int main(int argc, char *argv[])
{
/* parameters */
char output_file[256];
if(argc != 12)
{
printf("usage %s train_file test_file output_file width height topology neighborhood decay max_training_examples seconds_between_reports number_of_runs\n", argv[0]);
return -1;
}
unsigned int width = atoi(argv[4]);
unsigned int height = atoi(argv[5]);
unsigned int max_training_examples = atoi(argv[9]);
double seconds_between_reports = atof(argv[10]);
unsigned int number_of_runs = atoi(argv[11]);
fann_train_data *train_data = fann_read_train_from_file(argv[1]);
fann_train_data *test_data = fann_read_train_from_file(argv[2]);
char *train_out_file = argv[3];
FILE *train_out = 0;
fann_seed_rand();
for(unsigned int i = 0; i < number_of_runs; i++)
{
/* test_collector->newCollection();
train_collector->newCollection();
*/
if(strlen(train_out_file) == 1 && train_out_file[0] == '-')
train_out = stdout;
else
{
if(number_of_runs == 1)
train_out = fopen(train_out_file, "w");
else
{
sprintf(output_file, "%s_%d_run", train_out_file, i);
train_out = fopen(output_file, "w");
}
}
fprintf(stdout, "Quality test of %s\n", argv[1]);
quality_benchmark_fann_som(width, height, argv[6], argv[7], argv[8], train_data, test_data,
train_out, train_data->num_input, max_training_examples, seconds_between_reports);
fclose(train_out);
}
fann_destroy_train(train_data);
fann_destroy_train(test_data);
return 0;
}
void cunit_xor_test(void)
{
fann_type *calc_out = NULL;
unsigned int i;
int ret = 0;
struct fann *ann = NULL;
struct fann_train_data *data = NULL;
#ifdef FIXEDFANN
ann = fann_create_from_file("xor_fixed.net");
#else
ann = fann_create_from_file("xor_float.net");
#endif
CU_ASSERT_PTR_NOT_NULL_FATAL(ann);
#ifdef FIXEDFANN
data = fann_read_train_from_file("xor_fixed.data");
#else
data = fann_read_train_from_file("xor.data");
#endif
CU_ASSERT_PTR_NOT_NULL_FATAL(data);
for(i = 0; i < fann_length_train_data(data); i++)
{
fann_reset_MSE(ann);
calc_out = fann_test(ann, data->input[i], data->output[i]);
CU_ASSERT_PTR_NOT_NULL_FATAL(calc_out);
#ifdef FIXEDFANN
/*printf("XOR test (%d, %d) -> %d, should be %d, difference=%f\n",
data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0],
(float) fann_abs(calc_out[0] - data->output[i][0]) / fann_get_multiplier(ann));*/
if((float) fann_abs(calc_out[0] - data->output[i][0]) / fann_get_multiplier(ann) > 0.2)
{
CU_FAIL("XOR test failed.");
ret = -1;
}
#else
/*printf("XOR test (%f, %f) -> %f, should be %f, difference=%f\n",
data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0],
(float) fann_abs(calc_out[0] - data->output[i][0]));*/
#endif
}
fann_destroy_train(data);
fann_destroy(ann);
}
int main()
{
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 32;
const float desired_error = (const float) 0.0001;
const unsigned int max_epochs = 300;
const unsigned int epochs_between_reports = 10;
struct fann *ann;
struct fann_train_data *train_data, *test_data;
unsigned int i = 0;
printf("Creating network.\n");
train_data = fann_read_train_from_file("../datasets/mushroom.train");
ann = fann_create_standard(num_layers,
train_data->num_input, num_neurons_hidden, train_data->num_output);
printf("Training network.\n");
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC_STEPWISE);
fann_set_activation_function_output(ann, FANN_SIGMOID_STEPWISE);
/*fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL); */
fann_train_on_data(ann, train_data, max_epochs, epochs_between_reports, desired_error);
printf("Testing network.\n");
test_data = fann_read_train_from_file("../datasets/mushroom.test");
fann_reset_MSE(ann);
for(i = 0; i < fann_length_train_data(test_data); i++)
{
fann_test(ann, test_data->input[i], test_data->output[i]);
}
printf("MSE error on test data: %f\n", fann_get_MSE(ann));
printf("Saving network.\n");
fann_save(ann, "mushroom_float.net");
printf("Cleaning up.\n");
fann_destroy_train(train_data);
fann_destroy_train(test_data);
fann_destroy(ann);
return 0;
}
void NeuralNet::trainNet(char* ptrDataFileName){
const unsigned int maxEpochs = 1000;
const float desiredError = 0;
this->ptrData = fann_read_train_from_file(ptrDataFileName);
const unsigned int epochsBetweenReports = 10;
fann_train_on_data(this->ptrNeuralNet, this->ptrData, maxEpochs, epochsBetweenReports, desiredError);
}
int main()
{
const unsigned int num_input = 1;
const unsigned int num_output = 1;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 10;
const float desired_error = (const float) 0.0003;
const unsigned int max_epochs = 50000;
const unsigned int epochs_between_reports = 1000;
struct fann_train_data *train_data;
struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
train_data=fann_read_train_from_file("seno.data");
//AQUI
//fann_set_scaling_params(ann,train_data,-1.0,1.0,-1.0,1.0);
//fann_scale_train(ann,train_data);
//fann_scale_train_data(train_data,-1,1);
fann_train_on_data(ann, train_data, max_epochs, epochs_between_reports, desired_error);
fann_save(ann, "mlp2.net");
fann_destroy(ann);
return 0;
}
int main()
{
fann_type *calc_out;
const unsigned int num_input = 2;
const unsigned int num_output = 1;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 9;
const float desired_error = (const float) 0;
const unsigned int max_epochs = 500000;
const unsigned int epochs_between_reports = 1000;
struct fann *ann;
struct fann_train_data *data;
unsigned int i = 0;
unsigned int decimal_point;
printf("Creating network.\n");
ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output);
data = fann_read_train_from_file("osyslec_train.data");
fann_set_activation_steepness_hidden(ann, 1);
fann_set_activation_steepness_output(ann, 1);
fann_set_activation_function_hidden(ann, FANN_SIGMOID);
fann_set_activation_function_output(ann, FANN_SIGMOID);
fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
fann_set_bit_fail_limit(ann, 0.01f);
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
fann_init_weights(ann, data);
printf("Training network.\n");
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
printf("Testing network. %f\n", fann_test_data(ann, data));
for(i = 0; i < fann_length_train_data(data); i++)
{
calc_out = fann_run(ann, data->input[i]);
printf("GG test (%f,%f) -> %f, should be %f, difference=%f\n",
