oneflow.nn.RNNCell

class oneflow.nn.RNNCell(input_size: int, hidden_size: int, bias: bool = True, nonlinearity: str = 'tanh', device=None, dtype=None)

An Elman RNN cell with tanh or ReLU non-linearity.

\[h' = \tanh(W_{ih} x + b_{ih} + W_{hh} h + b_{hh})\]

If nonlinearity is ‘relu’, then ReLU is used in place of tanh.

The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.RNNCell.html.

Parameters
  • input_size – The number of expected features in the input x

  • hidden_size – The number of features in the hidden state h

  • bias – If False, then the layer does not use bias weights b_ih and b_hh. Default: True

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

Inputs: input, hidden
  • input: tensor containing input features

  • hidden: tensor containing the initial hidden state Defaults to zero if not provided.

Outputs: h’
  • h’ of shape (batch, hidden_size): tensor containing the next hidden state for each element in the batch

Shape:
  • input: \((N, H_{in})\) or \((H_{in})\) tensor containing input features where \(H_{in}\) = input_size.

  • hidden: \((N, H_{out})\) or \((H_{out})\) tensor containing the initial hidden state where \(H_{out}\) = hidden_size. Defaults to zero if not provided.

  • output: \((N, H_{out})\) or \((H_{out})\) tensor containing the next hidden state.

weight_ih

the learnable input-hidden weights, of shape (hidden_size, input_size)

weight_hh

the learnable hidden-hidden weights, of shape (hidden_size, hidden_size)

bias_ih

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

bias_hh

the learnable hidden-hidden bias, of shape (hidden_size)

Note

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

For example:

>>> import oneflow as flow
>>> import oneflow.nn as nn

>>> rnn = nn.RNNCell(10, 20)
>>> input = flow.randn(6, 3, 10)
>>> hx = flow.randn(3, 20)
>>> hx = rnn(input[0], hx)
>>> hx.size()
oneflow.Size([3, 20])