mirror of
https://github.com/lxsang/antd-lua-plugin
synced 2024-12-29 10:48:21 +01:00
117 lines
3.4 KiB
C
117 lines
3.4 KiB
C
/*
|
|
Fast Artificial Neural Network Library (fann)
|
|
Copyright (C) 2003-2016 Steffen Nissen (steffen.fann@gmail.com)
|
|
|
|
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;
|
|
}
|