// Submission by StevenMacManus 20214549
// Cloned by StevenMacManus on 9 Dec 2020 from World "Character recognition 1 layer neural network" by StevenMacManus
// Please leave this clone trail here.
// Cloned by StevenMacManus on 4 Dec 2020 from World "Character recognition neural network (clone by StevenMacManus)" by StevenMacManus
// Please leave this clone trail here.
// Cloned by StevenMacManus on 1 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;
var weights = [];
var CurrentScore = 0;
var TrainedScore = 0;
var Memory = {Model: [], Score: 0};
var ModelLoad = false;
var EarlyStopCount = 0;
var PreviousScore = 0;
var BestScore = 0;
var checkpoint = [];
// User Code: ------------------------------------------------------------------
// Arrays to store Balanced Accuracy Metrics
var ClassPrecision = new Array(10);
var ClassRecall = new Array(10);
var ClassF1 = new Array(10);
var ClassMatrix = new Array(10);
for (i = 0; i < 10; i++)
{
ClassMatrix[i] = new Array(10);
}
for (i = 0; i < 10; i++)
for (j = 0; j < 10; j++)
{
ClassMatrix[i][j] = 0;
}
//--- can modify all these --------------------------------------------------
// no of nodes in network
const noinput = PIXELSSQUARED;
const nooutput = 10;
const nohidden = 4*PIXELS;
var learningrate_initial = 0.1; // default 0.1
var learningrate = learningrate_initial;
// User Code: ------------------------------------------------------------------
// FilterInputs sets whether or not we filter the training images
// SHIFT is the upper limit of Pixels to shift the images by
const FilterInputs = false;
const SHIFT = 5;
// should we train every timestep or not
let do_training = true;
// how many to train and test per timestep
const TRAINPERSTEP = 60;
const TESTPERSTEP = 10;
// 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 = 14; // 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
}
// User Code: ------------------------------------------------------------------
// Functions to save and recall the trained model from the server
function restoreData()
{
AB.restoreData ( function ( a )
{
LoadWeights(a);
trained_nn = new NeuralNetwork( noinput, nohidden, nooutput );
for (i = 0; i < Memory.Model.bias_h.rows; i++)
for (j = 0; j < Memory.Model.bias_h.cols; j++)
{
trained_nn.bias_h.data[i][j] = Memory.Model.bias_h.data[i][j];
}
for (i = 0; i < Memory.Model.bias_o.rows; i++)
for (j = 0; j < Memory.Model.bias_o.cols; j++)
{
trained_nn.bias_o.data[i][j] = Memory.Model.bias_o.data[i][j];
}
for (i = 0; i < Memory.Model.weights_ih.rows; i++)
for (j = 0; j < Memory.Model.weights_ih.cols; j++)
{
trained_nn.weights_ih.data[i][j] = Memory.Model.weights_ih.data[i][j];
}
for (i = 0; i < Memory.Model.weights_ho.rows; i++)
for (j = 0; j < Memory.Model.weights_ho.cols; j++)
{
trained_nn.weights_ho.data[i][j] = Memory.Model.weights_ho.data[i][j];
}
});
}
function LoadWeights( a )
{
Memory.Model = a.Model;
Memory.Score = a.Score;
}
function saveData()
{
Memory.Model = nn;
Memory.Score = CurrentScore;
AB.saveData( Memory )
console.log("Saving Model Parameters")
}
// User Code: ------------------------------------------------------------------
// Function to shuffle the training data, must ensure that the order is
// Maintained between images and label
function shuffleTrainingData()
{
var currentIndex = mnist.train_images.length
var temporaryValue, randomIndex;
console.log("Shuffling Training Data")
while (0 !== currentIndex) {
randomIndex = Math.floor(Math.random() * currentIndex);
currentIndex -= 1;
temporaryData = mnist.train_images[currentIndex];
temporaryLabel = mnist.train_labels[currentIndex];
mnist.train_images[currentIndex] = mnist.train_images[randomIndex];
mnist.train_images[randomIndex] = temporaryData;
mnist.train_labels[currentIndex] = mnist.train_labels[randomIndex];
