NNF2

a C++ library for neural networks


NNF2 is a C++ library for feed-forward neural networks (also called multi-layer perceptrons).

Its modular design makes it easily extensible by users.

Batch training and saving of networks are supported.


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Usage example

Programming a neural network to solve the classic XOR problem is as simple as:


// transfer functions

Sigmoid sigmoid(2.0f); // sigmoid of parameter 2.0

Heaviside heaviside; // heaviside of default parameter 0


// learning rate and initial weight range

float eps = 0.5f;

float range = 1.0f;


// input layer has 2 neurons, uses heaviside transfer function

InputLayer il(2, heaviside);


// hidden layer is connected to input layer, has 4 neurons, uses sigmoid,

// has learning rate 'eps' and initial weights in (-range, range)

Layer hl(&il, 2, sigmoid, eps, range);


// output layer is connected to hidden layer, has 1 neuron, uses heaviside

// transfer function

OutputLayer ol(&hl, 1, heaviside, eps, range);


// MLP network constructor takes input and output layers and

// a NULL-terminated list of hidden layers in the same order

// they were connected

MultiLayerPerceptron mlp(&il, &ol, &hl, NULL);


// a simple way to train the network is to generate some examples

// of input-output couples

for (int epochs = 0; epochs < 100; ++epochs)

     for (int i = 0; i <= 1; ++i)

          for (int j = 0; j <= 1; ++j) {

               float input[2] = {i, j};

               float desired_output = XOR(i, j);

               mlp.train(input, &desired_output);

          }


// then we can test the network fitness so far

float input[2] = {1, 0};

float output;

mlp.compute(input, &output);

cout << "XOR(1, 0) = " << output << endl;


// or we can set up a text file for batch training

ifstream datafile("xor.txt");


// trains the network and returns true if it reached a mean square error

// under 0.001f in no more than 100000 epochs

bool success = mlp.optimize(datafile, 0.001f, 100000);

cout << "Success: " << success << endl;



// we can save the network on a text file

ofstream savefile("net.txt");

mlp.save(savefile);

savefile.close();


// load the network from a text file

ifstream loadfile("net.txt");

mlp.load(loadfile);

loadfile.close();


Used by

- An Exploration of Monophonic Instrument Classification Using Multi-Threaded Artificial Neural Networks, Marc J. Rubin, Master’s of Science Degree of Computer Science, The University of Tennessee, Knoxville, December 2009.

- ptBS reconstruction, Keith E. Turpin, Indiana University.

- Weather report Haaksbergen.


Contact

For any questions or bug reports contact <alessandro.presta@gmail.com>.