Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/tensorflow/tfjs-examples

Examples built with TensorFlow.js
https://github.com/tensorflow/tfjs-examples

Last synced: 4 days ago
JSON representation

Examples built with TensorFlow.js

Awesome Lists containing this project

README 

        
# TensorFlow.js Examples



This repository contains a set of examples implemented in

[TensorFlow.js](http://js.tensorflow.org).



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](https://docs.npmjs.com/cli/npm) OR [Yarn](https://yarnpkg.com/en/)



## How to build an example

`cd` into the directory



If you are using `yarn`:



```sh

cd mnist-core

yarn

yarn watch

```



If you are using `npm`:

```sh

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](https://github.com/tensorflow/tfjs/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:



```sh

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`.