tabnet_nn: TabNet Model Architecture

In tabnet: Fit 'TabNet' Models for Classification and Regression

TabNet Model Architecture

Description

This is a nn_module representing the TabNet architecture from Attentive Interpretable Tabular Deep Learning.

Usage

tabnet_nn(
  input_dim,
  output_dim,
  n_d = 8,
  n_a = 8,
  n_steps = 3,
  gamma = 1.3,
  cat_idxs = c(),
  cat_dims = c(),
  cat_emb_dim = 1,
  n_independent = 2,
  n_shared = 2,
  epsilon = 1e-15,
  virtual_batch_size = 128,
  momentum = 0.02,
  mask_type = "sparsemax"
)

Arguments

input_dim	Initial number of features.
output_dim	Dimension of network output examples : one for regression, 2 for binary classification etc.. Vector of those dimensions in case of multi-output.
n_d	Dimension of the prediction layer (usually between 4 and 64).
n_a	Dimension of the attention layer (usually between 4 and 64).
n_steps	Number of successive steps in the network (usually between 3 and 10).
gamma	Float above 1, scaling factor for attention updates (usually between 1 and 2).
cat_idxs	Index of each categorical column in the dataset.
cat_dims	Number of categories in each categorical column.
cat_emb_dim	Size of the embedding of categorical features if int, all categorical features will have same embedding size if list of int, every corresponding feature will have specific size.
n_independent	Number of independent GLU layer in each GLU block of the encoder.
n_shared	Number of independent GLU layer in each GLU block of the encoder.
epsilon	Avoid log(0), this should be kept very low.
virtual_batch_size	Batch size for Ghost Batch Normalization.
momentum	Float value between 0 and 1 which will be used for momentum in all batch norm.
mask_type	Either "sparsemax" or "entmax" : this is the masking function to use.

tabnet documentation built on June 22, 2024, 10:22 a.m.