Bayesian-Compression-for-Deep-Learning | Remplementation of paper https : //arxiv.org/abs/1705.08665

by Lyken17 Python Version: Current License: No License

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

kandi X-RAY | Bayesian-Compression-for-Deep-Learning Summary

Bayesian-Compression-for-Deep-Learning is a Python library. Bayesian-Compression-for-Deep-Learning has no bugs, it has no vulnerabilities and it has low support. However Bayesian-Compression-for-Deep-Learning build file is not available. You can download it from GitHub.

Remplementation of paper

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Quality

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License

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Bayesian-Compression-for-Deep-Learning has a low active ecosystem.

It has 26 star(s) with 15 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 481 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Bayesian-Compression-for-Deep-Learning is current.

Quality

Bayesian-Compression-for-Deep-Learning has 0 bugs and 0 code smells.

Security

Bayesian-Compression-for-Deep-Learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Bayesian-Compression-for-Deep-Learning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Bayesian-Compression-for-Deep-Learning 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

Bayesian-Compression-for-Deep-Learning releases are not available. You will need to build from source code and install.

Bayesian-Compression-for-Deep-Learning has no build file. You will be need to create the build yourself to build the component from source.

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

It has 1080 lines of code, 106 functions and 11 files.

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

Top functions reviewed by kandi - BETA

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

Optimizes the objective function
Print the header
Maximum acquisition function
Print the current iteration
Compute compression rate
Reshape a matrix
Calculate float precision based on distance function
Calculate float precision
Compute the reduced weights
Compute the compression rate
Wrapper for rounding function
Evaluate the utility function
Calculate the Poisson distribution
Evaluate gaussian distribution
Initialize the model
Convert a dictionary to a list of points
Perform forward transformation
Reparametrize the logarithmic variance
Train the model
Evaluate the model
Computes the KL divergence of the KL divergence
Unit round - off
Perform a forward computation
Compute the attention matrix
Compute weight masks for each layer
Compute the KL divergence

Get all kandi verified functions for this library.

Bayesian-Compression-for-Deep-Learning Key Features

No Key Features are available at this moment for Bayesian-Compression-for-Deep-Learning.

Bayesian-Compression-for-Deep-Learning Examples and Code Snippets

No Code Snippets are available at this moment for Bayesian-Compression-for-Deep-Learning.

Community Discussions

No Community Discussions are available at this moment for Bayesian-Compression-for-Deep-Learning.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install Bayesian-Compression-for-Deep-Learning

You can download it from GitHub.
You can use Bayesian-Compression-for-Deep-Learning 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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