PyTorch-VAE | A Collection of Variational Autoencoders in PyTorch | Machine Learning library

by AntixK Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | PyTorch-VAE Summary

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

A Collection of Variational Autoencoders (VAE) in PyTorch.

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Quality

Security

License

Reuse

Support

PyTorch-VAE has a medium active ecosystem.

It has 4806 star(s) with 885 fork(s). There are 44 watchers for this library.

It had no major release in the last 6 months.

There are 37 open issues and 27 have been closed. On average issues are closed in 93 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of PyTorch-VAE is current.

Quality

PyTorch-VAE has 0 bugs and 0 code smells.

Security

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

PyTorch-VAE code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PyTorch-VAE is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

PyTorch-VAE 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.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Compute the similarity between two images
Compute the Sigma between two images
Create a new 2D window
Generate a Gaussian window
Compute the loss function
Calculate the log density of a gaussian distribution
Compute the loss function
Permute latent variables
Calculate the loss function
Calculate the loss of the VAE
Generate random samples from the model
Draw random samples
Draw num_samples samples from the model
Sample from the Gamma distribution
Sample num_samples
Sample num_samples from the model
Sample from the latent dimensions
Generate random samples from the latent dimension
Performs a training step
Compute loss function
Generate random samples
Sample num_samples samples
Setup the training dataset
Sample from the model
Sample from the latent space
Compute loss function

Get all kandi verified functions for this library.

PyTorch-VAE Key Features

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

PyTorch-VAE Examples and Code Snippets

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

Community Discussions

Trending Discussions on PyTorch-VAE

Loading model from checkpoint is not working

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

QUESTION

Loading model from checkpoint is not working

Asked 2020-Dec-03 at 02:22

I trained a vanilla vae which I modified from this repository. When I try and use the trained model I am unable to load the weights using load_from_checkpoint. It seems there is a mismatch between my checkpoint object and my lightningModule object.

I have setup an experiment (VAEXperiment) using pytorch-lightning LightningModule. I try to load the weights into the network with:

...

ANSWER

Answered 2020-Aug-04 at 12:45

Posting the answer from comments:

Source https://stackoverflow.com/questions/63243359

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 PyTorch-VAE

You can download it from GitHub.
You can use PyTorch-VAE 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

If you have trained a better model, using these implementations, by fine-tuning the hyper-params in the config file, I would be happy to include your result (along with your config file) in this repo, citing your name 😊. Additionally, if you would like to contribute some models, please submit a PR.

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