feedback-alignment-pytorch | Implementation of feedback alignment learning in PyTorch | Machine Learning library

by L0SG Python Version: Current License: No License

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kandi X-RAY | feedback-alignment-pytorch Summary

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

Implementation of feedback alignment learning in PyTorch

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feedback-alignment-pytorch has a low active ecosystem.

It has 23 star(s) with 6 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of feedback-alignment-pytorch is current.

Quality

feedback-alignment-pytorch has 0 bugs and 3 code smells.

Security

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

feedback-alignment-pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

feedback-alignment-pytorch 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.

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

feedback-alignment-pytorch has no build file. You will be need to create the build yourself to build the component from source.

It has 166 lines of code, 13 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Initialize the model .
Gradient of grad_output .
Compute the linear solution .
Create a random dataset .

Get all kandi verified functions for this library.

feedback-alignment-pytorch Key Features

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feedback-alignment-pytorch Examples and Code Snippets

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Community Discussions

Trending Discussions on feedback-alignment-pytorch

RuntimeError: tensors must be 2-D

QUESTION

RuntimeError: tensors must be 2-D

Asked 2021-Apr-19 at 14:06

I was running a simple MLP network with customized learning algorithms. It worked fine on the training set, but I got this error when I entered additional code to check the test accuracy. How can I fix it?

Test Accuracy code ...

ANSWER

Answered 2021-Apr-19 at 14:06

You just need to flatten your input before passing it to your model. Something like this:

Source https://stackoverflow.com/questions/67159259

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

Vulnerabilities

No vulnerabilities reported

Install feedback-alignment-pytorch

You can download it from GitHub.
You can use feedback-alignment-pytorch 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

This is a simple implementation of Random synaptic feedback weights support error backpropagation for deep learning in PyTorch. Base codes are adapted from official PyTorch tutorial. It implements simple MLP with one hidden layer, without non-linear activation function. Run train_fa_vs_bp_linear_model.py to compare performance between feedback alignment vs backpropagation.

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