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Genann is a minimal, well-tested library for training and using feedforward artificial neural networks (ANN) in C. Its primary focus is on being simple, fast, reliable, and hackable. It achieves this by providing only the necessary functions and little extra.


  • ANSI C with no dependencies.
  • Contained in a single source code and header file.
  • Simple.
  • Fast and thread-safe.
  • Easily extendible.
  • Implements backpropagation training.
  • Compatible with alternative training methods (classic optimization, genetic algorithms, etc)
  • Includes examples and test suite.
  • Released under the zlib license - free for nearly any use.


Genann is self-contained in two files: genann.c and genann.h. To use Genann, simply add those two files to your project.

Example Code

Four example programs are included with the source code.

Quick Example

We create an ANN taking 2 inputs, having 1 layer of 3 hidden neurons, and providing 2 outputs. It has the following structure:

NN Example Structure

We then train it on a set of labeled data using backpropagation and ask it to predict on a test data point:

#include "genann.h"

/* Not shown, loading your training and test data. */
double **training_data_input, **training_data_output, **test_data_input;

/* New network with 2 inputs,
 * 1 hidden layer of 3 neurons each,
 * and 2 outputs. */
genann *ann = genann_init(2, 1, 3, 2);

/* Learn on the training set. */
for (i = 0; i < 300; ++i) {
    for (j = 0; j < 100; ++j)
        genann_train(ann, training_data_input[j], training_data_output[j], 0.1);

/* Run the network and see what it predicts. */
double const *prediction = genann_run(ann, test_data_input[0]);
printf("Output for the first test data point is: %f, %f\n", prediction[0], prediction[1]);


This example is to show API usage, it is not showing good machine learning techniques. In a real application you would likely want to learn on the test data in a random order. You would also want to monitor the learning to prevent over-fitting.


Creating and Freeing ANNs

genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
genann *genann_copy(genann const *ann);
void genann_free(genann *ann);

Creating a new ANN is done with the genann_init() function. Its arguments are the number of inputs, the number of hidden layers, the number of neurons in each hidden layer, and the number of outputs. It returns a genann struct pointer.

Calling genann_copy() will create a deep-copy of an existing genann struct.

Call genann_free() when you’re finished with an ANN returned by genann_init().

Training ANNs

void genann_train(genann const *ann, double const *inputs,
        double const *desired_outputs, double learning_rate);

genann_train() will preform one update using standard backpropogation. It should be called by passing in an array of inputs, an array of expected outputs, and a learning rate. See example1.c for an example of learning with backpropogation.

A primary design goal of Genann was to store all the network weights in one contigious block of memory. This makes it easy and efficient to train the network weights using direct-search numeric optimizion algorthims, such as Hill Climbing, the Genetic Algorithm, Simulated Annealing, etc. These methods can be used by searching on the ANN’s weights directly. Every genann struct contains the members int total_weights; and double *weight;. *weight points to an array of total_weights size which contains all weights used by the ANN. See example2.c for an example of training using random hill climbing search.

Saving and Loading ANNs

genann *genann_read(FILE *in);
void genann_write(genann const *ann, FILE *out);

Genann provides the genann_read() and genann_write() functions for loading or saving an ANN in a text-based format.


double const *genann_run(genann const *ann, double const *inputs);

Call genann_run() on a trained ANN to run a feed-forward pass on a given set of inputs. genann_run() will provide a pointer to the array of predicted outputs (of ann->outputs length).


  • All functions start with genann_.
  • The code is simple. Dig in and change things.

Extra Resources

The FAQ is an excellent resource for an introduction to artificial neural networks.

If you need an even smaller neural network library, check out the excellent single-hidden-layer library tinn.

If you’re looking for a heavier, more opinionated neural network library in C, I recommend the FANN library. Another good library is Peter van Rossum’s Lightweight Neural Network, which despite its name, is heavier and has more features than Genann.