shitty neural network example

gistfile1.txt

/**
 * Trainins input vectors.
 *
 * Input used to train neural network.
 */

// Square
var s_1 = [
  -1, -1, -1, -1, -1,
  -1,  1,  1,  1, -1,
  -1,  1, -1,  1, -1,
  -1,  1,  1,  1, -1,
  -1, -1, -1, -1, -1
];

// X
var s_2 = [
   1, -1, -1, -1,  1,
  -1,  1, -1,  1, -1,
  -1, -1,  1, -1, -1,
  -1,  1, -1,  1, -1,
   1, -1, -1, -1,  1
];

var s = [s_1, s_2];

/**
 * Target vectors.
 *
 * The expected output of NN.
 */

var t_1 = -1;
var t_2 = 1;
var t = [t_1, t_2];

/**
 * Initial weight matrix (a single vector)
 */

var weights = [
   0, 0, 0, 0, 0,
   0, 0, 0, 0, 0,
   0, 0, 0, 0, 0,
   0, 0, 0, 0, 0,
   0, 0, 0, 0, 0
];

var threshold = 0;

function activationFunction(input, threshold) {
  if (input >= threshold) return 1;
  return -1;
}

/**
 * Hebb-train the network
 */
for (var s_i = 0; s_i < s.length; s_i++)   {
  console.log("training...");

  for (var i=0; i < weights.length; i++) {
    weights[i] = weights[i] + (s[s_i][i] * t[s_i]);
  }

  threshold = threshold - t[s_i];

  console.log("New weights...", weights);
  console.log("New threshold...", threshold);
}

console.log("Network is now trained on two inputs...");

function run(input) {
  var sum = 0;

  for (var i=0; i < input.length; i++) {
    sum += input[i] * weights[i];
  }

  return activationFunction(sum, threshold);
}


// some inputs to try now that neural network is trained

// thinks it's an X
var tryme_1 = [
  -1, -1, -1, -1,  1,
   1,  1, -1,  1, -1,
  -1, -1,  1, -1, -1,
  -1,  1, -1,  1, -1,
   1, -1, -1, -1,  1
];

// Doesn't think it's an X
var tryme_2 = [
   1, -1, -1, -1,  1,
  -1,  1,  1,  1, -1,
  -1,  1,  1,  1, -1,
  -1,  1, -1,  1, -1,
  -1, -1, -1, -1, -1
];

console.log("Running on input tryme_1...", run(tryme_1), " (thinks it's an X)");
console.log("Running on input tryme_2...", run(tryme_2), " (doesn't think it's an x)");