// Cloned by Brendan on 9 Dec 2019 from World "Character recognition neural network (clone by Brendan)" by Brendan
// Please leave this clone trail here.
// Cloned by Brendan on 1 Dec 2019 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 = 96;
const nooutput = 10;
var learningRate = 0.1;
const LEARNING_MULTIPLIER = 0.01;
var dynamicLearning = true;
// should we train every timestep or not
let do_training = true;
let show_training = true;
// how many to train and test per timestep
const TRAINPERSTEP = 60; // 6:1 train v test
const TESTPERSTEP = 10;
var PerStepFactor = 1;
// 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 = 15; // thickness of doodle lines
const DOODLE_BLUR = 6; // blur factor applied to doodles
const DOODLE_POSTERIZE = 2;
const DOODLE_COLOUR = '#dddddd';
var 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.
// test randomise range at different values
function randomWeight() {
return (AB.randomFloatAtoB(-0.1, 0.1));
// 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 series of AB.msgs to put data at various places in the run header
var thehtml;
// 1 Doodle header
thehtml = "<hr> <b>doodle:</b><br> " +
"<button onclick='wipeDoodle();' class='normbutton' >clear doodle</button> <br> ";
AB.msg(thehtml, 1);
// 2 Doodle variable data (guess)
// 3 Training header
thehtml = "<hr><b>training:</b><br> " +
" <button onclick='show_training = !show_training;' class='normbutton' >show</button>" +
" <button onclick='PerStepFactor = PerStepFactor * 2;' class='normbutton' >-</button>" +
" <button onclick='PerStepFactor = PerStepFactor /2;' class='normbutton' >+</button>" +
" <button onclick='dynamicTraining = !dynamicTraining;' class='normbutton' >dyn</button>" +
" <button onclick='do_training = !do_training;' class='normbutton' >training</button> <br>";
AB.msg(thehtml, 3);
// 4 variable training data
// 5 Testing header
thehtml = "<b><br>tests:</b> ";
AB.msg(thehtml, 5);
// 6 variable testing data
// 7 Demo header
thehtml = "<hr><b>demo:</b><br>" +
"<button onclick='makeDemo();' class='normbutton' >demo</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; color:darkgreen'> ";
//--- end of AB.msgs structure: ---------------------------------------------------------
function setup() {
createCanvas(canvaswidth, canvasheight);
doodle = createGraphics(ZOOMPIXELS, ZOOMPIXELS); // doodle on larger canvas
doodle.pixelDensity(4);
// 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 NeuralNetworkMulti( [noinput, nohidden, nohidden/2, nooutput] );
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]; // greyscale, so RGB the same
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
{
// todo: rotate image randomly
let startTracking = false;
var ignoreRows = 0;
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 targets_count = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0];
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 && show_training) // && (trainrun > 1))
{
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
targets_count[label] += 1;
// console.log(train_index);
// console.log(inputs);
// console.log(targets);
var plearningRate = learningRate;
if (dynamicLearning) {
// Reduce LearningRate as we become more accurate
if (accuracy >= 0.90) {
learningRate = 1;
} else if (accuracy >= 0.75) {
learningRate = 5;
} else if (accuracy >= 0.50) {
learningRate = 8;
} else {
// default rate is 12.5%
learningRate = 10;
}
learningRate = learningRate * LEARNING_MULTIPLIER;
//forget tiered learning, implement continuous rates
// learningRate = Math.round(1/(accuracy * 100)*100)/100;
nn.setLearningRate(learningRate);
}
train_inputs = inputs; // can inspect in console
nn.train(inputs, targets);
thehtml = "train: " + trainrun + " / " + train_index +
"<br>learning rate: " + learningRate;
let t1 = targets_count.reduce((a, b) => a + b, 0);
// console.log("the targets are : " + targets_count);
AB.msg(thehtml, 4);
train_index++;
if (train_index == NOTRAIN) {
train_index = 0;
