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GateNetwork Class

This class is responsible for constructing and managing gate networks. This is a custom type of feedforward network that contains a single hidden node that is specifically managed by several new functions. The point of this type of network is to process values in the simplest way possible while keeping the features of a feedforward network. This is used by LSTM networks. For this documentation, the single hidden node will be called the "gate node".

GateNetwork .new(array inputNamesArray, array outputNamesArray, dictionary customSettings) Creates and returns the GateNetwork according to the given settings as !customSettings!. The settings determine how the network will perform and function and is open to customization. The available setting parameters and their default values are below. 
local customSettings = {
    Optimizer = StochasticGradientDescent.new();
    SoftMax = false;

    HiddenActivationName = "ReLU";
    OutputActivationName = "Sigmoid";
    Bias = 0;
    LearningRate = 0.3;
    RandomizeWeights = true;
}
Node :GetNode() Returns the gate node.
Synapse :GetSynapseWithNodeName(string inputName) Returns the synapse that connects the input node with the name !inputName! and the gate node.
number :GetWeight(string inputName) Returns the weight of the synapse that connects the input node with the name !inputName! and the gate node.
void :AddWeight(string inputName, number weightDelta) Adds !weightDelta! to the weight of the synapse that connects the input node with the name !inputName! and the gate node.
void :SetWeight(string inputName, number weight) Sets weight of the synapse that connects the input node with the name !inputName! and the gate node to !weight!.
number :GetBias() Returns the gate node's bias.
void :AddBias(number biasDelta) Adds !biasDelta! to the gate node's bias.
void :SetBias(number bias) Sets the gate node's bias to !bias!.
number :GetLearningRate() Returns the learning rate of the gate node.
ActivationFunction :GetActivationFunction() Returns the ActivationFunction object of the gate node.

Inherited from FeedforwardNetwork:

Only unchanged and unmodified functions are listed below.

NeuralNetwork .newFromSave(string serial) Deserializes and returns the neural network from the !serial! string: the save string.
string :Save() Returns the serialized form of the neural network as a string, allowing you to save the network.
void :SetBiases(number bias) Sets the bias of every node to !bias!.
void :ConnectNodes(Node inNode, Node outNode, bool checkOveride=false) Creates a synapse for the 2 nodes with !inNode! as the input and !outNode! as the output. If !checkOveride! is true, this function will not check if the synapse between these 2 nodes already exists; this is purely for internal performance purposes where the check is unnecessary. Do not set this to true if you are experimenting unless you're confident.
dictionary :(dictionary inputValues, bool doNotClearOtherInputValues=false) Propagates the network with the given !inputValues! if provided. If not, the network will run with the input values already set previously. If !doNotClearOtherInputValues! is true, any inputs that are missing from the !inputValues! dictionary are set to 0.

This function is fired when the NeuralNetwork object is called, such as: 
neuralNetwork:({Input1 = 0.5, Input2 = -0.2})
dictionary :GetOutputValues() Returns the current output values.
void :SetInputValues(dictionary inputValues, bool doNotClearOtherInputValues=false) Sets the given input values as !inputValues! into the input nodes. If !doNotClearOtherInputValues! is true, any inputs that are missing from the !inputValues! dictionary are set to 0.
void :ClearInputValues() Sets all input values of the input nodes to 0.
void :ClearValues() Sets all the output values of every functional node to 0.
array :GetNodes() Returns every node in the network in an unreliable order.
array :GetInputNodes() Returns the input nodes in the network in the order they were assigned by the user upon network creation.
void :AddInputNode(InputNode inputNode) Adds input node !inputNode! to the network.
array :GetOutputNodes() Returns the output nodes in the network in the order they were assigned by the user upon network creation.
void :AddOutputNode(OutputNode outputNode) Adds output node !outputNode! to the network.
array :GetHiddenNodes() Returns the hidden nodes in the network in the order they were created (first node to last node of first layer, first node to last node of second layer, etc).
void :AddHiddenNode(Node node) Adds the hidden node !node! to the network.
array :GetFunctionNodes() Returns the functional nodes in the network. Functional nodes, in my definition, are nodes that have a working bias and weight set. Thus, hidden nodes and output nodes are all functional nodes.
void :AddNode(Node node) Adds the node !node! to the network.
BackPropagator :GetBackPropagator() Returns the BackPropagator object for this network.
void :RandomizeWeights(number min=-0.5, number max=0.5) Randomizes all weights in the network with the given minimum !min! and maximum !max! values.
Optimizer :GetOptimizer() Returns the Optimizer object the network is using.
void :AddRandomNoise(number min, number max) Adds a random noise to every parameter in the network with the given minimum !min! and maximum !max! values.