ganomaly | GANomaly : Semi-Supervised Anomaly Detection | Machine Learning library

by samet-akcay Python Version: Current License: MIT

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

kandi X-RAY | ganomaly Summary

ganomaly is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. ganomaly has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However ganomaly has 1 bugs. You can download it from GitHub.

This repository contains PyTorch implementation of the following paper: GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training [1].

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Quality

Security

License

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Support

ganomaly has a medium active ecosystem.

It has 771 star(s) with 214 fork(s). There are 25 watchers for this library.

It had no major release in the last 6 months.

There are 38 open issues and 43 have been closed. On average issues are closed in 95 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ganomaly is current.

Quality

ganomaly has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 13 code smells.

Security

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

ganomaly code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ganomaly 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

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

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

ganomaly saves you 325 person hours of effort in developing the same functionality from scratch.

It has 780 lines of code, 46 functions and 9 files.

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

Top functions reviewed by kandi - BETA

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

Train a Ganomaly model
Load data set
Get the cifar anomaly dataset
Get the MNIST2M dataset

Get all kandi verified functions for this library.

ganomaly Key Features

No Key Features are available at this moment for ganomaly.

ganomaly Examples and Code Snippets

No Code Snippets are available at this moment for ganomaly.

Community Discussions

Trending Discussions on ganomaly

Could it be possible to start multiple training at the same time?

QUESTION

Could it be possible to start multiple training at the same time?

Asked 2019-Jan-04 at 13:41

I just started coding DNN and I was wondering if it could be possible to launch multiple training at the same time? Like with different parameters. Or will it slow down a lot the training?

Here for the context: I use a Ganomaly architecture in order to found anomalies in pictures and I have to change and try a few different combinations. I use Anaconda and during training, it shows that ~20% of memory is being used. My setup: Nvidia Quadro M6000, Cuda 9.0, cudNN 7.0.

...

ANSWER

Answered 2019-Jan-04 at 13:41

Could it be possible to start multiple training at the same time?

Yes, it is possible to start multiple instances but surely have an impact on your training speed.

One possible solution is to integrate GridSearchCV in sklearn and Keras model. To this end, Keras also provide a wrapper, called keras.wrappers.scikit_learn which you can set the number of jobs, in the GridSearchCV, to -1 in order to run them in parallel. See here for more details.

Here is a list of the hyper-parameter optimization solution.

See here for more details.

Another possible option is Google-hyper-tuning, which of course needs to run on the cloud. See here for more details.

Source https://stackoverflow.com/questions/54039639

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

Vulnerabilities

No vulnerabilities reported

Install ganomaly

First clone the repository git clone https://github.com/samet-akcay/ganomaly.git
Create the virtual environment via conda conda create -n ganomaly python=3.7
Activate the virtual environment. conda activate ganomaly
Install the dependencies. conda install -c intel mkl_fft pip install --user --requirement requirements.txt

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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