Improved_WGAN | Improved Training of Wasserstein GANs | Machine Learning library

by bgavran Python Version: Current License: No License

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kandi X-RAY | Improved_WGAN Summary

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

Implementation of the "Improved Training of Wasserstein GANs" paper in TensorFlow

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Support

Improved_WGAN has a low active ecosystem.

It has 18 star(s) with 6 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 378 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Improved_WGAN is current.

Quality

Improved_WGAN has 0 bugs and 0 code smells.

Security

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

Improved_WGAN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Improved_WGAN 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

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

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

Improved_WGAN saves you 155 person hours of effort in developing the same functionality from scratch.

It has 385 lines of code, 38 functions and 10 files.

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

Top functions reviewed by kandi - BETA

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

Runs a session
Generate summary
Return the elapsed time in seconds
Generate a fake random batch
Reads an image
Center a crop
Read an image

Get all kandi verified functions for this library.

Improved_WGAN Key Features

No Key Features are available at this moment for Improved_WGAN.

Improved_WGAN Examples and Code Snippets

No Code Snippets are available at this moment for Improved_WGAN.

Community Discussions

Trending Discussions on Improved_WGAN

Implementing gradient penalty loss with tensorflow 2

QUESTION

Implementing gradient penalty loss with tensorflow 2

Asked 2020-Apr-08 at 17:06

Good morning,

I am trying to implement the improved WGAN for 1D data as described on this paper: https://arxiv.org/pdf/1704.00028.pdf

It has been implemented as an example in the keras-contrib github: https://github.com/keras-team/keras-contrib/blob/master/examples/improved_wgan.py Nevertheless, this implementation of the gradient penalty loss is not working anymore with tf2. K.gradients() returns [None].

...

ANSWER

Answered 2020-Apr-08 at 17:06

If you do what is proposed in the UPDATE, tf will just ignore the loss function

With Tensorflow 2, it seems imposible to to this the old way. I finally change the code to adapt it to this way of creating models. What I suggest?

Create the gen/disc models with keras
Join them extending tf.keras.Model class like the WGAN of : https://github.com/timsainb/tensorflow2-generative-models

Source https://stackoverflow.com/questions/61058119

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

Vulnerabilities

No vulnerabilities reported

Install Improved_WGAN

You can download it from GitHub.
You can use Improved_WGAN 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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