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-layers

v4.22.0

Published

TensorFlow layers API in JavaScript

Downloads

463,888

Readme

TensorFlow.js Layers: High-Level Machine Learning Model API

A part of the TensorFlow.js ecosystem, TensorFlow.js Layers is a high-level API built on TensorFlow.js Core, enabling users to build, train and execute deep learning models in the browser. TensorFlow.js Layers is modeled after Keras and tf.keras and can load models saved from those libraries.

Importing

There are three ways to import TensorFlow.js Layers

  1. You can access TensorFlow.js Layers through the union package between the TensorFlow.js Core and Layers: @tensorflow/tfjs
  2. You can get TensorFlow.js Layers as a module: @tensorflow/tfjs-layers. Note that tfjs-layers has peer dependency on tfjs-core, so if you import @tensorflow/tfjs-layers, you also need to import @tensorflow/tfjs-core.
  3. As a standalone through unpkg.

Option 1 is the most convenient, but leads to a larger bundle size (we will be adding more packages to it in the future). Use option 2 if you care about bundle size.

Getting started

Building, training and executing a model

The following example shows how to build a toy model with only one dense layer to perform linear regression.

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: 1, inputShape: [1]}));

// 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 output = model.predict(tf.tensor2d([[5]], [1, 1]));
output.print();

Loading a pretrained Keras model

You can also load a model previously trained and saved from elsewhere (e.g., from Python Keras) and use it for inference or transfer learning in the browser.

For example, in Python, save your Keras model using tensorflowjs, which can be installed using pip install tensorflowjs.

import tensorflowjs as tfjs

# ... Create and train your Keras model.

# Save your Keras model in TensorFlow.js format.
tfjs.converters.save_keras_model(model, '/path/to/tfjs_artifacts/')

# Then use your favorite web server to serve the directory at a URL, say
#   http://foo.bar/tfjs_artifacts/model.json

To load the model with TensorFlow.js Layers:

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

const model = await tf.loadLayersModel('http://foo.bar/tfjs_artifacts/model.json');
// Now the model is ready for inference, evaluation or re-training.

For more information