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

Applies a multi-layer Elman RNN with tanhtanh or text{ReLU}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 \(\tanh\).

The interface is consistent with PyTorch. The documentation is referenced from:

  • 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) 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: 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: tensor of shape \((L, N, H_{in})\) when batch_first=False or \((N, L, H_{in})\) when batch_first=True containing the features of the input sequence.

  • h_0: tensor of shape \((D * \text{num\_layers}, N, H_{out})\) containing the initial hidden state for each element in the batch. Defaults to zeros if not provided.


\[\begin{split}\begin{aligned} N ={} & \text{batch size} \\ L ={} & \text{sequence length} \\ D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\ H_{in} ={} & \text{input_size} \\ H_{out} ={} & \text{hidden_size} \end{aligned}\end{split}\]
Outputs: output, h_n
  • output: tensor of shape \((L, N, D * H_{out})\) when batch_first=False or \((N, L, D * H_{out})\) when batch_first=True containing the output features (h_t) from the last layer of the RNN, for each t.

  • h_n: tensor of shape \((D * \text{num\_layers}, N, H_{out})\) containing the final hidden state for each element in the batch.


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)


the learnable hidden-hidden weights of the k-th layer, of shape (hidden_size, hidden_size)


the learnable input-hidden bias of the k-th layer, of shape (hidden_size)


the learnable hidden-hidden bias of the k-th layer, of shape (hidden_size)


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


For bidirectional RNNs, forward and backward are directions 0 and 1 respectively. Example of splitting the output layers when batch_first=False: output.view((seq_len, batch, num_directions, hidden_size)).

For example:

>>> import oneflow as flow
>>> import numpy as np
>>> rnn = flow.nn.RNN(10, 20, 2)
>>> input = flow.tensor(np.random.randn(5, 3, 10), dtype=flow.float32)
>>> h0 = flow.tensor(np.random.randn(2, 3, 20), dtype=flow.float32)
>>> output, hn = rnn(input, h0)
>>> output.size()
oneflow.Size([5, 3, 20])