// Cloned by Stefano Marzo on 23 Oct 2021 from World "Character recognition neural network" by "Coding Train" project
// Please leave this clone trail here.
// Port of Character recognition neural network from here:
// https://github.com/CodingTrain/Toy-Neural-Network-JS/tree/master/examples/mnist
// with many modifications
//STEFANO'S MODEL FOR GENERALIZED ANN:
/**
* Class: ANN
* e: an array containing the structure of the nn in terms of layers
* e.g. e = [3,5,4] -> 3 input neurons, 5 hidden neurons, 4 output neurons
* a: array of activation functions called at every layer e.g. sigmoid
* aDer: derivative of activation functions e.g. sigmoidDerivative
* lr: learning rate
* bias: boolean that determines if there is a bias neuron
*/
/**
* Class: ANN
* e: an array containing the structure of the nn in terms of layers
* e.g. e = [3,5,4] -> 3 input neurons, 5 hidden neurons, 4 output neurons
* a: array of activation functions called at every layer e.g. sigmoid
* aDer: derivative of activation functions e.g. sigmoidDerivative
* lr: learning rate
* bias: boolean that determines if there is a bias neuron
*/
class ANN{
constructor(structure = [2,2,2], activationFunction = [sigmoid, sigmoid], lr = .2, bias = false) {
this.structure = structure
this.activation = activationFunction;
this.lr = lr;
this.bias = bias;
this.x = this.generateLayers();
this.w = this.generateWeights();
this.activation.splice(0, 0, null);
this.trainingExampleFed = 0;
this.testingExampleFed = 0;
this.rightPrediction = 0; //prediction are valid only while testing, not while training
//this.checkInitialization();
}
generateLayers() {
let layers = [];
for (let j in this.structure) {
layers.push(new Matrix(this.structure[j], 1));
}
return layers;
}
generateWeights(){
let ep = this.structure.length - 1; //do not consider the last hidden layer
let w = new Array(ep-1);
w[0] = null;
for (let n = 0; n < ep; n++) {
let b = this.x[n].rows;
let bp = this.x[n+1].rows;
w[n+1] = new Matrix(bp, b);
w[n+1].randomize();
}
return w;
}
net(i_layer) {
return Matrix.multiply(this.w[i_layer], this.x[i_layer-1]);
}
act(i_layer) {
return this.net(i_layer).map(this.activation[i_layer].func);
}
onlyPropagate(input) {
input = this.transformInput(input);
this.x[0] = input;
for(let i = 1; i < this.x.length; i++) {
this.x[i] = this.act(i);
}
}
propagate(input) {
this.x[0] = input;
for(let i = 1; i < this.x.length; i++) {
this.x[i] = this.act(i);
}
}
calculateError(target) {
let etot = 0;
for(let i in target.data) {
for(let j in target.data[i])
etot += Math.pow((target.data[i][j] - this.x[this.x.length-1].data[i][j]),2)/2;
}
return etot;
}
backpropagate(target) {
let errors = []
let output_errors = Matrix.subtract(target, this.x[this.x.length-1]);
let gradient = Matrix.map(this.x[this.x.length-1], this.activation[this.x.length-1].dfunc);
gradient.multiply(output_errors);
gradient.multiply(this.lr);
let deltaOut = Matrix.multiply(gradient, Matrix.transpose(this.x[this.x.length-2]));
this.w[this.x.length-1].add(deltaOut);
errors[this.x.length-1] = output_errors;
for(let i = this.x.length-2; i > 0; i--) {
let h_errors = Matrix.multiply(Matrix.transpose(this.w[i+1]), errors[i+1]);
let h_gradients = Matrix.map(this.x[i], this.activation[i].dfunc);
h_gradients.multiply(h_errors);
h_gradients.multiply(this.lr);
let delta_h = Matrix.multiply(h_gradients, Matrix.transpose(this.x[i-1]));
