npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@tensorflow/tfjs-data

v4.22.0

Published

TensorFlow Data API in JavaScript

Downloads

460,251

Readme

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.

To keep track of issues we use the tensorflow/tfjs Github repo with comp:data tag.

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