ggml_layer_gru: Add a GRU Layer

In ggmlR: 'GGML' Tensor Operations for Machine Learning

Add a GRU Layer

Description

Gated Recurrent Unit recurrent layer. Implemented as an unrolled computation graph (BPTT).

Usage

ggml_layer_gru(
  model,
  units,
  return_sequences = FALSE,
  activation = "tanh",
  recurrent_activation = "sigmoid",
  input_shape = NULL,
  name = NULL,
  trainable = TRUE
)

Arguments

model	A ggml_sequential_model or ggml_tensor_node.
units	Integer, number of hidden units.
return_sequences	Logical; return all hidden states or only the last.
activation	Activation for the candidate hidden state ("tanh").
recurrent_activation	Activation for z/r gates ("sigmoid").
input_shape	Input shape c(seq_len, input_size) – required for the first layer only.
name	Optional layer name.
trainable	Logical.

Value

Updated model or a new ggml_tensor_node.

Weight layout

• W_zh [input_size, 2*units] — input kernel for z and r gates.

• U_zh [units, 2*units] — recurrent kernel for z and r.

• b_zh [2*units] — bias for z and r.

• W_n [input_size, units] — input kernel for candidate.

• U_n [units, units] — recurrent kernel for candidate.

• b_n [units] — bias for candidate.

Examples

model <- ggml_model_sequential() |>
  ggml_layer_gru(64L, input_shape = c(10L, 32L)) |>
  ggml_layer_dense(10L, activation = "softmax")

ggmlR documentation built on July 14, 2026, 1:08 a.m.