<?php
function sigmoid($x)
{
$steepness = 0.00069315;
return 1 / (1 + exp(-$x * $steepness));
}
for ($i = 0; $i <= 10000; ++$i) {
echo $i, ': ', sigmoid($i), '<br>';
}
function feedForward($input)
{
global $numLayers;
global $layerSize;
global $alpha;
global $beta;
global $weight;
global $prevWeight;
global $out;
//Assign input to 0th layer
foreach ($input as $key => $value) {
$out[0][$key] = $value;
}
//Assign output for each neuron using sigmoid function
for ($i = 1; $i < $numLayers; $i++) {
for ($j = 0; $j < $layerSize[$i]; $j++) {
$sum = 0;
if ($i == 1) {
$k = 0;
foreach ($out[0] as $key => $value) {
$sum += $value * $weight[$i][$j][$k++];
}
} else {
for ($k = 0; $k < $layerSize[$i - 1]; $k++) {
$sum += $out[$i - 1][$k] * $weight[$i][$j][$k];
}
}
//Apply bias
$sum += $weight[$i][$j][$layerSize[$i - 1]];
//Finally
$out[$i][$j] = sigmoid($sum);
}
}
}