/// <summary>
/// nitialize with l2 weight
/// </summary>
/// <param name="l2">The l2 weight value.</param>
/// <returns>Regulizers.</returns>
public static Regulizers RegL2(double l2 = 0.01)
{
Regulizers result = new Regulizers()
{
IsL2 = true,
L2 = l2,
};
return(result);
}
/// <summary>
/// nitialize with l1 weight
/// </summary>
/// <param name="l1">The l1 weight value.</param>
/// <returns>Regulizers.</returns>
public static Regulizers RegL1(double l1 = 0.01)
{
Regulizers result = new Regulizers()
{
IsL1 = true,
L1 = l1,
};
return(result);
}
/// <summary>
/// Initialize with l1 and l2 weight
/// </summary>
/// <param name="l1">The l1 weight value.</param>
/// <param name="l2">The l2 weight value.</param>
/// <returns>Regulizers.</returns>
public static Regulizers L1L2(double l1 = 0.01, double l2 = 0.01)
{
Regulizers result = new Regulizers()
{
IsL1 = true,
IsL2 = true,
L1 = l1,
L2 = l2
};
return(result);
}
/// <summary>
/// Initialize with l2 weight
/// </summary>
/// <param name="l2">The l2 weight value.</param>
/// <param name="gradientClippingWithTruncation">if set to <c>true</c> [gradient clipping with truncation].</param>
/// <param name="gradientClippingThresholdPerSample">The gradient clipping threshold per sample.</param>
/// <returns>
/// Regulizers.
/// </returns>
public static Regulizers RegL2(double l2 = 0.01, bool gradientClippingWithTruncation = false, double?gradientClippingThresholdPerSample = null)
{
Regulizers result = new Regulizers()
{
IsL2 = true,
L2 = l2,
GradientClippingWithTruncation = gradientClippingWithTruncation,
GradientClippingThresholdPerSample = gradientClippingThresholdPerSample
};
return(result);
}
/// <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(string optimizer, string loss, string metric, Regulizers regulizer = null)
{
CompileModel();
BaseOptimizer optimizerInstance = new BaseOptimizer(optimizer);
learners.Add(optimizerInstance.GetDefault(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;
}
}