instantiate_EQRN_network: Instantiates the default networks for training a EQRN_iid...

In EQRN: Extreme Quantile Regression Neural Networks for Risk Forecasting

Instantiates the default networks for training a EQRN_iid model

Description

Instantiates the default networks for training a EQRN_iid model

Usage

instantiate_EQRN_network(
  net_structure,
  shape_fixed,
  D_in,
  hidden_fct,
  p_drop = 0,
  orthogonal_gpd = TRUE,
  device = default_device()
)

Arguments

net_structure	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.
shape_fixed	Whether the shape estimate depends on the covariates or not (bool).
D_in	Number of covariates (including the intermediate quantile feature if used).
hidden_fct	Activation function for the hidden layers. Can be either a callable function (preferably from the torch library), or one of the the strings "SNN", "SSNN" for self normalizing networks (with common or separated networks for the scale and shape estimates, respectively). In the latter cases, shape_fixed has no effect.
p_drop	Probability parameter for dropout before each hidden layer for regularization during training. alpha-dropout is used with SNNs.
orthogonal_gpd	Whether to use the orthogonal reparametrization of the estimated GPD parameters (recommended).
device	(optional) A torch::torch_device(). Defaults to default_device().

Value

A torch::nn_module network used to regress the GPD parameters in EQRN_fit().

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