MLP module for GPD parameter prediction

A fully-connected network (or multi-layer perception) as a torch::nn_module, designed for generalized Pareto distribution parameter prediction.

Usage

FC_GPD_net(
  D_in,
  Hidden_vect = c(5, 5, 5),
  activation = torch::nnf_sigmoid,
  p_drop = 0,
  shape_fixed = FALSE,
  device = EQRN::default_device()
)

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,
activation: the activation function for the hidden layers (should be either a callable function, preferably from the torch library),
p_drop: probability parameter for dropout before each hidden layer for regularization during training,
shape_fixed: whether the shape estimate depends on the covariates or not (bool),
device: a torch::torch_device() for an internal constant vector. Defaults to default_device().

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,
activation: the activation function for the hidden layers (should be either a callable function, preferably from the torch library),
p_drop: probability parameter for dropout before each hidden layer for regularization during training,
shape_fixed: whether the shape estimate depends on the covariates or not (bool),
device: a torch::torch_device() for an internal constant vector. Defaults to default_device().