Logical XOR function: Difference between revisions
From Artificial Neural Network for PHP
(12 intermediate revisions by the same user not shown) | |||
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== FAQ == |
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For information about dat-files have a view to the [[FAQ]] page. |
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== Training == |
== Training == |
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<source lang="php"> |
<source lang="php"> |
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require_once 'ANN/ |
require_once 'ANN/Loader.php'; |
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use ANN\Network; |
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use ANN\Values; |
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try |
try |
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{ |
{ |
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$ |
$objNetwork = Network::loadFromFile('xor.dat'); |
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} |
} |
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catch(Exception $e) |
catch(Exception $e) |
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print 'Creating a new one...'; |
print 'Creating a new one...'; |
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$ |
$objNetwork = new Network; |
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$objValues = new Values; |
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⚫ | |||
->input(0,0)->output(0) |
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->input(0,1)->output(1) |
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->input(1,0)->output(1) |
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->input(1,1)->output(0); |
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unset($objValues); |
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} |
} |
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try |
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$inputs = array( |
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{ |
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array(0, 0), |
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$objValues = Values::loadFromFile('values_xor.dat'); |
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array(0, 1), |
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} |
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array(1, 0), |
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catch(Exception $e) |
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array(1, 1) |
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{ |
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); |
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die('Loading of values failed'); |
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} |
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$objNetwork->setValues($objValues); // to be called as of version 2.0.6 |
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$outputs = array( |
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array(0), |
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array(1), |
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array(1), |
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array(0) |
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); |
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$boolTrained = $objNetwork->train(); |
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$network->setInputs($inputs); |
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print ($boolTrained) |
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$network->setOutputs($outputs); |
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? 'Network trained' |
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: 'Network not trained completely. Please re-run the script'; |
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$objNetwork->saveToFile('xor.dat'); |
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$objNetwork->printNetwork(); |
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⚫ | |||
</source> |
</source> |
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<source lang="php"> |
<source lang="php"> |
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require_once 'ANN/ |
require_once 'ANN/Loader.php'; |
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use ANN\Network; |
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use ANN\Values; |
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try |
try |
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{ |
{ |
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$ |
$objNetwork = Network::loadFromFile('xor.dat'); |
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} |
} |
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catch(Exception $e) |
catch(Exception $e) |
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} |
} |
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try |
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$inputs = array( |
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{ |
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array(0, 0), |
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$objValues = Values::loadFromFile('values_xor.dat'); |
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array(0, 1), |
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} |
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array(1, 0), |
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catch(Exception $e) |
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array(1, 1) |
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{ |
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); |
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die('Loading of values failed'); |
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} |
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$objValues->input(0, 1) // input values appending the loaded ones |
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->input(1, 1) |
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->input(1, 0) |
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->input(0, 0) |
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->input(0, 1) |
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->input(1, 1); |
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$objNetwork->setValues($objValues); |
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$network->setInputs($inputs); |
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print_r($ |
print_r($objNetwork->getOutputs()); |
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</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());