// Cloned by Fergus on 29 Nov 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;
//--- can modify all these --------------------------------------------------
// no of nodes in network
const noinput = PIXELSSQUARED;
const nohidden = 256; //64. Altered by FD.
const nooutput = 10; //10 as there are 10 numbers 0->9
const learningrate = 0.1; // default 0.1 adapt to changing with learning
let learning_array=[]; // use this to track learning progress. fd
let learning_target=5; // added to track learning rate versus number of test iamges. fd
// should we train every timestep or not
let do_training = true;
// how many to train and test per timestep. There is 6x more training data so best to keep train/test ratio per step 6/1. fd
const TRAINPERSTEP = 24; //30
const TESTPERSTEP = 4; //5
// multiply it by this to magnify for display
const ZOOMFACTOR = 6; //7
const ZOOMPIXELS = ZOOMFACTOR * PIXELS;
let percent = 0.1; //make global fdowney
// 3 rows of
// large image + 50 gap + small image
// 50 gap between rows
const canvaswidth = ( PIXELS + ZOOMPIXELS ) + 50;
//const canvasheight = ( ZOOMPIXELS * 3 ) + 100;
const canvasheight = ( ZOOMPIXELS * 3 ) + 100;
const DOODLE_THICK = 14; // thickness of doodle lines .... orig = 18 fd edit
const DOODLE_BLUR = 0.2; // blur factor applied to doodles.... orig = 4 fd edit
let mnist;
// all data is loaded into this
// mnist.train_images
// mnist.train_labels
// mnist.test_images
// mnist.test_labels
let nn;
//let nn1, nn2; // this was an experiment to see if I could use the same nn.js to create a second layer but this did not work. fd
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, doodle_inputs_c;
// Matrix.randomize() is changed to point to this. Must be defined by user of Matrix.
function randomWeight()
{
//return ( AB.randomFloatAtoB ( -0.5, 0.5 ) );
return ( AB.randomFloatAtoB ( -0.5, 0.5 ) ); //fd edit
// 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 = "
1. Doodle Top row: Doodle (left) and shrunk (right). " +
" Draw your doodle in top LHS. Clear doodle ";
AB.msg ( thehtml, 1 );
// 2 Doodle variable data (guess)
// 3 Training header
thehtml = " 2. Training Middle row: Training image magnified (left) and original (right). " +
" Stop training ";
AB.msg ( thehtml, 3 );
// 4 variable training data
// 5 Testing header
thehtml = " Hidden tests " ;
AB.msg ( thehtml, 5 );
// 6 variable testing data
// 7 Demo header
thehtml = " 3. Demo Bottom row: Test image magnified (left) and original (right). " +
" The network is not trained on any of these images. " +
" Demo test image ";
AB.msg ( thehtml, 7 );
// 8 Demo variable data (random demo ID)
// 9 Demo variable data (changing guess)
const greenspan = " " ;
//--- end of AB.msgs structure: ---------------------------------------------------------
function setup()
{
createCanvas ( canvaswidth, canvasheight );
doodle = createGraphics ( ZOOMPIXELS, ZOOMPIXELS ); // doodle on larger canvas
doodle.pixelDensity(0.15); //fd edit changed from original = 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/fergus24/nn.js", function() //point to my own version of nn.js
{
$.getScript ( "/uploads/codingtrain/mnist.js", function()
{
console.log ("All JS loaded");
nn = new NeuralNetwork( noinput, nohidden, nooutput );
nn.setLearningRate ( learningrate );
//nn1 = new NeuralNetwork( noinput, nohidden, nohidden );
//nn1.setLearningRate ( learningrate );
//nn2 = new NeuralNetwork( nohidden, nohidden, nooutput );
//nn2.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]*1; //fdedit original = *1
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
image ( theimage, 0, ZOOMPIXELS+ (ZOOMPIXELS/5), ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS+50, ZOOMPIXELS+ (ZOOMPIXELS/5), 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: " + train_index);
//console.log("inputs: " + inputs);
//console.log("targets: " + targets);
train_inputs = inputs; // can inspect in console
nn.train ( inputs, targets );
//let initial_prediction = [];
//nn1.train ( inputs, nn1.predict(inputs) );
//nn2.train ( nn1.predict( inputs), targets );
thehtml = " trainrun: " + trainrun + " 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 );
//console.log ("inputs " + inputs )
test_inputs = inputs; // can inspect in console
let prediction = nn.predict(inputs); // array of outputs
//let prediction = nn2.predict(nn1.predict(inputs));
let guess = findMax(prediction); // the top output
//console.log ("guess " + guess )
//console.log ("label " + label )
total_tests++;
if (guess == label) total_correct++;
percent = (total_correct / total_tests) * 100 ;
//console.log ("learning rate " + learningrate )
let mod_learning_rate = (-2*((Math.log(percent/100))/Math.log(60)));
if ( mod_learning_rate > 0.5){
mod_learning_rate = 0.5;
//mod_learning_rate = 0.5;
}
//console.log ("percent " + percent);
//console.log ("rate calc " + (-2*((Math.log(percent/100))/Math.log(60))) );
console.log ("modified learning rate " + mod_learning_rate/1);
//nn.setLearningRate ( mod_learning_rate/10 ); // rectifier
nn.setLearningRate ( mod_learning_rate/1 ); // tanh
//nn1.setLearningRate ( mod_learning_rate/4 );
//nn2.setLearningRate ( mod_learning_rate/4 );
if ( percent > learning_target){
learning_array[learning_target/5] = total_tests;
console.log ("learning target" + learning_target);
console.log ("learning array" + learning_array);
learning_target=learning_target+5;
}
thehtml = " testrun: " + testrun + " no: " + total_tests + " " +
" correct: " + total_correct + " " +
" score: " + greenspan + percent.toFixed(2) + " ";
AB.msg ( thehtml, 6 ); //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;
// how can we get white doodle on black background on yellow canvas?
