TensorFlow.js Examples

This repository contains a set of examples implemented in TensorFlow.js.

Each example directory is standalone so the directory can be copied to another project.

Overview of Examples

Example name

Demo link

Input data type

Task type

Model type

Training

Inference

API type

Save-load operations

abalone-node

Numeric

Loading data from local file and training in Node.js

Multilayer perceptron

Node.js

Node.js

Layers

Saving to filesystem and loading in Node.js

addition-rnn

🔗

Text

Sequence-to-sequence

RNN: SimpleRNN, GRU and LSTM

Browser

Browser

Layers

addition-rnn-webworker

Text

Sequence-to-sequence

RNN: SimpleRNN, GRU and LSTM

Browser: Web Worker

Browser: Web Worker

Layers

angular-predictive-prefetching

Numeric

Multiclass predictor

DNN

Browser: Service Worker

Layers

baseball-node

Numeric

Multiclass classification

Multilayer perceptron

Node.js

Node.js

Layers

boston-housing

🔗

Numeric

Regression

Multilayer perceptron

Browser

Browser

Layers

cart-pole

🔗

Reinforcement learning

Policy gradient

Browser

Browser

Layers

IndexedDB

chrome-extension

Image

(Deploying TF.js in Chrome extension)

Convnet

Browser

custom-layer

🔗

(Defining a custom Layer subtype)

Browser

Layers

data-csv

🔗

Building a tf.data.Dataset from a remote CSV

data-generator

🔗

Building a tf.data.Dataset using a generator

Regression

Browser

Browser

Layers

date-conversion-attention

🔗

Text

Text-to-text conversion

Attention mechanism, RNN

Node.js

Browser and Node.js

Layers

Saving to filesystem and loading in browser

electron

Image

(Deploying TF.js in Electron-based desktop apps)

Convnet

Node.js

fashion-mnist-vae

Image

Generative

Variational autoencoder (VAE)

Node.js

Browser

Layers

Export trained model from tfjs-node and load it in browser

interactive-visualizers

Image

Multiclass classification, object detection, segmentation

Browser

iris

🔗

Numeric

Multiclass classification

Multilayer perceptron

Browser

Browser

Layers

iris-fitDataset

🔗

Numeric

Multiclass classification

Multilayer perceptron

Browser

Browser

Layers

jena-weather

🔗

Sequence

Sequence-to-prediction

MLP and RNNs

Browser and Node

Browser

Layers

lstm-text-generation

🔗

Text

Sequence prediction

RNN: LSTM

Browser

Browser

Layers

IndexedDB

mnist

🔗

Image

Multiclass classification

Convolutional neural network

Browser

Browser

Layers

mnist-acgan

🔗

Image

Generative Adversarial Network (GAN)

Convolutional neural network; GAN

Node.js

Browser

Layers

Saving to filesystem from Node.js and loading it in the browser

mnist-core

🔗

Image

Multiclass classification

Convolutional neural network

Browser

Browser

Core (Ops)

mnist-node

Image

Multiclass classification

Convolutional neural network

Node.js

Node.js

Layers

Saving to filesystem

mnist-transfer-cnn

🔗

Image

Multiclass classification (transfer learning)

Convolutional neural network

Browser

Browser

Layers

Loading pretrained model

mobilenet

🔗

Image

Multiclass classification

Convolutional neural network

Browser

Layers

Loading pretrained model

polynomial-regression

🔗

Numeric

Regression

Shallow neural network

Browser

Browser

Layers

polynomial-regression-core

🔗

Numeric

Regression

Shallow neural network

Browser

Browser

Core (Ops)

quantization

Various

Demonstrates the effect of post-training weight quantization

Various

Node.js

Node.js

Layers

sentiment

🔗

Text

Sequence-to-binary-prediction

LSTM, 1D convnet

Node.js or Python

Browser

Layers

Load model from Keras and tfjs-node

simple-object-detection

🔗

Image

Object detection

Convolutional neural network (transfer learning)

Node.js

Browser

Layers

Export trained model from tfjs-node and load it in browser

snake-dqn

🔗

Reinforcement learning

Deep Q-Network (DQN)

Node.js

Browser

Layers

Export trained model from tfjs-node and load it in browser

translation

🔗

Text

Sequence-to-sequence

LSTM encoder and decoder

Node.js or Python

Browser

Layers

Load model converted from Keras

tsne-mnist-canvas

Dimension reduction and data visualization

tSNE

Browser

Browser

Core (Ops)

webcam-transfer-learning

🔗

Image

Multiclass classification (transfer learning)

Convolutional neural network

Browser

Browser

Layers

Loading pretrained model

website-phishing

🔗

Numeric

Binary classification

Multilayer perceptron

Browser

Browser

Layers

Dependencies

Except for getting_started, all the examples require the following dependencies to be installed.

• Node.js version 8.9 or higher
• NPM cli OR Yarn

How to build an example

cd into the directory

If you are using yarn:

cd mnist-core
yarn
yarn watch

If you are using npm:

cd mnist-core
npm install
npm run watch

Details

The convention is that each example contains two scripts:

• yarn watch or npm run watch: starts a local development HTTP server which watches the filesystem for changes so you can edit the code (JS or HTML) and see changes when you refresh the page immediately.

• yarn build or npm run build: generates a dist/ folder which contains the build artifacts and can be used for deployment.

Contributing

If you want to contribute an example, please reach out to us on Github issues before sending us a pull request as we are trying to keep this set of examples small and highly curated.

Running Presubmit Tests

Before you send a pull request, it is a good idea to run the presubmit tests and make sure they all pass. To do that, execute the following commands in the root directory of tfjs-examples:

yarn
yarn presubmit

The yarn presubmit command executes the unit tests and lint checks of all the exapmles that contain the yarn test and/or yarn lint scripts. You may also run the tests for individual exampls by cd'ing into their respective subdirectory and executing yarn, followed by yarn test and/or yarn lint.