l2norm

• namespace: Rindow\NeuralNetworks\Gradient\Func
• classname: L2Norm

Differentiable L2 norm function.

Methods

l2norm

$g->l2norm(
    Variable|NDArray $x,
    ?int $axis=null,
) : Variable

Create and execute the function in the builder method

Arguments

• x: The argument is Variable or NDArray. Implicitly create Variable for NDArray.

Options

• axis: Axis to compute the norm. If null, return the norm of the whole array. If it is negative, it is negative from the maximum dimension.

use Rindow\Math\Matrix\MatrixOperator;
use Rindow\NeuralNetworks\Builder\NeuralNetworks;
$mo = new MatrixOperator();
$nn = new NeuralNetworks($mo);
$g = $nn->gradient();
$a = $g->Variable([[3,4]]);
$c = $nn->with($tape=$g->GradientTape(),function() use ($g,$a) {
    return $g->l2norm($a,axis:-1);
});
$da = $tape->gradient($c,$a);
echo $mo->toString($c)."\n";
echo $mo->toString($da)."\n";

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