Tinn: Tiny Neural Network

Tinn is a tiny and dependency free neural network implementation for dotnet. It has three configurable layers: an input layer, a hidden layer and an output layer.

How to get started?

Create a neural network:

var network = new TinyNeuralNetwork(inputCount: 2, hiddenCount: 4, outputCount: 1); 

Load a data set:

// This is XOR operation example.
var input = new float[][]
{
    new []{ 1f, 1f }, // --> 0f
    new []{ 1f, 0f }, // --> 1f
    new []{ 0f, 1f }, // --> 1f
    new []{ 0f, 0f }, // --> 0f
};
var expected = new float[][]
{
    new []{ 0f }, // <-- 1f ^ 1f
    new []{ 1f }, // <-- 1f ^ 0f
    new []{ 1f }, // <-- 0f ^ 1f
    new []{ 0f }, // <-- 0f ^ 0f
};

Train the network until a desired accuracy is achieved:

for (int i = 0; i < input.Length; i++)
{
    network.Train(input[i], expected[i], 1f);
}
// Note: you will probably have to loop this for a few times until network improves.

Try to predict some values:

var prediction = network.Predict(new [] { 1f, 1f });  
// Will return probability close to 0f, since 1 ^ 1 = 0.

For more examples see the examples directory and automated tests.

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The original library was written by glouw in C.