Logic Gate Neural Network Trainer

Overview

This project implements a simple neural network to learn the behavior of basic logic gates (AND, OR, NAND, XOR) using Python. The neural network uses a single-layer perceptron model with a sigmoid activation function. The training process uses numerical gradient estimation to update the weights and bias based on the mean squared error cost function.

Neural Network Architecture

Below is a diagram of the single neuron model used in this project:

graph TD
    A(Input 1) -->|Weight 1| D[Sum]
    B(Input 2) -->|Weight 2| D
    C(Bias) -->|Bias value| D
    D --> E{Sigmoid Activation}
    E --> F(Output)

    style D fill:#f9f,stroke:#333,stroke-width:2px
    style E fill:#ccf,stroke:#333,stroke-width:2px

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This model consists of two input nodes (representing the inputs of the logic gate), each connected to a summing junction through weights. A bias is also added to the sum. The result is passed through a sigmoid activation function, producing the final output.

Features

• Training neural networks to simulate logic gates.
• Configurable parameters such as learning rate, number of iterations, and epsilon for gradient estimation.
• Easy switching between different logic gate training sets via configuration.

Installation

No external libraries are required for the main functionality as it uses only NumPy, which can be installed via pip if not already available:

pip install numpy

Usage

The project is divided into two main files:

• config.py: Contains all configurable parameters and training data sets.
• main.py: Contains the neural network model, training logic, and performance evaluation.

To run the training process, execute the following command in your terminal:

python main.py

Configuration

Modify the config.py file to change the learning parameters or switch between different logic gates. Here’s a brief explanation of the configuration options available:

• learning_rate: How quickly the network adjusts its weights in response to the error.
• epsilon: A small number used for numerical gradient approximation.
• max_iterations: The total number of training iterations to perform.
• random_seed: Seed for NumPy's random number generator to ensure reproducibility.
• default_training_set: Choose which logic gate to train on (AND, OR, NAND, XOR).

Example of changing a configuration in config.py:

learning_rate = 0.05  # Reduce learning rate for slower convergence
default_training_set = training_data_xor  # Change to XOR gate training

Customizing Training Data

You can add new logic gates or different kinds of training data by editing the config.py file. Ensure each new dataset follows the format shown for existing gates, i.e., an array of input-output pairs.

Output

After running main.py, the terminal will display the cost after each training epoch, along with the final learned weights and biases. It also evaluates the trained model on all possible inputs for the selected logic gate to demonstrate the performance.

Contributing

Contributions to this project are welcome. Please ensure to keep the structure consistent, and add or update the corresponding configurations and documentation as needed.