Applies a multi-layer long short-term memory (LSTM) RNN to an input sequence.

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

Usage

nn_lstm(
  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 LSTMs together to form a stacked LSTM, with the second LSTM taking in outputs of the first LSTM 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 LSTM layer except the last layer, with dropout probability equal to dropout. Default: 0
bidirectional: If TRUE, becomes a bidirectional LSTM. Default: FALSE
...: currently unused.

Details

$$ \begin{array}{ll} \\ i_t = \sigma(W_{ii} x_t + b_{ii} + W_{hi} h_{(t-1)} + b_{hi}) \\ f_t = \sigma(W_{if} x_t + b_{if} + W_{hf} h_{(t-1)} + b_{hf}) \\ g_t = \tanh(W_{ig} x_t + b_{ig} + W_{hg} h_{(t-1)} + b_{hg}) \\ o_t = \sigma(W_{io} x_t + b_{io} + W_{ho} h_{(t-1)} + b_{ho}) \\ c_t = f_t c_{(t-1)} + i_t g_t \\ h_t = o_t \tanh(c_t) \\ \end{array} $$

where $h_t$ is the hidden state at time t, $c_t$ is the cell 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 $i_t$, $f_t$, $g_t$, $o_t$ are the input, forget, cell, and output 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, c_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() or nn_utils_rnn_pack_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.
c_0 of shape (num_layers * num_directions, batch, hidden_size): tensor containing the initial cell state for each element in the batch.

If (h_0, c_0) is not provided, both h_0 and c_0 default to zero.

Outputs

Outputs: output, (h_n, c_n)

output of shape (seq_len, batch, num_directions * hidden_size): tensor containing the output features (h_t) from the last layer of the LSTM, for each t. If a torch_nn.utils.rnn.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(c(num_layers, num_directions, batch, hidden_size)) and similarly for c_n.
c_n (num_layers * num_directions, batch, hidden_size): tensor containing the cell state for t = seq_len

Attributes

weight_ih_l[k] : the learnable input-hidden weights of the $\mbox{k}^{th}$ layer (W_ii|W_if|W_ig|W_io), of shape (4*hidden_size x input_size)
weight_hh_l[k] : the learnable hidden-hidden weights of the $\mbox{k}^{th}$ layer (W_hi|W_hf|W_hg|W_ho), of shape (4*hidden_size x hidden_size)
bias_ih_l[k] : the learnable input-hidden bias of the $\mbox{k}^{th}$ layer (b_ii|b_if|b_ig|b_io), of shape (4*hidden_size)
bias_hh_l[k] : the learnable hidden-hidden bias of the $\mbox{k}^{th}$ layer (b_hi|b_hf|b_hg|b_ho), of shape (4*hidden_size)

Examples

if (torch_is_installed()) {
rnn <- nn_lstm(10, 20, 2)
input <- torch_randn(5, 3, 10)
h0 <- torch_randn(2, 3, 20)
c0 <- torch_randn(2, 3, 20)
output <- rnn(input, list(h0, c0))
}