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/// A high-level encapsulation of a neural net
and NeuralNet(numInputs: int, numOutputs: int) =
do
if numInputs <= 0 then invalidArg "numInputs" "There must be at least one neuron in the input layer";
if numOutputs <= 0 then invalidArg "numOutputs" "There must be at least one neuron in the output layer";
let inputLayer: NeuralNetLayer = new NeuralNetLayer(numInputs);
let outputLayer: NeuralNetLayer = new NeuralNetLayer(numOutputs);
let mutable hiddenLayers: NeuralNetLayer seq = Seq.empty;
let mutable isConnected: bool = false;
let connectLayers (n1:NeuralNetLayer) (n2:NeuralNetLayer) = n1.Connect(n2);
let layersMinusInput: NeuralNetLayer seq =
seq {
for layer in hiddenLayers do yield layer;
yield outputLayer;
}
let layersMinusOutput: NeuralNetLayer seq =
seq {
yield inputLayer;
for layer in hiddenLayers do yield layer;
}
/// Yields all connections to nodes inside of the network
let connections = Seq.collect (fun (l:NeuralNetLayer) -> l.Neurons) layersMinusInput
|> Seq.collect (fun (n:Neuron) -> n.Inputs);
/// Gets the layers of the neural network, in sequential order
member this.Layers: NeuralNetLayer seq =
seq {
yield inputLayer;
for layer in hiddenLayers do
yield layer;
yield outputLayer;
}
/// Represents the input layer for the network which take in values from another system
member this.InputLayer = inputLayer;
/// Represents the last layer in the network which has the values that will be taken out of the network
member this.OutputLayer = outputLayer;
/// Connects the various layers of the neural network
member this.Connect() =
if isConnected then invalidOp "The Neural Network has already been connected";
Seq.iter2 (fun l lNext -> connectLayers l lNext) layersMinusOutput layersMinusInput
isConnected <- true;
/// Determines whether or not the network has been connected. After the network is connected, it can no longer be added to
member this.IsConnected = isConnected;
/// Adds a hidden layer to the middle of the neural net
member this.AddHiddenLayer(layer: NeuralNetLayer) =
if isConnected then invalidOp "Hidden layers cannot be added after the network has been connected.";
hiddenLayers <- Seq.append hiddenLayers [layer];
/// Sets the weights on all connections in the neural network
member this.SetWeights(weights: decimal seq) =
if isConnected = false then do this.Connect();
Seq.iter2 (fun (w:decimal) (c:NeuronConnection) -> c.Weight <- w) weights connections;
/// Evaluates the entire neural network and yields the result of the output layer
member this.Evaluate(): decimal seq =
if not isConnected then do this.Connect();
// Iterate through the layers and run calculations
let mutable result: decimal seq = Seq.empty;
for layer in this.Layers do
result <- layer.Evaluate();
result;
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