FANN XOR training - c++

Question

I am developing a piece of software that uses FANN, the Fast Artificial Neural Network library.
I have tried after numerous failed attempts at writing my own ANN code to compile a FANN sample program, here the C++ XOR approximation program. Here is the source.
#include "../include/floatfann.h"
#include "../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 = 10000;
////Make array for create_standard() workaround (prevent "FANN Error 11: Unable to allocate memory.")
const unsigned int num_input_num_hidden_num_output__array[3] = {num_input, num_hidden, num_output};
cout << endl << "Creating network." << endl;
FANN::neural_net net;
// cout<<"Debug 1"<<endl;
//net.create_standard(num_layers, num_input, num_hidden, num_output);//doesn't work
net.create_standard_array(num_layers, num_input_num_hidden_num_output__array);//this might work -- create_standard() workaround
net.set_learning_rate(learning_rate);
net.set_activation_steepness_hidden(1.0);
net.set_activation_steepness_output(1.0);
//Sample Code, changed below
net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE);
net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE);
//changed above to sigmoid
//net.set_activation_function_hidden(FANN::SIGMOID);
//net.set_activation_function_output(FANN::SIGMOID);
// 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://only connected to next layer
cout << "LAYER" << endl;
break;
case FANN::SHORTCUT://connected to all other layers
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. (not really)" << endl;
//I don't really get this code --- the funny for loop. Whatever. I'll skip it.
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. Synchronizes C and C++ output, calls the test function
and reports any exceptions */
int main(int argc, char **argv)
{
try
{
std::ios::sync_with_stdio(); // Synchronize cout and printf output
xor_test();
}
catch (...)
{
cerr << endl << "Abnormal exception." << endl;
}
return 0;
}
Here's my output.
XOR test started.
Creating network.
Network Type : LAYER
Input layer : 2 neurons, 1 bias
Hidden layer : 3 neurons, 1 bias
Output layer : 1 neurons
Total neurons and biases : 8
Total connections : 13
Connection rate : 1.000
Network type : FANN_NETTYPE_LAYER
Training algorithm : FANN_TRAIN_RPROP
Training error function : FANN_ERRORFUNC_TANH
Training stop function : FANN_STOPFUNC_MSE
Bit fail limit : 0.350
Learning rate : 0.700
Learning momentum : 0.000
Quickprop decay : -0.000100
Quickprop mu : 1.750
RPROP increase factor : 1.200
RPROP decrease factor : 0.500
RPROP delta min : 0.000
RPROP delta max : 50.000
Cascade output change fraction : 0.010000
Cascade candidate change fraction : 0.010000
Cascade output stagnation epochs : 12
Cascade candidate stagnation epochs : 12
Cascade max output epochs : 150
Cascade min output epochs : 50
Cascade max candidate epochs : 150
Cascade min candidate epochs : 50
Cascade weight multiplier : 0.400
Cascade candidate limit :1000.000
Cascade activation functions[0] : FANN_SIGMOID
Cascade activation functions[1] : FANN_SIGMOID_SYMMETRIC
Cascade activation functions[2] : FANN_GAUSSIAN
Cascade activation functions[3] : FANN_GAUSSIAN_SYMMETRIC
Cascade activation functions[4] : FANN_ELLIOT
Cascade activation functions[5] : FANN_ELLIOT_SYMMETRIC
Cascade activation functions[6] : FANN_SIN_SYMMETRIC
Cascade activation functions[7] : FANN_COS_SYMMETRIC
Cascade activation functions[8] : FANN_SIN
Cascade activation functions[9] : FANN_COS
Cascade activation steepnesses[0] : 0.250
Cascade activation steepnesses[1] : 0.500
Cascade activation steepnesses[2] : 0.750
Cascade activation steepnesses[3] : 1.000
Cascade candidate groups : 2
Cascade no. of candidates : 80
Training network.
Max Epochs 300000. Desired Error: 0.001
Epochs 1. Current Error: 0.25
Epochs 10000. Current Error: 0.25
Epochs 20000. Current Error: 0.25
Epochs 30000. Current Error: 0.25
Epochs 40000. Current Error: 0.25
Epochs 50000. Current Error: 0.25
Epochs 60000. Current Error: 0.25
Epochs 70000. Current Error: 0.25
Epochs 80000. Current Error: 0.25
Epochs 90000. Current Error: 0.25
Epochs 100000. Current Error: 0.25
Epochs 110000. Current Error: 0.25
Epochs 120000. Current Error: 0.25
Epochs 130000. Current Error: 0.25
Epochs 140000. Current Error: 0.25
Epochs 150000. Current Error: 0.25
Epochs 160000. Current Error: 0.25
Epochs 170000. Current Error: 0.25
Epochs 180000. Current Error: 0.25
Epochs 190000. Current Error: 0.25
Epochs 200000. Current Error: 0.25
Epochs 210000. Current Error: 0.25
Epochs 220000. Current Error: 0.25
Epochs 230000. Current Error: 0.25
Epochs 240000. Current Error: 0.25
Epochs 250000. Current Error: 0.25
Epochs 260000. Current Error: 0.25
Epochs 270000. Current Error: 0.25
Epochs 280000. Current Error: 0.25
Epochs 290000. Current Error: 0.25
Epochs 300000. Current Error: 0.25
Testing network. (not really)
XOR test (+0, -1.875) -> +0, should be +0, difference = -0
XOR test (+0, -1.875) -> +0, should be +0, difference = -0
XOR test (+0, +1.875) -> +0, should be +0, difference = -0
XOR test (+0, +1.875) -> +0, should be +0, difference = -0
Saving network.
XOR test completed.
The training data (xor.data) is here:
4 2 1
-1 -1
-1
-1 1
1
1 -1
1
1 1
-1
What explains the eerie lack of learning in the ANN? I'm pretty convinced that I have something configured very wrong somewhere, especially given that this is the sample program. ANN experts, any advice?

Answer 1

Apply the FANN patch and make sure that all references to floatfann, doublefann, etc. are congruent.