o-gan | Extremely Concise Approach for Auto-Encoding Generative | Machine Learning library

by bojone Python Version: Current License: No License

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

kandi X-RAY | o-gan Summary

o-gan is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Generative adversarial networks applications. o-gan has no bugs, it has no vulnerabilities and it has low support. However o-gan build file is not available. You can download it from GitHub.

O-GAN: Extremely Concise Approach for Auto-Encoding Generative Adversarial Networks

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Support

o-gan has a low active ecosystem.

It has 119 star(s) with 25 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 191 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of o-gan is current.

Quality

o-gan has no bugs reported.

Security

o-gan has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

o-gan 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

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

o-gan 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.

Top functions reviewed by kandi - BETA

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

This method is called after each step
Sample an eigenvalue matrix
Read image
Sample a PNG image
Apply weights to the model
Reset weights
Self - modulated batch normalization
Inject weights into the model
Compute the correlation coefficient

Get all kandi verified functions for this library.

o-gan Key Features

No Key Features are available at this moment for o-gan.

o-gan Examples and Code Snippets

No Code Snippets are available at this moment for o-gan.

Community Discussions

Trending Discussions on o-gan

DCGANs discriminator accuracy metric using PyTorch

QUESTION

DCGANs discriminator accuracy metric using PyTorch

Asked 2021-Feb-25 at 10:51

I am implementing DCGANs using PyTorch.

It works well in that I can get reasonable quality generated images, however now I want to evaluate the health of the GAN models by using metrics, mainly the ones introduced by this guide https://machinelearningmastery.com/practical-guide-to-gan-failure-modes/

Their implementation uses Keras which SDK lets you define what metrics you want when you compile the model, see https://keras.io/api/models/model/. In this case the accuracy of the discriminator, i.e. percentage of when it successfully identifies an image as real or generated.

With the PyTorch SDK, I can't seem to find a similar feature that would help me easily acquire this metric from my model.

Does Pytorch provide the functionality to be able to define and extract common metrics from a model?

...

ANSWER

Answered 2021-Feb-25 at 10:51

Pure PyTorch does not provide metrics out of the box, but it is very easy to define those yourself.

Also there is no such thing as "extracting metrics from model". Metrics are metrics, they measure (in this case accuracy of discriminator), they are not inherent to the model.

Binary accuracy

In your case, you are looking for binary accuracy metric. Below code works with either logits (unnormalized probability outputed by discriminator, probably last nn.Linear layer without activation) or probabilities (last nn.Linear followed by sigmoid activation):

Source https://stackoverflow.com/questions/66365566

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

Vulnerabilities

No vulnerabilities reported

Install o-gan

You can download it from GitHub.
You can use o-gan 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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