// Cloned by Deborah Djon on 1 Dec 2022 from World "Letter recognition neural network (clone by Deborah Djon)" by Deborah Djon
// Please leave this clone trail here.
// Cloned by Deborah Djon on 29 Nov 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
// --- 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 = 26; //altereed by Deborah Djon
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
/*altered by Deborah Djon
Added some letters.
*/
const LETTERS = ["A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z"];
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;
//altered by Deborah Djon
let data;
// 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/tharealog/matrix_tharealog.js", function()
{
$.getScript ( "/uploads/tharealog/nn_tharealog.js", function()
{
$.getScript ( "/uploads/tharealog/mnist.js", function() // todo: alter this
{
$.getScript("https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-vis@1.0.2/dist/tfjs-vis.umd.min.js", function(){ // todo outsource into cnn
$.getScript("https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.0.0/dist/tf.min.js", function(){
$.getScript ( "/uploads/tharealog/data.js", function() // todo: alter this
{
console.log ("All JS loaded");
nn = new NeuralNetwork( noinput, nohidden, nooutput );
nn.setLearningRate ( learningrate );
loadData();
data = new MnistData();
//data2 = new EMnistData();
});
});
});
});
});
});
}
// load data set from local file (on this server)
/*
altered by Deborah Djon
added this code copied from Shiffman's mnist.js file,
only changed the file names to already existing files from other users
*/
function loadData()
{
loadMNIST ( function(data)
{
mnist = data;
console.log ("All data loaded into mnist object:")
console.log(mnist);
console.log("training data cnt: "+ mnist.train_images.length);
console.log("training label cnt: "+ mnist.train_labels.length);
console.log("testing data cnt: "+mnist.test_images.length);
console.log("testing label cnt: "+mnist.test_labels.length);
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
/*
altered by Deborah Djon
changed the targets to be an Array of 26 elements, for each letter in the alphabet
*/
//let targets = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
let targets = Array(26).fill(0);
console.log(label)
//todo: figure out why this is an off by 1 error
targets[label-1] = 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
}
/*
altered by Deborah Djon
change guess from number to letter
*/
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 + LETTERS[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 +LETTERS[b[0]] + "</span> <br>" +
" No.2 guess is: " + greenspan + LETTERS[b[0]] + "</span>";
AB.msg ( thehtml, 2 );
}
function wipeDoodle()
{
doodle_exists = false;
doodle.background('black');
run();
}
// --- 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);
}
//------------- altered by Deborah Djon ----------------------
//------------- show stuff
async function showExamples(data) {
// Create a container in the visor // the place where we see our data
const surface =
tfvis.visor().surface({ name: 'Input Data Examples', tab: 'Input Data'});
// Get the examples
const examples = data.nextTestBatch(20);
const numExamples = examples.xs.shape[0];
// Create a canvas element to render each example
for (let i = 0; i < numExamples; i++) {
const imageTensor = tf.tidy(() => {
// Reshape the image to 28x28 px
return examples.xs
.slice([i, 0], [1, examples.xs.shape[1]])
.reshape([28, 28, 1]);
});
const canvas = document.createElement('canvas');
canvas.width = 28;
canvas.height = 28;
canvas.style = 'margin: 4px;';
await tf.browser.toPixels(imageTensor, canvas);
surface.drawArea.appendChild(canvas);
imageTensor.dispose();
}
}
// ---------------- model --------------
function getModel() {
const model = tf.sequential();
const IMAGE_WIDTH = 28;
const IMAGE_HEIGHT = 28;
const IMAGE_CHANNELS = 1;
// In the first layer of our convolutional neural network we have
// to specify the input shape. Then we specify some parameters for
// the convolution operation that takes place in this layer.
model.add(tf.layers.conv2d({
inputShape: [IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS],
kernelSize: 5,
filters: 8,
strides: 1,
activation: 'relu',
kernelInitializer: 'varianceScaling'
}));
// The MaxPooling layer acts as a sort of downsampling using max values
// in a region instead of averaging.
model.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
// Repeat another conv2d + maxPooling stack.
// Note that we have more filters in the convolution.
model.add(tf.layers.conv2d({
kernelSize: 5,
filters: 16,
strides: 1,
activation: 'relu',
kernelInitializer: 'varianceScaling'
}));
model.add(tf.layers.maxPooling2d({poolSize: [2, 2], strides: [2, 2]}));
// Now we flatten the output from the 2D filters into a 1D vector to prepare
// it for input into our last layer. This is common practice when feeding
// higher dimensional data to a final classification output layer.
model.add(tf.layers.flatten());
// Our last layer is a dense layer which has 10 output units, one for each
// output class (i.e. 0, 1, 2, 3, 4, 5, 6, 7, 8, 9).
const NUM_OUTPUT_CLASSES = 10;
model.add(tf.layers.dense({
units: NUM_OUTPUT_CLASSES,
kernelInitializer: 'varianceScaling',
activation: 'softmax'
}));
// Choose an optimizer, loss function and accuracy metric,
// then compile and return the model
const optimizer = tf.train.adam();
model.compile({
optimizer: optimizer,
loss: 'categoricalCrossentropy',
metrics: ['accuracy'],
});
return model;
}
// ---------------train
async function train(model, data) {
const metrics = ['loss', 'val_loss', 'acc', 'val_acc'];
const container = {
name: 'Model Training', tab: 'Model', styles: { height: '1000px' }
};
const fitCallbacks = tfvis.show.fitCallbacks(container, metrics);
const BATCH_SIZE = 512;
const TRAIN_DATA_SIZE = 5500;
const TEST_DATA_SIZE = 1000;
const [trainXs, trainYs] = tf.tidy(() => {
const d = data.nextTrainBatch(TRAIN_DATA_SIZE);
return [
d.xs.reshape([TRAIN_DATA_SIZE, 28, 28, 1]),
d.labels
];
});
const [testXs, testYs] = tf.tidy(() => {
const d = data.nextTestBatch(TEST_DATA_SIZE);
return [
d.xs.reshape([TEST_DATA_SIZE, 28, 28, 1]),
d.labels
];
});
return model.fit(trainXs, trainYs, {
batchSize: BATCH_SIZE,
validationData: [testXs, testYs],
epochs: 10,
shuffle: true,
callbacks: fitCallbacks
});
}
const classNames = ['Zero', 'One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven', 'Eight', 'Nine'];
function doPrediction(model, data, testDataSize = 500) {
const IMAGE_WIDTH = 28;
const IMAGE_HEIGHT = 28;
const testData = data.nextTestBatch(testDataSize);
const testxs = testData.xs.reshape([testDataSize, IMAGE_WIDTH, IMAGE_HEIGHT, 1]);
const labels = testData.labels.argMax(-1);
const preds = model.predict(testxs).argMax(-1);
testxs.dispose();
return [preds, labels];
}
async function showAccuracy(model, data) {
const [preds, labels] = doPrediction(model, data);
const classAccuracy = await tfvis.metrics.perClassAccuracy(labels, preds);
const container = {name: 'Accuracy', tab: 'Evaluation'};
tfvis.show.perClassAccuracy(container, classAccuracy, classNames);
labels.dispose();
}
async function showConfusion(model, data) {
const [preds, labels] = doPrediction(model, data);
const confusionMatrix = await tfvis.metrics.confusionMatrix(labels, preds);
const container = {name: 'Confusion Matrix', tab: 'Evaluation'};
tfvis.render.confusionMatrix(container, {values: confusionMatrix, tickLabels: classNames});
labels.dispose();
}