// Cloned by Sumit Khopkar on 3 Dec 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
// --- 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 = 3;
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 = 3;
const TESTPERSTEP = 1;
// 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 + ZOOMPIXELS ) + 50;
const canvasheight = ( 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: ---------------------------------------------------------
//CA686//
function sigmoid(x) {
return 1 / (1 + Math.exp(-x));
}
function dsigmoid(y) {
// return sigmoid(x) * (1 - sigmoid(x));
return y * (1 - y);
}
class Matrix {
constructor(rows, cols) {
this.rows = rows;
this.cols = cols;
this.data = [];
for (let i = 0; i < this.rows; i++) {
this.data[i] = [];
for (let j = 0; j < this.cols; j++) {
this.data[i][j] = 0;
}
}
}
static fromArray(arr) {
let m = new Matrix(arr.length, 1);
for (let i = 0; i < arr.length; i++) {
m.data[i][0] = arr[i];
}
return m;
}
static subtract(a, b) {
// Return a new Matrix a-b
let result = new Matrix(a.rows, a.cols);
for (let i = 0; i < result.rows; i++) {
for (let j = 0; j < result.cols; j++) {
result.data[i][j] = a.data[i][j] - b.data[i][j];
}
}
return result;
}
toArray() {
let arr = [];
for (let i = 0; i < this.rows; i++) {
for (let j = 0; j < this.cols; j++) {
arr.push(this.data[i][j]);
}
}
return arr;
}
randomize() {
for (let i = 0; i < this.rows; i++) {
for (let j = 0; j < this.cols; j++) {
this.data[i][j] = Math.random() * 2 - 1;
}
}
}
add(n) {
if (n instanceof Matrix) {
for (let i = 0; i < this.rows; i++) {
for (let j = 0; j < this.cols; j++) {
this.data[i][j] += n.data[i][j];
}
}
} else {
for (let i = 0; i < this.rows; i++) {
for (let j = 0; j < this.cols; j++) {
this.data[i][j] += n;
}
}
}
}
static transpose(matrix) {
let result = new Matrix(matrix.cols, matrix.rows);
for (let i = 0; i < matrix.rows; i++) {
for (let j = 0; j < matrix.cols; j++) {
result.data[j][i] = matrix.data[i][j];
}
}
return result;
}
static multiply(a, b) {
// Matrix product
console.log("static multiply");
if (a.cols !== b.rows) {
console.log('Columns of A must match rows of B.')
return undefined;
}
let result = new Matrix(a.rows, b.cols);
for (let i = 0; i < result.rows; i++) {
for (let j = 0; j < result.cols; j++) {
// Dot product of values in col
let sum = 0;
for (let k = 0; k < a.cols; k++) {
sum += a.data[i][k] * b.data[k][j];
}
result.data[i][j] = sum;
}
}
return result;
}
multiply(n) {
console.log("non-static multiply");
if (n instanceof Matrix) {
// hadamard product
for (let i = 0; i < this.rows; i++) {
for (let j = 0; j < this.cols; j++) {
this.data[i][j] *= n.data[i][j];
}
}
} else {
// Scalar product
for (let i = 0; i < this.rows; i++) {
for (let j = 0; j < this.cols; j++) {
this.data[i][j] *= n;
}
}
}
}
map(func) {
// Apply a function to every element of matrix
for (let i = 0; i < this.rows; i++) {
for (let j = 0; j < this.cols; j++) {
let val = this.data[i][j];
this.data[i][j] = func(val);
}
}
}
static map(matrix, func) {
let result = new Matrix(matrix.rows, matrix.cols);
// Apply a function to every element of matrix
for (let i = 0; i < matrix.rows; i++) {
for (let j = 0; j < matrix.cols; j++) {
let val = matrix.data[i][j];
result.data[i][j] = func(val);
}
}
return result;
}
print() {
console.table(this.data);
}
}
if (typeof module !== 'undefined') {
module.exports = Matrix;
}
class NeuralNetwork {
//Added code for learning rate
constructor(input_nodes, hidden_nodes, output_nodes, learning_rate) {
console.log("Constructor");
console.error("error");
this.input_nodes = input_nodes;
this.hidden_nodes = hidden_nodes;
this.output_nodes = output_nodes;
this.weights_ih = new Matrix(this.hidden_nodes, this.input_nodes);
this.weights_ho = new Matrix(this.output_nodes, this.hidden_nodes);
this.weights_ih.randomize();
this.weights_ho.randomize();
this.bias_h = new Matrix(this.hidden_nodes, 1);
this.bias_o = new Matrix(this.output_nodes, 1);
this.bias_h.randomize();
this.bias_o.randomize();
this.learning_rate = learning_rate;
}
feedforward(input_array) {
// Generating the Hidden Outputs
let inputs = Matrix.fromArray(input_array);
let hidden = Matrix.multiply(this.weights_ih, inputs);
hidden.add(this.bias_h);
// activation function!
