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<!DOCTYPE html>
<html lan="en">
<head>
<title> TFJS CNN Test </title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/tensorflow/0.10.3/tf.min.js"></script>
<script src="https://d3js.org/d3.v5.min.js"></script>
<script src="data.js"></script>
<script src="model.js"></script>
<link rel="stylesheet" type="text/css" href="styles.css">
</head>
<body>
<section>
<h3>CNN Tensorflow JS</h3>
<p>
A 2 layer CNN with Tensorflow JS to recognize hand-drawn digits (MNIST). Use the sliders to adjust the layer parameter below, and then
click Train to train the neural network. Once trained, draw a digit on the canvas to run live-prediction.
</p>
<br>
<div class="row">
<!-- Left Panel -->
<div>
<!-- Layer 1 -->
<div class="row">
<span><strong>Layer 1:</strong> Kernel <input id="row-one-kernel" type="range" min="2" max="8" value="5" step="1" oninput="updateSliders()"><span id="row-one-kernel-value"></span></span>
<span>Filters <input id="row-one-filters" type="range" min="4" max="24" value="8" step="1" oninput="updateSliders()"><span id="row-one-filters-value"></span></span>
</div>
<br>
<!-- Layer 2 -->
<div class="row">
<span><strong>Layer 2:</strong> Kernel <input id="row-two-kernel" type="range" min="2" max="8" value="5" step="1" oninput="updateSliders()"><span id="row-two-kernel-value"></span></span>
<span>Filters <input id="row-two-filters" type="range" min="4" max="24" value="16" step="1" oninput="updateSliders()"><span id="row-two-filters-value"></span></span>
</div>
<br>
<!-- Batch -->
<div class="row">
<span># Training batches <input style="width:15rem" id="num-batch" type="range" min="10" max="100" value="25" step="5" size="30" oninput="updateSliders()"><span id="num-batch-value"></span></span>
</div>
<br>
<!-- Train -->
<div>
<button style="font-size:120%" onclick="run()">Train</button>
<span id="training">...not trained...</span>
</div>
<!-- Prediction -->
<div class="row">
<div style="padding-left: 30px" id="predictions-one"></div>
<div style="padding-left: 30px" id="predictions-two"></div>
</div>
</div>
<!-- Right Panel -->
<div style="padding-left: 30px">
<div class="row">
<div>
<canvas id="input"></canvas><br>
<button style="font-size:120%" onclick="clearInputCanvas()">Clear canvas</button>
</div>
<div style="padding-left: 10px; font-size: 150%">
<div>Prediction Is</div>
<div style="font-size:300%" id="user-prediction">???</div>
</div>
</div>
</div>
</div>
<br>
</section>
</body>
<script>
let mnistData = new MnistData();
let cnnModel = new Model(0.15);
cnnModel.build({}, {}); // Just init a default model so stuff doesn't error out
function updateTraining(history) {
d3.select('#training').text('Accuracy: ' + history.history.acc[0].toFixed(2));
}
function updateSliders() {
let r1kernel = d3.select('#row-one-kernel').node().value;
let r1filters = d3.select('#row-one-filters').node().value;
let r2kernel = d3.select('#row-two-kernel').node().value;
let r2filters = d3.select('#row-two-filters').node().value;
let numBatch = d3.select('#num-batch').node().value;
console.log(r1kernel, r1filters, r2kernel, r2filters);
d3.select('#row-one-kernel-value').text(r1kernel);
d3.select('#row-one-filters-value').text(r1filters);
d3.select('#row-two-kernel-value').text(r2kernel);
d3.select('#row-two-filters-value').text(r2filters);
d3.select('#num-batch-value').text(numBatch);
}
/**
* Copied from https://github.com/tensorflow/tfjs-examples/blob/master/mnist/ui.js
*/
function draw(image, canvas) {
const [width, height] = [28, 28];
canvas.width = width;
canvas.height = height;
const ctx = canvas.getContext('2d');
const imageData = new ImageData(width, height);
const data = image.dataSync();
for (let i = 0; i < height * width; ++i) {
const j = i * 4;
imageData.data[j + 0] = data[i] * 255;
imageData.data[j + 1] = data[i] * 255;
imageData.data[j + 2] = data[i] * 255;
imageData.data[j + 3] = 255;
}
ctx.putImageData(imageData, 0, 0);
}
function showPredictions(batch, predictions, labels) {
const len = batch.xs.shape[0];
