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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.