GraphTR | Graph Neural Network for Tag Ranking | Machine Learning library

by lqfarmer Python Version: Current License: No License

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

kandi X-RAY | GraphTR Summary

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

Graph Neural Network for Tag Ranking in Tag-enhanced Video Recommendation（CIKM-2020) Qi Liu*, Ruobing Xie*, Lei Chen, Shukai Liu, Ke Tu, Peng Cui, Bo Zhang and Leyu Lin.

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GraphTR has a low active ecosystem.

It has 50 star(s) with 9 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 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of GraphTR is current.

Quality

GraphTR has no bugs reported.

Security

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

License

GraphTR 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

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

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

Top functions reviewed by kandi - BETA

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

Train the network
Construct placeholders
Restore the model
Save the model
Calculate the graph
Reshape the last two dimensions
Reshape x into n dimensions
Connects the model
Builds the graph
Builds the convolution layer
Compute the accuracy
Computes the cost of the objective function
Compute the loss between two inputs
Compute the hee loss
Compute the loss of the skipgram loss

Get all kandi verified functions for this library.

GraphTR Key Features

No Key Features are available at this moment for GraphTR.

GraphTR Examples and Code Snippets

No Code Snippets are available at this moment for GraphTR.

Community Discussions

Trending Discussions on GraphTR

Wrap Complex C++ Class using the Python Extension API

QUESTION

Wrap Complex C++ Class using the Python Extension API

Asked 2020-Feb-18 at 16:29

I'm pretty new to creating C++ class that I can use from within Python. I've skimmed through a lot of posts on the internet. Be it on StackOverflow, gist, github, ... I've also read the documentation, but I'm not sure how I can solve my issue.

Basically, the idea is to do this: http://www.speedupcode.com/c-class-in-python3/ As I want to avoid the burden of creating my own python newtype, I thought that using PyCapsule_New and PyCapsule_GetPointer as in the example above could be a workaround, but maybe I'm misleading, and I still need to create complex datatype.

Here is the header of my class I want to be able to call from python:

...

ANSWER

Answered 2020-Feb-18 at 16:29

I answer my own question.

I actually found the flaw in my code.

Both functions PyCapsule_GetPointer and PyCapsule_New work perfectly fine. As mentioned in my question, the issue arouse just after I was trying to Parse the capsule with the following code:

Source https://stackoverflow.com/questions/60205277

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

Vulnerabilities

No vulnerabilities reported

Install GraphTR

You can download it from GitHub.
You can use GraphTR 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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