// Cloned by Mohamed Hafez on 9 Dec 2020 from World "Character recognition neural network (clone by Mohamed Hafez)" by Mohamed Hafez
// Please leave this clone trail here.
// Cloned by Mohamed Hafez on 3 Dec 2020 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;
const conv=1;
//--- can modify all these --------------------------------------------------
// no of nodes in network
const noinput = PIXELSSQUARED;
const nohidden = 64;
const nooutput = 10;
const dataaugment =1;
const doodleit=0;
const doodlealter=0;
const shuffle=0;
const learningrate = 0.06; // 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 + 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 nn2;
let xs,ys,xs2,xs3,xs4;
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?
let img,label;
let trainimages=[];
let trainlabels=[];
let dataset_chunk=[];
let bigloop=[];
let firstloop=0;
let Newtrain_images_left=[];
let Newtrain_images_right=[];
let Newtrain_images_up=[];
let Newtrain_images_down=[];
let Newtrain_images_fade=[];
let Newtrain_images_highlight=[];
let Newtrain_images_rotate90=[];
let Newtrain_images_rotateneg90=[];
let firststart=0;
let ziko;
let variko=1;
// 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
}
// CSS trick
// make run header bigger
$("#runheaderbox").css ( { "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 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();
$.getScript ( "/uploads/mhafez/matrix.js", function()
{
$.getScript ( "/uploads/mhafez/nn_multilayer.js", function()
{
$.getScript ("https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js",function()
{
$.getScript ( "/uploads/codingtrain/mnist.js", function()
{
console.log ("All JS loaded");
const optimizer=tf.train.sgd(learningrate);
//[MH] Convolutiona neural network
if (conv==1)
{
nn2 = tf.sequential();
nn2.add(tf.layers.conv2d({inputShape:[28,28,1],kernelSize: 5,filters: 32,strides: 1,activation: 'relu'}));
nn2.add(tf.layers.maxPooling2d({poolSize: [2, 2]}));
nn2.add(tf.layers.conv2d({kernelSize: 5, filters: 64, strides: 1, activation: 'relu'}));
nn2.add(tf.layers.maxPooling2d({poolSize: [2, 2]}));
nn2.add(tf.layers.flatten());
nn2.add(tf.layers.dense({units:128,activation:'relu'}));
nn2.add(tf.layers.dense({units: 10,activation: 'softmax'}));
nn2.compile({optimizer:optimizer,loss:'meanSquaredError',metrics:['accuracy']});
}
//nn2.add(tf.layers.conv2d({inputShape:[28,28,1],kernelSize: 5,filters: 8,strides: 1,activation: 'relu',kernelInitializer: 'varianceScaling'}));
//nn2.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
//nn2.add(tf.layers.conv2d({kernelSize: 5, filters: 16, strides: 1, activation: 'relu',kernelInitializer: 'varianceScaling' }));
//nn2.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
//[MH] Non convolutional neural network
else
{
nn2 = tf.sequential();
nn2.add(tf.layers.dense({units:64,activation:'sigmoid',inputShape:784}));
nn2.add(tf.layers.dense({units:10,activation:'sigmoid'}));
nn2.compile({optimizer:optimizer,loss:'meanSquaredError',metrics:['accuracy']});
}
loadData();
});
});
});
});
setTimeout (console.log('hello'),500);
}
// 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);
for ( var i=0 ; i<mnist.train_labels.length;i++)
{
let targets = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
targets[mnist.train_labels[i]]=1;
bigloop.push(targets);
}
/*
xs = tf.tensor2d(mnist.train_images);
ys = tf.tensor2d(bigloop);
nn2.fit(xs,ys,{epochs : 1,callbacks:{onTrainBegun: () => console.log('train begun'), onTrainEnd:() => console.log('train end'), onEpochBegin : async() => await tf.nextFrame()}}).then(h=>console.log(h.history.loss[0]));
*/
AB.removeLoading(); // if no loading screen exists, this does nothing
});
}
function shiftt(arr,value)
{
// return arr.map((_, i, a) => a[(i + a.length + value) % a.length]);
var newarr=[];
for (var i=0; i<arr.length;i++)
{
if ((i+value)<=arr.length-1 && (i+value)>=0) newarr[i]=arr[i+value];
else newarr[i]=0;
}
return newarr;
}
function shiftcolumns(arr,value)
{