console.log("finished trainrun: " + trainrun);
trainrun++;
}
}
var accuracy;
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++;
accuracy = (total_correct / total_tests);
let percent = accuracy * 100;
/*
thehtml = "run: <table><tr><td>" + testrun + " ( " + total_correct +
" / " + total_tests +
" ) " + greenspan + percent.toFixed(2) + "%</span>";
*/
thehtml = "<table><tr><th>run</th><th>correct</th><th>tests</th><th>accuracy</th><tr>" +
"<td>" + testrun + "</td><td>" + total_correct + "</td><td>" + total_tests +
"</td><td>" + greenspan + percent.toFixed(2) + "%</span></td></tr>";
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;
}
var pauseWhenDrawing = false;
// --- 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');
if (!pauseWhenDrawing) {
background('black');
stroke(127);
fill(0, 0, 0);
rect(0, 0, ZOOMPIXELS, ZOOMPIXELS, 10);
rect(0, (ZOOMPIXELS * 1) + 49, ZOOMPIXELS+2, ZOOMPIXELS+2, 10);
rect(0, (ZOOMPIXELS * 2) + 90, ZOOMPIXELS+2, ZOOMPIXELS+2, 10);
if (do_training) {
// do some training per step
for (let i = 0; i < (TRAINPERSTEP * PerStepFactor); 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 * PerStepFactor); 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
{
pauseWhenDrawing = true;
// 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(DOODLE_COLOUR); // change colour to match MNIST
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.");
// pixelate(doodle, 4);
doodle.filter(POSTERIZE, DOODLE_POSTERIZE); // run posterize filter to reduce edges
doodle.filter(BLUR, DOODLE_BLUR); // just blur once
// console.log (doodle);
}
pauseWhenDrawing = false;
}
}
var resizeDoodle = false;
//--- 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[" + i + "]" +
" = " + 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 = "predict: " + greenspan + guess + "</span>";
AB.msg(thehtml, 9);
}
//--- doodle -------------------------------------------------------------
function drawDoodle() {
// doodle is createGraphics not createImage
let theimage = doodle.get();
// best method for matching MNIST is to reduce and enlarge
theimage.loadPixels();
theimage.resize(PIXELS, PIXELS);
theimage.resize(ZOOMPIXELS, ZOOMPIXELS);
theimage.updatePixels();
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();
// best simulation of MNIST is resize down and up
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
showMatrix = false;
thehtml = "predict 1: " + greenspan + b[0] + "</span><br>" +
"predict 2: " + greenspan + b[1] + "</span><br>";
// two methods of drawing prediction - second one is draw on demo.
// prediction.forEach(addMiniGraph);
fill(128);
for (var pLoop = 0; pLoop < prediction.length; pLoop++) {
var value = prediction[pLoop].toFixed(1) * 10;
var w = 10;
var h = 10 * (value + 1);
var x = (pLoop * 18) + 10;
var y = (ZOOMPIXELS * 3) + 90 - h;
rect(x, y, w, h);
}
AB.msg(thehtml, 2);
}
// run through a print a minigraph in html5 boxes
function addMiniGraph(element) {
var character;
switch (element.toFixed(1) * 10) {
case 1:
character = "▁";
break;
case 2:
character = "▁";
break;
case 3:
character = "▂";
break;
case 4:
character = "▃";
break;
case 5:
character = "▄";
break;
case 6:
character = "▅";
break;
case 7:
character = "▆";
break;
case 8:
character = "▇";
break;
case 9:
character = "█";
break;
default:
character = "_";
}
thehtml = thehtml + "|" + character;
}
var showMatrix = false;
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);
}
// Other techniques for learning
/*
class ActivationFunction {
constructor(func, dfunc) {
this.func = func;
this.dfunc = dfunc;
}
}
let sigmoid = new ActivationFunction(
x => 1 / (1 + Math.exp(-x)),
y => y * (1 - y)
);
let tanh = new ActivationFunction(
x => Math.tanh(x),
y => 1 - (y * y)
);
let rhlu = new ActivationFunction(
x => x < 0 ? 0 : x,
y => y < 0 ? 0 : 1
);
let dsigmoid = new ActivationFunction(
x => x * (1 - x),
y => 1 / (1 + Math.exp(-y))
);
*/
// New Activation Functions with storage
class ActivationFunction {
constructor(func, dfunc, use_X_values = false) {