mnist.train_labels[randomIndex] = temporaryLabel;
}
}
// User Code: ------------------------------------------------------------------
// Function to save checkpoints, and stop learning when accuracy decreases
function EarlyStop(CurrentScore)
{
if (testrun == 1)
{
PreviousScore = CurrentScore;
BestScore = PreviousScore;
}
else
{
if (BestScore > CurrentScore)
{
EarlyStopCount++
}
else
{
BestScore = CurrentScore;
checkpoint = new NeuralNetwork( nn, nohidden, nooutput );
EarlyStopCount = 0;
}
PreviousScore = CurrentScore;
}
if (EarlyStopCount == 2)
{
do_training = false;
console.log("Early Stop: Reverting Model to Checkpoint")
trained_nn = checkpoint;
ModelLoad = true;
}
}
// 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>" +
" <button onclick='restoreData(); ModelLoad = true; do_training = false;' class='normbutton' >Load Weights</button> <br> ";
AB.msg ( thehtml, 3 );
// 4 variable training data
// 5 Testing header
thehtml = "<h3> Classification Report </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/themacmac/matrix.js", function()
{
$.getScript ( "/uploads/themacmac/1_layer_nn.js", function()
{
$.getScript ( "/uploads/codingtrain/mnist.js", function()
{
console.log ("All JS loaded");
nn = new NeuralNetwork( noinput, nohidden, nooutput );
nn.setLearningRate ( learningrate );
nn.setActivationFunction ( sigmoid );
loadData();
});
});
});
restoreData();
}
// 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);
shuffleTrainingData();
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;
}
// User Code: ------------------------------------------------------------------
// Function to filter the MNIST images
function Filter (img)
{
aug_img = getImage ( img );
aug_img.filter (THRESHOLD, 0.4);
aug_img.filter (BLUR, 1);
aug_img.resize ( PIXELS, PIXELS );
aug_img.loadPixels();
// set up inputs
let aug_inputs = [];
for (let i = 0; i < PIXELSSQUARED ; i++)
{
aug_inputs[i] = aug_img.pixels[i * 4];
}
return aug_inputs
}
// User Code: ------------------------------------------------------------------
// 4 functions to shift the image by one pixel in each cardinal direction
function ShiftImageLeft( arr )
{
let pic = [];
let shifted_pic = [];
var temp;
for (let i = 0; i < PIXELS; i++)
for (let j = 0; j < PIXELS; j++)
{
pic.push(arr[j + i*PIXELS]);
}
for (let i = 0; i < PIXELS; i++)
for (let j = 0; j < PIXELS; j++)
{
// Note temp, can set third line to temp instead of 0 if wrapping
// is desired
temp = pic[i*PIXELS];
shifted_pic[i*PIXELS + j] = pic[i*PIXELS + j + 1];
shifted_pic[PIXELS * (1 + i) - 1] = 0;
}
return shifted_pic
}
function ShiftImageRight( arr )
{
let pic = [];
let shifted_pic = [];
var temp;
for (let i = 0; i < PIXELS; i++)
for (let j = 0; j < PIXELS; j++)
{
pic.push(arr[j + i*PIXELS]);
}
for (let i = 0; i < PIXELS; i++)
for (let j = 0; j < PIXELS; j++)
{
// Note temp, can set third line to temp instead of 0 if wrapping
// is desired
temp = pic[PIXELS * (1 + i) - 1];
shifted_pic[i*PIXELS + j] = pic[i*PIXELS + j - 1];
shifted_pic[i*PIXELS] = 0;
}
return shifted_pic
}
function ShiftImageUp( arr )
{
let pic = [];
let shifted_pic = [];
var temp;
for (let i = 0; i < PIXELS; i++)
for (let j = 0; j < PIXELS; j++)
{
pic.push(arr[j + i*PIXELS]);
}
for (let i = 0; i < PIXELS; i++)
for (let j = 0; j < PIXELS; j++)
{
// Note temp, can set third line to temp instead of 0 if wrapping
// is desired
temp = pic[j];
shifted_pic[i*PIXELS + j] = pic[i*PIXELS + j+PIXELS];
shifted_pic[(PIXELS * (PIXELS - 1)) + j] = 0;
}
return shifted_pic;
}
function ShiftImageDown( arr )
{
let pic = [];
let shifted_pic = [];
var temp;
for (let i = 0; i < PIXELS; i++)
for (let j = 0; j < PIXELS; j++)
{
pic.push(arr[j + i*PIXELS]);
}
for (let i = 0; i < PIXELS; i++)
for (let j = 0; j < PIXELS; j++)
{
// Note temp, can set third line to temp instead of 0 if wrapping
