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 = 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 todefault_device()
.
Details
The constructor allows specifying:
D_inthe input size (i.e. the number of features),
Hidden_vecta 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,
activationthe activation function for the hidden layers (should be either a callable function, preferably from the
torch
library),p_dropprobability parameter for dropout before each hidden layer for regularization during training,
shape_fixedwhether the shape estimate depends on the covariates or not (bool),
devicea
torch::torch_device()
for an internal constant vector. Defaults todefault_device()
.