Note Management System

Edit Note

Title:

Content:

private $weights = [
    [w1, w2],  // Weights for the first input neuron to the hidden neurons
    [w3, w4],  // Weights for the second input neuron to the hidden neurons
    [w5, w6]   // Weights for the hidden neurons to the output neuron
];

private $weights = [
    [w1, w2, w3, w4],  // Weights for the first input neuron to the hidden neurons
    [w5, w6, w7, w8],  // Weights for the second input neuron to the hidden neurons
    [w9, w10, w11, w12], // Weights for the third input neuron to the hidden neurons
    [w13, w14]  // Weights for the hidden neurons to the output neurons
];

private $weights = [
    [w1, w2, w3, w4],    // Weights for the first input neuron to the first hidden layer
    [w5, w6, w7, w8],    // Weights for the second input neuron to the first hidden layer
    [w9, w10, w11, w12],  // Weights for the third input neuron to the first hidden layer
    [w13, w14, w15, w16], // Weights for the first hidden layer to the second hidden layer
    [w17, w18, w19, w20], // Weights for the second hidden layer to the output neurons
    [w21, w22, w23, w24], // Weights for the second hidden layer to the output neurons
    [w25, w26, w27, w28]  // Weights for the second hidden layer to the output neurons
];

w1, w2, w3, w4 are the weights connecting the first input neuron to the 4 hidden neurons.
w5, w6, w7, w8 are the weights connecting the second input neuron to the 4 hidden neurons.
w9, w10, w11, w12 are the weights connecting the third input neuron to the 4 hidden neurons.
w13, w14 are the weights connecting the 4 hidden neurons to the 2 output neurons.

The $weights array in these examples helps define the connections between neurons in different layers. Each connection has a corresponding weight that will be adjusted during the training process. These weights are critical for the network's ability to learn from input data and make accurate predictions.

A = [1, 0, 0, 0, 0]
B = [0, 1, 0, 0, 0]
C = [0, 0, 1, 0, 0]
D = [0, 0, 0, 1, 0]
E = [0, 0, 0, 0, 1]

<?php

class NeuralNetwork {
    private $weights; // Weights for each layer

public function __construct($layers) {
        // Initialize the weights array
        $this->weights = [];

// Initialize weights with random values
        for ($i = 1; $i < count($layers); $i++) {
            $this->weights[$i] = $this->randomMatrix($layers[$i], $layers[$i-1]);
        }
    }

private function randomMatrix($rows, $cols) {
        // Generate a matrix with random values between -1 and 1
        $matrix = [];
        for ($i = 0; $i < $rows; $i++) {
            for ($j = 0; $j < $cols; $j++) {
                $matrix[$i][$j] = mt_rand(-1000, 1000) / 1000; // Random value between -1 and 1
            }
        }
        return $matrix;
    }
}

// Initialize neural network
$nn = new NeuralNetwork([3, 2, 1]);

// Example usage: Display weights for each layer
if ($_SERVER['REQUEST_METHOD'] === 'POST') {
    // Example to display the current weights on form submission
    echo "<pre>";
    print_r($nn);
    echo "</pre>";
}

?>

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Neural Network - Weights Display</title>
</head>
<body>
    <h1>Display Neural Network Weights</h1>

<form method="POST">
        <label for="input1">Input 1:</label>
        <input type="number" step="any" id="input1" name="input1" required><br>

<label for="input2">Input 2:</label>
        <input type="number" step="any" id="input2" name="input2" required><br>

<label for="input3">Input 3:</label>
        <input type="number" step="any" id="input3" name="input3" required><br>

</body>
</html>