Single neuron decision boundary using perceptron learning rule

Note: This project is submitted as part of class projects for CSE 5368 at University of Texas Arlington

This code should not be used for the course CSE 5368 offered at University of Texas Arlington

System requirements:

• RAM: min 4GB

Features:

• The project uses a random number generator to generate four random points in two classes.
• The neural network classifies the points into two classes(0 and 1) based on the perceptron learning rule.

Setup and installation:

The following libraries are required to run this program.

The project is tested using the below versions. It may or may not work with earlier or newer versions.

• Numpy v1.11
• Tk Inter v8.5.18

I recommend to use anaconda library to make life simpler instead of installing those libraries manually to deploy this project.

Download links:

• Anaconda library

Description:

• Input Weight 1, Input Weight 2 and Bias are used to select desired weights.
• Generate button generates four random points in two classes.
• Train button displays generated decision boundary.

Deployment:

1. Run Raja_01_01 program.

Additional information:

For more information refer the project wiki.