Logical XOR function: Difference between revisions
From Artificial Neural Network for PHP
m (→Training) |
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Line 42: | Line 42: | ||
print ($boolTrained) |
print ($boolTrained) |
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? 'Network trained' |
? 'Network trained' |
||
: 'Network not trained |
: 'Network not trained completely. Please re-run the script'; |
||
$objNetwork->saveToFile('xor.dat'); |
$objNetwork->saveToFile('xor.dat'); |
Revision as of 16:29, 25 May 2009
Training
require_once 'ANN/ANN_Loader.php';
try
{
$objNetwork = ANN_Network::loadFromFile('xor.dat');
}
catch(Exception $e)
{
print 'Creating a new one...';
$objNetwork = new ANN_Network;
$objValues = new ANN_Values;
$objValues->train()
->input(0,0)->output(0)
->input(0,1)->output(1)
->input(1,0)->output(1)
->input(1,1)->output(0);
$objValues->saveToFile('values_xor.dat');
unset($objValues);
}
try
{
$objValues = ANN_Values::loadFromFile('values_xor.dat');
}
catch(Exception $e)
{
die('Loading of values failed');
}
$objNetwork->setValues($objValues); // to be called as of version 2.0.6
$boolTrained = $objNetwork->train();
print ($boolTrained)
? 'Network trained'
: 'Network not trained completely. Please re-run the script';
$objNetwork->saveToFile('xor.dat');
$objNetwork->printNetwork();
Using trained network
require_once 'ANN/ANN_Loader.php';
try
{
$objNetwork = ANN_Network::loadFromFile('xor.dat');
}
catch(Exception $e)
{
die('Network not found');
}
try
{
$objValues = ANN_Values::loadFromFile('values_xor.dat');
}
catch(Exception $e)
{
die('Loading of values failed');
}
$objValues->input(0, 1) // input values appending the loaded ones
->input(1, 1)
->input(1, 0)
->input(0, 0)
->input(0, 1)
->input(1, 1);
$objNetwork->setValues($objValues);
print_r($objNetwork->getOutputs());