Beta-VAE | Pytorch implementation | Machine Learning library

by 1Konny Python Version: Current License: MIT

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

kandi X-RAY | Beta-VAE Summary

Beta-VAE is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch applications. Beta-VAE has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Beta-VAE build file is not available. You can download it from GitHub.

Pytorch implementation of β-VAE

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Quality

Security

License

Reuse

Support

Beta-VAE has a low active ecosystem.

It has 345 star(s) with 86 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 5 have been closed. On average issues are closed in 4 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Beta-VAE is current.

Quality

Beta-VAE has 0 bugs and 0 code smells.

Security

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

Beta-VAE code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Beta-VAE is licensed under the MIT License. This license is Permissive.

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

Reuse

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

Beta-VAE has no build file. You will be need to create the build yourself to build the component from source.

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

Beta-VAE saves you 280 person hours of effort in developing the same functionality from scratch.

It has 677 lines of code, 41 functions and 5 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Train the model
Return empty data dictionary
Return tensor tensor
Removes all data from the queue
Visualize the log lines
Convert grid to gif
Set the net mode
Insert new values
Wrapper for training images
Calculate reconstruction loss
Compute KL divergence
Save checkpoint to file
Traverse the dataset
Perform the forward computation
Reparametrize a Gaussian distribution
Return the decoded value
Encodes the given value
Compute the logarithm
Internal function to decode a string
Encodes x using encoder

Get all kandi verified functions for this library.

Beta-VAE Key Features

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

Beta-VAE Examples and Code Snippets

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

Community Discussions

Trending Discussions on Beta-VAE

How to run .py codes from github on jupyter lab?

Beta Variational AutoEncoders

QUESTION

How to run .py codes from github on jupyter lab?

Asked 2019-Jul-04 at 07:20

How can I run .py files from jupyter lab? I have spent my all coding life using jupyter notebook and jupyter lab but replication codes of research papers are mostly in .py file format

For instance, this is a github repository for beta variational autoencoder. As you can see from the repository, these kinds of repositories are usually comprised of main.py, model.py, which looks a lot different from .ipynb format that I usually use.

Can someone share how to comfortably run these kinds of .py codes from github on jupyter lab? I would appreciate it a lot if someone tells me a video or an article explaining how to run these .py codes on jupyter lab comfortably.

...

ANSWER

Answered 2019-Jun-30 at 14:34

Find File-> new launcher -> other -> terminal, then you use command line run your python file, like "python xxx.py"

Source https://stackoverflow.com/questions/56825494

QUESTION

Beta Variational AutoEncoders

Asked 2019-Jan-17 at 22:39

I have followed the variational autoencoders part in this tutorial. My first task in my project is to regenerate some vectors which represent how the grid layout is divided. So , I created my own dataset which contains at least 5000 rows of vectors of dimensions (1,36). Those vectors represent a 6 by 6 grid layouts. So I used some of the dataset as training set for my model which is the variational autoencoders. Then, since my project task requires that I use Disentangled VAE or Beta-VAE, I read some articles about this kind of VAE and figured that you just need to change the beta value.

So the code that I used is in this github link.

First, according to what I have read on the internet, when the beta value is superior to 1, we will have better construction results which is exactly the opposite of what I have found in my model.

Second, I have changed many hyperparameters in my model like the beta, the batch_size, number of epochs, the standard variation of the sampling vector but still I don't get a nice reconstruction of the data. I guess I am missing something in understanding this model but I couldn't figure what is it. Did I understand the beta-variational autoencoders right by writing this code ?

...

ANSWER

Answered 2019-Jan-17 at 22:39

The Beta term is for the KL term which is acting upon the prior and your variational approximation, the higher it is, the worse will be the reconstruction. So what you found makes sense.

Source https://stackoverflow.com/questions/51746327

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

Vulnerabilities

No vulnerabilities reported

Install Beta-VAE

You can download it from GitHub.
You can use Beta-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

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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