fonet is a deep neural network package for Go. It's mainly created because I wanted to learn about neural networks and create my own package. I'm planning to continue the development of the package and add more function to it, for example exporting/importing a model.

It's the same as everywhere, you just have to run the

go get github.com/Fontinalis/fonet

I focused (and still focusing) on creating an easy to use package, but let me know if something is not clear.

As in the xor example, it's not so complicated to create a network. When you creating the network, you always have to define the layers.

n := fonet.NewNetwork([]int{2, 3, 1}, fonet.Sigmond)
/*
2 nodes in the INPUT LAYER
3 nodes in the HIDDEN LAYER
1 node in the OUTPUT LAYER
*/

But my goal was also to create a package, which can create deep neural networks too, so here is another example for that.

n := fonet.NewNetwork([]int{6, 12, 8, 4}, fonet.Sigmond)
/*
6 nodes in the INPUT LAYER
12 nodes in the HIDDEN LAYER (1)
8 nodes in the HIDDEN LAYER (2)
4 nodes in the OUTPUT LAYER
*/

After creating the network, you have to train your network. To do that, you have to specify your training set, which should be like the next

var trainingData = [][][]float64{
    [][]float64{ // The actual training sample
        []float64{
            /*
            The INPUT data
            */
        },
        []float64{
            /*
            The OUTPUT data
            */
        },
    },
}

After giving the training data, you can set the epoch and the learning rate.

n.Train(trainingData, epoch, lrate, true)
// Train(trainingData [][][]float64, epochs int, lrate float64, debug bool)

Note: When 'debug' is true, it'll show when and which epoch is finished

After training your network, using the Predict(..) function you can calculate the output for the given input.

In the case of XOR, it looks like the next

input := []float64{
    1,
    1,
}
out := n.Predict(input)