// // Cloned by Vaibhav on 3 Dec 2022 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
var canvas = document.getElementById("defaultCanvas0");
var model;
var thehtml;
thehtml = "<hr> <h1> Predict </h1>" +
" <button onclick='erase();' class='normbutton' >Erase</button> <br>" + "<br>" +
" <button onclick='prediction(); ' class='normbutton' >Predict</button> <br>";
AB.msg ( thehtml, 1 );
function erase() {
clear();
background(0,0,0);
}
function canvas_to_image(image) {
let tensor = tf.browser.fromPixels(image).resizeNearestNeighbor([28, 28]).mean(2).expandDims(2).expandDims().toFloat();
return tensor.div(255.0);
}
async function prediction() {
var imageData = canvas.toDataURL();
let tensor = canvas_to_image(canvas);
console.log(tensor.data());
let predictions = await model.predict(tensor).data();
let results = Array.from(predictions);
// console.log(results);
var max = results[0];
var maxIndex = 0;
for (var i = 1; i < results.length; i++) {
if (results[i] > max) {
maxIndex = i;
max = results[i];
}
}
output = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'J', 10: 'K', 11: 'L', 12: 'M', 13: 'N', 14: 'O', 15: 'P', 16: 'Q', 17: 'R', 18: 'S', 19: 'T', 20: 'U', 21: 'V', 22: 'W', 23: 'X', 24: 'Y', 25: 'Z'};
console.log(output[maxIndex]);
var pred;
pred = "<br><b style='font-size: 2rem'>" + "Output: " + "</b>" + "<b style='font-size: 3rem; color:#2ecc71'>" + output[maxIndex] + "</b>";
AB.msg ( pred, 2 );
}
function setup() {
createCanvas(300, 300);
background(0,0,0);
$.getScript ( "/uploads/daddyyankee/tf.min.js", async function()
{
model = await tf.loadLayersModel("/uploads/daddyyankee/model.json");
});
noStroke();
}
function draw() {
stroke(255);
if (mouseIsPressed === true) {
strokeWeight(20);
line(mouseX, mouseY, pmouseX, pmouseY);
}
}
// // --- 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 + 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: ---------------------------------------------------------
// 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/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 );
// nn.setLearningRate ( learningrate );
// loadData();
// });
// });
// });
// }
// // 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);
// }