LSGAN | Chainer implementation of Least Squares GAN | Machine Learning library

by musyoku Python Version: Current License: No License

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

kandi X-RAY | LSGAN Summary

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

Chainer implementation of Least Squares GAN (LSGAN)

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Quality

Security

License

Reuse

Support

LSGAN has a low active ecosystem.

It has 52 star(s) with 16 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

LSGAN has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of LSGAN is current.

Quality

LSGAN has 0 bugs and 135 code smells.

Security

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

LSGAN code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

LSGAN 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

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

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

LSGAN saves you 1211 person hours of effort in developing the same functionality from scratch.

It has 2728 lines of code, 241 functions and 34 files.

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

Top functions reviewed by kandi - BETA

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

Run the method
Tile binary images
Tile RGB images
Backward computation
Convert x into a matrix
Compute the required padding for convolutional layers
Calculate padding
Calculate the size of a convolution
Get the padding for the decoder
Plot kernel density
Perform the forward computation
Plot a scatter plot
Performs the forward computation
Implements the GPU
Forward computation
Sample from images
Plots the original image
Plots samples for a given epoch
Plot the KDE density
Adds a layer to the layer
Plot binary images
Sample data from images
Generate a Gaussian mixture of a Gaussian distribution
Compute the size of a convolutional layer
Returns the W data
Get the W matrix W
Load MNIST images

Get all kandi verified functions for this library.

LSGAN Key Features

No Key Features are available at this moment for LSGAN.

LSGAN Examples and Code Snippets

No Code Snippets are available at this moment for LSGAN.

Community Discussions

Trending Discussions on LSGAN

pytorch cyclegann gives a Missing key error when testing

QUESTION

pytorch cyclegann gives a Missing key error when testing

Asked 2021-May-26 at 11:04

I have trained a model using the pix2pix pytorch implementation and would like to test it.

However when I test it I get the error

...

ANSWER

Answered 2021-May-26 at 11:04

I think the problem here is some layer the bias=None but in testing the model required this, you should check the code for details.

After I check your config in train and test, the norm is different. For the code in GitHub, the norm difference may set the bias term is True or False.

Source https://stackoverflow.com/questions/67701029

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

Vulnerabilities

No vulnerabilities reported

Install LSGAN

You can download it from GitHub.
You can use LSGAN 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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