Regularized_autoencoders-RAE-

by ParthaEth Python Version: Current License: GPL-3.0

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kandi X-RAY | Regularized_autoencoders-RAE- Summary

Regularized_autoencoders-RAE- is a Python library. Regularized_autoencoders-RAE- has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

Regularized_autoencoders-RAE-

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Quality

Security

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Regularized_autoencoders-RAE- has a low active ecosystem.

It has 96 star(s) with 11 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. There are 8 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Regularized_autoencoders-RAE- is current.

Quality

Regularized_autoencoders-RAE- has no bugs reported.

Security

Regularized_autoencoders-RAE- has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Regularized_autoencoders-RAE- is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

Regularized_autoencoders-RAE- 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.

Top functions reviewed by kandi - BETA

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

Processes a directory
Load image
Generate interpolation rows
Generate random image
Load data loader
Get a generator from the given directory
Creates an iterator for the given generator generator
Get an iterator from a numpy array
Retrieve the tfid for a given dataset
Calculate the activation statistics for each image
Calculate fid value for given paths
Stores training images
Compute the z - variance covariance matrix
Get VAE_Cifar_wae
Calculate fid - value for given paths
Solve the polynomial
Builds a VAE_Cifar
Calculate the total loss function
Get WAE_MNISTAN
Wrapper for VAE
Wrapper for celeba
Embed images in inception
Calculate the max f_beta
Compute precision recall from embedding
Compute the z - variance tensor
Loads the model
Calculate out the output from a given convolution
Download inception model
Calculate the activation statistics

Get all kandi verified functions for this library.

Regularized_autoencoders-RAE- Key Features

No Key Features are available at this moment for Regularized_autoencoders-RAE-.

Regularized_autoencoders-RAE- Examples and Code Snippets

No Code Snippets are available at this moment for Regularized_autoencoders-RAE-.

Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install Regularized_autoencoders-RAE-

Create a virtual environment virtualenv --no-site-packages <your_home_dir>/.virtualenvs/rae
Activate your environment source <your_home_dir>/.virtualenvs/rae/bin/activate
clone the repo git clone ...
Navigate to RAE directory cd Regularized_autoencoders-RAE-
Install requirements pip install -r requirements.txt
Run training python train_test_var_reduced_vaes.py <config_id>

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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