Genann: A Lightweight C Language Neural Network Library

Genann is a lightweight neural network library written in C, focusing on simplicity, speed, and reliability. It provides functionalities for creating, training, and using feedforward artificial neural networks (ANN), making it ideal for beginners and developers who need to use neural networks in resource-constrained environments.

1. Features of Genann

The core features of Genann are its lightweight nature and lack of dependencies. It fully adheres to the C99 standard and consists of only two files: genann.c and genann.h. This means you can easily integrate it into any C language project without worrying about external dependency issues. Additionally, Genann supports thread safety, ensuring stable operation in multi-threaded environments.

2. Creating a Neural Network

Creating a neural network is as simple as building with blocks. You just need to call the genann_init function and specify the number of input nodes, the number of hidden layers, the number of hidden nodes in each layer, and the number of output nodes. For example, to create a network with 2 input nodes, 1 hidden layer (3 neurons), and 2 output nodes, the code is as follows:

#include "genann.h"

genann* ann = genann_init(2, 1, 3, 2);

This code creates a simple neural network, like a small “brain,” ready to receive input and learn.

3. Training the Neural Network

Training the neural network is the process of making the “brain” learn. Genann uses the backpropagation algorithm to train the network, which is a classic method for training neural networks. You just need to provide input data, expected output data, and the learning rate, then call the genann_train function. For example:

double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
double output[4] = {0, 1, 1, 0};

for (int i = 0; i < 500; ++i) {
    for (int j = 0; j < 4; ++j) {
        genann_train(ann, input[j], &output[j], 3);
    }
}

This code trains a simple XOR function model. Just like teaching a child to solve math problems, we gradually teach the network the correct output by continuously providing it with input data and expected results.

4. Using the Neural Network for Prediction

Once training is complete, the network can make predictions. Using the genann_run function, you can pass in input data to get the network’s output. For example:

double const* prediction = genann_run(ann, input[0]);
printf("Input: 0 0, Output: %f\n", prediction[0]);

This code allows the network to predict the output when the input is [0, 0].

5. Saving and Loading the Neural Network

Genann also supports saving the trained network to a file or loading a network from a file. This is as convenient as saving and loading game progress. You can achieve this using the genann_write and genann_read functions:

genann_write(ann, stdout); // Save to file
genann* loaded_ann = genann_read(stdin); // Load from file

6. Application Scenarios

Although Genann is simple, it has a wide range of applications. It can be used for teaching and learning, helping beginners understand the basic concepts of neural networks. In practical applications, it can also be used for data prediction, pattern recognition, embedded systems, and more. For example, in embedded devices, Genann’s lightweight design makes it an ideal choice.

Tips

• When training the network, the learning rate is an important parameter that needs to be adjusted based on the actual situation.
• If you are interested in the underlying implementation of neural networks, the code of Genann is very concise, and you can read and modify it directly.

Summary

Genann is a simple, efficient, and easy-to-use neural network library. With its concise API and lightweight design, it provides developers with a quick tool for building and using neural networks. Whether you are a beginner or an experienced developer, Genann can meet your needs.