EquivariantNetworks | Code Accompanying Paper : https : //www

by luntergroup Python Version: Current License: No License

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

kandi X-RAY | EquivariantNetworks Summary

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

Code Accompanying Paper:

Support

Quality

Security

License

Reuse

Support

EquivariantNetworks has a low active ecosystem.

It has 3 star(s) with 0 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of EquivariantNetworks is current.

Quality

EquivariantNetworks has no bugs reported.

Security

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

License

EquivariantNetworks 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

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

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

Train the model
Load sequences from gz file
Load training data
Load data from recomb
Augment training data
Split the test validation
Save a model to a yaml file
Generates a model
This method runs the training step
Assign weights to the model
Return cached Rxc
Return the CRC of the circuit
Blend the RC gradient
Unstrained unconstrained
Get the constrained weights
Assigns to the bias layer
Call dropout
Compute the noise shape
Return the shape of the input tensor
Load a model from a YAML file

Get all kandi verified functions for this library.

EquivariantNetworks Key Features

No Key Features are available at this moment for EquivariantNetworks.

EquivariantNetworks Examples and Code Snippets

No Code Snippets are available at this moment for EquivariantNetworks.

Community Discussions

No Community Discussions are available at this moment for EquivariantNetworks.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install EquivariantNetworks

You can download it from GitHub.
You can use EquivariantNetworks 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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