tsf.js

import * as tf from '@tensorflow/tfjs-node-gpu';

const csvUrl =
    'https://storage.googleapis.com/tfjs-examples/multivariate-linear-regression/data/boston-housing-train.csv';

async function run() {
    // We want to predict the column "medv", which represents a median value of
    // a home (in $1000s), so we mark it as a label.
    const csvDataset = tf.data.csv(
        csvUrl, {
        columnConfigs: {
            medv: {
                isLabel: true
            }
        }
    });

    // Number of features is the number of column names minus one for the label
    // column.
    const numOfFeatures = (await csvDataset.columnNames()).length - 1;

    // Prepare the Dataset for training.
    const flattenedDataset =
        csvDataset
            .map(({ xs, ys }) => {
                // Convert xs(features) and ys(labels) from object form (keyed by
                // column name) to array form.
                return { xs: Object.values(xs), ys: Object.values(ys) };
            })
            .batch(10);

    // Define the model.
    const model = tf.sequential();
    model.add(tf.layers.dense({
        inputShape: [numOfFeatures],
        units: 1
    }));
    model.compile({
        optimizer: tf.train.sgd(0.000001),
        loss: 'meanSquaredError'
    });

    // Fit the model using the prepared Dataset
    return model.fitDataset(flattenedDataset, {
        epochs: 30,
        callbacks: {
            onEpochEnd: async (epoch, logs) => {
                console.log(epoch + ':' + logs.loss);
            }
        }
    });
}

await run();