data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0],
fann_abs(calc_out[0] - data->output[i][0]));
}
printf("Saving network.\n");
fann_save(ann, "osyslec_train_float.net");
decimal_point = fann_save_to_fixed(ann, "osyslec_train_fixed.net");
fann_save_train_to_fixed(data, "osyslec_train_fixed.data", decimal_point);
printf("Cleaning up.\n");
fann_destroy_train(data);
fann_destroy(ann);
return 0;
}
int main() {
printf("Neural Network training\n");
const unsigned int num_input = 3;
const unsigned int num_output = 4;
const unsigned int num_layers = 4;
const unsigned int num_neurons_hidden = 30;
const float desired_error = (const float) 0.02;
const unsigned int max_epochs = 500000;
const unsigned int epochs_between_reports = 10;
struct fann *ann = fann_create_standard(
num_layers,
num_input,
num_neurons_hidden,
num_neurons_hidden,
// num_neurons_hidden,
num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
// fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
struct fann_train_data *data = fann_read_train_from_file("../training.data");
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
fann_destroy_train(data);
fann_save(ann, "../neural.net");
fann_destroy(ann);
return 0;
}
int
main(int argc, char **argv)
{
const unsigned int num_input = 360;
const unsigned int num_output = 1;
const unsigned int num_layers = 4;
const unsigned int num_neurons_hidden = num_input / 2;
const float desired_error = (const float) 0.001;
const unsigned int max_epochs = 300;
const unsigned int epochs_between_reports = 10;
struct fann_train_data * data = NULL;
struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_neurons_hidden / 2, num_output);
unsigned int i, j;
if (argc != 2)
{
fprintf(stderr, "Use: %s arquivoTreino\n", argv[0]);
exit(1);
}
fann_set_activation_function_hidden(ann, FANN_ELLIOT);
fann_set_activation_function_output(ann, FANN_LINEAR);
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
fann_set_learning_rate(ann, 0.1);
fann_set_learning_momentum(ann, 0.6);
data = fann_read_train_from_file(argv[1]);
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
free(data);
fann_save(ann, ARQ_RNA);
fann_destroy(ann);
return 0;
}
int main( int argc, char** argv )
{
fann_type *calc_out;
unsigned int i;
int ret = 0;
struct fann *ann;
struct fann_train_data *data;
printf("Creating network.\n");
ann = fann_create_from_file("xor_float.net");
if(!ann)
{
printf("Error creating ann --- ABORTING.\n");
return 0;
}
fann_print_connections(ann);
fann_print_parameters(ann);
printf("Testing network.\n");
data = fann_read_train_from_file("5K.txt");
for(i = 0; i < fann_length_train_data(data); i++)
{
fann_reset_MSE(ann);
fann_scale_input( ann, data->input[i] );
calc_out = fann_run( ann, data->input[i] );
fann_descale_output( ann, calc_out );
printf("Result %f original %f error %f\n",
calc_out[0], data->output[i][0],
(float) fann_abs(calc_out[0] - data->output[i][0]));
}
printf("Cleaning up.\n");
fann_destroy_train(data);
fann_destroy(ann);
return ret;
}
int main(int argc, const char* argv[])
{
const unsigned int max_epochs = 1000;
unsigned int num_threads = 1;
struct fann_train_data *data;
struct fann *ann;
long before;
float error;
unsigned int i;
if(argc == 2)
num_threads = atoi(argv[1]);
data = fann_read_train_from_file("../../datasets/mushroom.train");
ann = fann_create_standard(3, fann_num_input_train_data(data), 32, fann_num_output_train_data(data));
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID);
before = GetTickCount();
for(i = 1; i <= max_epochs; i++)
{
error = num_threads > 1 ? fann_train_epoch_irpropm_parallel(ann, data, num_threads) : fann_train_epoch(ann, data);
printf("Epochs %8d. Current error: %.10f\n", i, error);
}
printf("ticks %d", GetTickCount()-before);
fann_destroy(ann);
fann_destroy_train(data);
return 0;
}
int main( int argc, char** argv )
{
const unsigned int num_input = 3;
const unsigned int num_output = 1;
const unsigned int num_layers = 4;
const unsigned int num_neurons_hidden = 5;
const float desired_error = (const float) 0.0001;
const unsigned int max_epochs = 5000;
const unsigned int epochs_between_reports = 1000;
struct fann_train_data * data = NULL;
struct fann *ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_neurons_hidden, num_output);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_LINEAR);
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
data = fann_read_train_from_file("../../datasets/scaling.data");
fann_set_scaling_params(
ann,
data,
-1, /* New input minimum */
1, /* New input maximum */
-1, /* New output minimum */
1); /* New output maximum */
fann_scale_train( ann, data );
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
fann_destroy_train( data );
fann_save(ann, "scaling.net");
fann_destroy(ann);
return 0;
}
int main()
{
struct fann *ann;
struct fann_train_data *train_data, *test_data;
const float desired_error = (const float) 0.001;
unsigned int max_neurons = 40;
unsigned int neurons_between_reports = 1;
printf("Reading data.\n");
train_data = fann_read_train_from_file("../benchmarks/datasets/two-spiral.train");
test_data = fann_read_train_from_file("../benchmarks/datasets/two-spiral.test");
fann_scale_train_data(train_data, 0, 1);
fann_scale_train_data(test_data, 0, 1);
printf("Creating network.\n");
ann = fann_create_shortcut(2, fann_num_input_train_data(train_data), fann_num_output_train_data(train_data));
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_LINEAR_PIECE);
fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR);
fann_print_parameters(ann);
printf("Training network.\n");
fann_cascadetrain_on_data(ann, train_data, max_neurons, neurons_between_reports, desired_error);
fann_print_connections(ann);
printf("\nTrain error: %f, Test error: %f\n\n", fann_test_data(ann, train_data),
fann_test_data(ann, test_data));
printf("Saving network.\n");
fann_save(ann, "two_spirali.net");
printf("Cleaning up.\n");
fann_destroy_train(train_data);
fann_destroy_train(test_data);
fann_destroy(ann);
return 0;
}
/*
Creer le meilleur fichier .net (apprentissage) possible
basé sur des tests effectués au cours de l'apprentissage
en fonction du nombre de neuronnes cachés choisis en entrée.
