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/*
*
* Fast Artificial Neural Network (fann) C++ Wrapper Sample
*
* C++ wrapper XOR sample with functionality similar to xor_train.c
*
* Copyright (C) 2004-2006 created by freegoldbar (at) yahoo dot com
*
* This wrapper 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 wrapper 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 "floatfann.h"
#include "fann_cpp.h"
#include <ios>
#include <iostream>
#include <iomanip>
using std::cout;
using std::cerr;
using std::endl;
using std::setw;
using std::left;
using std::right;
using std::showpos;
using std::noshowpos;
// Callback function that simply prints the information to cout
int print_callback(FANN::neural_net &net, FANN::training_data &train,
unsigned int max_epochs, unsigned int epochs_between_reports,
float desired_error, unsigned int epochs, void *user_data)
{
cout << "Epochs " << setw(8) << epochs << ". "
<< "Current Error: " << left << net.get_MSE() << right << endl;
return 0;
}
// Test function that demonstrates usage of the fann C++ wrapper
void xor_test()
{
cout << endl << "XOR test started." << endl;
const float learning_rate = 0.7f;
const unsigned int num_layers = 3;
const unsigned int num_input = 2;
const unsigned int num_hidden = 3;
const unsigned int num_output = 1;
const float desired_error = 0.001f;
const unsigned int max_iterations = 300000;
const unsigned int iterations_between_reports = 1000;
cout << endl << "Creating network." << endl;
FANN::neural_net net;
net.create_standard(num_layers, num_input, num_hidden, num_output);
net.set_learning_rate(learning_rate);
net.set_activation_steepness_hidden(1.0);
net.set_activation_steepness_output(1.0);
net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE);
net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE);
// Set additional properties such as the training algorithm
//net.set_training_algorithm(FANN::TRAIN_QUICKPROP);
// Output network type and parameters
cout << endl << "Network Type : ";
switch (net.get_network_type())
{
case FANN::LAYER:
cout << "LAYER" << endl;
break;
case FANN::SHORTCUT:
cout << "SHORTCUT" << endl;
break;
default:
cout << "UNKNOWN" << endl;
break;
}
net.print_parameters();
cout << endl << "Training network." << endl;
FANN::training_data data;
if (data.read_train_from_file("xor.data"))
{
// Initialize and train the network with the data
net.init_weights(data);
cout << "Max Epochs " << setw(8) << max_iterations << ". "
<< "Desired Error: " << left << desired_error << right << endl;
net.set_callback(print_callback, NULL);
net.train_on_data(data, max_iterations,
iterations_between_reports, desired_error);
cout << endl << "Testing network." << endl;
for (unsigned int i = 0; i < data.length_train_data(); ++i)
{
// Run the network on the test data
fann_type *calc_out = net.run(data.get_input()[i]);
cout << "XOR test (" << showpos << data.get_input()[i][0] << ", "
<< data.get_input()[i][1] << ") -> " << *calc_out
<< ", should be " << data.get_output()[i][0] << ", "
<< "difference = " << noshowpos
<< fann_abs(*calc_out - data.get_output()[i][0]) << endl;
}
cout << endl << "Saving network." << endl;
// Save the network in floating point and fixed point
net.save("xor_float.net");
unsigned int decimal_point = net.save_to_fixed("xor_fixed.net");
data.save_train_to_fixed("xor_fixed.data", decimal_point);
cout << endl << "XOR test completed." << endl;
}
}
/* Startup function. Syncronizes C and C++ output, calls the test function
and reports any exceptions */
int main(int argc, char **argv)
{
try
{
std::ios::sync_with_stdio(); // Syncronize cout and printf output
xor_test();
}
catch (...)
{
cerr << endl << "Abnormal exception." << endl;
}
return 0;
}
/******************************************************************************/
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