Metadata-Version: 2.1
Name: pygop
Version: 0.1.1
Summary: Python package that implements various algorithms using Generalized Operational Perceptron
Home-page: https://github.com/viebboy/PyGOP
Author: Dat Tran
Author-email: viebboy@gmail.com
License: LICENSE.txt
Description: # PyGOP: A Python library for Generalized Operational Perceptron (GOP) based algorithms
        This package implements progressive learning algorithms using [Generalized Operational Perceptron](https://www.sciencedirect.com/science/article/pii/S0925231216312851). This implementation includes the following algorithms: Progressive Operational Perceptron (POP); Heterogeneous Multilayer Generalized Operational Perceptron ([HeMLGOP](https://arxiv.org/abs/1804.05093)) and its variants; Fast Progressive Operational Perceptron ([POPfast](https://arxiv.org/abs/1808.06377)) and Progressive Operational Perceptron with Memory ([POPmemO](https://arxiv.org/abs/1808.06377), [POPmemH](https://arxiv.org/abs/1808.06377))
        
        What is Generalized Operational Perceptron?
        ===========================================
        
        
        [Generalized Operational Perceptron](https://www.sciencedirect.com/science/article/pii/S0925231216312851) is an artificial neuron model that was proposed to replace the traditional McCulloch-Pitts neuron model. While standard perceptron model only performs a linear transformation followed by non-linear thresholding, GOP model encapsulates a diversity of both linear and non-linear operations (with traditional perceptron as a special case). Each GOP is characterized by learnable synaptic weights and an operator set comprising of 3 types of operations: nodal operation, pooling operation and activation operation. The 3 types of operations performed by a GOP loosely resemble the neuronal activities in a biological learning system of mammals in which each neuron conducts electrical signals over three distinct operations:
        
        * Modification of input signal from the synapse connection in the Dendrites.
        * Pooling operation of the modified input signals in the Soma.
        * Sending pulses when the pooled potential exceeds a limit in the Axon hillock.
        
        By defining a set of nodal operators, pooling operators and activation operators, each GOP can select the suitable operators based on the problem at hand. Thus learning a GOP-based network involves finding the suitable operators as well as updating the synaptic weights. The author of GOP proposed Progressive Operational Perceptron (POP) algorithm to progressively learn GOP-based networks. Later, [Heterogeneous Multilayer Generalized Operational Perceptron (HeMLGOP)](https://arxiv.org/pdf/1804.05093.pdf) algorithm and its variants (HoMLGOP, HeMLRN, HoMLRN) were proposed to learn heterogeneous architecture of GOPs with efficient operator set search procedure. In addition, fast version of POP [POPfast](https://arxiv.org/pdf/1808.06377.pdf) was proposed together with memory extensions [POPmemO](https://arxiv.org/pdf/1808.06377.pdf), [POPmemH](https://arxiv.org/pdf/1808.06377.pdf) that augment POPfast by incorporating memory path.
        
        Installation
        ============
        
        PyPi installation
        -----------------
        
        Tensorflow is required before installing PyGOP.
        To install tensorflow CPU version through *pip*::
        
            pip install tensorflow
        
        Or the GPU version::
        
            pip install tensorflow-gpu
        
        To install PyGOP with required dependencies::
        
            pip install pygop
        
        At the moment, PyGOP only supports Linux with both python 2 and python 3 (tested on Python 2.7 and Python 3.5)
        
        Installation from source
        ------------------------
        
        To install latest version from github, clone the source from the project repository and install with setup.py::
        
            git clone https://github.com/viebboy/PyGOP
            cd PyGOP
            python setup.py install --user
         
        
        Documentation
        =============
        
        Full documentation can be found [here](https://pygop.readthedocs.io)
        
        
        
        
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Classifier: Operating System :: POSIX
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