*/
void train(struct fann *ann, char* trainFile, char *testFile, char *netFile , unsigned int max_epochs, unsigned int epochs_between_reports, float desired_error, const unsigned int num_neurons_hidden) {
struct fann_train_data *trainData, *testData;
struct fann *annBest = fann_copy(ann);
float error;
unsigned int i;
char buffer[1024];
float testError = 1;
float testErrorBest = 1;
trainData = fann_read_train_from_file(trainFile);
testData = fann_read_train_from_file(testFile);
for(i = 1; i <= max_epochs; i++){
fann_shuffle_train_data(trainData); //melange les données
error = fann_train_epoch(ann, trainData); //fait une epoque, ann : le réseaux créer, erreur : l'erreur d'apprentissage
//Toute les 5 epoques
if(i % epochs_between_reports == 0 || error < desired_error){
fann_test_data(ann,testData);// teste le reseau sur les donnée de test
testError = fann_get_MSE(ann);
if (testError < testErrorBest) {
testErrorBest = testError;
annBest = fann_copy(ann);
printf("Epochs %8d; trainError : %f; testError : %f;\n", i, error,testError);
sprintf(buffer,"%s_%u_%d.net",netFile,num_neurons_hidden,i);
fann_save(annBest, buffer);
}
}
if(error < desired_error){
break;
}
}
sprintf(buffer,"%s_%u.net",netFile,num_neurons_hidden);
fann_save(annBest, buffer);
fann_destroy_train(trainData);
fann_destroy_train(testData);
}
int
main(int argc, char **argv)
{
fann_type *calc_out;
unsigned int i, j;
struct fann *ann;
struct fann_train_data *data;
float error = 0.0;
if (argc < 3)
{
fprintf(stderr, "Use: %s FANN_network.net patternsFile\n", argv[0]);
exit(1);
}
printf("Openning ANN `%s'\n", argv[1]);
ann = fann_create_from_file(argv[1]);
if (!ann)
{
fprintf(stderr, "Error creating the ANN.\n");
return (1);
}
printf("Running ANN.\n");
data = fann_read_train_from_file(argv[2]);
for(i = 0; i < fann_length_train_data(data); i++)
{
calc_out = fann_run(ann, data->input[i]);
printf("ANN: %f ", calc_out[0]);
printf("Expected: %f ", data->output[i][0]);
printf("Error: %f ", (float) (data->output[i][0] -calc_out[0]));
printf("Throttle_Effort: %f Brake_Effort: %f Current_Velocity: %f\n",
// essa multiplicacao ocorre para desfazer o que o 'gerarEntrada.c' fez
data->input[i][360-3]*100.0, data->input[i][360-2]*100.0, data->input[i][360-1]*5.0);
error += (float) powf(fann_abs(calc_out[0] - data->output[i][0]),2);
}
printf("Test:: Squared Error: %f Mean Squared Error: %f\n", error, error/(fann_length_train_data(data)-1));
printf("Cleaning memory.\n");
fann_destroy_train(data);
fann_destroy(ann);
return (0);
}
FANN_EXTERNAL void FANN_API fann_train_on_file(struct fann *ann, const char *filename,
unsigned int max_epochs,
unsigned int epochs_between_reports,
float desired_error)
{
struct fann_train_data *data = fann_read_train_from_file(filename);
if(data == NULL)
{
return;
}
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
fann_destroy_train(data);
}
void NeuralNet::runNet(char* ptrDataFileName){
struct fann_train_data *ptrDataTest = fann_read_train_from_file(ptrDataFileName);
fann_reset_MSE(this->ptrNeuralNet);
fann_test_data(this->ptrNeuralNet, ptrDataTest);
printf("Mean Square Error: %f\n", fann_get_MSE(this->ptrNeuralNet));
fann_type *calc_out;
for(int i = 0; i < fann_length_train_data(ptrDataTest); i++){
calc_out = fann_run(this->ptrNeuralNet, ptrDataTest->input[i]);
cout << "Sample testing: "<< calc_out[0] << " " << ptrDataTest->output[i][0] << " " << fann_abs(calc_out[0] - ptrDataTest->output[i][0]) << endl;
}
fann_destroy_train(ptrDataTest);
}
int main()
{
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 96;
const float desired_error = (const float) 0.001;
struct fann *ann;
struct fann_train_data *train_data, *test_data;
float momentum;
train_data = fann_read_train_from_file("../benchmarks/datasets/robot.train");
test_data = fann_read_train_from_file("../benchmarks/datasets/robot.test");
for ( momentum = 0.0; momentum < 0.7; momentum += 0.1 )
{
printf("============= momentum = %f =============\n", momentum);
ann = fann_create_standard(num_layers,
train_data->num_input, num_neurons_hidden, train_data->num_output);
fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL);
fann_set_learning_momentum(ann, momentum);
fann_train_on_data(ann, train_data, 2000, 500, desired_error);
printf("MSE error on train data: %f\n", fann_test_data(ann, train_data));
printf("MSE error on test data : %f\n", fann_test_data(ann, test_data));
fann_destroy(ann);
}
fann_destroy_train(train_data);
fann_destroy_train(test_data);
return 0;
}
int main(int argc , char **argv) {
fann_type *calc_out;
int i;
