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Simple APIs to load and prepare data for use in machine learning models
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README.md

TensorFlow.js Data

This repo is under active development and is not production-ready. We are actively developing as an open source project.

TensorFlow.js Data provides simple APIs to load and parse data from disk or over the web in a variety of formats, and to prepare that data for use in machine learning models (e.g. via operations like filter, map, shuffle, and batch).

This project is the JavaScript analogue of tf.data on the Python/C++ side. TF.js Data will match the tf.data API to the extent possible.

Importing

There are two ways to import TensorFlow.js Data

  1. You can access TensorFlow.js Data through the union package: @tensorflow/tfjs
  2. You can get TensorFlow.js Data as a module: @tensorflow/tfjs-data. Note that tfjs-data has peer dependency on tfjs-core, so if you import @tensorflow/tfjs-data, you also need to import @tensorflow/tfjs-core.

Sample Usage

Reading a CSV file

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

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: 10,
    callbacks: {
      onEpochEnd: async (epoch, logs) => {
        console.log(epoch, logs.loss);
      }
    }
  });
}

run().then(() => console.log('Done'));

For more information

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