// is desired
temp = pic[(PIXELS * (PIXELS - 1)) + j];
shifted_pic[i*PIXELS + j] = pic[i*PIXELS + j-PIXELS];
shifted_pic[j] = 0;
}
return shifted_pic;
}
// User Code: ------------------------------------------------------------------
// Function that calls the shift functions to shift the image by up to a user
// defined limit of pixels in each cardinal and ordinal direction
function ShiftInputsByN(img)
{
let rand1 = Math.random()
let rand2 = Math.random()
for (i = 0; i < Math.ceil(rand2*SHIFT); i++)
{
if (rand1 < 0.1) img = img;
else if (0.1 < rand1 < 0.2) img = ShiftImageUp( img );
else if (0.2 < rand1 < 0.3) img = ShiftImageLeft( ShiftImageUp( img ) ) ;
else if (0.3 < rand1 < 0.4) img = ShiftImageLeft(img);
else if (0.4 < rand1 < 0.5) img = ShiftImageDown( ShiftImageLeft( img ) );
else if (0.5 < rand1 < 0.6) img = ShiftImageDown(img);
else if (0.6 < rand1 < 0.7) img = ShiftImageRight( ShiftImageDown( img ) );
else if (0.7 < rand1 < 0.8) img = ShiftImageRight( img );
else img = ShiftImageUp(ShiftImageRight( img ) );
}
return img;
}
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
{
// User Code: ----------------------------------------------------------------
// Now shifting the inputs
let img = ShiftInputsByN( mnist.train_images[train_index]);
let label = mnist.train_labels[train_index];
if (FilterInputs) img = Filter(img)
// 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
train_inputs = inputs; // can inspect in console
nn.train ( inputs, targets );
thehtml = "<br> Learning Rate: " + nn.learning_rate.toFixed(5) + "<br> Epoch: " + trainrun + "<br><br> Training Sample: " + train_index ;
AB.msg ( thehtml, 4 );
train_index++;
if ( train_index == NOTRAIN )
{
train_index = 0;
trainrun++;
}
}
// User Code: ------------------------------------------------------------------
// 2 Functions for calculating Balanced Accuracy Metrics
function SumRow(matrix, index)
{
let Sum = 0;
for (i = 0; i < (matrix.length); i++)
{
Sum = Sum + matrix[index][i];
}
return Sum
}
function SumColumn(matrix, index)
{
let Sum = 0;
for (i = 0; i < (matrix.length); i++)
{
Sum = Sum + matrix[i][index];
}
return Sum
}
function testit() // test the network with a single exemplar, from global var "test_index"
{
// User Code: ----------------------------------------------------------------
// Now shifting the inputs
let img = ShiftInputsByN(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);
let guess = findMax(prediction); // the top output
// User Code: ------------------------------------------------------------------
// Calculate Precision, Recall, and F1 for each digit
// Can console.table(ClassMatrix) for a truth table.
ClassMatrix[guess][label]++
for ( c = 0; c < 10; c++)
{
let PreDen = SumRow(ClassMatrix, c);
let RecDen = SumColumn(ClassMatrix, c);
ClassPrecision[c] = (ClassMatrix[c][c] / PreDen);
ClassRecall[c] = (ClassMatrix[c][c] / RecDen);
ClassF1[c] = ((2 * ClassPrecision[c] * ClassRecall[c])/(ClassPrecision[c] + ClassRecall[c])).toFixed(2);
}
total_tests++;
if (guess == label) total_correct++;
let percent = (total_correct / total_tests) * 100 ;
CurrentScore = percent;
// User Code: ------------------------------------------------------------------
// Show the Classification Report
thehtml = "<table style=width:400px> <tr> <th> </th> <th>precision</th> <th>recall</th> <th>F1</th> </tr>"
+ "<tr> <th style=text-align:right>0</th> <th>" + ClassPrecision[0].toFixed(2) + "</th> <th>" + ClassRecall[0].toFixed(2) + "</th> <th>" + ClassF1[0] + "</th> </tr>"
+ "<tr> <th style=text-align:right>1</th> <th>" + ClassPrecision[1].toFixed(2) + "</th> <th>" + ClassRecall[1].toFixed(2) + "</th> <th>" + ClassF1[1] + "</th> </tr>"
+ "<tr> <th style=text-align:right>2</th> <th>" + ClassPrecision[2].toFixed(2) + "</th> <th>" + ClassRecall[2].toFixed(2) + "</th> <th>" + ClassF1[2] + "</th> </tr>"
