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162
NeuralNetwork.js
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class NeuralNetwork
{
constructor(numInputs, numOutputs, numHiddenLayers, numNeuronsPerHiddenLayer)
{
this.numInputs = numInputs;
this.numOutputs = numOutputs;
this.numHiddenLayers = numHiddenLayers;
this.numNeuronsPerHiddenLayer = numNeuronsPerHiddenLayer;
this.bias = 0.0;
this.activationResponse = 1.0;
this.neuronLayers = [];
this.createNetwork();
}
createNetwork()
{
//create the layers of the network
if (this.numHiddenLayers > 0)
{
//create first hidden layer
var firstHiddenLayer = new NeuronLayer(this.numNeuronsPerHiddenLayer, this.numInputs);
this.neuronLayers.push(firstHiddenLayer);
for (var i=0; i<this.numHiddenLayers-1; ++i)
{
var newHiddenLayer = new NeuronLayer(this.numNeuronsPerHiddenLayer, this.numNeuronsPerHiddenLayer);
this.neuronLayers.push(newHiddenLayer);
}
//create output layer
var outputLayer = new NeuronLayer(this.numOutputs, this.numNeuronsPerHiddenLayer);
this.neuronLayers.push(outputLayer);
}
else
{
//create output layer
var outputLayer = new NeuronLayer(this.numOutputs, this.numInputs);
this.neuronLayers.push(outputLayer);
}
}
update(inputs)
{
var outputs = [];
var cWeight = 0;
// If the number of inputs supplied is incorrect...
if (inputs.length!=this.numInputs)
{
return outputs; // Return empty outputs
}
// Loop through all layers
var inputLayer = true;
for (var i=0; i < this.numHiddenLayers + 1; ++i)
{
var neuronLayer = this.neuronLayers[i];
if (!inputLayer)
{
inputs = [];
inputs = inputs.concat(outputs);
}
else
{
inputLayer = false;
}
outputs = [];
cWeight = 0;
// For each neuron sum the (inputs * corresponding weights).
// Throw the total at our sigmoid function to get the output.
for (var j=0; j < neuronLayer.neurons.length; ++j)
{
var neuron = neuronLayer.neurons[j];
var totalInput = 0;
// For each weight...
for (var k=0; k < neuron.numInputs ; ++k) // -1 ???
{
// Multiply it with the input.
totalInput += neuron.weights[k] *
inputs[cWeight];
console.log("cweight "+cWeight);
console.log("neuron weight "+neuron.weights[k]);
console.log("input: "+inputs[cWeight]);
console.log("total input "+totalInput);
cWeight++;
}
// Add in the bias (final weight)
// totalInput += neuron.weights[neuron.weights.length-1] * this.bias;
// We can store the outputs from each layer as we generate them.
// The combined activation is first filtered through the sigmoid function
outputs.push(this.sigmoid(totalInput, this.activationResponse));
cWeight = 0;
}
}
return outputs;
}
sigmoid(totalInput, activationResponse)
{
return ( 1 / ( 1 + Math.exp(-totalInput / activationResponse)));
}
getWeights()
{
var weights = [];
//for each layer
for (var i=0; i<this.numHiddenLayers + 1; ++i)
{
//for each neuron
for (var j=0; j<this.neuronLayers[i].neurons.length; ++j)
{
//for each weight
for (var k=0; k<this.neuronLayers[i].neurons[j].numInputs; ++k)
{
weights.push(this.neuronLayers[i].neurons[j].weights[k]);
}
}
}
return weights;
}
setWeights(weights)
{
var cWeight = 0;
//for each layer
for (var i=0; i<this.numHiddenLayers + 1; ++i)
{
//for each neuron
for (var j=0; j<this.neuronLayers[i].neurons.length; ++j)
{
//for each weight
for (var k=0; k<this.neuronLayers[i].neurons[j].numInputs; ++k)
{
this.neuronLayers[i].neurons[j].weights[k] = weights[cWeight++];
}
}
}
}
}

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Neuron.js
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class Neuron
{
constructor(numInputs)
{
this.weights = [];
this.numInputs = numInputs;
for (var i=0; i<numInputs+1; ++i)
{
var newWeight = -1 + (Math.random()*2);
//var newWeight = 1;
this.weights.push(newWeight);
}
}
}

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NeuronLayer.js
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class NeuronLayer
{
constructor(numNeuronsPerHiddenLayer, numInputs)
{
this.neurons = [];
for (var i = 0; i < numNeuronsPerHiddenLayer; ++i)
{
var newNeuron = new Neuron(numInputs);
this.neurons.push(newNeuron);
}
}
}

