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number_cognition_4.html
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<!DOCTYPE html>
<html>
<head>
<title>Neural Net</title>
<meta charset="utf-8">
<style>
body {
background-color: #323232;
}
canvas {
position: absolute;
left: 50%;
top:50%;
transform: translate(-50%, -50%);
background-color: white;
image-rendering: optimizeSpeed; /* FUCK SMOOTHING, GIVE ME SPEED */
image-rendering: -moz-crisp-edges; /* Firefox */
image-rendering: -o-crisp-edges; /* Opera */
image-rendering: -webkit-optimize-contrast; /* Chrome (and eventually Safari) */
image-rendering: optimize-contrast; /* CSS3 Proposed */
-ms-interpolation-mode: nearest-neighbor; /* IE8+ */
}
</style>
</head>
<body>
<div id="canvas_container"></div>
<script type="text/javascript">
function random_normal(shape, mean=0.0, stddev=1.0) {
if (shape.length==0) {
var u = 1 - Math.random();
var v = 1 - Math.random();
return (Math.sqrt( -2.0 * Math.log( u ) ) * Math.cos( 2.0 * Math.PI * v ))*stddev + mean;
} else {
var arr = []
for (let i = 0; i<shape[0]; i++) {
arr.push(random_normal(shape.slice(1),mean,stddev));
}
return arr;
}
}
function ones(shape) {
if (shape.length==0) {
return 1;
} else {
var arr = []
for (let i = 0; i<shape[0]; i++) {
arr.push(ones(shape.slice(1)));
}
return arr;
}
}
function zeros(shape) {
if (shape.length==0) {
return 0;
} else {
var arr = []
for (let i = 0; i<shape[0]; i++) {
arr.push(zeros(shape.slice(1)));
}
return arr;
}
}
function sigmoid(i_arr) {
if (!Array.isArray(i_arr)) {
return 1/(1 + Math.exp(-i_arr));
} else {
let o_arr = [];
for (let i=0;i<i_arr.length;i++) {
o_arr.push(sigmoid(i_arr[i]));
}
return o_arr;
}
}
function softmax(i_arr) {
let o_arr = [];
let sum = 0;
let max = -Infinity;
for (let i=0;i<i_arr.length;i++) {
if (max < i_arr[i]) {max = i_arr[i]};
}
for (let i=0;i<i_arr.length;i++) {
o_arr.push(Math.exp(i_arr[i] - max));
sum += o_arr[i]
}
for (let i=0;i<i_arr.length;i++) {
o_arr[i] /= sum;
}
return o_arr;
}
function matmul(a, b) {
var aNumRows = a.length, aNumCols = a[0].length,
bNumRows = b.length, bNumCols = b[0].length,
m = new Array(aNumRows); // initialize array of rows
for (var r = 0; r < aNumRows; ++r) {
m[r] = new Array(bNumCols); // initialize the current row
for (var c = 0; c < bNumCols; ++c) {
m[r][c] = 0; // initialize the current cell
for (var i = 0; i < aNumCols; ++i) {
m[r][c] += a[r][i] * b[i][c];
}
}
}
return m;
}
function one_hot_encoding(arr) {
let max = -Infinity;
let flag = true;
for (let i=0;i<arr.length;i++) {
if (max < arr[i]) {max = arr[i]};
}
for (let i=0;i<arr.length;i++) {
if (max == arr[i] && flag) {
arr[i] = 1;
flag = false;
} else {
arr[i] = 0;
}
}
return arr;
}
class NeuralLayer {
constructor (inputNum, outputNum, hypothesisFunction = sigmoid) {
this.inputNum = inputNum;
this.outputNum = outputNum;
this.hypothesisFunction = hypothesisFunction;
