Logical XOR function: Difference between revisions
From Artificial Neural Network for PHP
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<source lang="php"> |
<source lang="php"> |
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<?php |
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require_once 'ANN/Loader.php'; |
require_once 'ANN/Loader.php'; |
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try |
try |
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{ |
{ |
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$objNetwork = Network::loadFromFile('xor.dat'); |
$objNetwork = Network::loadFromFile('xor.dat'); |
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} |
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| ⚫ | |||
{ |
{ |
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$objNetwork = new Network(); |
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$objNetwork = new Network; |
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$objValues = new Values; |
$objValues = new Values(); |
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$objValues->train() |
$objValues->train() |
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->input(0, 0) |
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->output(0) |
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->input(0, 1) |
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->output(1) |
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->input(1, 0) |
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->output(1) |
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->input(1, 1) |
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->output(0); |
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$objValues->saveToFile('values_xor.dat'); |
$objValues->saveToFile('values_xor.dat'); |
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unset($objValues); |
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} |
} |
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try |
try |
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{ |
{ |
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$objValues = Values::loadFromFile('values_xor.dat'); |
$objValues = Values::loadFromFile('values_xor.dat'); |
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} |
} |
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catch(Exception $e) |
catch (Exception $e) |
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{ |
{ |
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die('Loading of values failed'); |
die('Loading of values failed'); |
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} |
} |
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$objNetwork->setValues($objValues); |
$objNetwork->setValues($objValues); |
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$boolTrained = $objNetwork->train(); |
$boolTrained = $objNetwork->train(); |
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print ($boolTrained) |
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? 'Network trained' |
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$objNetwork->saveToFile('xor.dat'); |
$objNetwork->saveToFile('xor.dat'); |
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$objNetwork->printNetwork(); |
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</source> |
</source> |
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<source lang="php"> |
<source lang="php"> |
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<?php |
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require_once 'ANN/Loader.php'; |
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 = |
$objNetwork = Network::loadFromFile('xor.dat'); |
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} |
} |
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catch(Exception $e) |
catch (Exception $e) |
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{ |
{ |
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die('Network not found'); |
die('Network not found'); |
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} |
} |
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try |
try |
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{ |
{ |
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$objValues = |
$objValues = Values::loadFromFile('values_xor.dat'); |
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} |
} |
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catch(Exception $e) |
catch (Exception $e) |
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{ |
{ |
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die('Loading of values failed'); |
die('Loading of values failed'); |
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} |
} |
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$objValues->input(0, 1) |
$objValues->input(0, 1) |
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-> // input values appending the loaded ones |
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input(1, 1) |
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->input(1, 0) |
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->input(0, 0) |
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->input(0, 1) |
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->input(1, 1); |
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$objNetwork->setValues($objValues); |
$objNetwork->setValues($objValues); |
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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());