Created
September 7, 2019 18:23
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/// <summary> | |
/// Represents a layer in a neural network. This could be an input, output, or hidden layer. | |
/// </summary> | |
public class NeuralNetLayer : IEnumerable<Neuron> | |
{ | |
private readonly IList<Neuron> _neurons; | |
[CanBeNull] | |
private NeuralNetLayer _nextLayer; | |
/// <summary> | |
/// Creates a new neural network layer with the given count of neurons. | |
/// </summary> | |
/// <param name="numNeurons">The number of neurons in the layer</param> | |
/// <exception cref="ArgumentOutOfRangeException"> | |
/// Thrown if <paramref name="numNeurons" /> was less than 1 | |
/// </exception> | |
public NeuralNetLayer(int numNeurons) | |
{ | |
if (numNeurons <= 0) | |
{ | |
throw new ArgumentOutOfRangeException(nameof(numNeurons), "Each layer must have at least one Neuron"); | |
} | |
_neurons = new List<Neuron>(numNeurons); | |
numNeurons.Each(n => _neurons.Add(new Neuron())); | |
} | |
/// <summary> | |
/// Gets the Neurons belonging to this layer. | |
/// </summary> | |
public IEnumerable<Neuron> Neurons => _neurons; | |
/// <summary> | |
/// Sets the values of the layer to the given values set. One value will be used for each neuron in the layer. | |
/// </summary> | |
/// <param name="values">The values to use.</param> | |
/// <exception cref="ArgumentException">Thrown if <paramref name="values"/> did not have an expected values count.</exception> | |
internal void SetValues([NotNull] IEnumerable<decimal> values) | |
{ | |
if (values == null) throw new ArgumentNullException(nameof(values)); | |
if (values.Count() != _neurons.Count) throw new ArgumentException("The number of inputs must match the number of neurons in a layer", nameof(values)); | |
int i = 0; | |
values.Each(v => _neurons[i++].Value = v); | |
} | |
/// <inheritdoc /> | |
public IEnumerator<Neuron> GetEnumerator() => _neurons.GetEnumerator(); | |
/// <inheritdoc /> | |
IEnumerator IEnumerable.GetEnumerator() => GetEnumerator(); | |
/// <summary> | |
/// Evaluates each node in the layer, as well as the next layer if one is present. | |
/// </summary> | |
/// <returns>The outputs from the Output layer</returns> | |
internal IEnumerable<decimal> Evaluate() | |
{ | |
// Calculate all neurons. | |
_neurons.Each(n => n.Evaluate()); | |
// If this is the last layer, return its values, otherwise delegate to the next layer and return its results | |
return _nextLayer == null | |
? _neurons.Select(n => n.Value) | |
: _nextLayer.Evaluate(); | |
} | |
/// <summary> | |
/// Connects this layer to the <paramref name="nextLayer"/>, forming connections between each node in this | |
/// layer and each node in the next layer. | |
/// </summary> | |
/// <param name="nextLayer">The layer to connect to</param> | |
internal void ConnectTo([NotNull] NeuralNetLayer nextLayer) | |
{ | |
_nextLayer = nextLayer ?? throw new ArgumentNullException(nameof(nextLayer)); | |
_neurons.Each(source => nextLayer.Each(source.ConnectTo)); | |
} | |
/// <summary> | |
/// Sets the weights in the layer to the values provided | |
/// </summary> | |
/// <param name="weights">The weights to use to set in the connections</param> | |
[UsedImplicitly] | |
public void SetWeights(IList<decimal> weights) | |
{ | |
int weightIndex = 0; | |
_neurons.Each(neuron => neuron.OutgoingConnections.Each(c => c.Weight = weights[weightIndex++])); | |
} | |
} |
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