const tf = require('tensorflow2');

// load mnist dataset.
const dataset = tf.keras.dataset.mnist();
// {
//   train: { x: [Getter], y: [Getter] },
//   test: { x: [Getter], y: [Getter] }
// }

// create model.
const model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten({
    input_shape: [28, 28]
  }),
  tf.keras.layers.Dense(128, {
    activation: 'relu'
  }),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
]);
model.summary();

// compile the model.
const loss_fn = tf.keras.losses.SparseCategoricalCrossentropy({ from_logits: true });
model.compile({
  optimizer: 'adam',
  loss: loss_fn,
  metrics: [ 'accuracy' ],
});

// train the model.
model.fit(dataset.train.x, dataset.train.y, { epochs: 5 });

// save the model
model.save('your-model.h5');