#Genann

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

##Features

• 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 training by alternative methods (classic optimization, genetic algorithms, etc)
• Includes examples and test suite.
• Released under the zlib license - free for nearly any use.

##Example Code

Four example programs are included.

• example1.c - Trains an ANN on the XOR function using backpropagation.
• example2.c - Trains an ANN on the XOR function using random search.
• example3.c - Loads and runs an ANN from a file.
• example4.c - Trains an ANN on the IRIS data-set using backpropagation.

##Quick Example

Here we create an ANN, train it on a set of labeled data using backpropagation, ask it to predict on a test data point, and then free it:

#include "genann.h"

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

/* New network with 5 inputs,
 * 2 hidden layer of 10 neurons each,
 * and 1 output. */
genann *ann = genann_init(5, 2, 10, 1);

/* 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. */
printf("Output for the first test data point is: %f\n", *genann_run(ann, test_data_input[0]));

genann_free(ann);

Not that 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.

##Usage

###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. It's 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 output, 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.

###Evaluating

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).

##Hints

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

##Extra Resources

The comp.ai.neural-nets FAQ is an excellent resource for an introduction to artificial neural networks.

If you're looking for a heavier, more opinionated neural network library in C, I highly 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.