keras_dr: Compile a Deep Distributional Regression Model

In deepregression: Fitting Deep Distributional Regression

Description

Compile a Deep Distributional Regression Model

Usage

keras_dr(
  list_pred_param,
  weights = NULL,
  optimizer = tf$keras$optimizers$Adam(),
  model_fun = keras_model,
  monitor_metrics = list(),
  from_preds_to_output = from_preds_to_dist,
  loss = from_dist_to_loss(family = list(...)$family, weights = weights),
  additional_penalty = NULL,
  ...
)

Arguments

list_pred_param	list of input-output(-lists) generated from subnetwork_init
weights	vector of positive values; optional (default = 1 for all observations)
optimizer	optimizer used. Per default Adam
model_fun	which function to use for model building (default keras_model)
monitor_metrics	Further metrics to monitor
from_preds_to_output	function taking the list_pred_param outputs and transforms it into a single network output
loss	the model's loss function; per default evaluated based on the arguments family and weights using from_dist_to_loss
additional_penalty	a penalty that is added to the negative log-likelihood; must be a function of model$trainable_weights with suitable subsetting
...	arguments passed to from_preds_to_output

Value

a list with input tensors and output tensors that can be passed to, e.g., keras_model

deepregression documentation built on Oct. 5, 2021, 1:06 a.m.