RNNLayer Class — RNNLayer • RHealth

Recurrent neural network layer (GRU/LSTM/RNN) with built‑in 1‑based‑index safety, masking, dropout, bidirectionality and a learnable fallback hidden vector for empty sequences.

Usage

RNNLayer(
  input_size,
  hidden_size,
  rnn_type = "GRU",
  num_layers = 1,
  dropout = 0.5,
  bidirectional = FALSE
)

Arguments

input_size: The number of expected features in the input x
hidden_size: The number of features in the hidden state h
rnn_type: Character, one of "GRU", "LSTM", or "RNN". Default "GRU".
num_layers: Number of recurrent layers. E.g., setting num_layers=2 would mean stacking two GRUs together to form a stacked GRU, with the second GRU taking in outputs of the first GRU and computing the final results. Default: 1
dropout: If non-zero, introduces a Dropout layer on the outputs of each GRU layer except the last layer, with dropout probability equal to dropout. Default: 0
bidirectional: If TRUE, becomes a bidirectional GRU. Default: FALSE

Details

Key design points

Accepts mask indicating valid time steps (1‑based indexing respected).
Uses pack_padded_sequence + pad_packed_sequence for efficiency.
Samples whose sequence length is zero (all‑zero rows) are skipped during RNN computation and later filled with a learnable parameter null_hidden so downstream layers always receive a hidden vector.
Works for unidirectional or bidirectional networks. In the bidirectional case, the last hidden state is built from the forward last step and the backward first step, then projected back to hidden_size.