orthog_control: Options for orthogonalization
In deepregression: Fitting Deep Distributional Regression

orthog_control

R Documentation

Options for orthogonalization

Description

Options for orthogonalization

Usage

orthog_control(
  split_fun = split_model,
  orthog_type = c("tf", "manual"),
  orthogonalize = options()$orthogonalize,
  identify_intercept = options()$identify_intercept,
  deep_top = NULL,
  orthog_fun = NULL,
  deactivate_oz_at_test = TRUE
)

Arguments

`split_fun`	a function separating the deep neural network in two parts so that the orthogonalization can be applied to the first part before applying the second network part; per default, the function `split_model` is used which assumes a dense layer as penultimate layer and separates the network into a first part without this last layer and a second part only consisting of a single dense layer that is fed into the output layer
`orthog_type`	one of two options; If `"manual"`, the QR decomposition is calculated before model fitting, otherwise (`"tf"`) a QR is calculated in each batch iteration via TF. The first only works well for larger batch sizes or ideally batch_size == NROW(y).
`orthogonalize`	logical; if set to `TRUE`, automatic orthogonalization is activated
`identify_intercept`	whether to orthogonalize the deep network w.r.t. the intercept to make the intercept identifiable
`deep_top`	function; optional function to put on top of the deep network instead of splitting the function using `split_fun`
`orthog_fun`	function; for custom orthogonaliuation. if NULL, `orthog_type` is used to define the function that computes the orthogonalization
`deactivate_oz_at_test`	logical; whether to deactive the orthogonalization cell at test time when using `orthog_tf` for `orthog_fun` (the default).