this.w[i].add(delta_h);
errors[i] = h_errors;
}
}
backpropagateCodingTrain(target) {
let errors = []
let output_errors = Matrix.subtract(target, this.x[this.x.length-1]);
let gradient = Matrix.map(this.x[this.x.length-1], this.activation[this.x.length-1].dfunc);
gradient.multiply(output_errors);
gradient.multiply(this.lr);
let deltaOut = Matrix.multiply(gradient, Matrix.transpose(this.x[this.x.length-2]));
this.w[this.x.length-1].add(deltaOut);
errors[this.x.length-1] = output_errors;
for(let i = this.x.length-2; i > 0; i--) {
let h_errors = Matrix.multiply(Matrix.transpose(this.w[i+1]), errors[i+1]);
let h_gradients = Matrix.map(this.x[i], this.activation[i].dfunc);
h_gradients.multiply(h_errors);
h_gradients.multiply(this.lr);
let delta_h = Matrix.multiply(h_gradients, Matrix.transpose(this.x[i-1]));
this.w[i].add(delta_h);
errors[i] = h_errors;
}
}
train(input, target) {
input = this.transformInput(input);
target = this.transformtarget(target);
this.propagate(input);
this.backpropagate(target);
let t = outNumeric(target);
let p = this.getPrediction();
this.trainingExampleFed += 1;
//if(t == p) this.rightPrediction += 1;
console.log('error: ', this.calculateError(target).toFixed(4),
' target: ', t,
' prediction: ', p,
' examples fed: ', this.trainingExampleFed,
(t == p) ? 'GOT IT': ' ');
}
test(input, target) {
input = this.transformInput(input);
target = this.transformtarget(target);
this.propagate(input);
//this.backpropagate(target);
let t = outNumeric(target);
let p = this.getPrediction();
this.testingExampleFed += 1;
if(t == p) this.rightPrediction += 1;
console.log('error: ', this.calculateError(target).toFixed(4),
' target: ', t,
' prediction: ', p,
' precision: ', this.rightPrediction, '/', this.testingExampleFed,
(t == p) ? 'GOT IT': ' ');
}
getPrediction() {
return outNumeric(this.x[this.x.length-1]);
}
transformInput(input) {
input = Array.from(input);
input = input.map((x) => x/255);
input = Matrix.fromArray(input);
return input;
}
transformtarget(target) {
target = outOneHot(this.structure[this.structure.length-1], target);
target = Matrix.fromArray(target);
return target;
}
// HTML GRAPHICS
// USAGE: Create an Html Div with ID #ANNDiv, instanciate one ANN in a variable called 'nn'
generateHtml() {
return `
<div id="ANNGenerator"><h4>Artificial Neural Network</h4>
<p class="ANNSubtitle"><i>customize it</i></p>
`+ this.generateInputSection() +`
`+ this.generateHiddenSections() +`
`+ this.generateOutputSection() +`
</div>
`;
}
generateInputSection() {
return `
<div class="ANNSection">
<div class="ANNTitleContainer">
<span class="ANNTitle"><b>Input layer</b></span>
<span class="ANNFunction"></span>
</div>
<div class="ANNCenter"># of Neurons: <b>`+ this.structure[0] +`</b></div>
<div class="ANNNeurons"><b><i>I</i></b><sub>0</sub> ... <b><i>I</i></b><sub>` + (this.structure[0]-1) +`</sub></div>
</div>
`;
}
generateHiddenSection(num) {
return `
<div class="ANNSectionAdd" onclick="nn.createNewLayer(` + (num) + `)">
<b>+</b>
</div>
<div class="ANNSection" id="hiddenLayer` + num + `">
<div class="ANNTitleContainer">
<span class="ANNTitle"><b>Hidden ` + num + ` layer</b></span>
<span class="ANNFunction">f = `+ this.activation[num].name +`</span>
</div>
<div class="ANNCenter"># of Neurons: <b>`+ this.structure[num] +`</b></div>
<div class="ANNNeurons">
<span class="ANNNeuronNames">
<b><i>H` + num + `</i></b><sub>0</sub> ...
