Logical XOR function: Difference between revisions
From Artificial Neural Network for PHP
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$objNetwork = new ANN_Network; |
$objNetwork = new ANN_Network; |
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⚫ | |||
$objValues = new ANN_Values; |
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$arrInputs = array( |
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array(0, 0), |
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array(0, 1), |
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array(1, 0), |
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array(1, 1) |
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); |
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$objValues->train() |
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$arrOutputs = array( |
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->input(0,0)->output(0) |
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array(0), |
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->input(0,1)->output(1) |
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array(1), |
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->input(1,0)->output(1) |
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array(1), |
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->input(1,1)->output(0); |
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array(0) |
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); |
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$objValues->saveToFile('values_xor.dat'); |
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$objNetwork->setInputs($arrInputs); |
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unset($objValues); |
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⚫ | |||
try |
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{ |
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$objValues = ANN_Values::loadFromFile('values_xor.dat'); |
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} |
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catch(Exception $e) |
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{ |
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die('Loading of values failed'); |
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} |
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$objNetwork-> |
$objNetwork->setValues($objValues); // to be called as of version 2.0.6 |
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$objNetwork->train(); |
$objNetwork->train(); |
Revision as of 17:51, 18 December 2008
Training
require_once 'ANN/ANN_Loader.php';
try
{
$objNetwork = ANN_Network::loadFromFile('xor.dat');
}
catch(Exception $e)
{
print 'Creating a new one...';
$objNetwork = new ANN_Network;
$objValues = new ANN_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 = ANN_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
$objNetwork->train();
$objNetwork->saveToFile('xor.dat');
Using trained network
require_once 'ANN/ANN_Loader.php';
try
{
$objNetwork = ANN_Network::loadFromFile('xor.dat');
}
catch(Exception $e)
{
die('Network not found');
}
$arrInputs = array(
array(0, 0),
array(0, 1),
array(1, 0),
array(1, 1)
);
$objNetwork->setInputs($arrInputs);
print_r($objNetwork->getOutputs());