this.func = func;
this.dfunc = dfunc;
this.use_X_values = use_X_values;
}
}
// Range (0, 1)
let sigmoid = new ActivationFunction(
x => 1 / (1 + Math.exp(-x)),
y => y * (1 - y)
);
// Range (-1, 1)
let tanh = new ActivationFunction(
x => Math.tanh(x),
y => 1 - (y * y)
);
// Range (-PI/2, PI/2)
let arctan = new ActivationFunction(
x => Math.atan(x),
y => 1 / ((y * y) + 1),
use_X_values = true
);
// Range (-1, 1)
let softsign = new ActivationFunction(
x => x / (1 + Math.abs(x)),
y => 1 / Math.pow((Math.abs(y) + 1), 2),
use_X_values = true
);
// Range [0, INFINITY)
let relu = new ActivationFunction(
x => x < 0 ? 0 : x,
y => y < 0 ? 0 : 1,
use_X_values = true
);
// Range (-INFINITY, INFINITY)
let leaky_relu = new ActivationFunction(
x => x < 0 ? 0.01 * x : x,
y => y < 0 ? 0.01 : 1,
use_X_values = true
);
// Range (0, INFINITY)
let softplus = new ActivationFunction(
x => Math.log(1 + Math.exp(x)),
y => 1 / (1 + Math.exp(-y)),
use_X_values = true
);
// Range (0, 1]
let gaussian = new ActivationFunction(
x => Math.exp(-1 * (x * x)),
y => -2 * y * Math.exp(-1 * (y * y)),
use_X_values = true
);
// nn.js imported to make it easier to code in Ancient Brain
class NeuralNetwork {
/*
* if first argument is a NeuralNetwork the constructor clones it
* USAGE: cloned_nn = new NeuralNetwork(to_clone_nn);
*/
constructor(in_nodes, hid_nodes, out_nodes) {
if (in_nodes instanceof NeuralNetwork) {
let a = in_nodes;
this.input_nodes = a.input_nodes;
this.hidden_nodes = a.hidden_nodes;
this.output_nodes = a.output_nodes;
this.weights_ih = a.weights_ih.copy();
this.weights_ho = a.weights_ho.copy();
this.bias_h = a.bias_h.copy();
this.bias_o = a.bias_o.copy();
} else {
this.input_nodes = in_nodes;
this.hidden_nodes = hid_nodes;
this.output_nodes = out_nodes;
this.weights_ih = new Matrix(this.hidden_nodes, this.input_nodes);
this.weights_ho = new Matrix(this.output_nodes, this.hidden_nodes);
this.weights_ih.randomize();
this.weights_ho.randomize();
this.bias_h = new Matrix(this.hidden_nodes, 1);
this.bias_o = new Matrix(this.output_nodes, 1);
this.bias_h.randomize();
this.bias_o.randomize();
}
// TODO: copy these as well
this.setLearningRate();
this.setActivationFunction();
}
predict(input_array) {
/*
if (this.activation_function.use_X_values) {
for (var i = 0; i < input_array.length; i++)
input_array[i] = (input_array[i] / 255);
}
*/
// Generating the Hidden Outputs
let inputs = Matrix.fromArray(input_array);
let hidden = Matrix.multiply(this.weights_ih, inputs);
hidden.add(this.bias_h);
// activation function!
hidden.map(this.activation_function.func);
// crude debugging
var arr = this.weights_ho.toArray();
for (var x = 0; x < arr.length; x++) {
arr[x] = Math.round(arr[x] * 10);
}
if (showMatrix) console.log("predict.hidden : " + arr); // trying to do debugging
// Generating the output's output!
let output = Matrix.multiply(this.weights_ho, hidden);
output.add(this.bias_o);
output.map(this.activation_function.func);
// Sending back to the caller!
return output.toArray();
}
setLearningRate(learning_rate = 0.1) {
this.learning_rate = learning_rate;
}
// Test Activation Functions
// preferred : sigmoid, tanh, softsign
// don't work: relu, leaky_relu
setActivationFunction(func = sigmoid) {
this.activation_function = func;
}
train(input_array, target_array) {
// Generating the Hidden Outputs
// if one that uses X values, then transpose brightness between 0 and 1
/* if (this.activation_function.use_X_values) {
for (var i = 0; i < input_array.length; i++)
input_array[i] = (input_array[i] / 255);
}
*/
let inputs = Matrix.fromArray(input_array);
let hidden = Matrix.multiply(this.weights_ih, inputs);
hidden.add(this.bias_h);
// activation function!
let xHidden;
if (this.activation_function.use_X_values) {
// Duplicate matrix => not in all nn.js files
xHidden = new Matrix(hidden.rows, hidden.cols).add(hidden);
// xHidden = Matrix.duplicate(hidden)
}
hidden.map(this.activation_function.func);
// Generating the output's output!
