neural_utils.h File Reference

#include <aio.h>
#include <math.h>

Include dependency graph for neural_utils.h:

This graph shows which files directly or indirectly include this file:

Go to the source code of this file.

Data Structures
struct	neuron
struct	neural_network
struct	training_data

Functions
double	sigmoid (double x)
double	double_rand (double size)
void	init_neuron (neuron *n, size_t nb_connection)
void	init_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_network (neural_network *network)
double	get_network_cost (neural_network *network, training_data expected_data)
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)
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)
char	get_data_char_prediction (training_data data, size_t nb_output)
char	get_network_char_prediction (neural_network *network, size_t AdaFactor)
void	print_network_activations (neural_network *network)
void	network_set_input_data (neural_network *network, training_data data)
void	print_network_state (neural_network *network)
void	print_training_debug (neural_network *network, training_data *data, size_t data_len)

Function Documentation

double_rand()

double double_rand

(

double

size

)

get_data_char_prediction()

char get_data_char_prediction	(	training_data	data,
		size_t	nb_output )

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_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 )

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_network()

void process_network

(

neural_network *

network

)

read_file()

char * read_file

(

const char *

file

)

save_neural_network()

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

sigmoid()

double sigmoid

(

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 )