<b><i>H` + num + `</i></b><sub>`+ (this.structure[num]-1) +`</sub>
</span>
<span class="ANNSettings">
<span class="jsLink" onclick="$('#hiddenLayer` + num + `').html(nn.generateHiddenSectionSettings(` + num + `))">change settings</span>
<span>
</div>
</div>
`;
}
generateHiddenSectionSettings(num) {
return `
<div class="ANNSection" id="hiddenLayer` + num + `">
<div class="ANNTitleContainer">
<span class="ANNTitle"><b>Hidden ` + num + ` layer</b></span>
<span class="ANNFunction">`+ this.activationFunctionSelection('hidFunction' + num) +`</span>
</div>
<div class="ANNCenter"># of Neurons:
<input type="number" id="hidNumber` + num + `" value="`+ this.structure[num] +`">
</div>
<div class="ANNNeurons">
<span class="ANNNeuronNames">
<b><i>H` + num + `</i></b><sub>0</sub> ...
<b><i>H` + num + `</i></b><sub>`+ (this.structure[num]-1) +`</sub>
</span>
<span class="ANNSettings">
<span class="jsLink" onclick="nn.updateHiddenSettings(` + num + `, $('#hidNumber` + num + `').val(), $('#hidFunction` + num + `').val())">save settings</span>
<span>
</div>
</div>
`;
}
updateHiddenSettings(layer, num, func) {
this.structure[layer] = Number(num);
this.activation[layer] = allFunctions[Number(func)];
this.x = this.generateLayers();
this.w = this.generateWeights();
this.writeHtmlOnPage();
}
generateOutputSectionSettings() {
return `
<div class="ANNTitleContainer">
<span class="ANNTitle"><b>Output layer</b></span>
<span class="ANNFunction">`+ this.activationFunctionSelection('outFunction') +` </span>
</div>
<div class="ANNCenter"># of Neurons:
<input type="number" id="outNumber" value="`+ this.structure[this.structure.length-1] +`">
</div>
<div class="ANNNeurons">
<span class="ANNNeuronNames">
<b><i>O</i></b><sub>0</sub> ...
<b><i>O</i></b><sub>`+ (this.structure[this.structure.length-1]-1) +`</sub>
</span>
<span class="ANNSettings">
<span class="jsLink" onclick="nn.updateOutputSettings($('#outNumber').val(), $('#outFunction').val())">save settings</span>
<span>
</div>
`;
}
generateHiddenSections() {
let s = '';
for(let i = 1; i < this.structure.length-1; i++) {
s += this.generateHiddenSection(i);
}
return s;
}
generateOutputSection() {
return `
<div class="ANNSectionAdd" onclick="nn.createNewLayer(` + (this.structure.length-1) + `)">
<b>+</b>
</div>
<div class="ANNSection" id="outputLayer">
<div class="ANNTitleContainer">
<span class="ANNTitle"><b>Output layer</b></span>
<span class="ANNFunction">f = `+ this.activation[this.structure.length-1].name +` </span>
</div>
<div class="ANNCenter"># of Neurons: <b>`+ this.structure[this.structure.length-1] +`</b></div>
<div class="ANNNeurons">
<span class="ANNNeuronNames">
<b><i>O</i></b><sub>0</sub> ...
<b><i>O</i></b><sub>`+ (this.structure[this.structure.length-1]-1) +`</sub>
</span>
<span class="ANNSettings">
<span class="jsLink" onclick="$('#outputLayer').html(nn.generateOutputSectionSettings())">change settings</span>
<span>
</div>
</div>
`;
}
generateOutputSectionSettings() {
return `
<div class="ANNTitleContainer">
<span class="ANNTitle"><b>Output layer</b></span>
<span class="ANNFunction">`+ this.activationFunctionSelection('outFunction') +` </span>
</div>
<div class="ANNCenter"># of Neurons:
<input type="number" id="outNumber" value="`+ this.structure[this.structure.length-1] +`">
</div>
<div class="ANNNeurons">
<span class="ANNNeuronNames">
<b><i>O</i></b><sub>0</sub> ...