hidden.map(sigmoid);
// Generating the output's output!
let output = Matrix.multiply(this.weights_ho, hidden);
output.add(this.bias_o);
output.map(sigmoid);
// Sending back to the caller!
return output.toArray();
}
train(input_array, target_array) {
// Generating the Hidden Outputs
let inputs = Matrix.fromArray(input_array);
console.log("inputs", inputs);
let hidden = Matrix.multiply(this.weights_ih, inputs);
hidden.add(this.bias_h);
// activation function!
hidden.map(sigmoid);
// Generating the output's output!
console.log("this.weights_ho", this.weights_ho);
let outputs = Matrix.multiply(this.weights_ho, hidden);
outputs.add(this.bias_o);
outputs.map(sigmoid);
// Convert array to matrix object
let targets = Matrix.fromArray(target_array);
// Calculate the error
// ERROR = TARGETS - OUTPUTS
let output_errors = Matrix.subtract(targets, outputs);
console.log("output_errors", output_errors);
console.log("outputs", outputs);
// let gradient = outputs * (1 - outputs);
// Calculate gradient
let gradients = Matrix.map(outputs, dsigmoid);
console.log("gradients", gradients);
gradients.multiply(output_errors);
gradients.multiply(this.learning_rate);
// Calculate deltas
let hidden_T = Matrix.transpose(hidden);
let weight_ho_deltas = Matrix.multiply(gradients, hidden_T);
// Adjust the weights by deltas
this.weights_ho.add(weight_ho_deltas);
// Adjust the bias by its deltas (which is just the gradients)
this.bias_o.add(gradients);
// Calculate the hidden layer errors
let who_t = Matrix.transpose(this.weights_ho);
let hidden_errors = Matrix.multiply(who_t, output_errors);
// Calculate hidden gradient
let hidden_gradient = Matrix.map(hidden, dsigmoid);
hidden_gradient.multiply(hidden_errors);
hidden_gradient.multiply(this.learning_rate);
// Calcuate input->hidden deltas
let inputs_T = Matrix.transpose(inputs);
let weight_ih_deltas = Matrix.multiply(hidden_gradient, inputs_T);
this.weights_ih.add(weight_ih_deltas);
// Adjust the bias by its deltas (which is just the gradients)
this.bias_h.add(hidden_gradient);
// outputs.print();
// targets.print();
// error.print();
}
predict(input_array) {
// Generating the Hidden Outputs
let inputs = Matrix.fromArray(input_array);
let hidden = Matrix.multiply(this.weights_ih, inputs);
hidden.add(this.bias_h);
// activation function!
//hidden.map(this.activation_function.func);
hidden.map(sigmoid);
// Generating the output's output!
let output = Matrix.multiply(this.weights_ho, hidden);
output.add(this.bias_o);
//output.map(this.activation_function.func);
output.map(sigmoid);
// Sending back to the caller!
return output.toArray();
}
}
const len = 784;
const totalData = 500;
const CAT = 0;
const RAINBOW = 1;
const TRAIN = 2;
let catsData;
let trainsData;
let rainbowsData;
let cats = {};
let trains = {};
let rainbows = {};
var doodle_list = [];
var doodle_list_data = [];
var doodle_num_list = [];
var epochCounter = 0;
function preload() {
catsData = loadBytes('uploads/sumitkhopkar25/cats1000.bin');
trainsData = loadBytes('uploads/sumitkhopkar25/trains1000.bin');
rainbowsData = loadBytes('uploads/sumitkhopkar25/rainbows1000.bin');
}
/*function prepareData(category, data, label) {
category.training = [];
category.testing = [];
for (let i = 0; i < totalData; i++) {
let offset = i * len;
let threshold = floor(0.8 * totalData);
if (i < threshold) {
category.training[i] = data.bytes.subarray(offset, offset + len);
category.training[i].label = label;
} else {
category.testing[i - threshold] = data.bytes.subarray(offset, offset + len);
category.testing[i - threshold].label = label;
}
}
console.log("category.training", category.training);
console.log("cats.training", cats.training);
}*/
//CA686//
function setup()
{
createCanvas ( canvaswidth, canvasheight );
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();
//CA686//
/*$.getScript ( "/uploads/codingtrain/matrix.js", function()
{
$.getScript ( "/uploads/codingtrain/nn.js", function()
{
$.getScript ( "/uploads/codingtrain/mnist.js", function()
{*/
console.log ("All JS loaded");
nn = new NeuralNetwork( noinput, nohidden, nooutput, learningrate);
//nn.setLearningRate ( learningrate );
loadData();
/*});
});
});*/
console.log("cats.training", cats.training);
//CA686//
}
// load data set from local file (on this server)
function loadData()
{
//CA680//
/*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
});*/