if (argc==1) {
printf("usage : eval testFile.txt network.net\n");
return 0;
}
struct fann_train_data *testData = fann_read_train_from_file(argv[1]);
struct fann *ann = ann = fann_create_from_file(argv[2]);
int output_size = fann_get_num_output(ann);
for(i=0;i<testData->num_data ;i++) {
// calcul du résultat de la prédiction
calc_out = fann_run(ann, testData->input[i]);
// interprétation et affichage du résultat
switch (maxIndex(calc_out,output_size)){
case 0 :
printf ("fr\n");
break;
case 1 :
printf ("en\n");
break;
case 2 :
printf ("de\n");
break;
case 3 :
printf ("es\n");
break;
case 4 :
printf ("pt\n");
break;
case 5 :
printf ("it\n");
break;
case 6 :
printf ("tr\n");
break;
}
}
fann_destroy(ann);
return 0;
}
int main()
{
const unsigned int num_input = 2;
const unsigned int num_output = 1;
const unsigned int num_layers = 3;
const unsigned int num_neurons_hidden = 3;
const float desired_error = (const float) 0.001;
const unsigned int max_epochs = 500000;
const unsigned int epochs_between_reports = 1000;
unsigned int i;
fann_type *calc_out;
struct fann_train_data *data;
struct fann *ann = fann_create_standard(num_layers,
num_input, num_neurons_hidden, num_output);
data = fann_read_train_from_file("xor.data");
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_training_algorithm(ann, FANN_TRAIN_QUICKPROP);
train_on_steepness_file(ann, "xor.data", max_epochs,
epochs_between_reports, desired_error, (float) 1.0, (float) 0.1,
(float) 20.0);
fann_set_activation_function_hidden(ann, FANN_THRESHOLD_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_THRESHOLD_SYMMETRIC);
for(i = 0; i != fann_length_train_data(data); i++)
{
calc_out = fann_run(ann, data->input[i]);
printf("XOR test (%f, %f) -> %f, should be %f, difference=%f\n",
data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0],
(float) fann_abs(calc_out[0] - data->output[i][0]));
}
fann_save(ann, "xor_float.net");
fann_destroy(ann);
fann_destroy_train(data);
return 0;
}
int
main(int argc, char **argv)
{
fann_type *calc_out;
unsigned int i, j;
struct fann *ann;
struct fann_train_data *data;
if (argc < 2)
{
fprintf(stderr, "Use: %s arquivoTeste\n", argv[0]);
exit(1);
}
printf("Abrindo a Rede `%s'\n", ARQ_RNA);
ann = fann_create_from_file(ARQ_RNA);
if (!ann)
{
fprintf(stderr, "Erro criando a RNA.\n");
return (1);
}
//fann_print_connections(ann);
//fann_print_parameters(ann);
printf("Testando a RNA.\n");
data = fann_read_train_from_file(argv[1]);
for(i = 0; i < fann_length_train_data(data); i++)
{
fann_reset_MSE(ann);
calc_out = fann_run(ann, data->input[i]);
printf("Resultado: %f ", calc_out[0]);
printf("Original: %f " , data->output[i][0]);
printf("Erro: %f\n" , (float) fann_abs(calc_out[0] - data->output[i][0]));
}
printf("Limpando memoria.\n");
fann_destroy_train(data);
fann_destroy(ann);
return (0);
}
void train_on_steepness_file(struct fann *ann, char *filename,
unsigned int max_epochs, unsigned int epochs_between_reports,
float desired_error, float steepness_start,
float steepness_step, float steepness_end)
{
float error;
unsigned int i;
struct fann_train_data *data = fann_read_train_from_file(filename);
if(epochs_between_reports)
{
printf("Max epochs %8d. Desired error: %.10f\n", max_epochs, desired_error);
}
fann_set_activation_steepness_hidden(ann, steepness_start);
fann_set_activation_steepness_output(ann, steepness_start);
for(i = 1; i <= max_epochs; i++)
{
/* train */
error = fann_train_epoch(ann, data);
/* print current output */
if(epochs_between_reports &&
(i % epochs_between_reports == 0 || i == max_epochs || i == 1 || error < desired_error))
{
printf("Epochs %8d. Current error: %.10f\n", i, error);
}
if(error < desired_error)
{
steepness_start += steepness_step;
if(steepness_start <= steepness_end)
{
printf("Steepness: %f\n", steepness_start);
fann_set_activation_steepness_hidden(ann, steepness_start);
fann_set_activation_steepness_output(ann, steepness_start);
}
else
{
break;
}
}
}
fann_destroy_train(data);
}
int main(int argc, char *argv[])
{
int i = 0;
const unsigned int width = 10;
const unsigned int height = 10;
const unsigned int num_dimensions = 3;
const float desired_error = (const float) 0.001;
const unsigned int max_epochs = 500000;
const unsigned int epochs_between_reports = 1000;
struct fann *ann = fann_create_som(width, height, num_dimensions);
ann->som_params->som_topology = FANN_SOM_TOPOLOGY_HEXAGONAL;
ann->som_params->som_neighborhood = FANN_SOM_NEIGHBORHOOD_DISTANCE;
ann->som_params->som_learning_decay = FANN_SOM_LEARNING_DECAY_LINEAR;
ann->som_params->som_learning_rate = 0.01;
