xor_ops.js

//this example was tested in electron; if anyone knows how to get around
//this silliness when using nodejs/electron (without including the .js file in
//html, please let me know)
var tfCore = require("@tensorflow/tfjs-core");
var tfLayers = require("@tensorflow/tfjs-layers");
var tf = Object.assign({}, tfCore, tfLayers);

var xs = tf.tensor([[0, 0], [0, 1], [1, 0], [1, 1]]);
var ys = tf.tensor([[0], [1], [1], [0]]);

var hiddenSize = 10;

var hiddenWeights = tf.variable(tf.randomNormal([2, hiddenSize]), true);
var hiddenBias = tf.variable(tf.zeros([hiddenSize]), true);
var hiddenActivation = tf.relu;

var outputWeights = tf.variable(tf.randomNormal([hiddenSize, 1]), true);
var outputBias = tf.variable(tf.zeros([1]), true);
var outputActivation = tf.tanh;

function apply(x)
{
  x = x.dot(hiddenWeights);
  x = x.add(hiddenBias);
  x = hiddenActivation(x);

  x = x.dot(outputWeights);
  x = x.add(outputBias);
  x = outputActivation(x);

  return x;
}

var opt = tf.train.adam(0.01);
var epochs = 1000;

for(var i = 0; i < epochs; i++)
{
  var loss = opt.minimize(() =>
  {
    var pred = apply(xs);

    return tf.losses.meanSquaredError(ys, pred);
  }, true).dataSync()[0];

  console.log(loss);
}

console.log(apply(xs).dataSync());

basically-no-cruft XOR solution in tfjs ops api