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README.md

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
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.

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.