ann->som_params->som_learning_rate_constant = 0.01;
/* Example using init_weights */
struct fann_train_data *data = fann_read_train_from_file("som_train.data");
for (i = 1; i < argc; i++) {
if (strcmp(argv[i], "-init") == 0)
fann_init_weights_som(ann, data);
else if (strcmp(argv[i], "-epoch") == 0) {
fann_train_epoch(ann, data);
return EXIT_SUCCESS;
}
}
/* Example without init_weights (random initialization)*/
fann_train_on_file(ann, "som_train.data", max_epochs, epochs_between_reports, desired_error);
/*fann_save(ann, "som_train.net"); */
fann_destroy(ann);
printf("\n");
return 0;
}
int main()
{
const float desired_error = (const float) 0.0001;
const unsigned int max_epochs = 350;
const unsigned int epochs_between_reports = 25;
struct fann *ann;
struct fann_train_data *train_data;
unsigned int i = 0;
printf("Creating network.\n");
train_data = fann_read_train_from_file("ann_training_data");
// Using incremental training -> shuffle training data
fann_shuffle_train_data(train_data);
// ann = fann_create_standard(num_layers,
// train_data->num_input, num_neurons_hidden, train_data->num_output);
//ann = fann_create_standard(500, 2, 50, 50, 21);
unsigned int layers[4] = {150, 70, 30, 22};
ann = fann_create_standard_array(4, layers);
printf("Training network.\n");
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC_STEPWISE);
fann_set_activation_function_output(ann, FANN_LINEAR_PIECE); //FANN_SIGMOID_STEPWISE);
fann_set_training_algorithm(ann, FANN_TRAIN_INCREMENTAL);
fann_train_on_data(ann, train_data, max_epochs, epochs_between_reports, desired_error);
printf("Saving network.\n");
fann_save(ann, "mymoves_gestures.net");
printf("Cleaning up.\n");
fann_destroy_train(train_data);
fann_destroy(ann);
return 0;
}
/*! fann.read_train_from_file(filename)
*# Creates a training object by reading a training data file.
*x train = fann.read_train_from_file("xor.data")
*-
*/
static int ann_read_train_from_file(lua_State *L)
{
struct fann_train_data **train;
const char *fname;
luaL_argcheck(L, lua_isstring(L,1), 1, "Argument to fann.open_file() must be a string");
fname = lua_tostring(L, 1);
#ifdef FANN_VERBOSE
printf("Opening training data from file '%s'\n", fname);
#endif
train = lua_newuserdata(L, sizeof *train);
luaL_getmetatable(L, FANN_TRAIN_METATABLE);
lua_setmetatable(L, -2);
*train = fann_read_train_from_file(fname);
if(!*train)
luaL_error(L, "Unable to read train data from %s", fname);
return 1;
}
int main()
{
fann_type *calc_out;
unsigned int i;
int ret = 0;
struct fann *ann;
struct fann_train_data *data;
printf("Creating network.\n");
#ifdef FIXEDFANN
ann = fann_create_from_file("digitde_validation_fixed.net");
#else
ann = fann_create_from_file("digitde_validation_float.net");
#endif
if(!ann)
{
printf("Error creating ann --- ABORTING.\n");
return -1;
}
fann_print_connections(ann);
fann_print_parameters(ann);
printf("Testing network.\n");
#ifdef FIXEDFANN
data = fann_read_train_from_file("digitde_validation_fixed.data");
#else
data = fann_read_train_from_file("digitde_validation.data");
#endif
for(i = 0; i < fann_length_train_data(data); i++)
{
fann_reset_MSE(ann);
calc_out = fann_test(ann, data->input[i], data->output[i]);
#ifdef FIXEDFANN
printf("GG test (%d, %d) -> %d, should be %d, difference=%f\n",
data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0],
(float) fann_abs(calc_out[0] - data->output[i][0]) / fann_get_multiplier(ann));
if((float) fann_abs(calc_out[0] - data->output[i][0]) / fann_get_multiplier(ann) > 0.2)
{
printf("Test failed\n");
ret = -1;
}
#else
printf("GG test (%f, %f) -> %f, should be %f, difference=%f\n",
data->input[i][0], data->input[i][1], calc_out[0], data->output[i][0],
(float) fann_abs(calc_out[0] - data->output[i][0]));
#endif
}
printf("Cleaning up.\n");
fann_destroy_train(data);
fann_destroy(ann);
return ret;
}
int main ( int argc, char **argv )
{
srand(time(NULL));
if ( argc<=1 )
{
// printf ( "neuro num\r\n" );
// exit ( 0 );
}
if (argc>2)
{
//desired_error=atof(argv[2]);
numn=atoi(argv[1]);
l1n=atoi(argv[2]);
if (argc>3)
l2n=atoi(argv[3]);
if (argc>4)
l3n=atoi(argv[4]);
if (argc>5)
l4n=atoi(argv[5]);
if (argc>6)
l5n=atoi(argv[6]);
if (argc>7)
l6n=atoi(argv[7]);
}
signal ( 2, sig_term );
srand ( time ( NULL ) );
printf("loading training data...");
train_data = fann_read_train_from_file ( "train.dat" );
test_data = fann_read_train_from_file ( "test.dat" );
weight_data=fann_merge_train_data(train_data,test_data);
cln_weight_data=fann_duplicate_train_data(weight_data);
cln_test_data=fann_duplicate_train_data(test_data);
