Metadata-Version: 2.1
Name: uncertainty-wizard
Version: 0.1.0
Summary: Quick access to uncertainty and confidence of Keras networks.
Home-page: https://github.com/testingautomated-usi/uncertainty_wizard
Author: Michael Weiss
Author-email: code@mweiss.ch
License: UNKNOWN
Description: ![UNCERTAINTY WIZARD](https://github.com/testingautomated-usi/uncertainty-wizard/raw/main/docs/uwiz_logo.PNG)
        
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        Uncertainty wizard is a plugin on top of `tensorflow.keras`,
         allowing to easily and efficiently create uncertainty-aware deep neural networks:
        
        * Plain Keras Syntax: Use the layers and APIs you know and love.
        * Conversion from keras: Convert existing keras models into uncertainty aware models.
        * Smart Randomness: Use the same model for point predictions and sampling based inference.
        * Fast ensembles: Train and evaluate deep ensembles lazily loaded and using parallel processing.
        * Super easy setup: Pip installable. Only tensorflow as dependency.
        
        #### Installation
        
        It's as easy as `pip install uncertainty-wizard`
        
        #### Requirements
        - tensorflow >= 2.3.0
        - python 3.6* / 3.7 / 3.8
        
        Note that **tensorflow 2.4** has just been released. 
        We will test and create compatibility with uncertainty wizard in the next couple of weeks.
        Until then, please stick to tensorflow 2.3.x.
        
        *python 3.6 requires to `pip install dataclasses`
        
        #### Documentation
        Our documentation is deployed to:
        [uncertainty-wizard.readthedocs.io/](https://uncertainty-wizard.readthedocs.io/)
        
        Note that we have a 100% docstring coverage on public method and classes.
        Hence, your IDE will be able to provide you with a good amount of docs out of the box.
        
        #### Examples
        A set of small and easy examples, perfect to get started can be found in the 
        [user guide for our models](https://uncertainty-wizard.readthedocs.io/en/latest/user_guide_models.html)
        and the [user guide for our quantifiers](https://uncertainty-wizard.readthedocs.io/en/latest/user_guide_quantifiers.html)
        
        Larger and examples are also provided - and you can run them in colab right away.
        You can find them here: [List of jupyter examples](https://uncertainty-wizard.readthedocs.io/en/latest/examples.html)
        
        #### Authors and Paper
        ``uncertainty-wizard`` was developed by Michael Weiss and Paolo Tonella at USI (Lugano, Switzerland).
        An early version was first presented in the following paper 
        (preprint can be found [here](https://uncertainty-wizard.readthedocs.io/en/latest/paper.html)):  
        
        <details>  
          <summary>Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring (expand for BibTex)</summary>  
        
            @inproceedings{Weiss2021,  
              title={Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring},  
              author={Weiss, Michael and Tonella, Paolo},  
              booktitle={2021 IEEE 14th International Conference on Software Testing,   
                Validation and Verification (ICST)},  
              year={2021},  
              organization={IEEE},  
              note={forthcoming}  
            }  
        
        </details>
        
        We are also currently writing a technical tool paper, describing design choices and challenges.
        We are happy to share a preprint upon request.
        
        #### Contributing
        Issues and PRs are welcome! 
        Before investing a lot of time for a PR, please open an issue first, describing your contribution.
        This way, we can make sure that the contribution fits well into this repository.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