const c1 = d3.select('#predictions-one');
const c2 = d3.select('#predictions-two');
for (let i=0; i < len; i++) {
const image = batch.xs.slice([i, 0], [1, batch.xs.shape[1]]);
const canvas = document.createElement('canvas');
canvas.className = 'prediction-canvas';
draw(image.flatten(), canvas);
const row = i % 2 === 0? c1.append('div') : c2.append('div');
row.style('height', '30px');
row.append('span').text(' Prediction: ' + predictions[i] + ' Actual:' + labels[i] + ' ');
row.node().appendChild(canvas);
}
}
let inputCanvas = null;
let inputCtx = null;
let isMouseDown = false;
let prev = null;
let curr = null;
function clearInputCanvas() {
d3.select('#user-prediction').text('???');
if (inputCtx !== null) {
inputCtx.fillStyle = '#000000';
inputCtx.fillRect(0, 0, 140, 140);
}
}
function setInputCanvas() {
const line = (ctx, x1, y1, x2, y2) => {
ctx.beginPath();
ctx.moveTo(x1, y1);
ctx.lineTo(x2, y2);
ctx.closePath();
ctx.stroke();
}
const getPosition = (canvas, clientX, clientY) => {
var rect = inputCanvas.getBoundingClientRect();
return {
x: clientX - rect.left,
y: clientY - rect.top
};
}
const extract = () => {
// Pull
const imgData = inputCtx.getImageData(0, 0, 140, 140);
// Grab single channel
const singleChannel = imgData.data.filter( (d, i) => i % 4 === 0);
// Down sample
const sample = [];
for (let i=0; i < 28; i++) {
for (let j=0; j < 28; j++) {
let c = 0;
for (let x = 0; x < 5; x++) {
for (let y = 0; y < 5; y++) {
c += singleChannel[140*(i*5 + x) + (j*5 + y)];
}
}
c /= 25;
sample.push(c);
}
}
console.log('sample', sample);
const tensor = tf.tensor(sample);
const userInput = tensor.reshape([-1, 28, 28, 1]);
const output = cnnModel.predict(userInput);
const axis = 1;
const predictions = Array.from(output.argMax(axis).dataSync());
d3.select('#user-prediction').text(predictions);
console.log('prediction', predictions);
}
const onMouseDown = (e) => {
isMouseDown = true;
prev = getPosition(inputCanvas,e.clientX, e.clientY);
}
const onMouseUp = (e) => {
isMouseDown = false;
extract();
}
const onMouseMove = (e) => {
if (isMouseDown === false) return;
curr = getPosition(inputCanvas, e.clientX, e.clientY);
inputCtx.lineWidth = 6;
inputCtx.lineCap = 'round';
inputCtx.fillStyle = '#FFFFFF';
inputCtx.strokeStyle = '#FFFFFF';
line(inputCtx, prev.x, prev.y, curr.x, curr.y);
prev = curr;
}
inputCanvas = document.getElementById('input');
inputCanvas.width = 140;
inputCanvas.height = 140;
inputCtx = inputCanvas.getContext('2d');
inputCtx.fillStyle = '#000000';
inputCtx.fillRect(0, 0, 140, 140);
inputCanvas.addEventListener('mousedown', onMouseDown);
inputCanvas.addEventListener('mouseup', onMouseUp);
inputCanvas.addEventListener('mousemove', onMouseMove);
window.inputCtx = inputCtx;
window.inputCanvas = inputCanvas;
}
async function load() {
console.log('Start loading...');
await mnistData.load();
console.log('Done loading...');
}
async function train() {
console.log('Start training');
let numBatch = +d3.select('#num-batch').node().value;
let r1kernel = +d3.select('#row-one-kernel').node().value;
let r1filters = +d3.select('#row-one-filters').node().value;
let r2kernel = +d3.select('#row-two-kernel').node().value;
let r2filters = +d3.select('#row-two-filters').node().value;
cnnModel.build({
kernelSize: r1kernel,
filters: r1filters
}, {
kernelSize: r2kernel,
filters: r2filters
});
await cnnModel.train(mnistData, numBatch, updateTraining);
console.log('Done training');
}
async function run() {
d3.select('#predictions-one').selectAll('*').remove();
d3.select('#predictions-two').selectAll('*').remove();
d3.select('#training').text('...loading...');
await load();
await train();
const batch = mnistData.nextTestBatch(10);
const output = cnnModel.predict(batch.xs.reshape([-1, 28, 28, 1]));
const axis = 1;
const labels = Array.from(batch.labels.argMax(axis).dataSync());
const predictions = Array.from(output.argMax(axis).dataSync());
showPredictions(batch, predictions, labels);
}
// run();
updateSliders();
setInputCanvas();
</script>
</html>