<?php
/*
dobu {
file:id(`4`),name(`binaryCrossEntropy-n-weights`) {
ascoos {
logo {`
__ _ ___ ___ ___ ___ ___ ___ ___
/ _` |/ / / __/ _ \ / _ \ / / / _ \ / /
| (_| |\ \| (_| (_) | (_) |\ \ | (_) |\ \
\__,_|/__/ \___\___/ \___/ /__/ \___/ /__/
`},
name {`ASCOOS OS`},
version {`1.0.0`},
},
example {
method {`TArtificialIntelligenceHandler::binaryCrossEntropy()`}
source {`examples/ai/TArtificialIntelligenceHandler/binaryCrossEntropy-n-weights.php`},
category:langs {
en {`N-Dimensional Numeric Arrays with weights`},
el {`???????????? ?????????? ?????????? ?? ????`}
},
description:langs {
en {`Computes the Binary Cross-Entropy loss for N-Dimensional arrays recursively with weights.`},
el {`?????????? ??? ??????? ?????????? ??????????????? ????????? ??? N-????????????? ??????? ?? ?????????? ???????? ??? ?? ????.`}
},
author {`Drogidis Christos`},
sincePHP {`8.4.0`}
}
}
}
*/
declare(strict_types=1);
use ASCOOS\OS\Kernel\AI\TArtificialIntelligenceHandler;
$ai = new TArtificialIntelligenceHandler([], []);
$y_true = [
[[ [1,0,0], [0,1,0] ],
[ [0,0,1], [1,0,0] ]],
[[ [0,1,0], [1,0,0] ],
[ [0,0,1], [0,1,0] ]]
];
$y_pred = [
[[ [0.7,0.2,0.1], [0.1,0.8,0.1] ],
[ [0.2,0.3,0.5], [0.6,0.3,0.1] ]],
[[ [0.2,0.7,0.1], [0.8,0.1,0.1] ],
[ [0.1,0.2,0.7], [0.2,0.6,0.2] ]]
];
$weights = [0.5,1.0,1.5,0.8,1.2,0.7,1.1,0.9];
$loss = $ai->binaryCrossEntropy($y_true, $y_pred, $weights);
echo "Binary Cross-Entropy Loss (3D) with Weights: {$loss}\n"; // Expected output: Binary Cross-Entropy Loss (3D) with Weights: 0.25228454778873
?>
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