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HTML 

              
                <a href="#controls">Jump to controls</a> 
<canvas id="graph" width="600" height="600"></canvas>
<div class="controls" id="controls">
    <button onclick="initialise()">initialise</button>
    <button onclick="train()">Train</button>
    <button onclick="classifyPoints()">Classify Points</button>
<button onclick="visualizeNeuronsAndWeights()">Visualize Neurons and Weights</button>
</div>
<label>Training Data Size
    <input id="trainingDataSize" value="5000">    
</label>
<label>training iterations
    <input id="trainingIterations" value="50000">    
</label>
<label>Learning Rate
    <input id="learningRate" value="0.03">    
</label>
<label>hidden nodes (more than 2!)
    <input id="hiddenNodes" value="8">    
</label>
<label>points to classify
    <input id="numPoints" value="400">    
</label>
!

CSS 

              
                label{
    display: block;
    padding: 10px;
}
a{
    display:block;
    margin-bottom: 20px;
}
.controls{
    display: flex;
    gap: 20px;
    padding: 10px;
}

body{
    padding: 20px;
}

canvas{
    outline: 2px solid #666;
    
}
!

JS 

              
                class NeuralNetwork {
    constructor(inputSize, hiddenSize, outputSize) {
        this.inputSize = inputSize;
        this.hiddenSize = hiddenSize;
        this.outputSize = outputSize;
        this.weightsInputToHidden = Array.from({ length: hiddenSize }, () =>
            Array.from({ length: inputSize }, () => Math.random() * 2 - 1)
        );
        this.biasHidden = Array(hiddenSize).fill(0);
        this.weightsHiddenToOutput = Array.from({ length: outputSize }, () =>
            Array.from({ length: hiddenSize }, () => Math.random() * 2 - 1)
        );
        this.biasOutput = Array(outputSize).fill(0);
        this.learningRate = document.querySelector('#learningRate').value; // Adjusted learning rate
        this.hiddenLayer = new Array(this.hiddenSize);
    }

    feedForward(inputs) {
        for (let i = 0; i < this.hiddenSize; i++) {
            this.hiddenLayer[i] = 0;
            for (let j = 0; j < this.inputSize; j++) {
                this.hiddenLayer[i] +=
                    this.weightsInputToHidden[i][j] * inputs[j];
            }
            this.hiddenLayer[i] += this.biasHidden[i];
            this.hiddenLayer[i] = sigmoid(this.hiddenLayer[i]);
        }

        const output = new Array(this.outputSize);
        for (let i = 0; i < this.outputSize; i++) {
            output[i] = 0;
            for (let j = 0; j < this.hiddenSize; j++) {
                output[i] +=
                    this.weightsHiddenToOutput[i][j] * this.hiddenLayer[j];
            }
            output[i] += this.biasOutput[i];
            output[i] = sigmoid(output[i]);
        }
        return output;
    }

    train(inputs, target) {
        for (let i = 0; i < this.hiddenSize; i++) {
            this.hiddenLayer[i] = 0;
            for (let j = 0; j < this.inputSize; j++) {
                this.hiddenLayer[i] +=
                    this.weightsInputToHidden[i][j] * inputs[j];
            }
            this.hiddenLayer[i] += this.biasHidden[i];
            this.hiddenLayer[i] = sigmoid(this.hiddenLayer[i]);
        }

        const output = new Array(this.outputSize);
        for (let i = 0; i < this.outputSize; i++) {
            output[i] = 0;
            for (let j = 0; j < this.hiddenSize; j++) {
                output[i] +=
                    this.weightsHiddenToOutput[i][j] * this.hiddenLayer[j];
            }
            output[i] += this.biasOutput[i];
            output[i] = sigmoid(output[i]);
        }

        const errorsOutput = new Array(this.outputSize);
        const errorsHidden = new Array(this.hiddenSize);

        for (let i = 0; i < this.outputSize; i++) {
            errorsOutput[i] = target[i] - output[i];
            for (let j = 0; j < this.hiddenSize; j++) {
                this.weightsHiddenToOutput[i][j] +=
                    this.learningRate *
                    errorsOutput[i] *
                    output[i] *
                    (1 - output[i]) *
                    this.hiddenLayer[j];
            }
            this.biasOutput[i] += this.learningRate * errorsOutput[i];
        }

        for (let i = 0; i < this.hiddenSize; i++) {
            errorsHidden[i] = 0;
            for (let j = 0; j < this.outputSize; j++) {
                errorsHidden[i] +=
                    this.weightsHiddenToOutput[j][i] * errorsOutput[j];
            }
            this.biasHidden[i] += this.learningRate * errorsHidden[i];
            for (let j = 0; j < this.inputSize; j++) {
                this.weightsInputToHidden[i][j] +=
                    this.learningRate *
                    errorsHidden[i] *
                    this.hiddenLayer[i] *
                    (1 - this.hiddenLayer[i]) *
                    inputs[j];
            }
        }
    }
}

const canvas = document.getElementById("graph");
const ctx = canvas.getContext("2d");
const pointRadius = 5; // Radius of the points

const trainingData = [];
const numDataPoints = document.querySelector('#trainingDataSize').value; // Adjust the number of data points as needed

for (let i = 0; i < numDataPoints; i++) {
    const x = Math.random() * 2 - 1; // Random x value between -1 and 1
    const y = Math.random() * 2 - 1; // Random y value between -1 and 1

    let label;
    if (x <= 0 && y < 0) {
        label = "blue";
    } else if (x <= 0 && y > 0) {
        label = "green";
    } else if (x > 0 && y <= 0) {
        label = "red";
    } else {
        label = "purple";
    }

    trainingData.push({ x, y, label });
}

var hiddenNodes = parseInt(document.querySelector('#hiddenNodes').value);


