keras-gcnn | reflection equivariant CNNs for Keras as presented in B. S

by basveeling Python Version: Current License: Non-SPDX

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

kandi X-RAY | keras-gcnn Summary

keras-gcnn is a Python library. keras-gcnn has no bugs, it has no vulnerabilities, it has build file available and it has low support. However keras-gcnn has a Non-SPDX License. You can download it from GitHub.

Conventional fully-convolutional NNs are 'equivariant' to translation: as the input shifts in the spatial plane, the output shifts accordingly. This can be extended to include other forms of transformations such as 90 degree rotations and reflection. This is formalized by [2].

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Support

keras-gcnn has a low active ecosystem.

It has 83 star(s) with 40 fork(s). There are 7 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 3 have been closed. On average issues are closed in 10 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keras-gcnn is current.

Quality

keras-gcnn has no bugs reported.

Security

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

License

keras-gcnn has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

keras-gcnn 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.

Top functions reviewed by kandi - BETA

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

Create a GDenseNet
Create a dense layer
Convolution block
Conv2D convolutional layer
Wrapper for BatchNorm
Multi - layer convolution layer
Transition block
Crops the data to fit into a crop
Return a name based on prefix and name
Call the convolutional layer
Apply transformation to a 2D filter
2d convolutional layer
Compute output shape
Convolutional CNN
Create a dense network
Transition up block

Get all kandi verified functions for this library.

keras-gcnn Key Features

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

keras-gcnn Examples and Code Snippets

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Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install keras-gcnn

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