cln_train_data=fann_duplicate_train_data(train_data);
//num_neurons_hidden = atoi ( argv[1] );
srand(time(NULL));
y=atoi(argv[2]);
lay=atoi(argv[1]);
ln=lay+2;
if (lay==1)
y2=train_data->num_output;
best_perc=1;
printf("\r\ndoing %ux%u [layers=%u,out=%u]",lay,y,ln, train_data->num_output);
while (true)
{
neur1=1+(rand()%y);
neur2=1+(rand()%y);
conn_rate=0.5f+((rand()%50)*0.01f);
printf("\r\n%2dx%-4d: ",neur1,neur2);
// printf("create network: layers=%d l1n=%d l2n=%d l3n=%d l4n=%d\ l5n=%d l6n=%dr\n",numn,l1n,l2n,l3n,l4n,l5n,l6n);
ann = fann_create_standard (//conn_rate,
ln,
train_data->num_input,
neur1,
neur2,
train_data->num_output );
//fann_init_weights ( ann, train_data );
printf(" [%p] ",ann);
if ( ( int ) ann==NULL )
{
printf ( "error" );
exit ( 0 );
}
fann_set_activation_function_hidden(ann,FANN_SIGMOID);
fann_set_activation_function_output(ann,FANN_SIGMOID);
rebuild_functions(neur1);
fann_set_training_algorithm ( ann, FANN_TRAIN_RPROP );
fann_set_sarprop_temperature(ann,15000.0f);
//fann_randomize_weights ( ann, -((rand()%10)*0.1f), ((rand()%10)*0.1f) );
fann_init_weights(ann,train_data);
got_inc=0;
prev_epoch_mse=1;
//
epochs=0;
unsigned last_best_perc_epoch=0;
unsigned last_sync_epoch=0;
unsigned last_ftest_secs=0;
last_sync_epoch=0;
last_best_perc_epoch=0;
if (good_ann)
fann_destroy(good_ann);
good_ann=fann_copy(ann);
unlink(histfile);
for (u=0;u<1000;u++)
{
fflush(NULL);
train_mse=fann_train_epoch(ann, train_data);
if (jitter_train)
apply_jjit(train_data,cln_train_data);
if (time(NULL)-last_ftest_secs>=1)
{
//printf("\r\n%5u %9.6f %5.2f ",epochs,train_mse,test_perc);
//printf(" %4.2f",test_perc);
printf(".");
last_ftest_secs=time(NULL);
}
ftest_data();
plot(epochs,train_mse,test_mse);
/* if (epochs>10&&((int)test_perc==43||(int)test_perc==57))
{
printf(" [excluded %.2f] ",test_perc);
break;
} else {
} */
//printf("excluded %f ",test_perc);
double prev_test_perc;
// if (prev_epoch_mse==best_perc)
// printf("o");
if ((int)test_perc>(int)train_perc&&epochs-last_stat_epoch>10)
{
fann_destroy(good_ann);
good_ann=fann_copy(ann);
if (test_perc!=prev_test_perc)
printf("%.2f [%f]",test_perc,train_mse);
//printf(" sync[%4.2f]",test_perc);
last_stat_epoch=epochs;
}
else if (epochs-last_sync_epoch>111500)
{
last_sync_epoch=epochs;
}
if (epochs>210&&test_perc>best_perc)
{
// u--;
// fann_destroy(good_ann);
// good_ann=fann_copy(ann);
printf(" [saved best %.0f] ",test_perc);
last_stat_epoch=epochs;
// printf("%f",test_perc);
// fann_destroy(ann);
// ann=fann_copy(good_ann);
fann_save(ann,"mutate-best.net");
best_perc=test_perc;
printf(" %6.2f [%f]",test_perc,train_mse);
last_best_perc_epoch=epochs;
}
else if (epochs>11100&&((int)test_perc<=63||(int)test_perc==(int)prev_test_perc))
{
//best_perc=test_perc;
// printf("x");
// printf(".");
//printf("\r%6.8f",train_mse);
// printf("done\r\n");
break;
}
static unsigned last_restore_epoch=0;
if (epochs>100&&test_mse-train_mse>=0.25f&&epochs-last_restore_epoch>=120)
{
/* fann_set_learning_rate ( ann,0.31f+(rand()%90)*0.01f);
fann_set_learning_momentum(ann,(rand()%90)*0.01f);
printf(" [restored @ %u lr %.2f mm %.2f]",epochs,fann_get_learning_rate(ann),
fann_get_learning_momentum(ann));
fann_destroy(ann);
ann=fann_copy(good_ann);
last_stat_epoch=epochs;
last_restore_epoch=epochs; */
double rdec,rinc;
rdec=0.0101f+((rand()%100)*0.00001f);
if (!rdec)
rdec=0.01f;
rinc=1.0001f+((rand()%90)*0.00001f);
if (!rinc)
rinc=1.1f;
static double prev_test_epoch_mse;
// rinc+=diff_mse*0.000001f;
// fann_set_rprop_increase_factor(ann,rinc );
// fann_set_rprop_decrease_factor(ann, rdec);
}
else if (test_mse-train_mse<=0.1f)
{
fann_destroy(good_ann);
good_ann=fann_copy(ann);
// printf("s");
}
else
{
fann_set_sarprop_temperature(ann,fann_get_sarprop_temperature(ann)-0.0001f);
}
static unsigned last_train_change_epoch=0;
if (test_mse>=train_mse&&epochs-last_train_change_epoch>=100)
{
last_train_change_epoch=epochs;
//fann_set_training_algorithm(ann,FANN_TRAIN_SARPROP);
jitter_train=0;
}
else
{
//fann_set_training_algorithm(ann,FANN_TRAIN_RPROP);
jitter_train=0;
}
got_inc=test_perc-prev_epoch_mse;
prev_epoch_mse=test_perc;
prev_test_perc=test_perc;
epochs++;
if (epochs-last_best_perc_epoch>511500)
{
printf(" failed");
break;
}
if (epochs>2200&&(int)train_perc<40)
{
printf("skip 1\r\n");
break;
}
if ((int)test_perc>=80)
{
printf("\r\ngot it %f\r\n",test_perc);
fann_save(ann,"good.net");
exit(0);
}
// printf("\n%6u ",epochs);
}
printf(" %6.2f inc: %.2f",test_perc,got_inc);
// printf("%6.2f %6.2f",train_perc,test_perc);