this.W = random_normal([inputNum, outputNum]);
this.b = random_normal([outputNum]);
this.nextLayer = undefined;
this.y_data = undefined;
this.input = [[]];
this.hypothesis = [[]];
this.gradient_W = [[[]]];
this.gradient_b = [[]];
this.error = [[]];
}
calc_hypothesis() {
let arr2 = matmul(this.input, this.W);
this.hypothesis.splice(0);
for (let i=0;i<this.input.length;i++) {
for (let j=0;j<this.outputNum;j++) {
arr2[i][j] += this.b[j];
}
this.hypothesis.push(this.hypothesisFunction(arr2[i]));
}
}
calc_error() {
let arr = [];
for (let n=0;n<this.input.length;n++) {
let arr2 = [];
for (let j=0;j<this.outputNum;j++) {
if (this.nextLayer != undefined) {
let sum = 0;
for (let k=0;k<this.nextLayer.outputNum;k++) {
sum += this.nextLayer.error[n][k] * this.nextLayer.W[j][k] * this.hypothesis[n][j] * (1-this.hypothesis[n][j]);
}
arr2.push(sum);
} else {
arr2.push((this.hypothesis[n][j] - this.y_data[n][j])* this.hypothesis[n][j] * (1-this.hypothesis[n][j]));
}
}
arr.push(arr2);
}
this.error = arr;
}
calc_gradient_W() {
let arr = [];
for (let i=0;i<this.inputNum;i++) {
let arr2 = [];
for (let j=0;j<this.outputNum;j++) {
let sum = 0;
for (let n=0;n<this.input.length;n++) {
sum += this.input[n][i] * this.error[n][j];
}
arr2.push(sum/this.input.length);
}
arr.push(arr2);
}
this.gradient_W = arr;
}
calc_gradient_b() {
let arr = [];
for (let j=0;j<this.outputNum;j++) {
let sum = 0;
for (let n=0;n<this.input.length;n++) {
sum += this.error[n][j];
}
arr.push(sum/this.input.length);
}
this.gradient_b = arr;
}
frontCalculate() {
this.calc_hypothesis();
}
backPropagate() {
this.calc_error();
this.calc_gradient_W();
this.calc_gradient_b();
}
train(learning_rate) {
for (let j=0; j<this.outputNum;j++) {
for (let i=0; i<this.inputNum; i++) {
this.W[i][j] -= learning_rate * this.gradient_W[i][j];
}
this.b[j] -= learning_rate * this.gradient_b[j];
}
}
}
class NeuralNet {
constructor(arr, x_data, y_data) {;
this.layers = [];
this.x_data = x_data;
this.y_data = y_data;
this._maxInputNum = Math.max.apply(null, arr);
for (let i=0;i<arr.length-1;i++) {
this.layers.push(new NeuralLayer(arr[i], arr[i+1]));
}
this.layers[0].input = this.x_data;
for (let i=1;i<this.layers.length;i++) {
this.layers[i].input = this.layers[i-1].hypothesis;
}
for (let i=0;i<this.layers.length-1;i++) {
this.layers[i].nextLayer = this.layers[i+1];
}
this.layers[this.layers.length-1].y_data = this.y_data;
}
frontCalculate() {
for(let i=0;i<this.layers.length;i++) {
this.layers[i].frontCalculate();
}
}
backPropagate() {
for(let i=this.layers.length-1;i>=0;i--) {
this.layers[i].backPropagate();
}
}
train(learning_rate) {
this.frontCalculate();
this.backPropagate();
for(let i=0;i<this.layers.length;i++) {
this.layers[i].train(learning_rate);
}
}
calc(input) {
this.layers[0].input = input;
this.frontCalculate();
let result = this.layers[this.layers.length-1].hypothesis.slice();
