Git Product home page Git Product logo

gumbel-softmax's Introduction

"categorical variational autoencoder using the Gumbel-Softmax estimator" 实现

基本流程

使用VAE结构. 在 Mnist 数据集,隐变量使用 Gumbel-softmax 进行采样. 损失函数使用 KL 损失 + Sigmoid重建损失.

重构可视化

左侧为原始图像,中间部分为 30*10 的隐变量,右侧为重构结果.

VAE

编码可视化

可视化 6000 张图片作为输入的 encoder 输出的编码,用T-SNE降维后的结果。 同一种颜色标志的为同类别的图片. 可以看出,编码的聚簇比较合理。

VAE

gumbel-softmax's People

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar

Forkers

luomuqinghan

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.