RNN

class torch.nn.RNN(*args, **kwargs)

Applies a multi-layer Elman RNN with $\tanh$ or $\text{ReLU}$ non-linearity to an input sequence.

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

$$h_t = \tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh})$$

where $h_t$ is the hidden state at time t, $x_t$ is the input at time t, and $h_{(t-1)}$ is the hidden state of the previous layer at time t-1 or the initial hidden state at time 0. If nonlinearity is 'relu', then $\text{ReLU}$ is used instead of $\tanh$.

Parameters

• 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 RNNs together to form a stacked RNN, with the second RNN taking in outputs of the first RNN and computing the final results. Default: 1

• nonlinearity – The non-linearity to use. Can be either 'tanh' or 'relu'. Default: 'tanh'

• 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 RNN layer except the last layer, with dropout probability equal to dropout. Default: 0

• bidirectional – If True, becomes a bidirectional RNN. Default: False

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 torch.nn.utils.rnn.pack_padded_sequence() or torch.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. Defaults to zero if not provided. If the RNN is bidirectional, num_directions should be 2, else it should be 1.

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 RNN, 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(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).

Shape:

• Input1: $(L, N, H_{in})$ tensor containing input features where $H_{in}=\text{input\_size}$ and L represents a sequence length.

• Input2: $(S, N, H_{out})$ tensor containing the initial hidden state for each element in the batch. $H_{out}=\text{hidden\_size}$ Defaults to zero if not provided. where $S=\text{num\_layers} * \text{num\_directions}$ If the RNN is bidirectional, num_directions should be 2, else it should be 1.

• Output1: $(L, N, H_{all})$ where $H_{all}=\text{num\_directions} * \text{hidden\_size}$

• Output2: $(S, N, H_{out})$ tensor containing the next hidden state for each element in the batch

Variables

• ~RNN.weight_ih_l[k] – the learnable input-hidden weights of the k-th layer, of shape (hidden_size, input_size) for k = 0. Otherwise, the shape is (hidden_size, num_directions * hidden_size)

• ~RNN.weight_hh_l[k] – the learnable hidden-hidden weights of the k-th layer, of shape (hidden_size, hidden_size)

• ~RNN.bias_ih_l[k] – the learnable input-hidden bias of the k-th layer, of shape (hidden_size)

• ~RNN.bias_hh_l[k] – the learnable hidden-hidden bias of the k-th layer, of shape (hidden_size)

Note

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

Warning

There are known non-determinism issues for RNN functions on some versions of cuDNN and CUDA. You can enforce deterministic behavior by setting the following environment variables:

On CUDA 10.1, set environment variable CUDA_LAUNCH_BLOCKING=1. This may affect performance.

On CUDA 10.2 or later, set environment variable (note the leading colon symbol) CUBLAS_WORKSPACE_CONFIG=:16:8 or CUBLAS_WORKSPACE_CONFIG=:4096:2.

See the cuDNN 8 Release Notes for more information.

Orphan

Note

If the following conditions are satisfied: 1) cudnn is enabled, 2) input data is on the GPU 3) input data has dtype torch.float16 4) V100 GPU is used, 5) input data is not in PackedSequence format persistent algorithm can be selected to improve performance.

Examples:

>>> rnn = nn.RNN(10, 20, 2)
>>> input = torch.randn(5, 3, 10)
>>> h0 = torch.randn(2, 3, 20)
>>> output, hn = rnn(input, h0)