/// <summary>
/// ClassificationNeuralNet learner using mini-batch gradient descent.
/// Several optimization methods is availible through the constructor.
/// </summary>
/// <param name="net">The neural net to learn</param>
/// <param name="loss">The loss measured and shown between each iteration</param>
/// <param name="learningRate">Controls the step size when updating the weights. (Default is 0.001)</param>
/// <param name="iterations">The maximum number of iterations before termination. (Default is 100)</param>
/// <param name="batchSize">Batch size for mini-batch stochastic gradient descent. (Default is 128)</param>
/// <param name="l1decay">L1 reguralization term. (Default is 0, so no reguralization)</param>
/// <param name="l2decay">L2 reguralization term. (Default is 0, so no reguralization)</param>
/// <param name="optimizerMethod">The method used for optimization (Default is RMSProp)</param>
/// <param name="momentum">Momentum for gradient update. Should be between 0 and 1. (Defualt is 0.9)</param>
/// <param name="rho">Squared gradient moving average decay factor (Default is 0.95)</param>
/// <param name="beta1">Exponential decay rate for estimates of first moment vector, should be in range 0 to 1 (Default is 0.9)</param>
/// <param name="beta2">Exponential decay rate for estimates of second moment vector, should be in range 0 to 1 (Default is 0.999)</param>
public ClassificationNeuralNetLearner(NeuralNet net, ILoss loss, double learningRate = 0.001, int iterations = 100, int batchSize = 128, double l1decay = 0, double l2decay = 0,
OptimizerMethod optimizerMethod = OptimizerMethod.RMSProp, double momentum = 0.9, double rho = 0.95, double beta1 = 0.9, double beta2 = 0.999)
{
if (!(net.Layers.Last() is IClassificationLayer))
{
throw new ArgumentException("Last layer must be a classification layer type. Was: " + net.Layers.Last().GetType().Name);
}
m_learner = new NeuralNetLearner(net, new OneOfNTargetEncoder(), loss,
learningRate, iterations, batchSize, l1decay, l2decay, optimizerMethod, momentum, rho, beta1, beta2);
}