return arr.map((_, i, a) => a[(i + a.length + value) % a.length]);
}
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 );
}
var obj1,obj2;
function shufflees(obj1, obj2) {
var index = obj1.length;
var rnd, tmp1, tmp2;
while (index) {
rnd = Math.floor(Math.random() * index);
index -= 1;
tmp1 = obj1[index];
tmp2 = obj2[index];
obj1[index] = obj1[rnd];
obj2[index] = obj2[rnd];
obj1[rnd] = tmp1;
obj2[rnd] = tmp2;
return [obj1,obj2];
}
}
function chunk(list, elementsPerSubArray) {
var matrix = [], i, k;
for (i = 0, k = -1; i < list.length; i++) {
if (i % elementsPerSubArray === 0) {
k++;
matrix[k] = [];
}
matrix[k].push(list[i]);
}
return matrix;
}
function transposeArray_anti(array, arrayLength){
var newArray = [];
for(var i = 0; i < array.length; i++){
newArray.push([]);
};
for(var i = 0; i < array.length; i++){
for(var j = 0; j < arrayLength; j++){
newArray[j].push(array[i][array.length-1-j]);
};
};
return newArray;
}
function transposeArray_clock(array, arrayLength){
var newArray = [];
for(var i = 0; i < array.length; i++){
newArray.push([]);
};
for(var i = 0; i < array.length; i++){
for(var j = 0; j < arrayLength; j++){
newArray[j].push(array[array.length-1-i][j]);
};
};
return newArray;
}
function rotate45 (array,angle){
var newArray = [];
for(var i = 0; i < array.length; i++){
newArray.push([]);
};
for(var i = 0; i < array.length; i++){
newArray[i]=[];
newArray[i].push([]);
};
for(var i = 0; i < array.length; i++){
for(var j = 0; j < array.length; j++){
var xo=(array.length/2)-Math.cos(angle)*(array.length/2)-Math.sin(angle)*(array.length/2);
var yo=(array.length/2)-Math.cos(angle)*(array.length/2)+Math.sin(angle)*(array.length/2);
var src_x=Math.abs(Math.cos(angle)*i + Math.sin(angle)*j+xo);
var src_y=Math.abs(-Math.sin(angle)*i + Math.cos(angle)*j+yo);
if (src_x > array.length-1 ) src_x=0;
if (src_y > array.length-1 ) src_y=0;
newArray[i][j]=array[Math.floor(src_x)][Math.floor(src_y)];
if (i==0 || j==0) newArray[i][j]=0;
}
}
return newArray;
};
function twodtooned(array)
{
var newArr = [];
for(var i = 0; i < array.length; i++)
{
newArr = newArr.concat(array[i]);
}
return newArr;
}
function shiftrows(array,value)
{
for (var i=0,v=[]; i<array.length;i++)
{
m=shiftt(array[i],value);
v[i]=m;
}
return v;
}
function brightnesss(array,value)
{
for (var i=0,v=[]; i<array.length;i++)
{
v[i]=array[i]*value;
if (v[i]>255) v[i]=255;
}
return v;
}
function distortionn(array,value)
{
for (var i=0,v=[]; i<array.length;i++)
{
if (value ==1) v[i]=array[i];
else v[i]=array[i]*(AB.randomFloatAtoB (0.5 , 2 ));
}
return v;
}
async function traintensor(xs,ys)
{
return await nn2.fit(xs,ys,{epochs : 1,batchSize: 1});
}
function trainit (show) // train the network with a single exemplar, from global var "train_index", show visual on or off
{
if (trainrun == 1 || shuffle ==0)
{
img = mnist.train_images[train_index];
label = mnist.train_labels[train_index];
}
else
{
img = trainimages[train_index];
label = trainlabels[train_index];
}
// xs = tf.tensor(mnist.train_images[0],[1,784]);
// ys = tf.tensor([1,0,0,0,0,0,0,0,0,0],[1,10]);
// trainnn(xs,ys);
// Data Augmentation
if (dataaugment==1)
{
img=chunk(img, 28); // divide 784 array to chunks of 28 2D Arrays
img=shiftrows(img,AB.randomElementOfArray([-2,0,0,0,2])); // shift to left/right;
img=shiftcolumns(img,AB.randomElementOfArray([-2,0,0,0,2])); // shift up/down
img=rotate45(img,AB.randomElementOfArray([-45,0,0,0,45]));
//var angel_select=AB.randomElementOfArray([0,1,2,2,2]);
//if (angel_select==0) img=transposeArray_clock(img,28); // rotate clockwise 90
//if (angel_select==1) img=transposeArray_anti(img,28); // rotate anti-clockwise 90
img=twodtooned(img); // make it one array of 784 again
// img=distortionn(img,AB.randomElementOfArray([1,1,1,0,0])); // adjust brightness
}
if (doodleit==1)
{
for (var i=0; i<img.length;i++)
{
if (img[i]>0) img[i]=255;
}
}
if (dataaugment==1)
{
img=brightnesss(img,AB.randomElementOfArray([0.8,1,1.2])); // adjust brightness
}
//AB.randomElementOfArray ( [-1,-2,1,2] );