this.layers[0].input = this.x_data;
this.frontCalculate();
return result;
}
render() {
neuralNet.layers.forEach((e, n, arr) => {
for (let i=0;i<e.inputNum;i++) {
for (let j=0;j<e.outputNum;j++) {
if (e.W[i][j] != 0) {
ctx.strokeStyle = e.W[i][j]>0?"green":"red";
ctx.lineWidth = Math.log(Math.abs(e.W[i][j]) + 1);
ctx.beginPath();
ctx.moveTo(n/(arr.length)*(WIDTH*5/7)+WIDTH/7,(i+(this._maxInputNum-e.inputNum+1)/2)/(this._maxInputNum)*(HEIGHT*5/7)+HEIGHT/7);
ctx.lineTo((n+1)/(arr.length)*(WIDTH*5/7)+WIDTH/7, (j+(this._maxInputNum-e.outputNum+1)/2)/(this._maxInputNum)*(HEIGHT*5/7)+HEIGHT/7);
ctx.closePath();
ctx.stroke();
}
}
}
for (let j=0;j<e.outputNum;j++) {
if (e.b[j] != 0) {
ctx.strokeStyle = e.b[j]>0?"green":"red";
ctx.lineWidth = Math.log(Math.abs(e.b[j]) + 1);
ctx.beginPath();
ctx.moveTo((n+0.5)/(arr.length)*(WIDTH*5/7)+WIDTH/7,((this._maxInputNum-Math.max(e.outputNum, e.inputNum))/2)/(this._maxInputNum)*(HEIGHT*5/7)+HEIGHT/7);
ctx.lineTo((n+1)/(arr.length)*(WIDTH*5/7)+WIDTH/7,(j+(this._maxInputNum-e.outputNum+1)/2)/(this._maxInputNum)*(HEIGHT*5/7)+HEIGHT/7);
ctx.closePath();
ctx.stroke();
}
}
});
neuralNet.layers.forEach((e, n, arr) => {
ctx.fillStyle = "white"
ctx.strokeStyle = "black";
ctx.lineWidth = 1;
ctx.beginPath();
ctx.arc((n+0.5)/(arr.length)*(WIDTH*5/7)+WIDTH/7,((this._maxInputNum-Math.max(e.outputNum, e.inputNum))/2)/(this._maxInputNum)*(HEIGHT*5/7)+HEIGHT/7, 5, 0, Math.PI*2);
ctx.closePath();
ctx.fill();
ctx.stroke();
if (n==0) {
for (let i=0;i<e.inputNum;i++) {
ctx.beginPath();
ctx.arc((n)/(arr.length)*(WIDTH*5/7)+WIDTH/7,(i +(this._maxInputNum-e.inputNum+1)/2)/(this._maxInputNum )*(HEIGHT*5/7)+HEIGHT/7, 5, 0, Math.PI*2);
ctx.closePath();
ctx.fill();
ctx.stroke();
}
}
for (let j=0;j<e.outputNum;j++) {
ctx.beginPath();
ctx.arc((n+1)/(arr.length)*(WIDTH*5/7)+WIDTH/7,(j+(this._maxInputNum-e.outputNum+1)/2)/(this._maxInputNum)*(HEIGHT*5/7)+HEIGHT/7, 5, 0, Math.PI*2);
ctx.closePath();
ctx.fill();
ctx.stroke();
}
});
}
}
let canvas = document.createElement("canvas");
let ctx = canvas.getContext("2d");
let mouse = {x:0, y:0, isClicked:false};
let NUMBER_WIDTH = 9;
let NUMBER_HEIGHT = 12;
let SIZE = 50;
canvas.setAttribute("width", NUMBER_WIDTH);
canvas.setAttribute("height", NUMBER_HEIGHT);
canvas.setAttribute("style", "width:" + NUMBER_WIDTH * SIZE + "px; height:" + NUMBER_HEIGHT * SIZE + "px;");
canvas.addEventListener("mousemove", (e) => {
mouse.x = Math.floor((e.pageX - canvas.offsetLeft + NUMBER_WIDTH*SIZE/2)/SIZE);
mouse.y = Math.floor((e.pageY - canvas.offsetTop + NUMBER_HEIGHT*SIZE/2)/SIZE);
if(mouse.isClicked) test_data[0][mouse.x + mouse.y*NUMBER_WIDTH] = 1;
});
document.addEventListener("mousedown", (e) => {
mouse.isClicked = true;
})
document.addEventListener("mouseup", (e) => {
one_hot_encoding(neuralNet.calc([normalize(test_data[0], NUMBER_WIDTH, NUMBER_HEIGHT)])[0]).forEach((e, i, arr) => {
