GCNN | Pytorch Implementation of GCNN model | Machine Learning library

by YingtongDou Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | GCNN Summary

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

Update: Please check out more GNN-based fake news detection models along with two real-world datasets in A collection of GNN-based fake news detectors with fake news propagation datasets.

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Quality

Security

License

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Support

GCNN has a low active ecosystem.

It has 17 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of GCNN is current.

Quality

GCNN has no bugs reported.

Security

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

License

GCNN is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

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

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Compute the accuracy of the logits
Compute test loss
Evaluate the F1 score

Get all kandi verified functions for this library.

GCNN Key Features

No Key Features are available at this moment for GCNN.

GCNN Examples and Code Snippets

No Code Snippets are available at this moment for GCNN.

Community Discussions

Trending Discussions on GCNN

Keras Custom Layer: ValueError: An operation has `None` for gradient

Execute SQL Database Backup from Access Front-End

QUESTION

Keras Custom Layer: ValueError: An operation has `None` for gradient

Asked 2019-Mar-30 at 12:37

I am trying to implement Graph Convolution Layer using Keras custom layer that is mentioned in the following paper: GCNN.

When I am trying to train my model, It gives me the following error:

...

ANSWER

Answered 2019-Mar-18 at 08:31

As error states, some of your function is non-differentiable. It' not easy to say why exactly it happens. For example, take a look

List of Differentiable Ops in Tensorflow

How to make sure your computation graph is differentiable

Edit: Consider example, where I use standard cifar10 data.

Source https://stackoverflow.com/questions/55181044

QUESTION

Execute SQL Database Backup from Access Front-End

Asked 2017-Jan-08 at 18:26

I have created a script to back up a specific database in SQL. I have tested it and it works fine when executed in SQL.

I want to be able to execute this script using Access Front-End to enable an end user to run a back-up when they require.

The query seems to be timing out and I've tried multiple different methods in executing the query.

This is my script to execute a backup.

...

ANSWER

Answered 2017-Jan-08 at 18:26

The most simple code and approach is to create a pass-through query in Access. That way you don’t mess with connection stings etc. in code.

The VBA code to do this would be:

Source https://stackoverflow.com/questions/41506641

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

Vulnerabilities

No vulnerabilities reported

Install GCNN

You can download it from GitHub.
You can use GCNN 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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