# tfjs-examples **Repository Path**: edencfc/tfjs-examples ## Basic Information - **Project Name**: tfjs-examples - **Description**: Examples built with TensorFlow.js 镜像加速 - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2019-07-15 - **Last Updated**: 2020-12-16 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## 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
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
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`.