setup_TabNet: Setup TabNet Hyperparameters
In egenn/rtemis: Machine Learning and Visualization

setup_TabNet

R Documentation

Setup TabNet Hyperparameters

Description

Setup hyperparameters for TabNet training.

Usage

setup_TabNet(
  batch_size = 1024^2,
  penalty = 0.001,
  clip_value = NULL,
  loss = "auto",
  epochs = 50L,
  drop_last = FALSE,
  decision_width = NULL,
  attention_width = NULL,
  num_steps = 3L,
  feature_reusage = 1.3,
  mask_type = "sparsemax",
  virtual_batch_size = 256^2,
  valid_split = 0,
  learn_rate = 0.02,
  optimizer = "adam",
  lr_scheduler = NULL,
  lr_decay = 0.1,
  step_size = 30,
  checkpoint_epochs = 10L,
  cat_emb_dim = 1L,
  num_independent = 2L,
  num_shared = 2L,
  num_independent_decoder = 1L,
  num_shared_decoder = 1L,
  momentum = 0.02,
  pretraining_ratio = 0.5,
  device = "auto",
  importance_sample_size = NULL,
  early_stopping_monitor = "auto",
  early_stopping_tolerance = 0,
  early_stopping_patience = 0,
  num_workers = 0L,
  skip_importance = FALSE,
  ifw = FALSE
)

Arguments

`batch_size`	(Tunable) Positive integer: Batch size.
`penalty`	(Tunable) Numeric: Regularization penalty.
`clip_value`	Numeric: Clip value.
`loss`	Character: Loss function.
`epochs`	(Tunable) Positive integer: Number of epochs.
`drop_last`	Logical: If TRUE, drop last batch.
`decision_width`	(Tunable) Positive integer: Decision width.
`attention_width`	(Tunable) Positive integer: Attention width.
`num_steps`	(Tunable) Positive integer: Number of steps.
`feature_reusage`	(Tunable) Numeric: Feature reusage.
`mask_type`	Character: Mask type.
`virtual_batch_size`	(Tunable) Positive integer: Virtual batch size.
`valid_split`	Numeric: Validation split.
`learn_rate`	(Tunable) Numeric: Learning rate.
`optimizer`	Character or torch function: Optimizer.
`lr_scheduler`	Character or torch function: "step", "reduce_on_plateau".
`lr_decay`	Numeric: Learning rate decay.
`step_size`	Positive integer: Step size.
`checkpoint_epochs`	(Tunable) Positive integer: Checkpoint epochs.
`cat_emb_dim`	(Tunable) Positive integer: Categorical embedding dimension.
`num_independent`	(Tunable) Positive integer: Number of independent Gated Linear Units (GLU) at each step of the encoder.
`num_shared`	(Tunable) Positive integer: Number of shared Gated Linear Units (GLU) at each step of the encoder.
`num_independent_decoder`	(Tunable) Positive integer: Number of independent GLU layers for pretraining.
`num_shared_decoder`	(Tunable) Positive integer: Number of shared GLU layers for pretraining.
`momentum`	(Tunable) Numeric: Momentum.
`pretraining_ratio`	(Tunable) Numeric: Pretraining ratio.
`device`	Character: Device "cpu" or "cuda".
`importance_sample_size`	Positive integer: Importance sample size.
`early_stopping_monitor`	Character: Early stopping monitor. "valid_loss", "train_loss", "auto".
`early_stopping_tolerance`	Numeric: Minimum relative improvement to reset the patience counter.
`early_stopping_patience`	Positive integer: Number of epochs without improving before stopping.
`num_workers`	Positive integer: Number of subprocesses for data loacding.
`skip_importance`	Logical: If TRUE, skip importance calculation.
`ifw`	Logical: If TRUE, use Inverse Frequency Weighting in classification.