, 11, 30); // get results error = GNN_LINE_E (line); derivative = GNN_LINE_DE (line);

Warning:
A line evaluation modifies the gnn_node's internal parameters. In order to recover the original vector, call this function with .
Parameters:

line A pointer to a Evaluations..

alpha The scalar .

flag The evaluation flag.

s The first pattern index .

n The size of the minibatch .

Returns:
Returns 0 if suceeded.

Definition at line 984 of file gnn_evaluation.c.

int gnn_line_set_direction ( gnn_line * line,

const gsl_vector * dir

)

This function sets a new directional vector for the line evaluation buffer. The vector should be of the correct size.
Parameters:

line A pointer to a Evaluations..

dir A pointer to a gsl_vector.

Returns:
Returns 0 if succeeded.

Definition at line 831 of file gnn_evaluation.c.

int gnn_line_set_origin ( gnn_line * line,

const gsl_vector * origin

)

This function sets a new origin vector for the line evaluation buffer. The vector should be of the correct size.
Parameters:

line A pointer to a Evaluations..

origin A pointer to a gsl_vector.

Returns:
Returns 0 if succeeded.

Definition at line 878 of file gnn_evaluation.c.

Generated on Sun Jun 13 20:50:32 2004 for libgnn Gradient Retropropagation Machine Library by

1.2.18


Data Structures
struct	_gnn_eval
struct	_gnn_grad
struct	_gnn_line
Defines
#define	GNN_EVAL_GET_NODE(eval) ((eval)->node)
	Returns a pointer to the associated gnn_node.
#define	GNN_EVAL_GET_INPUT(eval) ((eval)->in)
	Returns a pointer to the associated gnn_input : Reading and handling of sets of vectors..
#define	GNN_EVAL_GET_OUTPUT(eval) ((eval)->out)
	Returns a pointer to the associated gnn_output : Writing sets of vectors..
#define	GNN_EVAL_INPUT_SIZE(eval) ((eval)->n)
	Returns the size of the input.
#define	GNN_EVAL_OUTPUT_SIZE(eval) ((eval)->m)
	Returns the size of the output.
#define	GNN_EVAL_PARAMETER_SIZE(eval) ((eval)->l)
	Returns the size of the parameter vector.
#define	GNN_EVAL_AMOUNT(eval) ((eval)->P)
	Returns the amount of examples to be evaluated by the Evaluations..
#define	GNN_GRAD_GET_NODE(grad) ((grad)->node)
	Returns a pointer to the associated gnn_node.
#define	GNN_GRAD_GET_INPUT(grad) ((grad)->in)
	Returns a pointer to the associated gnn_input : Reading and handling of sets of vectors..
#define	GNN_GRAD_GET_OUTPUT(grad) ((grad)->out)
	Returns a pointer to the associated gnn_output : Writing sets of vectors..
#define	GNN_GRAD_INPUT_SIZE(grad) ((grad)->n)
	Returns the size of the input.
#define	GNN_GRAD_OUTPUT_SIZE(grad) ((grad)->m)
	Returns the size of the output.
#define	GNN_GRAD_PARAMETER_SIZE(grad) ((grad)->l)
	Returns the size of the parameter vector.
#define	GNN_GRAD_AMOUNT(grad) ((grad)->P)
	Returns the amount of examples considered by the Evaluations..
#define	GNN_GRAD_SUMP(grad) ((grad)->mp)
	Returns the last pattern weight sum.
#define	GNN_GRAD_SUMP(grad) ((grad)->mp)
	Returns the last pattern weight sum.
#define	GNN_GRAD_SUME(grad) ((grad)->mp * (grad)->me)
	Returns the sum of the last batch's error.
#define	GNN_GRAD_E(grad) ((grad)->me)
	Returns the last batch's mean error sum.
#define	GNN_GRAD_DX(grad) ((grad)->mdx)
	Returns the last batch's mean error sum.
#define	GNN_GRAD_DW(grad) ((grad)->mdw)
	Returns the last batch's mean error sum.
#define	GNN_LINE_W(line) ((line)->w)
	Returns the origin of the line evaluations.
#define	GNN_LINE_DIR(line) ((line)->d)
	Returns the direction of the line evaluations.
#define	GNN_LINE_E(line) ((line)->error)
	Returns the last computed error along the line evaluation.
#define	GNN_LINE_DE(line) ((line)->derivative)
	Returns the last computed derivative along the line evaluation.
Typedefs
typedef _gnn_eval	gnn_eval
	Evaluation buffer structure.
typedef _gnn_grad	gnn_grad
	Error and Gradients Evaluation buffer structure.
typedef enum _gnn_grad_eval	gnn_grad_eval
	Gradient evaluation flags.
typedef _gnn_line	gnn_line
	Directional Error and Gradients Evaluation buffer structure.
typedef enum _gnn_line_eval	gnn_line_eval
	Line evaluation flags.
Enumerations
enum	_gnn_grad_eval { gnnGradE, gnnGradDx, gnnGradDw }
enum	_gnn_line_eval { gnnLineE, gnnLineDE }
Functions
gnn_eval *	gnn_eval_new (gnn_node node, gnn_input in, gnn_output *out)
	Builds a new buffer for output evaluation.
void	gnn_eval_destroy (gnn_eval *eval)
	Destroys an evaluation buffer.
int	gnn_eval_pats (gnn_eval *eval, size_t s, size_t n)
	Computes the outputs for a given input set.
int	gnn_eval_all (gnn_eval *eval)
	Computes the outputs for a given input set.
gnn_grad *	gnn_grad_new (gnn_node node, gnn_criterion crit, gnn_dataset *data)
	Builds a new buffer for gradient evaluation.
void	gnn_grad_destroy (gnn_grad *grad)
	Destroys a gradient buffer.
int	gnn_grad_pats (gnn_grad *grad, gnn_grad_eval flag, size_t s, size_t n)
	Compute the mean cost and gradients.
int	gnn_grad_all (gnn_grad *grad, gnn_grad_eval flag)
	Compute the mean cost and gradients.
gnn_line *	gnn_line_new (gnn_grad grad, gsl_vector direction)
	Returns a new buffer structure for line evaluations.
void	gnn_line_destroy (gnn_line *line)
	Destroys a given line evaluation buffer.
int	gnn_line_set_direction (gnn_line line, const gsl_vector dir)
	Sets a new direction for the Evaluations. structure.
const gsl_vector *	gnn_line_get_direction (gnn_line *line)
	Gets the installed directional vector .
int	gnn_line_set_origin (gnn_line line, const gsl_vector origin)
	Sets a new origin for the Evaluations. structure.
const gsl_vector *	gnn_line_get_origin (gnn_line *line)
	Gets the installed origin vector .
gnn_grad *	gnn_line_get_grad (gnn_line *line)
	Gets the installed Evaluations. evaluation buffer.
int	gnn_line_pats (gnn_line *line, double alpha, gnn_line_eval flag, size_t s, size_t n)
	Compute mean cost and gradients along a direction for a minibatch.
int	gnn_line_all (gnn_line *line, double alpha, gnn_line_eval flag)
	Compute mean cost and gradients along a direction for a minibatch.