pytorch-EMM | EMM stands for External Memory Module

by bzcheeseman Python Version: Current License: GPL-3.0

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kandi X-RAY | pytorch-EMM Summary

pytorch-EMM is a Python library. pytorch-EMM has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However pytorch-EMM build file is not available. You can download it from GitHub.

EMM stands for External Memory Module. The initial implementation, EMM_NTM is very similar to a normal NTM except it's more self-contained. The other differences include an arbitrary number of read heads - which only requires minimal changes to the controller network to actually make use of. I have also implemented multiple memory banks which was inspired by Neural GPUs Learn Algorithms by Kaiser and Sutskever. I'm currently working also on EMM_GPU which is an external memory module even more inspired by the Neural GPU. I'm not sure how to do this, but I'm trying to create a 3D memory that's addressed by convolution filters to read in and out.

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Support

pytorch-EMM has a low active ecosystem.

It has 6 star(s) with 0 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

pytorch-EMM has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch-EMM is current.

Quality

pytorch-EMM has no bugs reported.

Security

pytorch-EMM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

pytorch-EMM is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

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

Forward computation
Write ww_t to memory
Helper function to read from memory
Return the number of features in x
Train GPU
Perform an EMM forward transformation
Forward forward computation

Get all kandi verified functions for this library.

pytorch-EMM Key Features

No Key Features are available at this moment for pytorch-EMM.

pytorch-EMM Examples and Code Snippets

No Code Snippets are available at this moment for pytorch-EMM.

Community Discussions

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

Vulnerabilities

No vulnerabilities reported

Install pytorch-EMM

You can download it from GitHub.
You can use pytorch-EMM 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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