<b><i>O</i></b><sub>`+ (this.structure[this.structure.length-1]-1) +`</sub>
</span>
<span class="ANNSettings">
<span class="jsLink" onclick="nn.updateOutputSettings($('#outNumber').val(), $('#outFunction').val())">save settings</span>
<span>
</div>
`;
}
updateOutputSettings(num, func) {
this.structure[this.structure.length-1] = Number(num);
this.activation[this.activation.length-1] = allFunctions[Number(func)];
this.x = this.generateLayers();
this.w = this.generateWeights();
this.writeHtmlOnPage();
//alert(num + func);
}
activationFunctionSelection(idName) {
let s = '';
for(let i in allFunctions) {
s += '<option value="' + i + '">'+allFunctions[i].name+'</option>';
}
return `
<label for="` + idName + `">f = </label>
<select id="` + idName + `">
`+ s +`
</select>
`;
}
createNewLayer(num) {
this.structure.splice(num, 0, 2); //add a layer with 2 neurons
this.activation.splice(num, 0, allFunctions[0]);
this.x = this.generateLayers();
this.w = this.generateWeights();
this.writeHtmlOnPage();
}
writeHtmlOnPage() {
$('#ANNDiv').html(this.generateHtml())
}
}
// END GENERALIZED MODEL
// --- defined by MNIST - do not change these ---------------------------------------
const PIXELS = 28; // images in data set are tiny
const PIXELSSQUARED = PIXELS * PIXELS;
// number of training and test exemplars in the data set:
const NOTRAIN = 60000;
const NOTEST = 10000;
//--- can modify all these --------------------------------------------------
// no of nodes in network
const noinput = PIXELSSQUARED;
const nohidden = 64;
const nooutput = 10;
const learningrate = 0.1; // default 0.1
// should we train every timestep or not
let do_training = true;
// how many to train and test per timestep
const TRAINPERSTEP = 30;
const TESTPERSTEP = 5;
// multiply it by this to magnify for display
const ZOOMFACTOR = 7;
const ZOOMPIXELS = ZOOMFACTOR * PIXELS;
// 3 rows of
// large image + 50 gap + small image
// 50 gap between rows
const canvaswidth = PIXELS//( PIXELS + ZOOMPIXELS ) + 50;
const canvasheight = PIXELS//( ZOOMPIXELS * 3 ) + 100;
const DOODLE_THICK = 18; // thickness of doodle lines
const DOODLE_BLUR = 3; // blur factor applied to doodles
let mnist;
// all data is loaded into this
// mnist.train_images
// mnist.train_labels
// mnist.test_images
// mnist.test_labels
let nn;
let trainrun = 1;
let train_index = 0;
let testrun = 1;
let test_index = 0;
let total_tests = 0;
let total_correct = 0;
// images in LHS:
let doodle, demo;
let doodle_exists = false;
let demo_exists = false;
let mousedrag = false; // are we in the middle of a mouse drag drawing?
// save inputs to global var to inspect
// type these names in console
var train_inputs, test_inputs, demo_inputs, doodle_inputs;
// Matrix.randomize() is changed to point to this. Must be defined by user of Matrix.
