FC_GPD_SNN: Self-normalized fully-connected network module for GPD...

In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

Self-normalized fully-connected network module for GPD parameter prediction

Description

A fully-connected self-normalizing network as a torch::nn_module, designed for generalized Pareto distribution parameter prediction.

Usage

FC_GPD_SNN(D_in, Hidden_vect = c(64, 64, 64), p_drop = 0.01)

Arguments

D_in	the input size (i.e. the number of features),
Hidden_vect	a vector of integers whose length determines the number of layers in the neural network and entries the number of neurons in each corresponding successive layer,
p_drop	probability parameter for the alpha-dropout before each hidden layer for regularization during training.

Details

The constructor allows specifying:

D_in

the input size (i.e. the number of features),

Hidden_vect

a vector of integers whose length determines the number of layers in the neural network and entries the number of neurons in each corresponding successive layer,

p_drop

probability parameter for the alpha-dropout before each hidden layer for regularization during training.

Value

The specified SNN MLP GPD network as a torch::nn_module.

References

Gunter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter. Self-Normalizing Neural Networks. Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017.

EQRN documentation built on April 4, 2025, 12:45 a.m.