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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 "fann.h"
#include <stdio.h>
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()
{
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;
}
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