Simple XOR Neural Network in C

Description

This project implements a simple neural network in C that can learn the XOR function. The XOR (exclusive OR) problem is a classic problem in neural network literature, demonstrating a case where linear classifiers fail, and non-linear solutions are required. This neural network uses a single hidden layer and is trained using backpropagation.

Features

• Sigmoid activation function for non-linearity.
• Initialization of weights with random values.
• Implementation of the forward pass and backpropagation to adjust weights based on errors.
• Use of a simple shuffle function to randomize the order of training data for each epoch.

Requirements

• C compiler (e.g., GCC, Clang)
• Standard C libraries (stdio.h, stdlib.h, math.h)

Usage

1. Clone this repository to your local machine.
2. Compile the source code using a C compiler. For example, using GCC:

   gcc -o xor_nn xor_nn.c -lm

   Note: The -lm flag is necessary to link the math library.
3. Run the compiled executable:

   ./xor_nn

Implementation Details

Activation Function:

The network uses the sigmoid function for activation, providing a smooth gradient for backpropagation.

Weight Initialization:

Weights are initialized to random values using init_weights function, which generates numbers between 0 and 1.

Training Data:

Hardcoded to represent the XOR truth table.

Learning Rate:

Set to 0.1, adjustable based on training needs.

Epochs:

The network trains for 10,000 epochs, which can be adjusted based on convergence requirements.

Contributing

Contributions to this project are welcome. Please fork the repository and submit a pull request with your improvements.

License

This project is open-source and available under the MIT License.