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What This Code Does:
Biases Initialization: The constructor (__construct()) generates random bias values for each layer based on the number of neurons in that layer.
Random Bias Values: The randomBiases() function generates random bias values between -1 and 1 for each neuron in the layer.
Output: The getBiases() function returns the biases for all layers.

Layer 1 (3 neurons): You will generate 3 random bias values, one for each neuron in this layer.
Layer 2 (4 neurons): You will generate 4 random bias values, one for each neuron in this layer.
Layer 3 (2 neurons): You will generate 2 random bias values, one for each neuron in this layer.

1. Weights:
Purpose: Weights control the strength and direction of the connection between neurons in different layers of the network.
Explanation: Each connection between neurons has a weight that represents how much influence one neuron has on the other. During the training process, these weights are adjusted to minimize the error in the network's output.

2. Biases:
Purpose: Biases allow the activation of neurons to shift, helping the network to better fit the data and improve its ability to learn.
Explanation: Each neuron has a bias, which is added to the weighted sum of inputs before applying the activation function. It helps the neuron to fire (activate) even when the inputs are zero or when no weighted sum is enough to activate it.

Summary:
Weights are the values that connect neurons between layers and are adjusted during training.
Biases are added to the weighted sum of inputs to each neuron and help adjust the output of the neuron.

<?php

class NeuralNetwork {
    private $biases;   // Biases for each layer

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

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

// Function to generate random biases for a layer
    private function randomBias($neurons) {
        $biases = [];
        for ($i = 0; $i < $neurons; $i++) {
            $biases[$i] = mt_rand(-1000, 1000) / 1000; // Random value between -1 and 1
        }
        return $biases;
    }

// Function to display only the biases
    public function displayBiases() {
        echo "<h2>Biases</h2>";
        echo "<pre>";
        print_r($this->biases);
        echo "</pre>";
    }
}

// Initialize neural network with layers (e.g., 3 input neurons, 4 hidden neurons, 2 output neurons)
$nn = new NeuralNetwork([3, 4, 2]);

// Check if form is submitted and display biases
if ($_SERVER['REQUEST_METHOD'] === 'POST') {
    $nn->displayBiases();
}

?>

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

<h1>Neural Network - Display Biases</h1>

<form method="POST">
    <label for="layer1">Layer 1 Neurons:</label>
    <input type="number" id="layer1" name="layer1" value="3" readonly><br>

<label for="layer2">Layer 2 Neurons:</label>
    <input type="number" id="layer2" name="layer2" value="4" readonly><br>

<label for="layer3">Layer 3 Neurons:</label>
    <input type="number" id="layer3" name="layer3" value="2" readonly><br>

</body>
</html>