Logical XOR function: Difference between revisions

From Artificial Neural Network for PHP
 
(4 intermediate revisions by the same user not shown)
Line 6: Line 6:


<source lang="php">
<source lang="php">
<?php
require_once 'ANN/Loader.php';
require_once 'ANN/Loader.php';

use ANN\Network;
use ANN\Values;


try
try
{
{
$objNetwork = ANN_Network::loadFromFile('xor.dat');
$objNetwork = Network::loadFromFile('xor.dat');
} catch (Exception $e)
}
catch(Exception $e)
{
{
print 'Creating a new one...';
$objNetwork = new Network();
$objNetwork = new ANN_Network;


$objValues = new ANN_Values;
$objValues = new Values();


$objValues->train()
$objValues->train()
->input(0,0)->output(0)
->input(0, 0)
->input(0,1)->output(1)
->output(0)
->input(1,0)->output(1)
->input(0, 1)
->input(1,1)->output(0);
->output(1)
->input(1, 0)
->output(1)
->input(1, 1)
->output(0);


$objValues->saveToFile('values_xor.dat');
$objValues->saveToFile('values_xor.dat');
unset($objValues);
}
}


try
try
{
{
$objValues = ANN_Values::loadFromFile('values_xor.dat');
$objValues = Values::loadFromFile('values_xor.dat');
}
}
catch(Exception $e)
catch (Exception $e)
{
{
die('Loading of values failed');
die('Loading of values failed');
}
}


$objNetwork->setValues($objValues); // to be called as of version 2.0.6
$objNetwork->setValues($objValues);


$boolTrained = $objNetwork->train();
$boolTrained = $objNetwork->train();


print ($boolTrained) ? 'Network trained' : 'Network not trained completely. Please re-run the script';
print ($boolTrained)
? 'Network trained'
: 'Network not trained completely. Please re-run the script';


$objNetwork->saveToFile('xor.dat');
$objNetwork->saveToFile('xor.dat');

$objNetwork->printNetwork();
</source>
</source>


Line 56: Line 55:


<source lang="php">
<source lang="php">
<?php
require_once 'ANN/Loader.php';
require_once 'ANN/Loader.php';

use ANN\Network;
use ANN\Values;


try
try
{
{
$objNetwork = ANN_Network::loadFromFile('xor.dat');
$objNetwork = Network::loadFromFile('xor.dat');
}
}
catch(Exception $e)
catch (Exception $e)
{
{
die('Network not found');
die('Network not found');
}
}


try
try
{
{
$objValues = ANN_Values::loadFromFile('values_xor.dat');
$objValues = Values::loadFromFile('values_xor.dat');
}
}
catch(Exception $e)
catch (Exception $e)
{
{
die('Loading of values failed');
die('Loading of values failed');
}
}


$objValues->input(0, 1) // input values appending the loaded ones
$objValues->input(0, 1)
->input(1, 1)
-> // input values appending the loaded ones
->input(1, 0)
input(1, 1)
->input(0, 0)
->input(1, 0)
->input(0, 1)
->input(0, 0)
->input(1, 1);
->input(0, 1)
->input(1, 1);


$objNetwork->setValues($objValues);
$objNetwork->setValues($objValues);

Latest revision as of 12:04, 4 October 2025

FAQ

For information about dat-files have a view to the FAQ page.

Training

<?php
require_once 'ANN/Loader.php';

use ANN\Network;
use ANN\Values;

try
{
    $objNetwork = Network::loadFromFile('xor.dat');
} catch (Exception $e)
{
    $objNetwork = new Network();

    $objValues = new 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');
}

try
{
    $objValues = Values::loadFromFile('values_xor.dat');
}
catch (Exception $e)
{
    die('Loading of values failed');
}

$objNetwork->setValues($objValues);

$boolTrained = $objNetwork->train();

print ($boolTrained) ? 'Network trained' : 'Network not trained completely. Please re-run the script';

$objNetwork->saveToFile('xor.dat');

Using trained network

<?php
require_once 'ANN/Loader.php';

use ANN\Network;
use ANN\Values;

try
{
    $objNetwork = Network::loadFromFile('xor.dat');
}
catch (Exception $e)
{
    die('Network not found');
}

try
{
    $objValues = 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());