225
index.html
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<html>
<head>
<script src="NeuralNetwork.js"></script>
<script src="Neuron.js"></script>
<script src="NeuronLayer.js"></script>
<style>
body{
padding:0;
margin:0;
overflow:hidden;
font-family:Helvetica;
font-size:10px;
}
input{
padding:0;
}
p{
padding:0;
margin:0;
}
#input{
position:absolute;
top:100px;
left:10px;
}
#first_layer{
position:absolute;
top: 100px;
left: 200px;
}
#second_layer{
position:absolute;
top: 100px;
left: 400px;
}
#output{
position:absolute;
top: 100px;
left: 600px;
}
#network_container{
position:absolute;
top: 100px;
left: 600px;
margin-left:-400px;
margin-top:-250px;
}
</style>
</head>
<body>
<canvas id="myCanvas" width="100%" height="100%"></canvas>
<div id="network_container">
<div id="input">
<input type="range" min="0" max="100" value="1" class="i0" id="i0">
<p id="i0_output"></p>
<input type="range" min="0" max="100" value="10" class="i1" id="i1">
<p id="i1_output"></p>
</div>
<div id = "first_layer">
<input type="range" min="-400" max="400" value="10" class="w0" id="w0">
<p id="w0_output"></p>
<input type="range" min="-400" max="400" value="10" class="w1" id="w1">
<p id="w1_output"></p><br><br>
<input type="range" min="-400" max="400" value="10" class="w2" id="w2">
<p id="w2_output"></p>
<input type="range" min="-400" max="400" value="10" class="w3" id="w3">
<p id="w3_output"></p><br><br>
<input type="range" min="-400" max="400" value="10" class="w4" id="w4">
<p id="w4_output"></p>
<input type="range" min="-400" max="400" value="10" class="w5" id="w5">
<p id="w5_output"></p>
</div>
<div id = "second_layer">
<input type="range" min="-400" max="400" value="10" class="w6" id="w6">
<p id="w6_output"></p>
<input type="range" min="-400" max="400" value="10" class="w7" id="w7">
<p id="w7_output"></p>
<input type="range" min="-400" max="400" value="10" class="w8" id="w8">
<p id="w8_output"></p><br><br>
<input type="range" min="-400" max="400" value="10" class="w9" id="w9">
<p id="w9_output"></p>
<input type="range" min="-400" max="400" value="10" class="w10" id="w10">
<p id="w10_output"></p>
<input type="range" min="-400" max="400" value="10" class="w11" id="w11">
<p id="w11_output"></p>
</div>
<div id="output">
<input id="clickMe" type="button" value="update network" onclick="update();" />
<input id="random" type="button" value="random network" onclick="random();" />
<p id="network_output"></p>
</div>
</div>
<script>
var numInputs = 2;
var numOutputs = 2;
var numHiddenLayers = 1;
var numNeuronsPerHiddenLayer = 3;
var slider = [];
var output = [];
var neuralNetwork = new NeuralNetwork(numInputs, numOutputs, numHiddenLayers, numNeuronsPerHiddenLayer);
var input0 = document.getElementById("i0")
var input_display0 = document.getElementById("i0_output")
input_display0.innerHTML = input0.value/100;
input0.oninput = function() {
input_display0.innerHTML = this.value/100;
}
var input1 = document.getElementById("i1")
var input_display1 = document.getElementById("i1_output")
input_display1.innerHTML = input1.value/100;
input1.oninput = function() {
input_display1.innerHTML = this.value/100;
}
//slider
for (var i = 0; i<12; i++){
slider.push(document.getElementById("w"+i));
output.push(document.getElementById("w"+i+"_output"));
output[i].innerHTML = slider[i].value/100;
slider[i].oninput = function() {
output[slider.indexOf(this)].innerHTML = this.value/100;
updateCanvas()
}
}
document.addEventListener('mousemove', function(e) {
console.log("move")
var x = e.clientX / window.innerWidth;
var y = e.clientY / window.innerHeight;
input0.value=x*100;
input1.value=y*100;
input_display0.innerHTML = x;
input_display1.innerHTML = y;
update([x,y])
})
//CANVAS STUFF
var c = document.getElementById("myCanvas");
var ctx = c.getContext("2d");
ctx.canvas.width = window.innerWidth;
ctx.canvas.height = window.innerHeight;
var stepsize = 50;
for(var x = 0; x < window.innerWidth; x = x+stepsize){
for(var y = 0; y < window.innerHeight; y = y+stepsize){
var outputs = neuralNetwork.update([x/window.innerWidth,y/window.innerHeight]);
ctx.fillStyle = 'hsl(' + 360 * outputs[0] + ', 50%,'+outputs[1]*100 +'%)';
//ctx.fillStyle= "rgb(255,20,255);"
console.log(outputs[1]*255)
ctx.fillRect(x,y,stepsize,stepsize);
}
}
function updateCanvas(){
var stepsize = 50;
for(var x = 0; x < window.innerWidth; x = x+stepsize){
for(var y = 0; y < window.innerHeight; y = y+stepsize){
var outputs = neuralNetwork.update([x/window.innerWidth,y/window.innerHeight]);
ctx.fillStyle = 'hsl(' + 360 * outputs[0] + ', 90%,'+outputs[1]*100+'%)';
//ctx.fillStyle= "rgb(255,20,255);"
console.log(outputs[1]*255)
ctx.fillRect(x,y,stepsize,stepsize);
}
}
}
function update(inputs){
var weights = neuralNetwork.getWeights();
var newWeights = [];
for (var i=0; i < weights.length; i++) {
newWeights.push(slider[i].value/100);
//newWeights.push(1);
}
neuralNetwork.setWeights(newWeights);
//var inputs = [input0.value/100, input1.value/100];
var outputs = neuralNetwork.update(inputs);
// console.log(neuralNetwork.getWeights());
var output_display = document.getElementById("network_output")
output_display.innerHTML = "<br> "+outputs[0] + "<br><br> "+outputs [1];
document.getElementById('network_container').style.left=outputs[0]*window.innerWidth;
document.getElementById('network_container').style.top=outputs[1]*window.innerHeight;
}
random()
function random(inputs){
for (var i = 0; i<slider.length; i++){
slider[i].value = Math.random()*800-400;
output[slider.indexOf(slider[i])].innerHTML = slider[i].value/100;
}
updateCanvas();
}
</script>
</body>
</html>
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