// background('#ffffcc'); doodle.background('black');
//background ('black'); //fdowney
background ('#ffffcc');
//var canvas = document.createElement("blank_board");
//canvas.width = img.width;
//canvas.height = img.height;
bg = createGraphics(ZOOMPIXELS*1.1, ZOOMPIXELS*1.1);
bg.background('red');
// background changed to make the doodle graphic area clearer and therefore allow for a better comparison to test graphic. fd
//red background for doodle, fd edit
image ( bg, 0, 0, ZOOMPIXELS*1.05, ZOOMPIXELS*1.05 ); // original
image ( bg, ZOOMPIXELS+50, 0, PIXELS*1.05, PIXELS*1.05 ); // shrunk
//red background for test, fd edit
image ( bg, 0, ZOOMPIXELS*1+ (ZOOMPIXELS*1/5), ZOOMPIXELS*1.05, ZOOMPIXELS*1.05 ); // original
image ( bg, ZOOMPIXELS+50, ZOOMPIXELS*1+ (ZOOMPIXELS*1/5), ZOOMPIXELS*1.05, PIXELS*1.05 ); // shrunk
//red background for demo, fd edit
image ( bg, 0, canvasheight - ZOOMPIXELS*1.05, ZOOMPIXELS*1.05, ZOOMPIXELS*1.05 ); // original
image ( bg, ZOOMPIXELS+50, canvasheight - ZOOMPIXELS*1.05, PIXELS*1.05, PIXELS*1.05 ); // shrunk
if ( do_training )
{
// do_training=false; // to stop it after one run, fd
// 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 + 0; // can draw up to this pixels in corner fd edit orig = +20
if ( (mouseX < MAX) && (mouseY < MAX) && (pmouseX < MAX) && (pmouseY < MAX) )
{
mousedrag = true; // start a mouse drag
doodle_exists = true;
//doodle.point(ZOOMPIXELS/2, ZOOMPIXELS/2);
doodle.stroke('white'); //orig=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 + " " +
"Classification: " + label + " " ;
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
image ( theimage, 0, canvasheight - ZOOMPIXELS+ (ZOOMPIXELS/100), ZOOMPIXELS, ZOOMPIXELS ); // magnified
image ( theimage, ZOOMPIXELS+50, canvasheight - ZOOMPIXELS+ (ZOOMPIXELS/100), 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 prediction = nn2.predict(nn1.predict(inputs)); // array of outputs
let guess = findMax(prediction); // the top output
thehtml = " We classify it as: " + greenspan + guess + "" ;
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;
}
/////////////////////////////////////////////f downey doodle recentre code//////////////////////
let image_h_start=0;
let image_h_stop=0;
let image_w_start=0;
let image_w_stop=0;
let c_inputs = inputs;
if (mousedrag === false)
{
//source https://stackoverflow.com/questions/1295584/most-efficient-way-to-create-a-zero-filled-javascript-array
c_inputs = Array.apply(null, Array(PIXELS*PIXELS)).map(Number.prototype.valueOf,0);
for (let h = 0; h < PIXELS ; h++)
{
for (let w = 0; w < PIXELS ; w++)
{
if (inputs[(h*PIXELS)+w] > 0 && image_h_start === 0)
{
image_h_start=h;
}
}
}
for ( h = PIXELS-1; h>0 ; h--)
{
for ( w = 0; w < PIXELS ; w++)
{
if (inputs[(h*PIXELS)+w] > 0 && image_h_stop === 0)
{
image_h_stop=h;
}
}
}
for ( w = 0; w < PIXELS ; w++)
{
for ( h = 0; h < PIXELS ; h++)
{
if (inputs[(h*PIXELS)+w] > 0 && image_w_start === 0)
{
image_w_start=w;
}
}
}
for ( w = PIXELS-1; w>0 ; w--)
{
for ( h = 0; h < PIXELS ; h++)
{
if (inputs[(h*PIXELS)+w] > 0 && image_w_stop === 0)
{
image_w_stop=w;
}
}
}
//console.log ("w start " + image_w_start );
//console.log ("w stop " + image_w_stop );
//console.log ("h start " + image_h_start );
//console.log ("h stop " + image_h_stop );
let h_offset = Math.round(((PIXELS/2) - ((image_h_stop-image_h_start)/2))-image_h_start);
let w_offset = Math.round(((PIXELS/2) - ((image_w_stop-image_w_start)/2))-image_w_start);
//console.log ("PIXELS " + PIXELS );
//console.log ("h offset " + h_offset );
//console.log ("w offset " + w_offset );
for (let h = 0; h < PIXELS ; h++)
{
for (let w = 0; w < PIXELS ; w++)
{
if (inputs[(h*PIXELS)+w] > 0 )
{
c_inputs[((h+h_offset)*PIXELS)+(w+w_offset)] = inputs[((h)*PIXELS)+w];
}
}
}
} //end of mousedrag if
doodle_inputs = inputs; // can inspect in console
doodle_inputs_c = c_inputs;
// feed forward to make prediction
let prediction = nn.predict(c_inputs); // array of outputs
//let prediction = nn2.predict(nn1.predict(inputs));
let b = find12(prediction); // get no.1 and no.2 guesses
thehtml = " We classify it as: " + greenspan + b[0] + " " +
" No.2 guess is: " + greenspan + b[1] + "";
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);
}