// let outputs = Matrix.multiply(this.weights_ho, hidden);
// outputs.add(this.bias_o);
// outputs.map(this.activation_function.func);
let outputs = Matrix.multiply(this.weights_ho, hidden);
outputs.add(this.bias_o);
// Create Copy of outputs matrix if needed.
let xOutputs;
if (this.activation_function.use_X_values) {
xOutputs = new Matrix(outputs.rows, outputs.cols).add(outputs);
// xOutputs = Matrix.duplicate(outputs)
}
outputs.map(this.activation_function.func);
// Convert array to matrix object
let targets = Matrix.fromArray(target_array);
// Calculate the error
// ERROR = TARGETS - OUTPUTS
let output_errors = Matrix.subtract(targets, outputs);
// let gradient = outputs * (1 - outputs);
// Calculate gradient
let gradients /*= Matrix.map(outputs, this.activation_function.dfunc)*/ ;
if (this.activation_function.use_X_values) {
gradients = Matrix.map(xOutputs, this.activation_function.dfunc);
} else {
gradients = Matrix.map(outputs, this.activation_function.dfunc);
}
gradients.multiply(output_errors);
gradients.multiply(this.learning_rate);
// Calculate deltas
let hidden_T = Matrix.transpose(hidden);
let weight_ho_deltas = Matrix.multiply(gradients, hidden_T);
// Adjust the weights by deltas
this.weights_ho.add(weight_ho_deltas);
// Adjust the bias by its deltas (which is just the gradients)
this.bias_o.add(gradients);
// Calculate the hidden layer errors
let who_t = Matrix.transpose(this.weights_ho);
let hidden_errors = Matrix.multiply(who_t, output_errors);
// Calculate hidden gradient
// let hidden_gradient = Matrix.map(hidden, this.activation_function.dfunc);
let hidden_gradient;
if (this.activation_function.use_X_values) {
hidden_gradient = Matrix.map(xHidden, this.activation_function.dfunc);
} else {
hidden_gradient = Matrix.map(hidden, this.activation_function.dfunc);
}
hidden_gradient.multiply(hidden_errors);
hidden_gradient.multiply(this.learning_rate);
// Calcuate input->hidden deltas
let inputs_T = Matrix.transpose(inputs);
let weight_ih_deltas = Matrix.multiply(hidden_gradient, inputs_T);
this.weights_ih.add(weight_ih_deltas);
// Adjust the bias by its deltas (which is just the gradients)
this.bias_h.add(hidden_gradient);
// outputs.print();
// targets.print();
// error.print();
}
serialize() {
return JSON.stringify(this);
}
static deserialize(data) {
if (typeof data == 'string') {
data = JSON.parse(data);
}
let nn = new NeuralNetwork(data.input_nodes, data.hidden_nodes, data.output_nodes);
nn.weights_ih = Matrix.deserialize(data.weights_ih);
nn.weights_ho = Matrix.deserialize(data.weights_ho);
nn.bias_h = Matrix.deserialize(data.bias_h);
nn.bias_o = Matrix.deserialize(data.bias_o);
nn.learning_rate = data.learning_rate;
return nn;
}
// Adding function for neuro-evolution
copy() {
return new NeuralNetwork(this);
}
// Accept an arbitrary function for mutation
mutate(func) {
this.weights_ih.map(func);
this.weights_ho.map(func);
this.bias_h.map(func);
this.bias_o.map(func);
}
}
// Did not work as well as double resize!
