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. Specifies the verbosity (printout detail level) through one or more of strings
"basic","progress","convergence","shapley"and"vS_details"."basic"(default) displays basic information about the computation which is being performed, in addition to some messages about parameters being sets or checks being unavailable due to specific input."progressdisplays information about where in the calculation process the function currently is. #'"convergence"displays information on how close to convergence the Shapley value estimates are (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. This is most relevant forapproach %in% c("regression_separate", "regression_surrogate", "vaeac").NULLmeans no printout. Note that any combination of four strings can be used. E.g.verbose = c("basic", "vS_details")will display basic information + details about the v(S)-estimation process.- 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.