Logical XOR function: Difference between revisions
From Artificial Neural Network for PHP
Line 45: | Line 45: | ||
try |
try |
||
{ |
{ |
||
$ |
$objNetwork = ANN_Network::loadFromFile('xor.dat'); |
||
} |
} |
||
catch(Exception $e) |
catch(Exception $e) |
||
Line 52: | Line 52: | ||
} |
} |
||
$ |
$arrInputs = array( |
||
array(0, 0), |
array(0, 0), |
||
array(0, 1), |
array(0, 1), |
||
Line 59: | Line 59: | ||
); |
); |
||
$ |
$objNetwork->setInputs($arrInputs); |
||
print_r($ |
print_r($objNetwork->getOutputs()); |
||
</source> |
</source> |
Revision as of 12:42, 18 December 2008
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;
}
$arrInputs = array(
array(0, 0),
array(0, 1),
array(1, 0),
array(1, 1)
);
$arrOutputs = array(
array(0),
array(1),
array(1),
array(0)
);
$objNetwork ->setInputs($arrInputs);
$objNetwork ->setOutputs($arrOutputs);
$objNetwork ->train();
$objNetwork ->saveToFile('xor.dat');
Using trained network
require_once 'ANN/ANN_Loader.php';
try
{
$objNetwork = ANN_Network::loadFromFile('xor.dat');
}
catch(Exception $e)
{
die('Network not found');
}
$arrInputs = array(
array(0, 0),
array(0, 1),
array(1, 0),
array(1, 1)
);
$objNetwork->setInputs($arrInputs);
print_r($objNetwork->getOutputs());