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.
Example name | Demo link | Input data type | Task type | Model type | Training | Inference | API type | Save-load operations |
---|---|---|---|---|---|---|---|---|
addition-rnn | ๐ | Text | Sequence-to-sequence | RNN: SimpleRNN, GRU and LSTM | Browser | Browser | Layers | |
baseball-node | Numeric | Multiclass classification | Multilayer perceptron | Node.js | Node.js | Layers | ||
boston-housing | ๐ | Numeric | Regression | Multilayer perceptron | Browser | Browser | Layers | |
cart-pole | ๐ | Reinforcement learning | Policy gradient | Browser | Browser | Layers | IndexedDB | |
chrome-extension | Image | (Deploying TF.js in Chrome extension) | Convnet | Browser | ||||
custom-layer | ๐ | (Defining a custom Layer subtype) | Browser | Layers | ||||
data-csv | ๐ | Building a tf.data.Dataset from a remote CSV | ||||||
data-generator | ๐ | Building a tf.data.Dataset using a generator | Regression | Browser | Browser | Layers | ||
date-conversion-attention | ๐ | Text | Text-to-text conversion | Attention mechanism, RNN | Node.js | Browser and Node.js | Layers | Saving to filesystem and loading in browser |
fashion-mnist-vae | Image | Generative | Variational autoencoder (VAE) | Node.js | Browser | Layers | Export trained model from tfjs-node and load it in browser | |
iris | ๐ | Numeric | Multiclass classification | Multilayer perceptron | Browser | Browser | Layers | |
iris-fitDataset | ๐ | Numeric | Multiclass classification | Multilayer perceptron | Browser | Browser | Layers | |
jena-weather | ๐ | Sequence | Sequence-to-prediction | MLP and RNNs | Browser and Node | Browser | Layers | |
lstm-text-generation | ๐ | Text | Sequence prediction | RNN: LSTM | Browser | Browser | Layers | IndexedDB |
mnist | ๐ | Image | Multiclass classification | Convolutional neural network | Browser | Browser | Layers | |
mnist-acgan | ๐ | 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 | ๐ | 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 | ๐ | Image | Multiclass classification (transfer learning) | Convolutional neural network | Browser | Browser | Layers | Loading pretrained model |
mobilenet | ๐ | Image | Multiclass classification | Convolutional neural network | Browser | Layers | Loading pretrained model | |
polynomial-regression | ๐ | Numeric | Regression | Shallow neural network | Browser | Browser | Layers | |
polynomial-regression-core | ๐ | 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 | ๐ | Text | Sequence-to-binary-prediction | LSTM, 1D convnet | Node.js or Python | Browser | Layers | Load model from Keras and tfjs-node |
simple-object-detection | ๐ | 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 | ๐ | Reinforcement learning | Deep Q-Network (DQN) | Node.js | Browser | Layers | Export trained model from tfjs-node and load it in browser | |
translation | ๐ | 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 | ๐ | Image | Multiclass classification (transfer learning) | Convolutional neural network | Browser | Browser | Layers | Loading pretrained model |
website-phishing | ๐ | Numeric | Binary classification | Multilayer perceptron | Browser | Browser | Layers |
Except for getting_started
, all the examples require the following dependencies to be installed.
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
The convention is that each example contains two scripts:
-
yarn watch
ornpm 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
ornpm run build
: generates adist/
folder which contains the build artifacts and can be used for deployment.
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.
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
.