• ml_perceptron/
  • src/
    • data.py
      • Contains methods for retrieving the vectors needed for the training and validation of the program.
    • main.py
      • The entry point to the program.
    • mlp.py
      • Contains the multi layer perceptron mlp class and handles its instantiation as well as forward and backward propogation of training vectors and error respectively.
    • res/
      • data.txt
        • Contains the raw data vectors.

• A basic understanding of programming and terminal emulator know how.
• An installation of Python3 (or Python) which can be found here.
• A Python package manager such as pip. It can be installed by following the instructions here.
• The NumPy package for Python. It can be installed with pip by running the terminal command pip install numpy.

Cloning from GitHub:

• You can Git clone the GitHub repo with git clone https://github.com/RLHerbert/ml_perceptron.git.

Follow these steps to run the program:

1. Navigate to the ml_perceptron folder in your terminal emulator of choice.
2. Run Python3 by typing and entering Python3 src/main.py in your terminal.
   • Alternatively, if you wish to save the resulting output you may enter Python3 src/main.py > [output file] on Linux/MacOS terminals and Windows PowerShell.

This project features a multilayer perceptron classifier which utilizes forward propogation training and backward propogation error correction to classify the provided data set. The program shows the initial and final hidden and output weights. It also outputs the precision, recall, sensitivity and specificity of a given class and the accuracy and error rates of the MLP in general.