chainer-semi-supervised

by mattya Python Version: Current License: No License

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

kandi X-RAY | chainer-semi-supervised Summary

chainer-semi-supervised is a Python library. chainer-semi-supervised has no bugs, it has no vulnerabilities and it has low support. However chainer-semi-supervised build file is not available. You can download it from GitHub.

chainer-semi-supervised

Support

Quality

Security

License

Reuse

Support

chainer-semi-supervised has a low active ecosystem.

It has 18 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 1120 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of chainer-semi-supervised is current.

Quality

chainer-semi-supervised has 0 bugs and 0 code smells.

Security

chainer-semi-supervised has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

chainer-semi-supervised code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

chainer-semi-supervised 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

chainer-semi-supervised releases are not available. You will need to build from source code and install.

chainer-semi-supervised has no build file. You will be need to create the build yourself to build the component from source.

chainer-semi-supervised saves you 167 person hours of effort in developing the same functionality from scratch.

It has 415 lines of code, 29 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

load MNIST dataset
Get n random n points
Calculate the adversarial direction
Computes the loss of the loss of the loss function .
Wrapper function .
Calculate the loss of unlabeled loss .
Forward computation .
Evaluate the loss function .
Computes the KL divergence .
Compute the KL divergence between two points .

Get all kandi verified functions for this library.

chainer-semi-supervised Key Features

No Key Features are available at this moment for chainer-semi-supervised.

chainer-semi-supervised Examples and Code Snippets

No Code Snippets are available at this moment for chainer-semi-supervised.

Community Discussions

No Community Discussions are available at this moment for chainer-semi-supervised.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install chainer-semi-supervised

You can download it from GitHub.
You can use chainer-semi-supervised 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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