processors: Function that creates layer for each processor

In deepregression: Fitting Deep Distributional Regression

Function that creates layer for each processor

Description

Function that creates layer for each processor

Usage

layer_generator(
  term,
  output_dim,
  param_nr,
  controls,
  layer_class = tf$keras$layers$Dense,
  without_layer = tf$identity,
  name = makelayername(term, param_nr),
  further_layer_args = NULL,
  layer_args_names = NULL,
  units = as.integer(output_dim),
  ...
)

int_processor(term, data, output_dim, param_nr, controls)

lin_processor(term, data, output_dim, param_nr, controls)

gam_processor(term, data, output_dim, param_nr, controls)

Arguments

term	character; term in the formula
output_dim	integer; number of units in the layer
param_nr	integer; identifier for models with more than one additive predictor
controls	list; control arguments which allow to pass further information
layer_class	a tf or keras layer function
without_layer	function to be used as layer if controls$with_layer is FALSE
name	character; name of layer. if NULL, makelayername will be used to create layer name
further_layer_args	named list; further arguments passed to the layer
layer_args_names	character vector; if NULL, default layer args will be used. Needs to be set for layers that do not provide the arguments of a default Dense layer.
units	integer; number of units for layer
...	other keras layer parameters
data	data frame; the data used in processors

Value

a basic processor list structure

deepregression documentation built on Jan. 18, 2023, 1:11 a.m.