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117 lines
3.5 KiB
C
117 lines
3.5 KiB
C
/*
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Fast Artificial Neural Network Library (fann)
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Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
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This library is free software; you can redistribute it and/or
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modify it under the terms of the GNU Lesser General Public
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License as published by the Free Software Foundation; either
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version 2.1 of the License, or (at your option) any later version.
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This library is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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Lesser General Public License for more details.
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You should have received a copy of the GNU Lesser General Public
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License along with this library; if not, write to the Free Software
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Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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*/
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#include <stdio.h>
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#include "fann.h"
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int main()
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{
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struct fann *ann;
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struct fann_train_data *train_data, *test_data;
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const float desired_error = (const float)0.0;
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unsigned int max_neurons = 30;
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unsigned int neurons_between_reports = 1;
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unsigned int bit_fail_train, bit_fail_test;
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float mse_train, mse_test;
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unsigned int i = 0;
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fann_type *output;
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fann_type steepness;
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int multi = 0;
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enum fann_activationfunc_enum activation;
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enum fann_train_enum training_algorithm = FANN_TRAIN_RPROP;
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printf("Reading data.\n");
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train_data = fann_read_train_from_file("../../datasets/parity8.train");
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test_data = fann_read_train_from_file("../../datasets/parity8.test");
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fann_scale_train_data(train_data, -1, 1);
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fann_scale_train_data(test_data, -1, 1);
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printf("Creating network.\n");
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ann = fann_create_shortcut(2, fann_num_input_train_data(train_data), fann_num_output_train_data(train_data));
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fann_set_training_algorithm(ann, training_algorithm);
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fann_set_activation_function_hidden(ann, FANN_SIGMOID_SYMMETRIC);
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fann_set_activation_function_output(ann, FANN_LINEAR);
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fann_set_train_error_function(ann, FANN_ERRORFUNC_LINEAR);
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if(!multi)
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{
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/*steepness = 0.5;*/
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steepness = 1;
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fann_set_cascade_activation_steepnesses(ann, &steepness, 1);
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/*activation = FANN_SIN_SYMMETRIC;*/
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activation = FANN_SIGMOID_SYMMETRIC;
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fann_set_cascade_activation_functions(ann, &activation, 1);
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fann_set_cascade_num_candidate_groups(ann, 8);
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}
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if(training_algorithm == FANN_TRAIN_QUICKPROP)
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{
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fann_set_learning_rate(ann, 0.35f);
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fann_randomize_weights(ann, -2.0f, 2.0f);
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}
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fann_set_bit_fail_limit(ann, (fann_type)0.9);
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fann_set_train_stop_function(ann, FANN_STOPFUNC_BIT);
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fann_print_parameters(ann);
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fann_save(ann, "cascade_train2.net");
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printf("Training network.\n");
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fann_cascadetrain_on_data(ann, train_data, max_neurons, neurons_between_reports, desired_error);
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fann_print_connections(ann);
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mse_train = fann_test_data(ann, train_data);
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bit_fail_train = fann_get_bit_fail(ann);
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mse_test = fann_test_data(ann, test_data);
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bit_fail_test = fann_get_bit_fail(ann);
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printf("\nTrain error: %f, Train bit-fail: %d, Test error: %f, Test bit-fail: %d\n\n",
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mse_train, bit_fail_train, mse_test, bit_fail_test);
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for(i = 0; i < train_data->num_data; i++)
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{
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output = fann_run(ann, train_data->input[i]);
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if((train_data->output[i][0] >= 0 && output[0] <= 0) ||
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(train_data->output[i][0] <= 0 && output[0] >= 0))
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{
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printf("ERROR: %f does not match %f\n", train_data->output[i][0], output[0]);
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}
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}
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printf("Saving network.\n");
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fann_save(ann, "cascade_train.net");
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printf("Cleaning up.\n");
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fann_destroy_train(train_data);
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fann_destroy_train(test_data);
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fann_destroy(ann);
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return 0;
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}
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