var neuralNetwork = "";

function initialise(){
    console.log("HN", hiddenNodes);
    clearCanvas();
    neuralNetwork = new NeuralNetwork(2, hiddenNodes, 4);
}


function train() {
    for (let i = 0; i < parseInt(document.querySelector('#trainingIterations').value); i++) {
        const data =
            trainingData[Math.floor(Math.random() * trainingData.length)];
        neuralNetwork.train([data.x, data.y], oneHotEncode(data.label));
    }
    alert("Training complete");
}

function classifyPoints() {
    ctx.clearRect(0, 0, canvas.width, canvas.height);
    drawAxes();
    this.points = [];
    for (let i = 0; i < parseInt(document.querySelector('#numPoints').value); i++) {
        const x = Math.random() * 2 - 1; // Random x-coordinate between -1 and 1
        const y = Math.random() * 2 - 1; // Random y-coordinate between -1 and 1
        const output = neuralNetwork.feedForward([x, y]);
        const predictedLabel = oneHotDecode(output);
        drawPoint(x, y, predictedLabel);
        points.push({ x, y, predictedLabel });
    }
    console.log(points);
    console.log(neuralNetwork.hiddenLayer);
}

function oneHotEncode(label) {
    const encoding = {
        blue: [1, 0, 0, 0],
        red: [0, 1, 0, 0],
        green: [0, 0, 1, 0],
        purple: [0, 0, 0, 1]
    };
    return encoding[label];
}

function oneHotDecode(output) {
    const labels = ["blue", "red", "green", "purple"];
    const maxIndex = output.indexOf(Math.max(...output));
    return labels[maxIndex];
}

function sigmoid(x) {
    return 1 / (1 + Math.exp(-x));
}
function drawPoint(x, y, color) {
    ctx.beginPath();
    ctx.arc(
        ((x + 1) * canvas.width) / 2,
        canvas.height - ((y + 1) * canvas.height) / 2,
        pointRadius,
        0,
        2 * Math.PI
    );
    ctx.fillStyle = color;
    ctx.fill();
    ctx.closePath();
}

function drawAxes() {
    ctx.beginPath();
    ctx.moveTo(0, canvas.height / 2);
    ctx.lineTo(canvas.width, canvas.height / 2); // X-axis
    ctx.moveTo(canvas.width / 2, 0);
    ctx.lineTo(canvas.width / 2, canvas.height); // Y-axis
    ctx.strokeStyle = "black";
    ctx.stroke();
    ctx.closePath();
}
function visualizeNeuronsAndWeights() {
    clearCanvas(); // Clear the canvas
    visualizeLayer(100, 100, neuralNetwork.inputSize, 'grey', 'Input');
    visualizeLayer(300, 100, neuralNetwork.hiddenSize, 'aqua', 'Hidden', true);
    visualizeLayer(500, 100, neuralNetwork.outputSize, 'red', 'Output');
    visualizeWeights();
}

function clearCanvas() {
    ctx.clearRect(0, 0, canvas.width, canvas.height);
}

function visualizeLayer(startX, startY, neuronCount, color, label, showBias) {
    const neuronSpacing = 100;
    const neuronRadius = 20;

    for (let i = 0; i < neuronCount; i++) {
        const x = startX;
        const y = startY + i * neuronSpacing;
        drawNeuron(x, y, neuronRadius, color, label);
        if (showBias) {
            drawBias(x, y, neuralNetwork.biasHidden[i]);
        }
        if (label === 'Input') {
            drawInputToHiddenWeights(x, y);
        }
    }
}

function drawInputToHiddenWeights(inputX, hiddenY) {
    for (let i = 0; i < neuralNetwork.inputSize; i++) {
        for (let j = 0; j < neuralNetwork.hiddenSize; j++) {
            const weight = neuralNetwork.weightsInputToHidden[j][i];
            const fromX = inputX;
            const fromY = hiddenY;
            const toX = 300;
            const toY = 100 + (j * 100);
            drawWeight(fromX, fromY, toX, toY, weight, "red");
        }
    }
}

function visualizeWeights() {
    for (let i = 0; i < neuralNetwork.outputSize; i++) {
        for (let j = 0; j < neuralNetwork.hiddenSize; j++) {
            const weight = neuralNetwork.weightsHiddenToOutput[i][j];
            const fromX = 300;
            const fromY = 100 + j * 100; // Increase vertical spacing
            const toX = 500;
            const toY = 100 + i * 100; // Increase vertical spacing
            drawWeight(fromX, fromY, toX, toY, weight);
        }
    }
}

function drawNeuron(x, y, radius, color, label) {
    ctx.beginPath();
    ctx.arc(x, y, radius, 0, 2 * Math.PI);
    ctx.fillStyle = color;
    ctx.fill();
    ctx.strokeStyle = 'black';
    ctx.stroke();
    ctx.font = '14px Arial';
    ctx.fillStyle = 'black';
    ctx.fillText(label, x - 15, y + 5);
    ctx.closePath();
}

function drawBias(x, y, bias) {
    ctx.font = '14px Arial';
    ctx.fillStyle = 'black';
    ctx.fillText('Bias: ' + bias.toFixed(2), x - 50, y + 30);
}

var yOffset = 0;

function drawWeight(fromX, fromY, toX, toY, weight, color) {
    ctx.beginPath();
    ctx.moveTo(fromX, fromY);
    ctx.lineTo(toX, toY);
    ctx.strokeStyle = color || 'green';
    ctx.stroke();

    const textX = (fromX + toX) / 2;
    const textY = (fromY + toY) / 2 + (toY / 3);
    ctx.font = '14px Arial';
    ctx.fillStyle = 'green';
    ctx.fillText(weight.toFixed(2), textX, textY);
    ctx.closePath();
}
!
999px

Console