//function randomWeight()
//{
// return ( AB.randomFloatAtoB ( -0.5, 0.5 ) );
// // Coding Train default is -1 to 1
//}
// make run header bigger
AB.headerCSS ( { "max-height": "95vh" } );
//--- start of AB.msgs structure: ---------------------------------------------------------
// We output a serious of AB.msgs to put data at various places in the run header
var thehtml;
// 1 Doodle header
thehtml = "<hr> <h1> 1. Doodle </h1> Top row: Doodle (left) and shrunk (right). <br> " +
" Draw your doodle in top LHS. <button onclick='wipeDoodle();' class='normbutton' >Clear doodle</button> <br> ";
AB.msg ( thehtml, 1 );
// 2 Doodle variable data (guess)
// 3 Training header
thehtml = "<hr> <h1> 2. Training </h1> Middle row: Training image magnified (left) and original (right). <br> " +
" <button onclick='do_training = false;' class='normbutton' >Stop training</button> <br> ";
AB.msg ( thehtml, 3 );
// 4 variable training data
// 5 Testing header
thehtml = "<h3> Hidden tests </h3> " ;
AB.msg ( thehtml, 5 );
// 6 variable testing data
// 7 Demo header
thehtml = "<hr> <h1> 3. Demo </h1> Bottom row: Test image magnified (left) and original (right). <br>" +
" The network is <i>not</i> trained on any of these images. <br> " +
" <button onclick='makeDemo();' class='normbutton' >Demo test image</button> <br> ";
AB.msg ( thehtml, 7 );
// 8 Demo variable data (random demo ID)
// 9 Demo variable data (changing guess)
const greenspan = "<span style='font-weight:bold; font-size:x-large; color:darkgreen'> " ;
//--- end of AB.msgs structure: ---------------------------------------------------------
function loadCss(url) {
var head = document.getElementsByTagName('head')[0];
var link = document.createElement('link');
link.rel = 'stylesheet';
link.type = 'text/css';
link.href = url;
link.media = 'all';
head.appendChild(link);
}
function setup()
{
/**
* Structure of ANN
*/
let layersStructure = [PIXELSSQUARED, 64, 64, 10];
let activationStructure;
let activationDerivative;
/**
* Div for containing GeneralANN html
*/
let divAnnContainer = createDiv('bla');
divAnnContainer.id('ANNDiv');
//make Ann Interface usable, hide AB interface
$('#ANNDiv').css('display', 'inline-block');
$('#ab-wrapper').css('display', 'none');
/**
* Load CSS
*/
loadCss('https://ancientbrain.com/uploads/stefano/general_ann_style.css');
loadCss('https://fonts.googleapis.com/css2?family=Noto+Serif:ital,wght@1,700&family=Nunito:wght@300&display=swap');
createCanvas ( canvaswidth, canvasheight );
background(220);
//doodle = createGraphics ( ZOOMPIXELS, ZOOMPIXELS ); // doodle on larger canvas
//doodle.pixelDensity(1);
// JS load other JS
// maybe have a loading screen while loading the JS and the data set
AB.loadingScreen();
$.getScript ( "/uploads/codingtrain/matrix.js", function() {
$.getScript( "/uploads/stefano/utils.js", function() {
$.getScript ( "/uploads/stefano/generalANN.js", function() {
$.getScript ( "/uploads/codingtrain/mnist.js", function() {
console.log ("All JS loaded");
activationStructure = [sigmoid, sigmoid, sigmoid];
nn = new ANN(layersStructure, activationStructure, 0.1, false);
loadData();
nn.writeHtmlOnPage();
});
});
});
});
}
// load data set from local file (on this server)
function loadData()
{
loadMNIST ( function(data)
{
mnist = data;
console.log ("All data loaded into mnist object:");
console.log(mnist);
AB.removeLoading(); // if no loading screen exists, this does nothing
});
}
function getImage ( img ) // make a P5 image object from a raw data array
{
let theimage = createImage (PIXELS, PIXELS); // make blank image, then populate it
theimage.loadPixels();
