L2 Regularization

Presentation

This optimizer is a layer optimizer that add a value (regularization penalty) to the error of a layer that depends on the average. This regularization corresponds to the average value of weights of each neurons in the layer multiply by a small factor.

Declaration

This is the function used to declare a L2 Regularization optimizer.

OptimizerModel L2Regularization(float value);

Arguments

• value: The value of the factor. This value must be very low, a good value would be between 1e-2 and 1e-5.

Here is an example of L2 Regularization.

    StraightforwardNeuralNetwork neuralNetwork({
        Input(784),
        FullyConnected(100, activation::sigmoid, L2Regularization(1e-4f)),
        GruLayer(10),
        FullyConnected(10)
    });