fann_destroy ( ann );
}
fann_destroy_train ( train_data );
fann_destroy_train ( test_data );
fann_destroy ( ann );
return 0;
}
int main()
{
struct fann *ann;
struct fann_train_data *train_data, *test_data;
const float desired_error = (const float)0.0;
unsigned int max_neurons = 30;
unsigned int neurons_between_reports = 1;
unsigned int bit_fail_train, bit_fail_test;
float mse_train, mse_test;
unsigned int i = 0;
fann_type *output;
fann_type steepness;
int multi = 0;
enum fann_activationfunc_enum activation;
enum fann_train_enum training_algorithm = FANN_TRAIN_RPROP;
printf("Reading data.\n");
train_data = fann_read_train_from_file("../benchmarks/datasets/parity8.train");
test_data = fann_read_train_from_file("../benchmarks/datasets/parity8.test");
fann_scale_train_data(train_data, -1, 1);
fann_scale_train_data(test_data, -1, 1);
printf("Creating network.\n");
ann = fann_create_shortcut(2, fann_num_input_train_data(train_data), fann_num_output_train_data(train_data));
fann_set_training_algorithm(ann, training_algorithm);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_LINEAR);
fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR);
if(!multi)
{
/*steepness = 0.5;*/
steepness = 1;
fann_set_cascade_activation_steepnesses(ann, &steepness, 1);
/*activation = FANN_SIN_SYMMETRIC;*/
activation = FANN_SIGMOID_SYMMETRIC;
fann_set_cascade_activation_functions(ann, &activation, 1);
fann_set_cascade_num_candidate_groups(ann, 8);
}
if(training_algorithm == FANN_TRAIN_QUICKPROP)
{
fann_set_learning_rate(ann, 0.35);
fann_randomize_weights(ann, -2.0,2.0);
}
fann_set_bit_fail_limit(ann, 0.9);
fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
fann_print_parameters(ann);
fann_save(ann, "cascade_train2.net");
printf("Training network.\n");
fann_cascadetrain_on_data(ann, train_data, max_neurons, neurons_between_reports, desired_error);
fann_print_connections(ann);
mse_train = fann_test_data(ann, train_data);
bit_fail_train = fann_get_bit_fail(ann);
mse_test = fann_test_data(ann, test_data);
bit_fail_test = fann_get_bit_fail(ann);
printf("\nTrain error: %f, Train bit-fail: %d, Test error: %f, Test bit-fail: %d\n\n",
mse_train, bit_fail_train, mse_test, bit_fail_test);
for(i = 0; i < train_data->num_data; i++)
{
output = fann_run(ann, train_data->input[i]);
if((train_data->output[i][0] >= 0 && output[0] <= 0) ||
(train_data->output[i][0] <= 0 && output[0] >= 0))
{
printf("ERROR: %f does not match %f\n", train_data->output[i][0], output[0]);
}
}
printf("Saving network.\n");
fann_save(ann, "cascade_train.net");
printf("Cleaning up.\n");
fann_destroy_train(train_data);
fann_destroy_train(test_data);
fann_destroy(ann);
return 0;
}
int main()
{
fann_type *calc_out;
const unsigned int num_input = 22500;
const unsigned int num_output = 1;
//const unsigned int num_layers = 4;
const unsigned int num_layers = 4;
/* this value can be changed to tweak the network */
const unsigned int num_neurons_hidden = 50;
//const unsigned int num_neurons_hidden = 150;
const float desired_error = (const float) 0.02;
const unsigned int max_epochs = 15000;
const unsigned int epochs_between_reports = 20;
float learning_rate = .5;
struct fann *ann;
struct fann_train_data *data;
int num_neurons = 0;
unsigned int i = 0;
unsigned int decimal_point;
/* CREATING NETWORK */
ann = fann_create_standard(num_layers, num_input,
num_neurons_hidden,
num_neurons_hidden, num_output);
/* reading training data */
data = fann_read_train_from_file("training.data");
fann_set_activation_steepness_hidden(ann, 1);
fann_set_activation_steepness_output(ann, 1);
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
fann_init_weights(ann, data);
/*
TRAINING NETWORK
run x epochs at learn rate .y
*/
//fann_set_learning_rate(ann, .7);
//fann_train_on_data(ann, data, 200, epochs_between_reports, .4);
//fann_train_on_data(ann, data, 500, epochs_between_reports, .002);
fann_set_learning_rate(ann, .5);
fann_train_on_data(ann, data,5000, epochs_between_reports, .2);
//fann_train_on_data(ann, data,50, epochs_between_reports, .2);
fann_set_learning_rate(ann, .2);
fann_train_on_data(ann, data,1000, epochs_between_reports, .15);
//fann_train_on_data(ann, data,100, epochs_between_reports, .15);
fann_set_learning_rate(ann, .1);
fann_train_on_data(ann, data,5000, epochs_between_reports, .002);
//fann_train_on_data(ann, data,200, epochs_between_reports, .00002);
/* TESTING NETWORK */
printf("Testing network. %f\n", fann_test_data(ann, data));
for(i = 0; i < fann_length_train_data(data); i++)
{
calc_out = fann_run(ann, data->input[i]);
