Metadata-Version: 2.1
Name: pyNeVer
Version: 0.0.1a2
Summary: Package for the training, pruning and verification of neural networks.
Home-page: https://github.com/darioguidotti/pyNeVer
Author: Dario Guidotti
Author-email: dario.guidotti@edu.unige.it
License: GNU General Public License with Commons Clause License Condition v1.0
Description: # pyNeVer
        
        Neural networks Verifier (__NeVer__) is a tool for the training, pruning and verification of neural networks.
        At present it supports sequential fully connected neural networks with ReLU and Sigmoid activation functions.
        __pyNeVer__ is the corresponding python package providing all the main capabilities of the __NeVer__ tool
        and can be easily installed using pip. The PyPI project page can be found at <https://pypi.org/project/pyNeVer/>
        whereas the github repository can be found at <https://github.com/darioguidotti/pyNeVer>.
        
        #### REQUIREMENTS AND INSTALLATION
        Given the characteristcs of PyTorch and ONNX we were not able to setup an auto-installation for these packages.
        Therefore the user is required to install the torch, torchvision and onnx packages indipendently.
        Guides on how to install such packages can be found at:
        * <https://pytorch.org/get-started/locally/>
        * <https://github.com/onnx/onnx>
        
        After the installation of the required packages __pyNeVer__ can be installed using the command:
        * _pip install pynever_
        
        #### DOCUMENTATION
        The documentation related to the __pyNeVer__ package can be found in the directory docs/pynever/ as html files.
        
        #### SUPPORTED INPUTS
        At present the __pyNeVer__ package supports only the abstraction and verification of fully connected neural networks 
        with ReLU and Sigmoid activation functions. The training, pruning and conversion supports also batch normalization
        layers. A network with batchnorm layers following fully connected layers can be converted to a "pure" fully connected
        neural networks using the capabilities provided in the [utilities.py](pynever/utilities.py) module.  
        The [conversion.py](pynever/strategies/conversion.py) provides the capabilities for the conversion of PyTorch and ONNX
        networks: therefore these kind of networks can be loaded using the respective frameworks and then converted to the
        internal representation used by __pyNeVer__. Examples of the loading of ONNX networks can be found in the scripts
        [mnist_experiment.py](examples/submissions/ATVA2021/mnist_experiments.py) and
        [james_experiments.py](examples/submissions/ATVA2021/james_experiments.py).  
        At present the properties for the verification and abstraction of the networks must be defined in python code following
        the specification which can be found in the documentation. Examples of the specification of the properties can be found
        in all the scripts in the directory examples/submissions/ATVA2021/.
        
        #### EXAMPLES
        **NB: All the scripts should be executed INSIDE the related directory!**  
        
        * The directory examples/ contains some examples of application of the __pyNeVer__ package. In particular the 
        [jupyter notebook](examples/notebooks/bidimensional_example_with_sigmoid.ipynb) shows a graphical example of the 
        application of the abstraction module for the reachability of a small network with bi-dimensional input and outputs.  
          
        * The [pruning_example.py](examples/pruning_example/pruning_example.py) script show how to train and prune some small
        fully connected neural networks with relu activation function. It also show how it is possible to combine batch norm
        layer and fully connected layers to make the networks compliant with the requirements of the verification and 
        abstraction modules.  
        
        * The directory examples/submissions/ATVA2021 contains the experimental setup used for the experimental evaluation
        in our ATVA2021 paper plus a novel experiment. The experiments can be easily replicated by executing the python scripts 
        [acas_experiment.py](examples/submissions/ATVA2021/acas_experiments.py) and 
        [mnist_experiment.py](examples/submissions/ATVA2021/mnist_experiments.py) from within the ATVA2021/ directory. 
        The log files will be generated and will be saved in the logs/ directory.  
        It is also possible to test the package on the script 
        [james_experiments.py](examples/submissions/ATVA2021/james_experiments.py) which consider small fully connected neural
        networks with Sigmoid activation functions.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.8
Classifier: Development Status :: 3 - Alpha
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
