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() { 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; }
void Trainer::set_learning_momentum(float learning_momentum) { std::cerr << "Set learning mementum to " << learning_momentum << std::endl; return fann_set_learning_momentum(ann_, learning_momentum); }
int main(int argc, char *argv[]) { struct fann_train_data *dadosTreino, *dadosTeste; struct fann *ANN; fann_type *ANN_Answers; int *layers, i, j, aux; chromosome chromo; float erro = 0.0; checkArgs(argc, argv); buildChromosome(argv, &chromo); checkDatasetFiles(); dadosTreino = fann_read_train_from_file(nomeArqTreino); layers = (int *) calloc(2+chromo.qntCamadasOcultas, sizeof(int)); layers[0] = qntNeuroniosEntrada; layers[2+chromo.qntCamadasOcultas-1] = qntNeuroniosSaida; aux = chromo.neurOcultos; for (i=1; i < 2+chromo.qntCamadasOcultas-1 ; i++) { layers[i] = aux; aux = aux/2; } // CRIANDO A RNA: ANN = fann_create_standard_array(2+chromo.qntCamadasOcultas, layers); // TREINO fann_set_learning_rate(ANN, chromo.learnRate); fann_set_learning_momentum(ANN, chromo.moment); fann_set_activation_function_hidden( ANN, chromo.fcOculta ); fann_set_activation_function_output( ANN, chromo.fcSaida ); fann_set_training_algorithm(ANN, chromo.algAprend ); if (fann_get_training_algorithm(ANN) == FANN_TRAIN_QUICKPROP) fann_set_quickprop_decay(ANN, chromo.decay); // Em python, o treino ficava entre um try. // Se desse erro, escrevia "Resultado: 999.0" e exit fann_train_on_data(ANN, dadosTreino, chromo.epocasTreino, 50, desiredError); fann_destroy_train(dadosTreino); // TESTES: dadosTeste = fann_read_train_from_file( nomeArqValidacao); // Em python, o teste também ficava entre um try. // Se desse erro, escrevia "Resultado: 999.0" e exit for(i = 0; i < fann_length_train_data(dadosTeste); i++) { ANN_Answers = fann_run(ANN, dadosTeste->input[i]); if (ANN_Answers == NULL) { printf("Resultado: 999.0\n"); exit(2); } for (j=0; j < qntNeuroniosSaida; j++) erro += (float) powf(fann_abs(ANN_Answers[j] - dadosTeste->output[i][j]), 2); } printf("Resultado: %f\n", erro/(fann_length_train_data(dadosTeste)-1)); fann_destroy_train(dadosTeste); saveANN(argc, argv, ANN); fann_destroy(ANN); }
void ViFann::setLearningMomentum(const qreal &momentum) { fann_set_learning_momentum(mNetwork, momentum); }