neural_utils.c File Reference

#include "neural_utils.h"
#include "../Application/ApplicationUtils.h"
#include "../Image/ImageUtils.h"
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <err.h>
#include <dirent.h>
#include <time.h>

Include dependency graph for neural_utils.c:

Macros
#define	_DEFAULT_SOURCE
#define	_GNU_SOURCE

Functions
double	sigmoid (double x)
double	sigmoid_derivate (double x)
double	double_rand (double size)
double	clamp (double value, double min, double max)
char *	get_formated_time (size_t time)
void	init_neuron (neuron *n, size_t nb_connection)
void	free_neuron (neuron *n)
void	init_neural_network (neural_network *network)
void	free_neural_network (neural_network *network)
void	reset_neural_network (neural_network *network)
void	save_neural_network (neural_network *network, const char *file_path)
char **	string_split (const char *string, char separator, size_t *res_len)
void	load_neural_network (neural_network *network, const char *content)
char *	read_file (const char *file)
void	process_hidden_layer (neural_network *network)
void	process_output_layer (neural_network *network)
void	process_network (neural_network *network)
double	get_network_cost (neural_network *network, training_data expected_data)
void	shuffle_dataset (training_data *datas, size_t data_len, size_t nb_shuffle)
double	calculate_hidden_local_gradiant (neural_network *network, size_t x, size_t y)
void	network_hidden_calculate_propagation (neural_network *network, neural_network *memory_network)
void	network_output_calculate_propagation (neural_network *network, neural_network *memory_network, training_data data)
void	network_hidden_apply_propagation (neural_network *network, neural_network *memory_network, size_t batch_size, double learning_rate)
void	network_output_apply_propagation (neural_network *network, neural_network *memory_network, size_t batch_size, double learning_rate)
void	network_back_propagation (neural_network *network, neural_network *memory_network, training_data *datas, size_t data_len, double learning_rate)
void	network_set_input_data (neural_network *network, training_data data)
void	network_train_data (neural_network *network, neural_network *memory_network, training_data *datas, size_t data_len, double learning_rate, double *cost)
void	network_process_epoche (neural_network *network, neural_network *memory_network, training_data *data, size_t data_len, size_t batch_size, size_t nb_shuffle, double learning_rate, double *total_cost)
double	get_network_total_cost (neural_network *network, training_data *datas, size_t data_len)
char	get_data_char_prediction (training_data data, size_t nb_output)
char	get_network_char_prediction (neural_network *network, size_t AdaFactor)
void	train_network (neural_network *network, training_data *datas, size_t data_len, float learning_rate, size_t batch_size, size_t warmup, size_t warmup_iterations, size_t iterations)
size_t	get_network_success_rate (neural_network *network, training_data *datas, size_t data_len, size_t AdaFactor)
training_data *	load_dataset (const char *directory, size_t AdaFactor, size_t *nb_data)
void	print_network_activations (neural_network *network)
void	print_network_state (neural_network *network)
void	print_training_debug (neural_network *network, training_data *data, size_t data_len)

Macro Definition Documentation

_DEFAULT_SOURCE

#define _DEFAULT_SOURCE

_GNU_SOURCE

#define _GNU_SOURCE

Function Documentation

calculate_hidden_local_gradiant()

double calculate_hidden_local_gradiant	(	neural_network *	network,
		size_t	x,
		size_t	y )

clamp()

double clamp	(	double	value,
		double	min,
		double	max )

double_rand()

double double_rand

(

double

size

)

free_neural_network()

void free_neural_network

(

neural_network *

network

)

free_neuron()

void free_neuron

(

neuron *

n

)

get_data_char_prediction()

char get_data_char_prediction	(	training_data	data,
		size_t	nb_output )

get_formated_time()

char * get_formated_time

(

size_t

time

)

get_network_char_prediction()

char get_network_char_prediction	(	neural_network *	network,
		size_t	AdaFactor )

get_network_cost()

double get_network_cost	(	neural_network *	network,
		training_data	expected_data )

get_network_success_rate()

size_t get_network_success_rate	(	neural_network *	network,
		training_data *	datas,
		size_t	data_len,
		size_t	AdaFactor )

get_network_total_cost()

double get_network_total_cost	(	neural_network *	network,
		training_data *	datas,
		size_t	data_len )

init_neural_network()

void init_neural_network

(

neural_network *

network

)

init_neuron()

void init_neuron	(	neuron *	n,
		size_t	nb_connection )

load_dataset()

training_data * load_dataset	(	const char *	directory,
		size_t	AdaFactor,
		size_t *	nb_data )

load_neural_network()

void load_neural_network	(	neural_network *	network,
		const char *	content )

network_back_propagation()

void network_back_propagation	(	neural_network *	network,
		neural_network *	memory_network,
		training_data *	datas,
		size_t	data_len,
		double	learning_rate )

network_hidden_apply_propagation()

void network_hidden_apply_propagation	(	neural_network *	network,
		neural_network *	memory_network,
		size_t	batch_size,
		double	learning_rate )

network_hidden_calculate_propagation()

void network_hidden_calculate_propagation	(	neural_network *	network,
		neural_network *	memory_network )

network_output_apply_propagation()

void network_output_apply_propagation	(	neural_network *	network,
		neural_network *	memory_network,
		size_t	batch_size,
		double	learning_rate )

network_output_calculate_propagation()

void network_output_calculate_propagation	(	neural_network *	network,
		neural_network *	memory_network,
		training_data	data )

network_process_epoche()

void network_process_epoche	(	neural_network *	network,
		neural_network *	memory_network,
		training_data *	data,
		size_t	data_len,
		size_t	batch_size,
		size_t	nb_shuffle,
		double	learning_rate,
		double *	total_cost )

network_set_input_data()

void network_set_input_data	(	neural_network *	network,
		training_data	data )

network_train_data()

void network_train_data	(	neural_network *	network,
		neural_network *	memory_network,
		training_data *	datas,
		size_t	data_len,
		double	learning_rate,
		double *	cost )

print_network_activations()

void print_network_activations

(

neural_network *

network

)

print_network_state()

void print_network_state

(

neural_network *

network

)

print_training_debug()

void print_training_debug	(	neural_network *	network,
		training_data *	data,
		size_t	data_len )

process_hidden_layer()

void process_hidden_layer

(

neural_network *

network

)

process_network()

void process_network

(

neural_network *

network

)

process_output_layer()

void process_output_layer

(

neural_network *

network

)

read_file()

char * read_file

(

const char *

file

)

reset_neural_network()

void reset_neural_network

(

neural_network *

network

)

save_neural_network()

void save_neural_network	(	neural_network *	network,
		const char *	file_path )

shuffle_dataset()

void shuffle_dataset	(	training_data *	datas,
		size_t	data_len,
		size_t	nb_shuffle )

sigmoid()

double sigmoid

(

double

x

)

sigmoid_derivate()

double sigmoid_derivate

(

double

x

)

string_split()

char ** string_split	(	const char *	string,
		char	separator,
		size_t *	res_len )

train_network()

void train_network	(	neural_network *	network,
		training_data *	datas,
		size_t	data_len,
		float	learning_rate,
		size_t	batch_size,
		size_t	warmup,
		size_t	warmup_iterations,
		size_t	iterations )