public NeuralNetwork(NeuronCount inputs, NeuronCount[] numberOfNeuronsPerHiddenLayer, NeuronCount outputs)
{
layers = new[] { inputs }
.Concat(numberOfNeuronsPerHiddenLayer)
.Concat(new[] { outputs })
.Select(n => new NeuralNetworkLayer(n))
.ToArray();
for (var i = layers.Length - 1; i > 1; i--)
{
((INeuralNetworkLayer)layers[i]).BuildSynapses(layers[i - 1]);
}
}
public NeuralNetwork(NeuronCount inputs, NeuronCount outputs, params NeuronCount[] hiddenLayers)
{
if (hiddenLayers == null || !hiddenLayers.Any())
{
throw new ArgumentException("At least one hidden layer is required", nameof(hiddenLayers));
}
NeuralNetworkLayer previousLayer = null;
layers = new[] { inputs }
.Concat(hiddenLayers)
.Concat(new[] { outputs })
.Select(nc =>
{
previousLayer = previousLayer == null ? new NeuralNetworkLayer(nc) : new NeuralNetworkLayer(nc, previousLayer);
return(previousLayer);
})
.ToArray();
}
public NeuralNetworkLayer(NeuronCount numberOfNeurons)
{
neurons = Enumerable.Range(1, numberOfNeurons)
.Select(_ => new Neuron())
.ToArray();
}