Difference between revisions of "Logical XOR function"

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...)
 
 
(20 intermediate revisions by the same user not shown)
Line 1: Line 1:
  +
== 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 12:29, 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());