mirror of
https://github.com/cupcakearmy/mnist.git
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initial push
This commit is contained in:
cupcakearmy
parent
ae60abd9fb
commit
727f750aec
4
.gitignore
vendored
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4
.gitignore
vendored
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@ -0,0 +1,4 @@
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yarn.lock
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.cache
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dist
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node_modules
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10
docker-compose.yml
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10
docker-compose.yml
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version: '3.7'
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services:
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server:
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image: cupcakearmy/static
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restart: unless-stopped
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ports:
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- 80:80
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volumes:
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- ./dist:/srv:ro
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16
package.json
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16
package.json
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{
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"scripts": {
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"start": "parcel src/index.html",
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"build": "parcel build src/index.html"
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},
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"browserslist": [
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"last 1 Chrome versions"
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],
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"dependencies": {
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"@tensorflow/tfjs": "^1.5.1"
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},
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"devDependencies": {
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"parcel-bundler": "^1.12.4",
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"parcel-plugin-static-files-copy": "^2.2.1"
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}
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}
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81
src/canvas.js
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81
src/canvas.js
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/* jslint esversion: 6, asi: true */
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var canvas, ctx, flag = false,
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prevX = 0,
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currX = 0,
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prevY = 0,
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currY = 0,
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dot_flag = false;
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var x = "black",
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y = 2;
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function init() {
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canvas = document.getElementById('can');
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ctx = canvas.getContext("2d");
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w = canvas.width;
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h = canvas.height;
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canvas.addEventListener("mousemove", function (e) {
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findxy('move', e)
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}, false);
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canvas.addEventListener("mousedown", function (e) {
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findxy('down', e)
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}, false);
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canvas.addEventListener("mouseup", function (e) {
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findxy('up', e)
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}, false);
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canvas.addEventListener("mouseout", function (e) {
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findxy('out', e)
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}, false);
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window.document.getElementById('clear').addEventListener('click', erase)
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}
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function draw() {
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ctx.beginPath();
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ctx.moveTo(prevX, prevY);
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ctx.lineTo(currX, currY);
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ctx.strokeStyle = x;
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ctx.lineWidth = y;
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ctx.stroke();
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ctx.closePath();
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function erase() {
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ctx.clearRect(0, 0, w, h);
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function findxy(res, e) {
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currX = e.clientX - canvas.offsetLeft;
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flag = true;
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ctx.beginPath();
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ctx.fillRect(currX, currY, 2, 2);
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dot_flag = false;
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}
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}
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if (res == 'up' || res == "out") {
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}
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if (res == 'move') {
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if (flag) {
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prevX = currX;
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prevY = currY;
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currX = e.clientX - canvas.offsetLeft;
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currY = e.clientY - canvas.offsetTop;
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draw();
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}
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}
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}
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init()
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61
src/index.html
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61
src/index.html
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<html>
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<head>
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<style>
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* {
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box-sizing: border-box;
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font-family: monospace;
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}
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html,
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body {
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padding: 0;
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margin: 0;
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height: 100vh;
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width: 100vw;
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display: flex;
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justify-content: center;
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align-items: center;
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}
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body>div {
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text-align: center;
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}
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div canvas {
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display: inline-block;
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border: 1px solid;
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}
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div input {
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display: inline-block;
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margin-top: .5em;
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padding: .5em 2em;
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background: white;
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outline: none;
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border: 1px solid;
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font-weight: bold;
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}
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</style>
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</head>
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<body>
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<div>
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<h1>MNIST (Pretrained)</h1>
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<canvas id="can" width="28" height="28"></canvas>
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<br />
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<input id="clear" type="button" value="clear">
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<br />
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<input id="test" type="button" value="test">
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<br />
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<h2 id="result"></h2>
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<a href="https://github.com/cupcakearmy/mnist">
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<h3>source code</h3>
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</a>
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</div>
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<script src="./tf.js"></script>
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<script src="./canvas.js"></script>
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</body>
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</html>
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22
src/tf.js
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22
src/tf.js
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/* jslint esversion: 8, asi: true */
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import * as tf from '@tensorflow/tfjs';
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let model
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tf.loadLayersModel('/model.json').then(m => {
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model = m
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})
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window.document.getElementById('test').addEventListener('click', async () => {
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const canvas = window.document.querySelector('canvas')
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const { data, width, height } = canvas.getContext('2d').getImageData(0, 0, 28, 28)
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const tensor = tf.tensor(new Uint8Array(data.filter((_, i) => i % 4 === 3)), [1, 28, 28, 1])
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const prediction = model.predict(tensor)
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const result = await prediction.data()
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const guessed = result.indexOf(1)
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console.log(guessed)
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window.document.querySelector('#result').innerText = guessed
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})
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BIN
static/group1-shard1of1.bin
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BIN
static/group1-shard1of1.bin
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Binary file not shown.
