/// <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)
{
}
