Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.

For each element in the input sequence, each layer computes the following function:

Usage

nn_gru(
  input_size,
  hidden_size,
  num_layers = 1,
  bias = TRUE,
  batch_first = FALSE,
  dropout = 0,
  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
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
bias: If FALSE, then the layer does not use bias weights b_ih and b_hh. Default: TRUE
batch_first: If TRUE, then the input and output tensors are provided as (batch, seq, feature). Default: FALSE
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
...: currently unused.

Details

$\begin{array}{ll} r_{t} = σ (W_{i r} x_{t} + b_{i r} + W_{h r} h_{(t - 1)} + b_{h r}) \\ z_{t} = σ (W_{i z} x_{t} + b_{i z} + W_{h z} h_{(t - 1)} + b_{h z}) \\ n_{t} = \tanh (W_{i n} x_{t} + b_{i n} + r_{t} (W_{h n} h_{(t - 1)} + b_{h n})) \\ h_{t} = (1 - z_{t}) n_{t} + z_{t} h_{(t - 1)} \end{array}$

where $h_{t}$ is the hidden state at time t, $x_{t}$ is the input at time t, $h_{(t - 1)}$ is the hidden state of the previous layer at time t-1 or the initial hidden state at time 0, and $r_{t}$ , $z_{t}$ , $n_{t}$ are the reset, update, and new gates, respectively. $σ$ is the sigmoid function.

Note

All the weights and biases are initialized from $U (- \sqrt{k}, \sqrt{k})$ where $k = \frac{1}{hidden\_size}$

Inputs

Inputs: input, h_0

input of shape (seq_len, batch, input_size): tensor containing the features of the input sequence. The input can also be a packed variable length sequence. See nn_utils_rnn_pack_padded_sequence() for details.
h_0 of shape (num_layers * num_directions, batch, hidden_size): tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided.

Outputs

Outputs: output, h_n

output of shape (seq_len, batch, num_directions * hidden_size): tensor containing the output features h_t from the last layer of the GRU, for each t. If a PackedSequence has been given as the input, the output will also be a packed sequence. For the unpacked case, the directions can be separated using output$view(c(seq_len, batch, num_directions, hidden_size)), with forward and backward being direction 0 and 1 respectively. Similarly, the directions can be separated in the packed case.
h_n of shape (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for t = seq_len Like output, the layers can be separated using h_n$view(num_layers, num_directions, batch, hidden_size).

Attributes

weight_ih_l[k] : the learnable input-hidden weights of the $k^{t h}$ layer (W_ir|W_iz|W_in), of shape (3*hidden_size x input_size)
weight_hh_l[k] : the learnable hidden-hidden weights of the $k^{t h}$ layer (W_hr|W_hz|W_hn), of shape (3*hidden_size x hidden_size)
bias_ih_l[k] : the learnable input-hidden bias of the $k^{t h}$ layer (b_ir|b_iz|b_in), of shape (3*hidden_size)
bias_hh_l[k] : the learnable hidden-hidden bias of the $k^{t h}$ layer (b_hr|b_hz|b_hn), of shape (3*hidden_size)

Examples

if (torch_is_installed()) {

rnn <- nn_gru(10, 20, 2)
input <- torch_randn(5, 3, 10)
h0 <- torch_randn(2, 3, 20)
output <- rnn(input, h0)
}