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Red Neuronal - PHP
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<?php | |
/* | |
Red Neuronal 3x12x8x2 - Mr.Jack - 10/2017 | |
3 neuronas de entrada | |
2 capas intermedias | |
12 neuronas escondidas en la capa 1 | |
8 neuronas escondidas en la capa 2 | |
2 neuronas de salida | |
*/ | |
class RedNeuronal{ | |
/*3x12*/ | |
private $w1 = [ | |
[.5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5] | |
]; | |
/*12x8*/ | |
private $w2 = [ | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5], | |
[.5, .5, .5, .5, .5, .5, .5, .5] | |
]; | |
/*8x2*/ | |
private $w3 = [ | |
[.5, .5], | |
[.5, .5], | |
[.5, .5], | |
[.5, .5], | |
[.5, .5], | |
[.5, .5], | |
[.5, .5], | |
[.5, .5] | |
]; | |
/*12*/ | |
private $u2 = [.5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5]; | |
/*8*/ | |
private $u3 = [.5, .5, .5, .5, .5, .5, .5, .5]; | |
/*3*/ | |
private $u4 = [.5, .5]; | |
function run($i,$X){ | |
return $this->Y($i,$X); | |
} | |
function A2($i,$X){ | |
$sumatoria=0; | |
for($q=0; $q<3; $q++){ | |
$sumatoria += ($X[$q]*$this->w1[$q][$i]); | |
} | |
$aux1 = $sumatoria + $this->u2[$i]; | |
return $this->f($aux1); | |
} | |
function A3($i,$X){ | |
$sumatoria=0; | |
for($q=0; $q<12; $q++){ | |
$sumatoria += ($this->A2($q, $X)*$this->w2[$q][$i]); | |
} | |
$aux1 = $sumatoria + $this->u3[$i]; | |
return $this->f($aux1); | |
} | |
function Y($i,$X){ | |
$sumatoria=0; | |
for($q=0; $q<8; $q++){ | |
$sumatoria += ($this->A3($q, $X)*$this->w3[$q][$i]); | |
} | |
$aux1 = $sumatoria + $this->u4[$i]; | |
return $this->f($aux1); | |
} | |
function f($x){ | |
return 1 / (1 + exp(-$x)); | |
} | |
function delta4($Y, $sal){ | |
return $Y*(1-$Y)*($Y-$sal); | |
} | |
function delta3($k, $d4, $X){ | |
$a3=$this->A3($k,$X); | |
$sumatoria=0; | |
for($i=0; $i<2; $i++){ | |
$sumatoria += ($this->w3[$k][$i]*$d4[$i]); | |
} | |
return $a3*(1-$a3)*$sumatoria; | |
} | |
function delta2($k, $d4, $X){ | |
$a2=$this->A2($k,$X); | |
$sumatoria=0; | |
for($i=0; $i<8; $i++){ | |
$sumatoria += ($this->w2[$k][$i]*$this->delta3($i,$d4,$X)); | |
} | |
return $a2*(1-$a2)*$sumatoria; | |
} | |
function error3($k, $d4, $X){ | |
return $this->A3($k, $X)*$d4; | |
} | |
function error2($j, $k, $d4, $X){ | |
return $this->A2($j, $X)* $this->delta3($k, $d4, $X); | |
} | |
function error($j,$k,$d4,$X){ | |
return $X[$j]*$this->delta2($k,$d4,$X); | |
} | |
function BackPropagation($X,$sal){ | |
$alfa = 1; | |
$Y = [1, 1]; | |
$d4 = [1, 1]; | |
for($i=0; $i<2; $i++){ | |
$Y[$i] = $this->run($i, $X); | |
$d4[$i] = $this->delta4($Y[$i], $sal[$i]); | |
} | |
for($j=0; $j<3; $j++){ | |
for($i=0; $i<12; $i++){ | |
$this->w1[$j][$i] -= $alfa * $this->error($j, $i, $d4, $X); | |
$this->u2[$i] -= $alfa * $this->delta2($i, $d4, $X); | |
} | |
} | |
for($j=0; $j<12; $j++){ | |
for($i=0; $i<8; $i++){ | |
$this->w2[$j][$i] -= $alfa * $this->error2($j, $i, $d4, $X); | |
$this->u3[$i] -= $alfa * $this->delta3($i, $d4, $X); | |
} | |
} | |
for($j=0; $j<8; $j++){ | |
for($i=0; $i<2; $i++){ | |
$this->w3[$j][$i] -= $alfa * $this->error3($i,$d4[$i],$X); | |
$this->u4[$i] -= $alfa * $d4[$i]; | |
} | |
} | |
} | |
} | |
$rn = new RedNeuronal(); | |
//Estado inicial de la red | |
echo "S1:". PHP_EOL; | |
print_r( $rn->run(0, [1, 1, 1]) ); | |
echo "<br>" . PHP_EOL; | |
echo "S2:". PHP_EOL; | |
print_r( $rn->run(1, [1, 1, 1]) ); | |
echo "<br>" . PHP_EOL; | |
//1000 épocas | |
for($i=0; $i<1000; $i++){ | |
$rn->BackPropagation([0, 0, 0], [0.1, 0.2]); //para la entrada 0,0,0 se espera la salida 0.1, 0.2 | |
$rn->BackPropagation([0, 0, 1], [0.3, 0.4]); //para la entrada 0,0,1 se espera la salida 0.3, 0.4 | |
$rn->BackPropagation([0, 1, 0], [0.5, 0.6]); //para la entrada 0,1,0 se espera la salida 0.5, 0.6 | |
$rn->BackPropagation([0, 1, 1], [0.7, 0.8]); //para la entrada 0,1,1 se espera la salida 0.7, 0.8 | |
} | |
//Estado final de la red | |
echo "<hr>0 0 0<br>". PHP_EOL;; | |
echo "S1:". PHP_EOL; | |
print_r( $rn->run(0, [0, 0, 0]) ); | |
echo "<br>" . PHP_EOL; | |
echo "S2:". PHP_EOL; | |
print_r( $rn->run(1, [0, 0, 0]) ); | |
echo "<br>" . PHP_EOL; | |
echo "<hr>0 0 1<br>". PHP_EOL;; | |
echo "S1:". PHP_EOL; | |
print_r( $rn->run(0, [0, 0, 1]) ); | |
echo "<br>" . PHP_EOL; | |
echo "S2:". PHP_EOL; | |
print_r( $rn->run(1, [0, 0, 1]) ); | |
echo "<br>" . PHP_EOL; | |
echo "<hr>0 1 0<br>". PHP_EOL;; | |
echo "S1:". PHP_EOL; | |
print_r( $rn->run(0, [0, 1, 0]) ); | |
echo "<br>" . PHP_EOL; | |
echo "S2:". PHP_EOL; | |
print_r( $rn->run(1, [0, 1, 0]) ); | |
echo "<br>" . PHP_EOL; | |
echo "<hr>0 1 1<br>". PHP_EOL;; | |
echo "S1:". PHP_EOL; | |
print_r( $rn->run(0, [0, 1, 1]) ); | |
echo "<br>" . PHP_EOL; | |
echo "S2:". PHP_EOL; | |
print_r( $rn->run(1, [0, 1, 1]) ); | |
echo "<br>" . PHP_EOL; |
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