nn_gru {torch}

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

Description

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.

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)

Note

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