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])