Metadata-Version: 2.1
Name: SeqNN
Version: 0.0.3
Summary: A sequential neural network python extension
Home-page: https://github.com/BrettCleary/SeqNN
Author: Brett Cleary
Author-email: your.email@domain.com
License: MIT
Description: # SeqNN
        A simple sequential neural network python extension
        
        <!-- PROJECT SHIELDS -->
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        <!-- TABLE OF CONTENTS -->
        ## Table of Contents
        
        * [About the Project](#about-the-project)
          * [Built With](#built-with)
        * [Getting Started](#getting-started)
          * [Installation](#installation)
        * [Usage](#usage)
        * [Contributing](#contributing)
        * [License](#license)
        * [Contact](#contact)
        
        
        <!-- ABOUT THE PROJECT -->
        ## About The Project
        SeqNN is a simple single threaded sequential neural network python extension written in c++ and compiled with SWIG.
        
        ### Features
        - Modular layers
            - 2 Dimensional Convolutional
            - 2 Dimensional Max/Min Pool
            - Fully Connected Dense Layer
        - Regularization
            - Weight decay
            - Soft weight sharing for the 2D Convolutional layer
            - Early Stopping
        
        ### Built With
        * [SWIG](http://www.swig.org/download.html)
        
        <!-- GETTING STARTED -->
        ## Getting Started
        
        ### Installation
        
        1. Clone the repo
        ```sh
        git clone https://github.com/BrettCleary/DigitCNN
        ```
        OR
        
        2. Install with PyPi
        ```sh
        pip install SeqNN
        ```
        
        <!-- USAGE EXAMPLES -->
        ## Usage
        
        Example usage with a subset of the MNIST dataset is given in DigitCNN.py. Here is a sample output:
        
            Error Rate (%) after training  2  number of epochs is  22.8
            Error Rate (%) after training  4  number of epochs is  14.4
            Error Rate (%) after training  6  number of epochs is  12.8
            Error Rate (%) after training  8  number of epochs is  12.0
            Error Rate (%) after training  10  number of epochs is  10.8
            Error Rate (%) after training  12  number of epochs is  10.8
            Error Rate (%) after training  14  number of epochs is  10.0
            Error Rate (%) after training  16  number of epochs is  10.8
            Error Rate (%) after training  18  number of epochs is  10.4
            Error Rate (%) after training  20  number of epochs is  9.6
        
            The error rate for test dataset is  6.4
        
        
        <!-- CONTRIBUTING -->
        ## Contributing
        
        Any contributions you make are **greatly appreciated**.
        
        1. Fork the Project
        2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
        3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
        4. Push to the Branch (`git push origin feature/AmazingFeature`)
        5. Open a Pull Request
        
        <!-- LICENSE -->
        ## License
        
        Distributed under the MIT License. See `LICENSE` for more information.
        
        <!-- CONTACT -->
        ## Contact
        
        Project Link: [https://github.com/BrettCleary/SeqNN](https://github.com/BrettCleary/SeqNN)
        
Keywords: neural,network,sequential
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Description-Content-Type: text/markdown
