weight_control: Options for weights of layers
In deepregression: Fitting Deep Distributional Regression

weight_control

R Documentation

Options for weights of layers

Description

Options for weights of layers

Usage

weight_control(
  specific_weight_options = NULL,
  general_weight_options = list(activation = NULL, use_bias = FALSE, trainable = TRUE,
    kernel_initializer = "glorot_uniform", bias_initializer = "zeros", kernel_regularizer
    = NULL, bias_regularizer = NULL, activity_regularizer = NULL, kernel_constraint =
    NULL, bias_constraint = NULL),
  warmstart_weights = NULL,
  shared_layers = NULL
)

Arguments

`specific_weight_options`	specific options for certain weight terms; must be a list of length `length(list_of_formulas)` and each element in turn a named list (names are term names as in the formula) with specific options in a list
`general_weight_options`	default options for layers
`warmstart_weights`	While all keras layer options are availabe, the user can further specify a list for each distribution parameter with list elements corresponding to term names with values as vectors corresponding to start weights of the respective weights
`shared_layers`	list for each distribution parameter; each list item can be again a list of character vectors specifying terms which share layers