310
static/model.json
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310
static/model.json
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{
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"format": "layers-model",
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"generatedBy": "keras v2.2.4-tf",
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"convertedBy": "TensorFlow.js Converter v1.4.0",
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"modelTopology": {
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"keras_version": "2.2.4-tf",
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"backend": "tensorflow",
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"model_config": {
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"class_name": "Sequential",
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"config": {
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"name": "sequential",
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"layers": [
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{
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"class_name": "Conv2D",
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"config": {
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"name": "conv2d",
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"trainable": true,
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"batch_input_shape": [
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null,
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28,
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28,
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1
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],
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"dtype": "float32",
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"filters": 64,
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"kernel_size": [
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3,
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3
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],
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"strides": [
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1,
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1
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],
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"padding": "valid",
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"data_format": "channels_last",
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"dilation_rate": [
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1,
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1
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],
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"activation": "relu",
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"use_bias": true,
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"kernel_initializer": {
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"class_name": "GlorotUniform",
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"config": {
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"seed": 420,
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"dtype": "float32"
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}
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},
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"bias_initializer": {
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"class_name": "Zeros",
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"config": {
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"dtype": "float32"
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}
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},
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"kernel_constraint": null,
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"bias_constraint": null
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}
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},
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{
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"class_name": "Conv2D",
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"config": {
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"name": "conv2d_1",
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"trainable": true,
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"dtype": "float32",
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"filters": 32,
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"kernel_size": [
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3,
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3
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],
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"strides": [
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1,
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1
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],
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"padding": "valid",
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"data_format": "channels_last",
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"dilation_rate": [
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1,
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1
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],
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"activation": "relu",
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"use_bias": true,
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"kernel_initializer": {
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"class_name": "GlorotUniform",
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"config": {
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"seed": 420,
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"dtype": "float32"
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}
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},
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"bias_initializer": {
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"class_name": "Zeros",
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"config": {
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"dtype": "float32"
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}
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},
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"bias_constraint": null
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}
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},
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{
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"class_name": "MaxPooling2D",
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"config": {
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"name": "max_pooling2d",
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"trainable": true,
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"dtype": "float32",
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"pool_size": [
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2,
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2
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],
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"padding": "valid",
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"strides": [
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2,
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2
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],
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"data_format": "channels_last"
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}
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},
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{
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"class_name": "Dropout",
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"config": {
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"name": "dropout",
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"trainable": true,
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"dtype": "float32",
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"rate": 0.25,
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"seed": 420
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}
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},
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{
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"class_name": "Flatten",
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"config": {
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"name": "flatten",
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"trainable": true,
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"dtype": "float32",
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"data_format": "channels_last"
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}
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},
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{
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"config": {
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"name": "dense",
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"trainable": true,
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"dtype": "float32",
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"activation": "relu",
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"kernel_initializer": {
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"class_name": "GlorotUniform",
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"config": {
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"dtype": "float32"
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"bias_constraint": null
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}
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},
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{
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"class_name": "Dropout",
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"config": {
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"name": "dropout_1",
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"trainable": true,
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"dtype": "float32",
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"rate": 0.5,
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"noise_shape": null,
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"seed": 420
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}
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},
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{
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"class_name": "Dense",
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"config": {
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"name": "dense_1",
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"trainable": true,
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"dtype": "float32",
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"units": 10,
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"activation": "softmax",
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}
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"bias_constraint": null
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}
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}
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]
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}
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},
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"training_config": {
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"loss": "categorical_crossentropy",
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"metrics": [
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"accuracy"
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],
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"weighted_metrics": null,
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"sample_weight_mode": null,
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"loss_weights": null,
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"optimizer_config": {
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"class_name": "Adam",
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"config": {
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"name": "Adam",
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"learning_rate": 0.0010000000474974513,
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"decay": 0.0,
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"beta_1": 0.8999999761581421,
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"beta_2": 0.9990000128746033,
|
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"epsilon": 1e-07,
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"amsgrad": false
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}
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}
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}
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},
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"weightsManifest": [
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{
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"paths": [
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"group1-shard1of1.bin"
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],
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"weights": [
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{
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"name": "conv2d/kernel",
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"shape": [
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3,
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3,
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1,
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64
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],
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"dtype": "float32"
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},
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{
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"name": "conv2d/bias",
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"shape": [
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64
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],
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"dtype": "float32"
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},
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{
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"name": "conv2d_1/kernel",
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"shape": [
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3,
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3,
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],
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||||
"dtype": "float32"
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},
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{
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"name": "conv2d_1/bias",
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||||
"shape": [
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32
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||||
],
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||||
"dtype": "float32"
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||||
},
|
||||
{
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||||
"name": "dense/kernel",
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"shape": [
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4608,
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128
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],
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||||
"dtype": "float32"
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},
|
||||
{
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||||
"name": "dense/bias",
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||||
"shape": [
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||||
128
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||||
],
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||||
"dtype": "float32"
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||||
},
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||||
{
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||||
"name": "dense_1/kernel",
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||||
"shape": [
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||||
128,
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10
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||||
],
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||||
"dtype": "float32"
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},
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||||
{
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||||
"shape": [
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10
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||||
],
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||||
"dtype": "float32"
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}
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]
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||||
}
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||||
]
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}
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