/// <summary>
/// Configures the model for training.
/// </summary>
/// <param name="optimizer">The optimizer function name used for training the model.</param>
/// <param name="loss">The function name with which the training loss will be minimized.</param>
/// <param name="metric"> The metric name to be evaluated by the model during training and testing.</param>
/// <param name="regulizer">The regulizer instance to apply penalty on layers parameters.</param>
public void Compile(BaseOptimizer optimizer, string loss, string metric = "", Regulizers regulizer = null)
{
CompileModel();
learners.Add(optimizer.Get(modelOut, regulizer));
lossName = loss;
lossFunc = Losses.Get(loss, labelVariable, modelOut);
if (!string.IsNullOrWhiteSpace(metric))
{
metricName = metric;
metricFunc = Metrics.Get(metric, labelVariable, modelOut);
}
else
{
metricName = loss;
metricFunc = lossFunc;
}
}
/// <summary>
/// Mean Absolute Percentage Error the specified labels.
/// </summary>
/// <param name="labels">The labels.</param>
/// <param name="predictions">The predictions.</param>
/// <returns>Function.</returns>
private static Function MAPE(Variable labels, Variable predictions)
{
return(Losses.MeanAbsPercentageError(labels, predictions));
}
/// <summary>
/// Mean Squared Error of the specified labels.
/// </summary>
/// <param name="labels">The labels.</param>
/// <param name="predictions">The predictions.</param>
/// <returns>Function.</returns>
private static Function MSE(Variable labels, Variable predictions)
{
return(Losses.MeanSquaredError(labels, predictions));
}
