TF.js

import * as tf from '@tensorflow/tfjs';

// A sequential model is a container which you can add layers to.
const model = tf.sequential();

// Add a dense layer with 1 output unit.
model.add(tf.layers.dense({units: 10,
                           inputShape: [1],
                           activation='relu'
                          }));

model.add(tf.layers.dense({units: 5,
                           activation='relu'
                          }));

model.add(tf.layers.dense({units: 1,
                           activation='softmax'
                          }));

// Specify the loss type and optimizer for training.
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

// Generate some synthetic data for training.
const xs = tf.tensor2d([[1], [2], [3], [4]], [4, 1]);
const ys = tf.tensor2d([[1], [3], [5], [7]], [4, 1]);

// Train the model.
await model.fit(xs, ys, {epochs: 500});

// After the training, perform inference.
const pred = model.predict(tf.tensor2d([[5]], [1, 1]));
pred.print();