An example solving XOR problem with tfjs

tfjs-example.js

var tf = require("@tensorflow/tfjs");

//code modified from:
//https://medium.com/tensorflow/a-gentle-introduction-to-tensorflow-js-dba2e5257702

//define our inputs (combinations of 2 bits, represented as 0s and 1s)
//https://js.tensorflow.org/api/0.12.0/#tensor
var xs = tf.tensor([[0, 0], [0, 1], [1, 0], [1, 1]]);

//define our outputs (xor operation, a simple non-linear problem)
//[0, 0] -> [0], [0, 1] -> [1], etc
var ys = tf.tensor([[0], [1], [1], [0]]);

//create a "sequential" model
//https://js.tensorflow.org/api/0.12.0/#sequential
var model = tf.sequential();

//add a dense/"fully-connected" layer to the model that takes inputs of size 2,
//contains 8 neurons, and uses "tanh" activation function
//https://js.tensorflow.org/api/0.12.0/#layers.dense
//see https://js.tensorflow.org/api/0.12.0/#layers.activation for other activations
model.add(tf.layers.dense({units: 8, inputDim: 2, activation: 'tanh'}));

//add a second dense layer with 1 neuron and "sigmoid" activation
model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));

//compile our model using "adam" optimizer, binary crossentropy loss,
//and a learning rate of 0.1
//https://js.tensorflow.org/api/0.12.0/#class:Model
model.compile({optimizer: 'adam', loss: 'binaryCrossentropy', lr: 0.3});

//train the model for 5000 epochs (complete passes over the dataset),
//using a batch size of 4 (4 input/output pairs per iteration)
//https://towardsdatascience.com/epoch-vs-iterations-vs-batch-size-4dfb9c7ce9c9
model.fit(xs, ys, {
  batchSize: 4,
  epochs: 1000,
  callbacks: {
    //after every epoch, print the current loss
    onEpochEnd: async (epoch, log) => {
      console.log(`Epoch ${epoch}: loss = ${log.loss}`);
    }
  }
}).then(() =>
{
  //after training, predict answers for our original input data
  model.predict(xs).print();
  //output should be close to [[0], [1], [1], [0]]
})