keras-gcn | Keras implementation of Graph Convolutional Networks | Machine Learning library

by tkipf Python Version: Current License: MIT

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

kandi X-RAY | keras-gcn Summary

keras-gcn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. keras-gcn has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Deep Learning on Graphs with Keras.

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Quality

Security

License

Reuse

Support

keras-gcn has a low active ecosystem.

It has 742 star(s) with 264 fork(s). There are 23 watchers for this library.

It had no major release in the last 6 months.

There are 25 open issues and 31 have been closed. On average issues are closed in 18 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras-gcn is current.

Quality

keras-gcn has 0 bugs and 0 code smells.

Security

keras-gcn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-gcn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-gcn is licensed under the MIT License. This license is Permissive.

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

Reuse

keras-gcn releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

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

keras-gcn saves you 105 person hours of effort in developing the same functionality from scratch.

It has 268 lines of code, 19 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Load a cora data file
Encode labels into one - hot encoded array
Calculate training and test splits
Generate a boolean mask
Evaluate the loss function
Compute the accuracy of each label
Calculates the crossentropy crossentropy
Preprocess the adjacency matrix
R Normalizes an adjacency matrix
R Return the covariance matrix

Get all kandi verified functions for this library.

keras-gcn Key Features

No Key Features are available at this moment for keras-gcn.

keras-gcn Examples and Code Snippets

No Code Snippets are available at this moment for keras-gcn.

Community Discussions

Trending Discussions on keras-gcn

keras-gcn fit model ValueError

QUESTION

keras-gcn fit model ValueError

Asked 2019-Nov-29 at 06:32

I'm using this library to create a model to learn graphs. Here is the code (from repository):

...

ANSWER

Answered 2019-Nov-29 at 06:19

I think the issue is mismatch between the shapes of your edge_layer and data_layer.

When you use the function keras.layers.Input you're giving data_layer a shape of shape=(None, 3) and then you're giving edge_layer a shape of shape=(None, None)

Match the shapes and let me know how it goes.

Source https://stackoverflow.com/questions/59099402

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

Vulnerabilities

No vulnerabilities reported

Install keras-gcn

You can download it from GitHub.
You can use keras-gcn 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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