/*printf("%f, should be %f, difference=%f\n",
calc_out[0], data->output[i][0],
fann_abs(calc_out[0] - data->output[i][0])); */
}
/* SAVING NETWORK */
fann_save(ann, "image_spam.net");
/* CLEANING UP */
fann_destroy_train(data);
fann_destroy(ann);
return 0;
}
int main() {
printf("Reading XML.. .. ..\n");
ezxml_t f1 = ezxml_parse_file("test.xml"), classification, temp, algo, temp2;
classification = ezxml_child(f1, "classification");
temp = ezxml_child(classification, "algorithm");
algo = ezxml_child(temp, "MultiLayerPerceptron");
const unsigned int num_input = atoi(ezxml_child(classification, "input")->txt);
const unsigned int num_output = atoi(ezxml_child(classification, "output")->txt);
const unsigned int num_layers = atoi(ezxml_child(classification, "numberOfLayers")->txt);
const unsigned int num_neurons_hidden = atoi(ezxml_child(algo, "hiddenNeurons")->txt);
const float desired_error = (const float) (atof(ezxml_child(algo, "desiredError")->txt));
const unsigned int max_epochs = atoi(ezxml_child(algo, "maxEpochs")->txt);
const unsigned int epochs_between_reports = atoi(ezxml_child(algo, "epochsBetweenReports")->txt);
fann_type *calc_out;
struct fann *ann;
struct fann_train_data *data;
unsigned int i = 0;
unsigned int decimal_point;
printf("Creating network.\n");
ann = fann_create_standard(num_layers, num_input, num_neurons_hidden, num_output);
data = fann_read_train_from_file(ezxml_child(classification, "datafile")->txt);
fann_set_activation_steepness_hidden(ann, atoi(ezxml_child(algo, "hiddenActivationSteepness")->txt));
fann_set_activation_steepness_output(ann, atoi(ezxml_child(algo, "outputActivationSteepness")->txt));
fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
temp2 = ezxml_child(algo, "trainStopFuction");
const char *stopFunc = temp2->txt;
if(stopFunc == "FANN_STOPFUNC_BIT"){
fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
} else {
fann_set_train_stop_function(ann, FANN_STOPFUNC_MSE);
}
fann_set_bit_fail_limit(ann, 0.01f);
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
fann_init_weights(ann, data);
printf("Training network.\n");
fann_train_on_data(ann, data, max_epochs, epochs_between_reports, desired_error);
printf("Testing network. %f\n", fann_test_data(ann, data));
for(i = 0; i < fann_length_train_data(data); i++)
{
calc_out = fann_run(ann, data->input[i]);
printf("Test Results (%f,%f,%f) -> %f, should be %f, difference=%f\n",
data->input[i][0], data->input[i][1], data->input[i][2], calc_out[0], data->output[i][0],
fann_abs(calc_out[0] - data->output[i][0]));
}
printf("Saving network.\n");
fann_save(ann, "xor_float.net");
decimal_point = fann_save_to_fixed(ann, "xor_fixed.net");
fann_save_train_to_fixed(data, "xor_fixed.data", decimal_point);
printf("Cleaning up.\n");
fann_destroy_train(data);
fann_destroy(ann);
ezxml_free(f1);
return 0;
}
/*
arguments (all required):
- data filename
- topology, as number of neurons per layer separated by dashes
- epochs (integer)
- learning rate (0.0-1.0 float)
- output filename
*/
int main(int argc, char **argv)
{
// Argument 1: data filename.
const char *datafn = argv[1];
// Argument 2: topology.
unsigned int layer_sizes[MAX_LAYERS];
unsigned int num_layers = 0;
char *token = strtok(argv[2], "-");
while (token != NULL) {
layer_sizes[num_layers] = atoi(token);
++num_layers;
token = strtok(NULL, "-");
}
// Argument 3: epoch count.
unsigned int max_epochs = atoi(argv[3]);
// Argument 4: learning rate.
float learning_rate = atof(argv[4]);
// Argument 5: output filename.
const char *outfn = argv[5];
struct fann *ann;
ann = fann_create_standard_array(num_layers, layer_sizes);
// Misc parameters.
fann_set_training_algorithm(ann, FANN_TRAIN_RPROP);
fann_set_activation_steepness_hidden(ann, 0.5);
fann_set_activation_steepness_output(ann, 0.5);
fann_set_activation_function_hidden(ann, FANN_SIGMOID);
fann_set_activation_function_output(ann, FANN_SIGMOID);
//fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
//fann_set_bit_fail_limit(ann, 0.01f);
struct fann_train_data *data;
data = fann_read_train_from_file(datafn);
fann_init_weights(ann, data);
fann_set_learning_rate(ann, learning_rate);
fann_train_on_data(
ann,
data,
max_epochs,
10, // epochs between reports
DESIRED_ERROR
);
printf("Testing network. %f\n", fann_test_data(ann, data));
fann_type *calc_out;
for(unsigned int i = 0; i < fann_length_train_data(data); ++i)
{
calc_out = fann_run(ann, data->input[i]);
}
printf("RMSE = %f\n", sqrt(fann_get_MSE(ann)));
fann_save(ann, outfn);
fann_destroy_train(data);
fann_destroy(ann);
return 0;
}
