/// <summary> /// Initialze the trainer object. The error function is used to decide how much the output is wrong and where. /// </summary> public Trainer(SequentialNet nn, Func <float[], float[], bool, float[]> errorFunction, float learningRate, bool printError = true) { NeuralNet = nn; ErrorFunc = errorFunction; if (ErrorFunc == null) { throw new NullReferenceException("Error function is null."); } LearningRate = learningRate; this.printError = printError; }
public BackPropagation(SequentialNet nn, Func <float[], float[], bool, float[]> errorFunction, float learningRate, bool printError = true) : base(nn, errorFunction, learningRate, printError) { }