Code viewer for World: XOR multi-layer network (c...

// Cloned by Michael Walsh on 8 Nov 2022 from World "XOR multi-layer network" by "Coding Train" project 
// Please leave this clone trail here.
 

// XOR multi-layer network

// Port from:
// https://github.com/CodingTrain/Toy-Neural-Network-JS/tree/master/examples/xor
// with modifications 

// libraries from:
// https://github.com/CodingTrain/Toy-Neural-Network-JS/tree/master/lib
// ported to here:
// https://ancientbrain.com/uploads.php?userid=codingtrain 

// 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)
);


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) {

    // 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);

    // 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;
  }

  setActivationFunction(func = sigmoid) {
    this.activation_function = func;
  }

  train(input_array, target_array) {
    // 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);

    // Generating the output's output!
    let outputs = Matrix.multiply(this.weights_ho, hidden);
    outputs.add(this.bias_o);
    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);
    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);
    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);
  }
}

// let m = new Matrix(3,2);


class Matrix {
  constructor(rows, cols) {
    this.rows = rows;
    this.cols = cols;
    this.data = Array(this.rows).fill().map(() => Array(this.cols).fill(0));
  }

  copy() {
    let m = new Matrix(this.rows, this.cols);
    for (let i = 0; i < this.rows; i++) {
      for (let j = 0; j < this.cols; j++) {
        m.data[i][j] = this.data[i][j];
      }
    }
    return m;
  }

  static fromArray(arr) {
    return new Matrix(arr.length, 1).map((e, i) => arr[i]);
  }

  static subtract(a, b) {
    if (a.rows !== b.rows || a.cols !== b.cols) {
      console.log('Columns and Rows of A must match Columns and Rows of B.');
      return;
    }

    // Return a new Matrix a-b
    return new Matrix(a.rows, a.cols)
      .map((_, i, j) => a.data[i][j] - b.data[i][j]);
  }

  toArray() {
    let arr = [];
    for (let i = 0; i < this.rows; i++) {
      for (let j = 0; j < this.cols; j++) {
        arr.push(this.data[i][j]);
      }
    }
    return arr;
  }

  randomize() {
    return this.map ( e => randomWeight() );       
  }

  add(n) {
    if (n instanceof Matrix) {
      if (this.rows !== n.rows || this.cols !== n.cols) {
        console.log('Columns and Rows of A must match Columns and Rows of B.');
        return;
      }
      return this.map((e, i, j) => e + n.data[i][j]);
    } else {
      return this.map(e => e + n);
    }
  }

  static transpose(matrix) {
    return new Matrix(matrix.cols, matrix.rows)
      .map((_, i, j) => matrix.data[j][i]);
  }

  static multiply(a, b) {
    // Matrix product
    if (a.cols !== b.rows) {
      console.log('Columns of A must match rows of B.');
      return;
    }

    return new Matrix(a.rows, b.cols)
      .map((e, i, j) => {
        // Dot product of values in col
        let sum = 0;
        for (let k = 0; k < a.cols; k++) {
          sum += a.data[i][k] * b.data[k][j];
        }
        return sum;
      });
  }

  multiply(n) {
    if (n instanceof Matrix) {
      if (this.rows !== n.rows || this.cols !== n.cols) {
        console.log('Columns and Rows of A must match Columns and Rows of B.');
        return;
      }

      // hadamard product
      return this.map((e, i, j) => e * n.data[i][j]);
    } else {
      // Scalar product
      return this.map(e => e * n);
    }
  }

  map(func) {
    // Apply a function to every element of matrix
    for (let i = 0; i < this.rows; i++) {
      for (let j = 0; j < this.cols; j++) {
        let val = this.data[i][j];
        this.data[i][j] = func(val, i, j);
      }
    }
    return this;
  }

  static map(matrix, func) {
    // Apply a function to every element of matrix
    return new Matrix(matrix.rows, matrix.cols)
      .map((e, i, j) => func(matrix.data[i][j], i, j));
  }

  print() {
    console.table(this.data);
    return this;
  }

  serialize() {
    return JSON.stringify(this);
  }

  static deserialize(data) {
    if (typeof data == 'string') {
      data = JSON.parse(data);
    }
    let matrix = new Matrix(data.rows, data.cols);
    matrix.data = data.data;
    return matrix;
  }
}

if (typeof module !== 'undefined') {
  module.exports = Matrix;
}



//=== Tweaker's box ============================================

// number of nodes in each layer:
const noinput = 2;
const nohidden = 4;
const nooutput = 1;

// define the exemplars to learn from:
let training_data = [
  {    inputs: [0, 0],    outputs: [0]  },
  {    inputs: [0, 1],    outputs: [1]  },
  {    inputs: [1, 0],    outputs: [1]  },
  {    inputs: [1, 1],    outputs: [0]  }
];

var nn;     // global var 

const learningrate = 1; 

// train this number of times per draw()
const notrain = 10;

// Take screenshot on this step:
AB.screenshotStep  = 200;   


// divide 0,1 into squares 
// show all squares or just the corner squares:
var showall = true;

const canvassize    = 400;
const squaresize    = 40;

const cols          = 10;
const rows          = 10;


// Matrix.randomize() is changed to point to this. Must be defined by user of Matrix. 

function randomWeight()
{
    return ( AB.randomFloatAtoB ( -1, 1 ));
            // Coding Train default is -1 to 1
}    

//=== End of tweaker's box ============================================




function setup() 
{
  createCanvas (canvassize, canvassize);
  nn = new NeuralNetwork ( noinput, nohidden, nooutput );
}



function draw() 
{
  // check if libraries loaded yet:
  if ( typeof nn == 'undefined' ) return;
  nn.setLearningRate ( learningrate );

  background ('#ffffcc'); 

  // train n times 
  for (let i = 0; i < notrain ; i++) 
  {
    let data = random ( training_data );
    nn.train ( data.inputs, data.outputs );
  }

// draw either some squares or all squares:

  if ( showall )
  {
    // redraw all squares each time round
    for (let i = 0; i < cols; i++) 
        for (let j = 0; j < rows; j++) 
            drawquare ( i, j );
  }
  else
  {
    // redraw just the 4 squares 
    for ( let i = 0; i < cols; i = i + cols-1 ) 
        for ( let j = 0; j < rows; j = j + rows-1 ) 
            drawquare ( i, j );
  }
}  
     
 
function drawquare ( i, j )
{
      let x1 = i / cols;
      let x2 = j / rows;
      let inputs = [x1, x2];
      let y = nn.predict(inputs);
      //console.log ( "input (" +x1 + "," + x2 + ") output " + y );
      
      strokeWeight(2);
      stroke('black');
      fill ( y * 255 );      // 0 is black, 1 is white 
      
      rect ( i * squaresize, j * squaresize, squaresize, squaresize );
}