Applies a multi-layer Elman RNN with or non-linearity to an input sequence.
For each element in the input sequence, each layer computes the following function:
where is the hidden state at time t, is the input at time t, and is the hidden state of the previous layer at time t-1 or the initial hidden state at time 0. If
'relu', then is used instead of .
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=2would 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
bias – If
False, then the layer does not use bias weights b_ih and b_hh. Default:
batch_first – If
True, then the input and output tensors are provided as (batch, seq, feature) instead of (seq, batch, feature). Note that this does not apply to hidden or cell states. See the Inputs/Outputs sections below for details. Default:
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:
- Inputs: input, h_0
input: tensor of shape when
batch_first=Truecontaining the features of the input sequence. The input can also be a packed variable length sequence. See
h_0: tensor of shape containing the initial hidden state for each element in the batch. Defaults to zeros if not provided.
- Outputs: output, h_n
output: tensor of shape when
batch_first=Truecontaining the output features (h_t) from the last layer of the RNN, for each t. If a
torch.nn.utils.rnn.PackedSequencehas been given as the input, the output will also be a packed sequence.
h_n: tensor of shape containing the final hidden state for each element in the batch.
~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)
All the weights and biases are initialized from where
For bidirectional RNNs, forward and backward are directions 0 and 1 respectively. Example of splitting the output layers when
output.view(seq_len, batch, num_directions, hidden_size).
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)
See the cuDNN 8 Release Notes for more information.
If the following conditions are satisfied: 1) cudnn is enabled, 2) input data is on the GPU 3) input data has dtype
torch.float164) V100 GPU is used, 5) input data is not in
PackedSequenceformat persistent algorithm can be selected to improve performance.
>>> rnn = nn.RNN(10, 20, 2) >>> input = torch.randn(5, 3, 10) >>> h0 = torch.randn(2, 3, 20) >>> output, hn = rnn(input, h0)