for (let i = 0; i < PIXELSSQUARED ; i++)
{
let bright = img[i];
let index = i * 4;
theimage.pixels[index + 0] = bright;
theimage.pixels[index + 1] = bright;
theimage.pixels[index + 2] = bright;
theimage.pixels[index + 3] = 255;
}
theimage.updatePixels();
return theimage;
}
function getInputs ( img ) // convert img array into normalised input array
{
let inputs = [];
for (let i = 0; i < PIXELSSQUARED ; i++)
{
let bright = img[i];
inputs[i] = bright / 255; // normalise to 0 to 1
}
return ( inputs );
}
function trainit (show) // train the network with a single exemplar, from global var "train_index", show visual on or off
{
let img = mnist.train_images[train_index];
let label = mnist.train_labels[train_index];
// optional - show visual of the image
if (show)
{
var theimage = getImage ( img ); // get image from data array
image ( theimage, 0, ZOOMPIXELS+50, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS+50, ZOOMPIXELS+50, PIXELS, PIXELS ); // original
}
// set up the inputs
let inputs = getInputs ( img ); // get inputs from data array
// set up the outputs
let targets = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
targets[label] = 1; // change one output location to 1, the rest stay at 0
// console.log(train_index);
// console.log(inputs);
// console.log(targets);
train_inputs = inputs; // can inspect in console
nn.train ( inputs, targets );
thehtml = " trainrun: " + trainrun + "<br> no: " + train_index ;
AB.msg ( thehtml, 4 );
train_index++;
if ( train_index == NOTRAIN )
{
train_index = 0;
console.log( "finished trainrun: " + trainrun );
trainrun++;
}
}
function testit() // test the network with a single exemplar, from global var "test_index"
{
let img = mnist.test_images[test_index];
let label = mnist.test_labels[test_index];
// set up the inputs
let inputs = getInputs ( img );
test_inputs = inputs; // can inspect in console
let prediction = nn.predict(inputs); // array of outputs
let guess = findMax(prediction); // the top output
total_tests++;
if (guess == label) total_correct++;
let percent = (total_correct / total_tests) * 100 ;
thehtml = " testrun: " + testrun + "<br> no: " + total_tests + " <br> " +
" correct: " + total_correct + "<br>" +
" score: " + greenspan + percent.toFixed(2) + "</span>";
AB.msg ( thehtml, 6 );
test_index++;
if ( test_index == NOTEST )
{
console.log( "finished testrun: " + testrun + " score: " + percent.toFixed(2) );
testrun++;
test_index = 0;
total_tests = 0;
total_correct = 0;
}
}
//--- find no.1 (and maybe no.2) output nodes ---------------------------------------
// (restriction) assumes array values start at 0 (which is true for output nodes)
function find12 (a) // return array showing indexes of no.1 and no.2 values in array
{
let no1 = 0;
let no2 = 0;
let no1value = 0;
let no2value = 0;
for (let i = 0; i < a.length; i++)
{
if (a[i] > no1value) // new no1
{
// old no1 becomes no2
no2 = no1;
no2value = no1value;
// now put in the new no1
no1 = i;
no1value = a[i];
}
else if (a[i] > no2value) // new no2
{
no2 = i;
no2value = a[i];
}
}
var b = [ no1, no2 ];
return b;
}
// just get the maximum - separate function for speed - done many times
// find our guess - the max of the output nodes array
function findMax (a)
{
let no1 = 0;
let no1value = 0;
for (let i = 0; i < a.length; i++)
{
if (a[i] > no1value)
{
no1 = i;
no1value = a[i];
}
}
return no1;
}
// --- the draw function -------------------------------------------------------------
// every step:
function draw()
{
/*
// check if libraries and data loaded yet:
if ( typeof mnist == 'undefined' ) return;
// how can we get white doodle on black background on yellow canvas?
