gan_numpy | simple step by step implementation

by shinseung428 Python Version: Current License: No License

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kandi X-RAY | gan_numpy Summary

gan_numpy is a Python library. gan_numpy has no vulnerabilities and it has low support. However gan_numpy has 1 bugs and it build file is not available. You can download it from GitHub.

This is a very simple step by step implementation of GAN using only numpy. Without the use of GPU, it takes too much time to generate all the numbers. To get the result quickly using only CPU, I suggest working with one number.

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Quality

Security

License

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Support

gan_numpy has a low active ecosystem.

It has 234 star(s) with 22 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 1 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of gan_numpy is current.

Quality

gan_numpy has 1 bugs (0 blocker, 0 critical, 0 major, 1 minor) and 19 code smells.

Security

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

gan_numpy code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

gan_numpy 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

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

gan_numpy has no build file. You will be need to create the build yourself to build the component from source.

gan_numpy saves you 81 person hours of effort in developing the same functionality from scratch.

It has 209 lines of code, 13 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Backpropative loss function
Backprop generation function
Tile an image
Read the MNIST dataset
Generate the generator
Compute discriminator
Lrelu function
Sigmoid function
Calculate tanh

Get all kandi verified functions for this library.

gan_numpy Key Features

No Key Features are available at this moment for gan_numpy.

gan_numpy Examples and Code Snippets

No Code Snippets are available at this moment for gan_numpy.

Community Discussions

No Community Discussions are available at this moment for gan_numpy.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install gan_numpy

You can download it from GitHub.
You can use gan_numpy 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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