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/*
Fast Artificial Neural Network Library (fann)
Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk)
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
#include <stdio.h>
#include "fann.h"
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 = 30;
unsigned int neurons_between_reports = 1;
fann_type *steepnesses = NULL;
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");
train_data = fann_read_train_from_file("../benchmarks/datasets/parity13.train");
test_data = fann_read_train_from_file("../benchmarks/datasets/parity13.test");
train_data = fann_read_train_from_file("../benchmarks/datasets/building.train");
test_data = fann_read_train_from_file("../benchmarks/datasets/building.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_learning_rate(ann, 0.1);
fann_set_quickprop_decay(ann, 0.0);
fann_set_quickprop_mu(ann, 2.0);
fann_set_cascade_weight_multiplier(ann, 1);
fann_set_cascade_max_out_epochs(ann, 150);
fann_set_bit_fail_limit(ann, 0.35);
fann_set_activation_steepness_output(ann, 1);
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_activation_function_output(ann, FANN_LINEAR_PIECE_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_SIGMOID_SYMMETRIC);
fann_set_activation_function_output(ann, FANN_LINEAR);
fann_set_train_error_function(ann, FANN_ERRORFUNC_TANH);
fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR);
fann_randomize_weights(ann, 0.1, 0.1);
/*
fann_set_cascade_weight_multiplier(ann, 0.4);
fann_set_cascade_candidate_limit(ann, 1000.0);
*/
fann_set_cascade_output_change_fraction(ann, 0.01);
fann_set_cascade_candidate_change_fraction(ann, 0.01);
/*
steepnesses = (fann_type *)calloc(1, sizeof(fann_type));
steepnesses[0] = (fann_type)1;
fann_set_cascade_activation_steepnesses(ann, steepnesses, 1);
*/
fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
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_spiral.net");
printf("Cleaning up.\n");
fann_destroy_train(train_data);
fann_destroy_train(test_data);
fann_destroy(ann);
return 0;
}
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