tfjs

授权协议 Apache-2.0 License
开发语言 Python
所属分类 神经网络/人工智能、 机器学习/深度学习
软件类型 开源软件
地区 不详
投 递 者 慕容安易
操作系统 跨平台
开源组织
适用人群 未知
 软件概览

TensorFlow.js

TensorFlow.js is an open-source hardware-accelerated JavaScript library fortraining and deploying machine learning models.

Develop ML in the Browser
Use flexible and intuitive APIs to build models from scratch using the low-levelJavaScript linear algebra library or the high-level layers API.

Develop ML in Node.js
Execute native TensorFlow with the same TensorFlow.js API under the Node.jsruntime.

Run Existing models
Use TensorFlow.js model converters to run pre-existing TensorFlow models rightin the browser.

Retrain Existing models
Retrain pre-existing ML models using sensor data connected to the browser orother client-side data.

About this repo

This repository contains the logic and scripts that combineseveral packages.

APIs:

Backends/Platforms:

If you care about bundle size, you can import those packages individually.

If you are looking for Node.js support, check out the TensorFlow.js Node directory.

Examples

Check out ourexamples repositoryand our tutorials.

Gallery

Be sure to check out the gallery of all projects related to TensorFlow.js.

Pre-trained models

Be sure to also check out our models repository where we host pre-trained modelson NPM.

Benchmarks

  • Local benchmark tool. Use this webpage tool to collect the performance related metrics (speed, memory, etc) of TensorFlow.js models and kernels on your local device with CPU, WebGL or WASM backends. You can benchmark custom models by following this guide.
  • Multi-device benchmark tool. Use this tool to collect the same performance related metrics on a collection of remote devices.

Getting started

There are two main ways to get TensorFlow.js in your JavaScript project:via script tags or by installing it from NPMand using a build tool like Parcel,WebPack, or Rollup.

via Script Tag

Add the following code to an HTML file:

<html>
  <head>
    <!-- Load TensorFlow.js -->
    <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>


    <!-- Place your code in the script tag below. You can also use an external .js file -->
    <script>
      // Notice there is no 'import' statement. 'tf' is available on the index-page
      // because of the script tag above.

      // Define a model for linear regression.
      const model = tf.sequential();
      model.add(tf.layers.dense({units: 1, inputShape: [1]}));

      // Prepare the model for training: Specify the loss and the optimizer.
      model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

      // Generate some synthetic data for training.
      const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
      const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

      // Train the model using the data.
      model.fit(xs, ys).then(() => {
        // Use the model to do inference on a data point the model hasn't seen before:
        // Open the browser devtools to see the output
        model.predict(tf.tensor2d([5], [1, 1])).print();
      });
    </script>
  </head>

  <body>
  </body>
</html>

Open up that HTML file in your browser, and the code should run!

via NPM

Add TensorFlow.js to your project using yarn or npm. Note: Becausewe use ES2017 syntax (such as import), this workflow assumes you are using a modern browser or a bundler/transpilerto convert your code to something older browsers understand. See ourexamplesto see how we use Parcel to buildour code. However, you are free to use any build tool that you prefer.

import * as tf from '@tensorflow/tfjs';

// Define a model for linear regression.
const model = tf.sequential();
model.add(tf.layers.dense({units: 1, inputShape: [1]}));

// Prepare the model for training: Specify the loss and the optimizer.
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

// Generate some synthetic data for training.
const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

// Train the model using the data.
model.fit(xs, ys).then(() => {
  // Use the model to do inference on a data point the model hasn't seen before:
  model.predict(tf.tensor2d([5], [1, 1])).print();
});

See our tutorials, examplesand documentation for more details.

Importing pre-trained models

We support porting pre-trained models from:

Various ops supported in different backends

Please refer below :

Find out more

TensorFlow.js is a part of theTensorFlow ecosystem. For more info:

Thanks, BrowserStack, for providing testing support.

  • 原文链接: tfjs 内存管理 tidy 上一篇: python acm 常见输入输出 下一篇: python acm 代码片段 因为TensorFlow.js使用了GPU来加速数学运算,因此当tensorflow处理张量和变量时就有必要来管理GPU内存。在TensorFlow.js中,我们可以通过dispose 和 tf.tidy这两种方法来管理内存。 dispose 您可以在张量或变量上调用d

  • 开始 年前的时候就有这个想法,想做一个能够人脸识别和物体识别,并且能简单对话识别指令的助手,类似小爱同学离线增强版,顺便能监控自己的小屋。 不过年底太忙根本没有时间精力去折腾,想着年初再搞,谁知道来了个疫情,突然多出那么多空闲时间,可惜树莓派还没来得及买,浪费了大把时间。 复工后中间还出了次差,这又快到年底了终于克服懒癌晚期,把基本的功能实现出来。 这里写下来做个记录,毕竟年级大了记性不太好。 树

 相关资料
  • This repository has been archived in favor of tensorflow/tfjs. This repo will remain around for some time to keep history but all future PRs should be sent to tensorflow/tfjs inside the tfjs-core fold

  • TFJS in NativeScript This is a POC to show that it is possible to useTFJS with NativeScript To run, make sure you have NativeScript environment setup on yourcomputer, https://docs.nativescript.org/sta

  • tfjs-model-view是一个用于浏览器可视化神经网络的库,旨在与TensorFlow.js一起使用。 特征: 自动渲染神经网络 自动更新权重/偏差/值 不同的渲染方法:canvas(默认),d3 该库还旨在提供灵活性,使您可以轻松融入你的应用。 演示: Movielens recommendation using Tensorflow.js Iris Prediction with Cus

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