neurips2019_disentanglement_challenge_starter_kit | Starter Kit for the NeurIPS 2019 Disentanglement

by AIcrowd Python Version: Current License: No License

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

kandi X-RAY | neurips2019_disentanglement_challenge_starter_kit Summary

neurips2019_disentanglement_challenge_starter_kit is a Python library. neurips2019_disentanglement_challenge_starter_kit has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Starter Kit for the NeurIPS 2019 Disentanglement Challenge

Support

Quality

Security

License

Reuse

Support

neurips2019_disentanglement_challenge_starter_kit has a low active ecosystem.

It has 33 star(s) with 8 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of neurips2019_disentanglement_challenge_starter_kit is current.

Quality

neurips2019_disentanglement_challenge_starter_kit has 0 bugs and 0 code smells.

Security

neurips2019_disentanglement_challenge_starter_kit has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

neurips2019_disentanglement_challenge_starter_kit code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

neurips2019_disentanglement_challenge_starter_kit 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

neurips2019_disentanglement_challenge_starter_kit 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, examples and code snippets are available.

neurips2019_disentanglement_challenge_starter_kit saves you 251 person hours of effort in developing the same functionality from scratch.

It has 610 lines of code, 46 functions and 8 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed neurips2019_disentanglement_challenge_starter_kit and discovered the below as its top functions. This is intended to give you an instant insight into neurips2019_disentanglement_challenge_starter_kit implemented functionality, and help decide if they suit your requirements.

Exports the given model
Return the path to the model
Get data loader
Export function to file
Return the path to the function
Evaluate a model using gin
Evaluate a model
Import function
Evaluate score with numpy
Train the model
Compute the loss function
Get dataset
Register training progress
This function is used to submit an event
Registration event handler
Forward the encoder function
Reparameterize the prior distribution
Test loader
Get a DataLoader for a dataset
Compute the decoder

Get all kandi verified functions for this library.

neurips2019_disentanglement_challenge_starter_kit Key Features

No Key Features are available at this moment for neurips2019_disentanglement_challenge_starter_kit.

neurips2019_disentanglement_challenge_starter_kit Examples and Code Snippets

No Code Snippets are available at this moment for neurips2019_disentanglement_challenge_starter_kit.

Community Discussions

No Community Discussions are available at this moment for neurips2019_disentanglement_challenge_starter_kit.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install neurips2019_disentanglement_challenge_starter_kit

Create your new conda environment. Your code specific dependencies.
Anaconda (By following instructions here) At least version 4.5.11 is required to correctly populate environment.yml.
Create your new conda environment
Your code specific dependencies
Follow the instructions here to download the publicly available datasets, and store them inside ./scratch/datasets folder. Please ensure that the datasets are not checked into the repository.

Support

Please find a list of frequently asked questions here, and please feel free to send a pull request with more questions that you think can help other participants.

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