training the model

training.cpp

ens::Adam optimizer(
   STEP_SIZE,  // Step size of the optimizer.
   BATCH_SIZE, // Batch size. Number of data points that are used in each iteration.
   0.9,        // Exponential decay rate for the first moment estimates.
   0.999, // Exponential decay rate for the weighted infinity norm estimates.
   1e-8,  // Value used to initialise the mean squared gradient parameter.
   MAX_ITERATIONS, // Max number of iterations.
   1e-8, // Tolerance.
   true);

model.Train(trainX,
           trainY,
           optimizer,
           ens::PrintLoss(),
           ens::ProgressBar(),
           ens::EarlyStopAtMinLoss(EPOCH),
           ens::EarlyStopAtMinLoss(
               [&](const arma::mat& /* param */)
               {
                 double validationLoss = model.Evaluate(validX, validY);
                 std::cout << "Validation loss: " << validationLoss
                     << "." << std::endl;
                 return validationLoss;
               }));