weightnorm | Example code for Weight Normalization | Machine Learning library

by openai Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | weightnorm Summary

weightnorm is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. weightnorm has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However weightnorm build file is not available. You can download it from GitHub.

Status: Archive (code is provided as-is, no updates expected).

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Quality

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License

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Support

weightnorm has a low active ecosystem.

It has 352 star(s) with 111 fork(s). There are 135 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 4 have been closed. On average issues are closed in 7 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of weightnorm is current.

Quality

weightnorm has 0 bugs and 0 code smells.

Security

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

weightnorm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

weightnorm is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

weightnorm 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.

weightnorm saves you 350 person hours of effort in developing the same functionality from scratch.

It has 838 lines of code, 52 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Gated resnet layer
Get a variable from ema
Inverse tensor
Generate a name for a given layer
Calculate updates for parameters
Calculate weights for weight norm
Add weightnorm parameter updates
Discretized mixture logistic loss
Calculates the log probability of the given logits
Computes the sum of the exp
Downshifted x
A 2D convolutional layer
Calculate Adam updates
Get weights from parameters
Sample from discretized logistic
Estimate the SVD
Return the whitening of x
Weight normalization layer
Calculate softmax loss
Unpickles data from a pickle file
Down - shifted reduction

Get all kandi verified functions for this library.

weightnorm Key Features

No Key Features are available at this moment for weightnorm.

weightnorm Examples and Code Snippets

No Code Snippets are available at this moment for weightnorm.

Community Discussions

Trending Discussions on weightnorm

Wrapper layer to change kernel weights

QUESTION

Wrapper layer to change kernel weights

Asked 2021-Mar-22 at 16:25

I'm trying to write a custom wrapper layer such as the following one (simplified), where I want to modify the kernel weights of the wrapped layer:

...

ANSWER

Answered 2021-Mar-22 at 16:25

A kernel of a layer is a tf.Variable. To change its value, use the assign method.

Source https://stackoverflow.com/questions/66748911

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

Vulnerabilities

No vulnerabilities reported

Install weightnorm

You can download it from GitHub.
You can use weightnorm 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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