// 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
//xs = tf.tensor2d(mnist.train_images);
// ys = tf.tensor2d(bigloop);
//[MH] Train tensorflow neural network and giving it tensor inputs
if (conv==0)
{
xs = tf.tensor(inputs,[1,784]);
ys = tf.tensor(targets,[1,10]);
nn2.fit(xs,ys,{epochs : 1,callbacks:{onTrainBegun: () => console.log('train begun'), onTrainEnd:() => train_index++ , onEpochEnd:()=> variko=1}}).then(h=>console.log(h.history.loss[0]));
}
else
{
//train CNN
let img2=chunk(inputs, 28);
xs=tf.tensor2d(img2); //[MH] Convert mnist images to tensors
xs=xs.reshape([-1,28,28,1]);
ys=tf.tensor(targets,[1,10]); //[MH] Convert mnist labels to tensors
nn2.fit(xs,ys,{epochs : 1,callbacks:{onTrainBegun: () => console.log('train begun'), onTrainEnd:() => train_index++}}); //[MH] Train neural networl
}
// nn2.fit(xs,ys,{epochs : 1,batchSize: 1,callbacks:{onTrainEnd: () => train_index++}}).then(h=>console.log(h.history.loss[0]));
//nn2.fit(xs,ys,{epochs : 1,batchSize: 1,callbacks:train_index++});
// nn.train ( inputs, targets );
thehtml = " trainrun: " + trainrun + "<br> no: " + train_index ;
AB.msg ( thehtml, 4 );
if ( train_index == NOTRAIN )
{
train_index = 0;
console.log( "finished trainrun: " + trainrun );
trainrun++;
if (shuffle==1)
{
var miko=[];
miko=shufflees(mnist.train_labels,mnist.train_images);
trainimages=miko[1];
trainlabels=miko[0];
}
}
}
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];
if (doodleit==1)
{
for (var i=0; i<img.length;i++)
{
if (img[i]>0) img[i]=255;
}
}
// 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
//tf.tidy (()=>
// {
tf.tidy (()=>
{
// predict neural network output
if (conv==0) xs2 = tf.tensor(inputs,[1,784]);
else
// predict CNN Output
{
let img2=chunk(inputs, 28);
xs2=tf.tensor2d(img2);
xs2=xs2.reshape([-1,28,28,1]);
}
var tftest = nn2.predict(xs2);
var tfvalues = tftest.dataSync();
var tfarr = Array.from(tfvalues);
console.log(tfarr);
let guess=findMax(tfarr);
console.log(guess);
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)
{
no1 = i;
no1value = a[i];
}
else if (a[i] > no2value)
{
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;
/* if (firstloop==0)
{
for ( var i=0 ; i<mnist.train_labels.length;i++)
{
let targets = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
targets[mnist.train_labels[i]]=1;
bigloop.push(targets);
}
firstloop++;
}
console.log(bigloop);
*/
// 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);
}
// console.log(xs);
//nn2.predict(xs).print();
// 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()
{
if (doodleit==1)
{
for (var i=0; i<demo.length;i++)
{
if (demo[i]>0) demo[i]=255;
}
}
let inputs = getInputs ( demo );
if (conv===0) xs3 = tf.tensor(inputs,[1,784]);
else
{
let img3=chunk(inputs, 28);
xs3=tf.tensor2d(img3);
xs3=xs3.reshape([-1,28,28,1]);
}
var tftest3 = nn2.predict(xs3);
var tfvalues3 = tftest3.dataSync();
var tfarr3 = Array.from(tfvalues3);
let guess=findMax(tfarr3);
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);
// console.log('image');
// 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 = [];
let inputs2=[];
let doodleimage=[];
for (let i = 0; i < PIXELSSQUARED ; i++)
{
inputs[i] = img.pixels[i * 4] / 255;
inputs2[i] = img.pixels[i * 4];
}
/*
for (let i = 0; i < PIXELSSQUARED ; i++)
{
if(doodlealter==1)
{
if (inputs2[i-1]==0 && inputs2[i]>0) inputs2[i-1]=inputs2[i]/2;
if (inputs2[i-1]>0 && inputs2[i]==0) inputs2[i]=inputs2[i-1]/2;
}
}
*/
var theimage = getImage ( inputs2 ); // get image from data array
image ( theimage, 0, 0, ZOOMPIXELS, ZOOMPIXELS ); // original
image ( theimage, ZOOMPIXELS+50, 0, PIXELS, PIXELS ); // shrunk
doodle_inputs = inputs; // can inspect in console
if (conv==0) xs4 = tf.tensor(inputs,[1,784]);
else
{
let img4=chunk(inputs, 28);
xs4=tf.tensor2d(img4);
xs4=xs4.reshape([-1,28,28,1]);
}
var tftest4 = nn2.predict(xs4);
var tfvalues4 = tftest4.dataSync();
var tfarr4 = Array.from(tfvalues4);
let b=find12(tfarr4);
// 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);
}