Function that loads a previously trained vaeac model and continue the training, either on new data or on the same dataset as it was trained on before. If we are given a new dataset, then we assume that new dataset has the same distribution and one_hot_max_sizes as the original dataset.
Usage
vaeac_train_model_continue(
explanation,
epochs_new,
lr_new = NULL,
x_train = NULL,
save_data = FALSE,
verbose = NULL,
seed = 1
)Arguments
- explanation
A
explain()object andvaeacmust be the used approach.- epochs_new
Positive integer. The number of extra epochs to conduct.
- lr_new
Positive numeric. If we are to overwrite the old learning rate in the adam optimizer.
- x_train
A data.table containing the training data. Categorical data must have class names \(1,2,\dots,K\).
- save_data
Logical (default is
FALSE). IfTRUE, then the data is stored together with the model. Useful if one are to continue to train the model later usingvaeac_train_model_continue().- verbose
String vector or NULL. Controls verbosity (printout detail level) via one or more of
"basic","progress","convergence","shapley"and"vS_details"."basic"(default) displays basic information about the computation and messages about parameters/checks."progress"displays where in the calculation process the function currently is."convergence"displays how close the Shapley value estimates are to convergence (only wheniterative = TRUE)."shapley"displays intermediate Shapley value estimates and standard deviations (only wheniterative = TRUE), and the final estimates."vS_details"displays information about the v(S) estimates, most relevant forapproach %in% c("regression_separate", "regression_surrogate", "vaeac").NULLmeans no printout. Any combination can be used, e.g.,verbose = c("basic", "vS_details").- seed
Positive integer (default is
1). Seed for reproducibility. Specifies the seed before any randomness based code is being run.
Value
A list containing the training/validation errors and paths to where the vaeac models are saved on the disk.