// Preparing the data
doodle_list = [cats, rainbows, trains];
doodle_list_data = [catsData, rainbowsData, trainsData];
doodle_num_list = [CAT, RAINBOW, TRAIN];
for(let j = 0; j < doodle_list.length; j++){
console.log("j", j);
doodle_list[j].training = [];
doodle_list[j].testing = [];
for (let i = 0; i < totalData; i++) {
let offset = i * len;
let threshold = floor(0.8 * totalData);
if (i < threshold) {
doodle_list[j].training[i] = doodle_list_data[j].bytes.subarray(offset, offset + len);
doodle_list[j].training[i].label = doodle_num_list[j];
} else {
doodle_list[j].testing[i - threshold] = doodle_list_data[j].bytes.subarray(offset, offset + len);
doodle_list[j].testing[i - threshold].label = doodle_num_list[j];
}
}
console.log("doodle_list[j].training", doodle_list[j].training);
}
console.log("cats.training", cats.training);
/*prepareData(cats, catsData, CAT);
prepareData(rainbows, rainbowsData, RAINBOW);
prepareData(trains, trainsData, TRAIN);*/
AB.removeLoading(); // if no loading screen exists, this does nothing
//CA680//
}
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 );
}
//CA686//
function trainEpoch(training) {
shuffle(training, true);
//console.log(training);
// Train for one epoch
for (let i = 0; i < training.length; i++) {
let data = training[i];
let inputs = Array.from(data).map(x => x / 255);
let label = training[i].label;
let targets = [0, 0, 0];
targets[label] = 1;
console.log("inputs", inputs);
console.log("targets", targets);
console.log("nn.weights_ih", nn.weights_ih);
nn.train(inputs, targets);
}
}
function testAll(testing) {
let correct = 0;
// Train for one epoch
for (let i = 0; i < testing.length; i++) {
// for (let i = 0; i < 1; i++) {
let data = testing[i];
let inputs = Array.from(data).map(x => x / 255);
let label = testing[i].label;
let guess = nn.predict(inputs);
let m = max(guess);
let classification = guess.indexOf(m);
// console.log(guess);
// console.log(classification);
// console.log(label);
if (classification === label) {
correct++;
}
}
let percent = 100 * correct / testing.length;
return percent;
}
//CA686//
function trainit (show) // train the network with a single exemplar, from global var "train_index", show visual on or off
{
//CA686//
/*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++;
}*/
// Randomizing the data
let training = [];
console.log("cats in training", cats);
training = training.concat(cats.training);
training = training.concat(rainbows.training);
training = training.concat(trains.training);
console.log("training", training);
trainEpoch(training);
epochCounter++;
console.log("Epoch: " + epochCounter);
}
function testit() // test the network with a single exemplar, from global var "test_index"
{
//CA686//
/*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;
}*/
let testing = [];
testing = testing.concat(cats.testing);
testing = testing.concat(rainbows.testing);
testing = testing.concat(trains.testing);
let percent = testAll(testing);
console.log("Percent: " + nf(percent, 2, 2) + "%");
let inputs = [];
let img = get();
img.resize(28, 28);
img.loadPixels();
for (let i = 0; i < len; i++) {
let bright = img.pixels[i * 4];
inputs[i] = (255 - bright) / 255.0;
}
let guess = nn.predict(inputs);
// console.log(guess);
let m = max(guess);
let classification = guess.indexOf(m);
if (classification === CAT) {
console.log("cat");
} else if (classification === RAINBOW) {
console.log("rainbow");
} else if (classification === TRAIN) {
console.log("train");
}
//CA686//
}
//--- 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:
//CA686//
//if ( typeof mnist == 'undefined' ) return;
console.log("doodle_list", doodle_list);
if ( typeof doodle_list == 'undefined' || doodle_list.length == 0 ) return;
//CA686//
// how can we get white doodle on black background on yellow canvas?
// background('#ffffcc'); doodle.background('black');
background ('black');
AB.queryDataExists ( function ( exists ) // asynchronous - need callback function
{
if ( exists ){
AB.restoreData ( function ( nn )
{
// object returned from server is an array of blocks
// console.log ( "Restoring " + a.length + " blocks from server" );
console.log(nn)
});
}
else{
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();
AB.saveData ( nn );
}
}
});
//throw new Error("Something went badly wrong!");
// 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);
}