<?php
require_once "../core/class_neuralnetwork.php";
// Create a new neural network with 3 input neurons,
// 4 hidden neurons, and 1 output neuron
$n = new NeuralNetwork(3, 4, 1);
$n->setVerbose(false);
// Add test-data to the network. In this case,
// we want the network to learn the 'XOR'-function
$n->addTestData(array(-1, -1, 1), array(-1));
$n->addTestData(array(-1, 1, 1), array(1));
$n->addTestData(array(1, -1, 1), array(1));
$n->addTestData(array(1, 1, 1), array(-1));
// we try training the network for at most $max times
$max = 3;
// train the network in max 1000 epochs, with a max squared error of 0.01
while (!($success = $n->train(1000, 0.01)) && $max-- > 0) {
echo "Nothing found...<hr />";
}
// print a message if the network was succesfully trained
if ($success) {
$epochs = $n->getEpoch();
echo "Success in {$epochs} training rounds!<hr />";
}
// in any case, we print the output of the neural network
for ($i = 0; $i < count($n->trainInputs); $i++) {
$output = $n->calculate($n->trainInputs[$i]);
print "<br />Testset {$i}; ";
print "expected output = (" . implode(", ", $n->trainOutput[$i]) . ") ";
print "output from neural network = (" . implode(", ", $output) . ")\n";
}
// Create a new neural network with 3 input neurons,
// 4 hidden neurons, and 1 output neuron
$n = new NeuralNetwork(3, 4, 1);
$n->setVerbose(false);
// Add test-data to the network. In this case,
// we want the network to learn the 'XOR'-function
$n->addTestData(array(-1, -1, 1), array(-1));
$n->addTestData(array(-1, 1, 1), array(1));
$n->addTestData(array(1, -1, 1), array(1));
$n->addTestData(array(1, 1, 1), array(-1));
// we try training the network for at most $max times
$max = 3;
$i = 0;
echo "<h1>Learning the XOR function</h1>";
// train the network in max 1000 epochs, with a max squared error of 0.01
while (!($success = $n->train(1000, 0.01)) && ++$i < $max) {
echo "Round {$i}: No success...<br />";
}
// print a message if the network was succesfully trained
if ($success) {
$epochs = $n->getEpoch();
echo "Success in {$epochs} training rounds!<br />";
}
echo "<h2>Result</h2>";
echo "<div class='result'>";
// in any case, we print the output of the neural network
for ($i = 0; $i < count($n->trainInputs); $i++) {
$output = $n->calculate($n->trainInputs[$i]);
echo "<div>Testset {$i}; ";
echo "expected output = (" . implode(", ", $n->trainOutput[$i]) . ") ";
echo "output from neural network = (" . implode(", ", $output) . ")\n</div>";
