disentangled_vae | Replicating "Understanding disentangling in β-VAE" | Dataset library

by miyosuda Python Version: Current License: Non-SPDX

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

kandi X-RAY | disentangled_vae Summary

disentangled_vae is a Python library typically used in Artificial Intelligence, Dataset applications. disentangled_vae has no bugs, it has no vulnerabilities and it has low support. However disentangled_vae build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Replicating "Understanding disentangling in β-VAE"

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Quality

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Support

disentangled_vae has a low active ecosystem.

It has 189 star(s) with 37 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 2 have been closed. On average issues are closed in 14 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of disentangled_vae is current.

Quality

disentangled_vae has 0 bugs and 0 code smells.

Security

disentangled_vae has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

disentangled_vae code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

disentangled_vae has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

disentangled_vae releases are not available. You will need to build from source code and install.

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

disentangled_vae saves you 164 person hours of effort in developing the same functionality from scratch.

It has 408 lines of code, 42 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Train model
Reconstruct the VAE output
Check disentangle
Transform the model
Get an image
Generate the model
Calculate encoding capacity
Get a list of images
Check for reconstructing images
Perform a partial fit
Create the network
Calculate the output size of deconv2d
Samples z_sigma
Creates the network
Create the generator network
Creates a weight variable
Convolution layer
Check for disentangle
Loads the dataset
Loads the old checkpoint

Get all kandi verified functions for this library.

disentangled_vae Key Features

No Key Features are available at this moment for disentangled_vae.

disentangled_vae Examples and Code Snippets

No Code Snippets are available at this moment for disentangled_vae.

Community Discussions

Trending Discussions on disentangled_vae

Beta Variational AutoEncoders

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 disentangled_vae

You can download it from GitHub.
You can use disentangled_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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