CI_ASS2_Neural Network Classification_PHP

// Generating weights
$layers_config = [2, 3, 1]; // nodes for each layer, excluding the hidden layer
$layer_last    = count($layers_config) - 1;
$weights       = [];
$biases        = [];
for ($layer = 1; $layer <= $layer_last; ++$layer) {
    $prev_layer = $layer - 1;
    $weights[$prev_layer] = [];
    for ($node = 0; $node < $layers_config[$layer]; ++$node) {
        for ($prev_node = 0; $prev_node < $layers_config[$prev_layer]; ++$prev_node) {
            $weights[$prev_layer][$prev_node][$node] = generateWeight(); // weight from i to j
        }
        $biases[$layer][$node] = generateWeight(); // bias weight for each layer
    }
}
//exit;

$learning_rate = .0001; // Initializing learning rate

echo '<pre>';
//echo '<table width="100%">';
print_r($weights);
print_r($biases);
$k = 0;
for ($i = 1; $i < 9; ++$i) {
    //echo '<tr><td><b>Training - iteration ' . $i . '</b></td></tr>';
    //echo '<tr><td><b>Weight</b></td></tr>';

    $old_weights = $weights;
    foreach ($training_sets as list($i0, $i1, $desired_output)) {

        // Forward activations
        $outputs = [[$i0, $i1]];
        for ($layer = 1; $layer <= $layer_last; ++$layer) {
            $prev_layer = $layer - 1;
            for ($node = 0; $node < $layers_config[$layer]; ++$node) {
                $sum = 0;
                for ($prev_node = 0; $prev_node < $layers_config[$prev_layer]; ++$prev_node) {
                    $sum += $outputs[$prev_layer][$prev_node] * $weights[$prev_layer][$prev_node][$node];
                }
                $outputs[$layer][$node] = activate($sum + $biases[$layer][$node]);
            }
        }

        // Back propagations
        //$error_total = pow($desired_output - $outputs[2][0], 2) / 2;
        $error_gradients = [];
        $prev_correction = [];
        for ($layer = $layer_last; $layer > 0; --$layer) {
            $next_layer = $layer + 1;
            $prev_layer = $layer - 1;
            for ($node = 0; $node < $layers_config[$layer]; ++$node) {
                $derivation = deriveActivation($outputs[$layer][$node]);
                if ($layer === $layer_last) {
                    $error_gradients[$layer][$node] = $derivation * ($desired_output - $outputs[$layer][$node]);
                } else {
                    $error_gradients[$layer][$node] = $biases[$layer][$node];
                    for ($next_node = 0; $next_node < $layers_config[$next_layer]; ++$next_node) {
                        $error_gradients[$layer][$node] += $error_gradients[$next_layer][$next_node] * $weights[$layer][$node][$next_node];
                    }
                    $error_gradients[$layer][$node] *= $derivation;
                }
                for ($prev_node = 0; $prev_node < $layers_config[$prev_layer]; ++$prev_node) {
                    $weights[$prev_layer][$prev_node][$node] += $learning_rate * $outputs[$prev_layer][$prev_node] * $error_gradients[$layer][$node];
                }
                $biases[$layer][$node] += $learning_rate * -$error_gradients[$layer][$node];
            }
        }
    }

    //echo '<tr><td>' . json_encode($weights) . '</td></tr>';
    if (json_encode($old_weights) === json_encode($weights)) break;
}
//echo '</table>';

print_r($weights);

echo '<table>';
echo '<tr><td colspan="3"><b>Solving</b></td></tr>';
foreach ($sets as $k => list($i0, $i1, $desired_output)) {
    $outputs = [[$i0, $i1]];
    for ($layer = 1; $layer <= $layer_last; ++$layer) {
        $prev_layer = $layer - 1;
        for ($node = 0; $node < $layers_config[$layer]; ++$node) {
            $sum = 0;
            for ($prev_node = 0; $prev_node < $layers_config[$prev_layer]; ++$prev_node) {
                $sum += $outputs[$prev_layer][$prev_node] * $weights[$prev_layer][$prev_node][$node];
            }
            $outputs[$layer][$node] = activate($sum - $biases[$layer][$node]);
        }
    }
    $outputs[2][0] = ($o = $outputs[2][0]) >= 0 ? 1 : -1;
    echo '<tr><td>' . $o . '</td><td>' . $outputs[2][0] . '</td><td>' . $desired_output . '</td><td>' . ($outputs[2][0] == $desired_output ? '' : '×') . '</td></tr>';
}
echo '</table>';