+ "<tr> <th style=text-align:right>3</th> <th>" + ClassPrecision[3].toFixed(2) + "</th> <th>" + ClassRecall[3].toFixed(2) + "</th> <th>" + ClassF1[3] + "</th> </tr>"
+ "<tr> <th style=text-align:right>4</th> <th>" + ClassPrecision[4].toFixed(2) + "</th> <th>" + ClassRecall[4].toFixed(2) + "</th> <th>" + ClassF1[4] + "</th> </tr>"
+ "<tr> <th style=text-align:right>5</th> <th>" + ClassPrecision[5].toFixed(2) + "</th> <th>" + ClassRecall[5].toFixed(2) + "</th> <th>" + ClassF1[5] + "</th> </tr>"
+ "<tr> <th style=text-align:right>6</th> <th>" + ClassPrecision[6].toFixed(2) + "</th> <th>" + ClassRecall[6].toFixed(2) + "</th> <th>" + ClassF1[6] + "</th> </tr>"
+ "<tr> <th style=text-align:right>7</th> <th>" + ClassPrecision[7].toFixed(2) + "</th> <th>" + ClassRecall[7].toFixed(2) + "</th> <th>" + ClassF1[7] + "</th> </tr>"
+ "<tr> <th style=text-align:right>8</th> <th>" + ClassPrecision[8].toFixed(2) + "</th> <th>" + ClassRecall[8].toFixed(2) + "</th> <th>" + ClassF1[8] + "</th> </tr>"
+ "<tr> <th style=text-align:right>9</th> <th>" + ClassPrecision[9].toFixed(2) + "</th> <th>" + ClassRecall[9].toFixed(2) + "</th> <th>" + ClassF1[9] + "</th> </tr>"
+ "<tr> <th></th> <th></th> <th></th> <th></th> </tr>"
+ "<tr> <th>Accuracy: </th> <th>" + percent.toFixed(2) + "</th> <th></th> <th></th> </tr> </table>"
AB.msg ( thehtml, 6 );
test_index++;
if ( test_index == NOTEST )
{
console.log( "Completed epoch: " + testrun + ", Accuracy: " + CurrentScore.toFixed(2) + ", Stored Model Accuracy: " + Memory.Score.toFixed(2));
// User Code: --------------------------------------------------------------
// Train Until Performance Decreases, Save the model if it bests the stored model's accuracy
if( CurrentScore.toFixed(2) > Memory.Score.toFixed(2)) saveData();
restoreData();
EarlyStop(CurrentScore)
// User Code: --------------------------------------------------------------
// Implement Exponential Learning Rate Decay -------------------------------------------
learningrate = Math.exp(-0.5 * testrun) * learningrate_initial;
nn.setLearningRate ( learningrate );
console.log("Decreasing Learning Rate to: " + nn.learning_rate.toFixed(5))
testrun++;
test_index = 0;
total_tests = 0;
total_correct = 0;
shuffleTrainingData()
}
}
//--- 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 ('grey');
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
{
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;
doodle.filter (BLUR, DOODLE_BLUR); // just blur once
}
}
}
//--- 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
// User Code: ----------------------------------------------------------------
// If we have loaded the pretrained model use it
// Model in training
if(!ModelLoad)
{
let prediction = nn.predict(inputs);
let guess = findMax(prediction);
thehtml = " We classify it as: " + greenspan + guess + "</span>" ;
AB.msg ( thehtml, 9 );
}
// Pretrained Model
else
{
let prediction = trained_nn.predict(inputs);
let guess = findMax(prediction);
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
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
// User Code: ----------------------------------------------------------------
// If we have loaded the pretrained model use it
// Model in training
if(!ModelLoad)
{
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 );
}
// Pretrained Model
else
{
let prediction = trained_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 );
}
}
// User Code: ----------------------------------------------------------------
// Modified so the background doesn't disappear after wiping, but need to wipe
// before drawing
function wipeDoodle()
{
doodle_exists = false;
doodle.background('black');
doodle_exists = true;
}
// --- 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);
}