Tensorflow.js NN-model

nn.js

// This is the model
const model = tf.sequential();

// Create hidden layer
const hiddens = tf.layers.dense({
  units: 4,
  inputShape: [2],
  activation: 'sigmoid'
});

// Add the hidden layer
model.add(hiddens);

// Create another layer
const outputs = tf.layers.dense({
  units: 1,
  activation: 'sigmoid'
});
model.add(outputs);

// Train it with gradient descent
model.complile({
  optimizer: tf.train.sgd(0.1),
  loss: tf.losses.meanSquaredError
});

const xs = tf.tensor2d([
  [0, 0],
  [0.5, 0.5],
  [1, 1]
]);

const ys = tf.tensor2d([
  [1],
  [0.5],
  [0]
]);

// Learning (or training if you prefer)
async function train(depth) {
  for (var i = 0; i < depth; i++) {
    const response = await model.fit(xs, ys, {epochs: 10, shiffle: true});
    console.log(response.history.loss[0]);
  }
}

// Predict presumably new data
train(100).then(() => {
  console.log('training complete!');
  model.predict(xs).print();
});