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 |
|---|---|---|---|---|---|---|---|---|
| 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 |
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 watchIf you are using npm:
cd mnist-core
npm install
npm run watchThe convention is that each example contains two scripts:
-
yarn watchornpm 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 buildornpm 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 presubmitThe 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.
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