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

Source:`R/nn-rnn.R`

`nn_gru.Rd`

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

$$ \begin{array}{ll} r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\ n_t = \tanh(W_{in} x_t + b_{in} + r_t (W_{hn} h_{(t-1)}+ b_{hn})) \\ h_t = (1 - z_t) n_t + z_t h_{(t-1)} \end{array} $$

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.
\(\sigma\) is the sigmoid function.

## Note

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

## 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 \(\mbox{k}^{th}\) 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 \(\mbox{k}^{th}\) 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 \(\mbox{k}^{th}\) layer (b_ir|b_iz|b_in), of shape`(3*hidden_size)`

`bias_hh_l[k]`

: the learnable hidden-hidden bias of the \(\mbox{k}^{th}\) 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)
}
```