function pixelate(doodle, sample_size) {
var image = doodle.pixels;
var w = ZOOMPIXELS;
var h = ZOOMPIXELS;
for (var y = 0; y < h; y += sample_size) {
for (var x = 0; x < w; x += sample_size) {
var pos = (x + y * w) * 4;
var red = doodle[pos];
var green = doodle[pos + 1];
var blue = doodle[pos + 2];
for (var n = 1; n < sample_size; n++) {
doodle[pos + (4 * n) + 0] = red;
doodle[pos + (4 * n) + 1] = green;
doodle[pos + (4 * n) + 2] = blue;
}
}
}
}
// MultiArray Neural Network
// from: https://github.com/Fir3will/Java-Neural-Network
// Allows multi hidden array
class NeuralNetworkMulti {
constructor(arr, lr) {
this.nodes = arr
this.lr = lr || 0.01
this.activation = NeuralNetworkMulti.rhlu;
this.dactivation = NeuralNetworkMulti.drhlu;
this.weights = []
this.biases = []
for (let i = 0; i < this.nodes.length - 1; i++) {
this.weights.push(new Matrix(this.nodes[i + 1], this.nodes[i]).randomize())
}
for (let i = 1; i < this.nodes.length; i++) {
this.biases.push(new Matrix(this.nodes[i], 1).randomize())
}
}
static tanh(x) {
var y = Math.tanh(x);
return y;
}
static dtanh(x) {
var y = 1 / (pow(Math.cosh(x), 2));
return y;
}
static sigmoid(x) {
return 1 / (1 + Math.exp(-x));
}
static dsigmoid(y) {
// return sigmoid(x) * (1s - sigmoid(x));
return y * (1 - y);
}
// define rhlu
static rhlu(x) {
return x < 0 ? 0 : x;
}
static drhlu(x) {
return x < 0 ? 0 : 1;
}
predict(input_arr) {
let input = Matrix.fromArray(input_arr)
for (let i = 0; i < this.weights.length; i++) {
input = Matrix.multiply(this.weights[i], input)
input.add(this.biases[i])
input.map(this.activation)
}
return input.toArray()
}
train(input_arr, target_arr) {
let target = Matrix.fromArray(target_arr)
let output = Matrix.fromArray(this.predict(input_arr))
let O = []
let input = Matrix.fromArray(input_arr)
for (let i = 0; i < this.weights.length; i++) {
O.push(input)
input = Matrix.multiply(this.weights[i], input)
input.add(this.biases[i])
input.map(this.activation)
}
let error = Matrix.subtract(target, output)
let gradient = Matrix.map(output, this.dactivation)
gradient.multiply(error)
gradient.multiply(this.lr)
for (let i = O.length - 1; i >= 0; i--) {
let dw = Matrix.multiply(gradient, Matrix.transpose(O[i]))
this.weights[i].add(dw)
this.biases[i].add(gradient)
error = Matrix.multiply(Matrix.transpose(this.weights[i]), error)
gradient = Matrix.map(O[i], this.dactivation)
gradient.multiply(error)
gradient.multiply(this.lr)
}
}
getModel() {
let model = this
let k = {
nodes: model.nodes,
lr: model.lr,
activation: model.activation,
dactivation: model.dactivation,
weights: [],
biases: []
}
for (let weight of model.weights) {
let s = {
rows: weight.rows,
cols: weight.cols,
data: []
}
for (let d of weight.data) {
let a = []
for (let l of d) {
a.push(l)
}
s.data.push(a)
}
k.weights.push(s)
}
for (let bias of model.biases) {
let s = {
rows: bias.rows,
cols: bias.cols,
data: bias.data
}
k.biases.push(s)
}
return k
}
static formModel(model) {
let nn = new NeuralNetworkMulti(model.nodes, model.lr)
nn.nodes = model.nodes
nn.lr = model.lr
nn.activation = model.activation
nn.dactivation = model.dactivation
for (let i = 0; i < nn.weights.length; i++) {
nn.weights[i].rows = model.weights[i].rows
nn.weights[i].cols = model.weights[i].cols
for (let j = 0; j < model.weights[i].rows; j++) {
for (let k = 0; k < model.weights[i].cols; k++) {
nn.weights[i].data[j][k] = model.weights[i].data[j][k]
}
}
nn.weights[i].rows = model.weights[i].rows
}
return nn
}
copy() {
let model = this.getModel()
return NeuralNetworkMulti.formModel(model)
}
mutate(func) {
for (let weight of this.weights) {
weight.map(func)
}
for (let bias of this.biases) {
bias.map(func);
}
}
merge(net, ratio = 0.5) {
let r1 = 1 - ratio;
let r2 = ratio;
for (let i = 0; i < this.nodes.length; i++) {
if (this.nodes[i] != net.nodes[i]) {
console.error("Neural Networks can not be merged");
return;
}
}
this.lr = (this.lr * r1) + (net.lr * r2);
for (let i = 0; i < this.weights.length; i++) {
for (let j = 0; j < this.weights[i].rows; j++) {
for (let k = 0; k < this.weights[i].cols; k++) {
this.weights[i].data[j][k] = (this.weights[i].data[j][k] * r1) + (net.weights[i].data[j][k] * r2);
}
}
}
for (let i = 0; i < this.biases.length; i++) {
for (let j = 0; j < this.biases[i].rows; j++) {
for (let k = 0; k < this.biases[i].cols; k++) {
this.biases[i].data[j][k] = (this.biases[i].data[j][k] * r1) + (net.biases[i].data[j][k] * r2);
}
}
}
return this;
}
setActivation(activation, dactivation) {
this.activation = activation;
this.dactivation = dactivation;
}
setLearningRate(lr) {
this.lr = lr;
}
}