if (e==1) {
if(window.confirm("It's " + i +".")) {
x_data[i+10*Math.floor(Math.random()*learning_fontName.length)] = test_data[0];
} else {
let answer = window.prompt("What was that?");
if (answer == null) {
} else if (answer.match(/^[0-9]$/)) {
x_data[parseInt(answer)+10*Math.floor(Math.random()*learning_fontName.length)] = test_data[0];
} else {
window.alert("I please write 0-9!");
}
}
}
});
mouse.isClicked = false;
for (let i=0;i<NUMBER_WIDTH*NUMBER_HEIGHT;i++) {
test_data[0][i] = 0;
}
})
document.body.append(canvas);
window.onload = function() {
initData();
init();
render();
}
let x_data = [];
let y_data = [];
let test_data = [[]];
for (let i=0;i<NUMBER_WIDTH*NUMBER_HEIGHT;i++) {
test_data[0].push(0);
}
let learning_fontName = ["AppleSDGothicNeo-UltraLight", "AppleSymbols", "AppleGothic", "AppleMyungjo", "AdobeGothicStd-Bold", "AdobeMyungjoStd-Medium", "SignPainter-HouseScript", "PTMono-Regular", "NanumGothic", "NanumMyeongjo", "NanumBarunpen", "NanumSquareL"];
function initData() {
let canvas = document.createElement("canvas");
let ctx = canvas.getContext("2d");
canvas.setAttribute("width", NUMBER_WIDTH);
canvas.setAttribute("height", NUMBER_HEIGHT);
for (let i=0;i<learning_fontName.length*5;i++) {
ctx.font = (i%4+NUMBER_HEIGHT) + "px " + learning_fontName[Math.floor(i/5)];
ctx.textAlign = "center";
ctx.textBaseline = "middle";
for (let j=0;j<10;j++) {
ctx.clearRect(0,0,NUMBER_WIDTH,NUMBER_HEIGHT);
ctx.fillText(j, 5, 6);
let data = ctx.getImageData(0, 0, NUMBER_WIDTH, NUMBER_HEIGHT);
let arr = [];
let arr2 = [];
for (let k=0;k<data.data.length;k+=4) {
arr.push(data.data[k+3]/255);
}
for (let k=0;k<10;k++) {
arr2.push(k==j?1:0);
}
normalize(arr, NUMBER_WIDTH, NUMBER_HEIGHT);
x_data.push(arr);
y_data.push(arr2);
}
}
}
function normalize(arr, width, height) {
let original_data = arr.slice();
let start_x=width;
let end_x=0;
let start_y=height;
let end_y=0;
for (let i=0;i<height;i++) {
for (let j=0;j<width;j++) {
if (original_data[i*width+j] != 0) {
start_x = Math.min(start_x, j);
end_x = Math.max(end_x, j);
start_y = Math.min(start_y, i);
end_y = Math.max(end_y, i);
}
}
}
for (let i=0;i<height;i++) {
for (let j=0;j<width;j++) {
arr[i*width+j] = original_data[Math.round((i/height)*(end_y-start_y+1)+start_y)*width+Math.round((j/width)*(end_x-start_x+1)+start_x)];
}
}
return arr;
}
function init() {
let shape = [NUMBER_WIDTH*NUMBER_HEIGHT, 10, 8, 8, 10];
neuralNet = new NeuralNet(shape, x_data, y_data);
neuralNet.layers[neuralNet.layers.length-1].hypothesisFunction = softmax;
neuralNet.input = x_data;
}
function render() {
ctx.clearRect(0, 0, NUMBER_WIDTH, NUMBER_HEIGHT);
for (let i=0;i<test_data[0].length;i++) {
ctx.fillStyle = "rgba(0,0,0," + test_data[0][i] + ")";
ctx.fillRect(i%NUMBER_WIDTH, Math.floor(i/NUMBER_WIDTH), 1, 1);
}
window.requestAnimationFrame(render);
neuralNet.train(1);
}
</script>
</body>
</html>