// background('#ffffcc'); doodle.background('black');
background ('black');
if ( do_training )
{
// do some training per step
for (let i = 0; i < TRAINPERSTEP; i++)
{
if (i == 0) trainit(true); // show only one per step - still flashes by
else trainit(false);
}
// do some testing per step
for (let i = 0; i < TESTPERSTEP; i++)
testit();
}
// keep drawing demo and doodle images
// and keep guessing - we will update our guess as time goes on
if ( demo_exists )
{
drawDemo();
guessDemo();
}
if ( doodle_exists )
{
drawDoodle();
guessDoodle();
}
// detect doodle drawing
// (restriction) the following assumes doodle starts at 0,0
if ( mouseIsPressed ) // gets called when we click buttons, as well as if in doodle corner
{
// console.log ( mouseX + " " + mouseY + " " + pmouseX + " " + pmouseY );
var MAX = ZOOMPIXELS + 20; // can draw up to this pixels in corner
if ( (mouseX < MAX) && (mouseY < MAX) && (pmouseX < MAX) && (pmouseY < MAX) )
{
mousedrag = true; // start a mouse drag
doodle_exists = true;
doodle.stroke('white');
doodle.strokeWeight( DOODLE_THICK );
doodle.line(mouseX, mouseY, pmouseX, pmouseY);
}
}
else
{
// are we exiting a drawing
if ( mousedrag )
{
mousedrag = false;
// console.log ("Exiting draw. Now blurring.");
doodle.filter (BLUR, DOODLE_BLUR); // just blur once
// console.log (doodle);
}
}*/
}
//--- demo -------------------------------------------------------------
// demo some test image and predict it
// get it from test set so have not used it in training
function makeDemo()
{
demo_exists = true;
var i = AB.randomIntAtoB ( 0, NOTEST - 1 );
demo = mnist.test_images[i];
var label = mnist.test_labels[i];
thehtml = "Test image no: " + i + "<br>" +
"Classification: " + label + "<br>" ;
AB.msg ( thehtml, 8 );
// type "demo" in console to see raw data
}
function drawDemo()
{
var theimage = getImage ( demo );
// console.log (theimage);
image ( theimage, 0, canvasheight - ZOOMPIXELS, ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS+50, canvasheight - ZOOMPIXELS, PIXELS, PIXELS ); // original
}
function guessDemo()
{
let inputs = getInputs ( demo );
demo_inputs = inputs; // can inspect in console
let prediction = nn.predict(inputs); // array of outputs
let guess = findMax(prediction); // the top output
thehtml = " We classify it as: " + greenspan + guess + "</span>" ;
AB.msg ( thehtml, 9 );
}
//--- doodle -------------------------------------------------------------
function drawDoodle()
{
// doodle is createGraphics not createImage
let theimage = doodle.get();
// console.log (theimage);
image ( theimage, 0, 0, ZOOMPIXELS, ZOOMPIXELS ); // original
image ( theimage, ZOOMPIXELS+50, 0, PIXELS, PIXELS ); // shrunk
}
function guessDoodle()
{
// doodle is createGraphics not createImage
let img = doodle.get();
img.resize ( PIXELS, PIXELS );
img.loadPixels();
// set up inputs
let inputs = [];
for (let i = 0; i < PIXELSSQUARED ; i++)
{
inputs[i] = img.pixels[i * 4] / 255;
}
doodle_inputs = inputs; // can inspect in console
// feed forward to make prediction
let prediction = nn.predict(inputs); // array of outputs
let b = find12(prediction); // get no.1 and no.2 guesses
thehtml = " We classify it as: " + greenspan + b[0] + "</span> <br>" +
" No.2 guess is: " + greenspan + b[1] + "</span>";
AB.msg ( thehtml, 2 );
}
function wipeDoodle()
{
doodle_exists = false;
doodle.background('black');
}
// --- debugging --------------------------------------------------
// in console
// showInputs(demo_inputs);
// showInputs(doodle_inputs);
function showInputs ( inputs )
// display inputs row by row, corresponding to square of pixels
{
var str = "";
for (let i = 0; i < inputs.length; i++)
{
if ( i % PIXELS == 0 ) str = str + "\n"; // new line for each row of pixels
var value = inputs[i];
str = str + " " + value.toFixed(2) ;
}
console.log (str);
}
function executeTraining() {
for (let j = 0; j < 6000; j++) {
nn.train(mnist.train_images[j], mnist.train_labels[j]);
}
}
function executeTesting() {
for (let j = 0; j < 1000; j++) {
nn.test(mnist.test_images[j], mnist.test_labels[j]);
}
}