VAE-CVAE-MNIST | Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch | Machine Learning library

by timbmg Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | VAE-CVAE-MNIST Summary

VAE-CVAE-MNIST is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch applications. VAE-CVAE-MNIST has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch

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Quality

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VAE-CVAE-MNIST has a low active ecosystem.

It has 442 star(s) with 95 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 5 have been closed. On average issues are closed in 90 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of VAE-CVAE-MNIST is current.

Quality

VAE-CVAE-MNIST has 0 bugs and 0 code smells.

Security

VAE-CVAE-MNIST has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

VAE-CVAE-MNIST code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

VAE-CVAE-MNIST does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

VAE-CVAE-MNIST releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed VAE-CVAE-MNIST and discovered the below as its top functions. This is intended to give you an instant insight into VAE-CVAE-MNIST implemented functionality, and help decide if they suit your requirements.

Compute the MLP model
Convert idx to one - hot array
Apply encoder
Reparameterize the model
Compute the MLP
Infer the decoder

Get all kandi verified functions for this library.

VAE-CVAE-MNIST Key Features

No Key Features are available at this moment for VAE-CVAE-MNIST.

VAE-CVAE-MNIST Examples and Code Snippets

No Code Snippets are available at this moment for VAE-CVAE-MNIST.

Community Discussions

Trending Discussions on VAE-CVAE-MNIST

How is KL-divergence in pytorch code related to the formula?

QUESTION

How is KL-divergence in pytorch code related to the formula?

Asked 2020-May-04 at 20:57

In VAE tutorial, kl-divergence of two Normal Distributions is defined by:

And in many code, such as here, hereand here, the code is implemented as:

...

ANSWER

Answered 2020-May-04 at 20:57

The expressions in the code you posted assume X is an uncorrelated multi-variate Gaussian random variable. This is apparent by the lack of cross terms in the determinant of the covariance matrix. Therefore the mean vector and covariance matrix take the forms

Using this we can quickly derive the following equivalent representations for the components of the original expression

Substituting these back into the original expression gives

Source https://stackoverflow.com/questions/61597340

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install VAE-CVAE-MNIST

You can download it from GitHub.
You can use VAE-CVAE-MNIST like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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