Logical XOR function: Difference between revisions

From Artificial Neural Network for PHP
(New page: = Logical XOR function = == Training == <source lang="php"> require_once '../ANN/ANN_Network.php'; try { $network = ANN_Network::loadFromFile('xor.dat'); } catch(Exception $e) { pri...)
 
 
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== FAQ ==
= Logical XOR function =

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


== Training ==
== Training ==


<source lang="php">
<source lang="php">

require_once '../ANN/ANN_Network.php';
require_once 'ANN/Loader.php';

use ANN\Network;
use ANN\Values;


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

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


try
$inputs = array(
{
array(0, 0),
$objValues = Values::loadFromFile('values_xor.dat');
array(0, 1),
}
array(1, 0),
catch(Exception $e)
array(1, 1)
{
);
die('Loading of values failed');
}


$objNetwork->setValues($objValues); // to be called as of version 2.0.6
$outputs = array(
array(0),
array(1),
array(1),
array(0)
);


$boolTrained = $objNetwork->train();
$network->setInputs($inputs);


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


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


$objNetwork->printNetwork();
$network->saveToFile('xor.dat');
</source>
</source>


== Using a trained network ==
== Using trained network ==


<source lang="php">
<source lang="php">
require_once('../ANN/ANN_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
try
{
{
$network = ANN_Network::loadFromFile('xor.dat');
$objValues = Values::loadFromFile('values_xor.dat');
}
}
catch(Exception $e)
catch(Exception $e)
{
{
die('Loading of values failed');
print "\nNetwork not found.";
}
}


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


$objNetwork->setValues($objValues);
$network->setInputs($inputs);


print_r($network->getOutputs());
print_r($objNetwork->getOutputs());
</source>
</source>

Latest revision as of 11:29, 1 June 2011

FAQ

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

Training

require_once 'ANN/Loader.php';

use ANN\Network;
use ANN\Values;

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

try
{
  $objValues = 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/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());