RNNCell

class torch.nn.RNNCell(input_size, hidden_size, bias=True, nonlinearity='tanh', device=None, dtype=None) [source]

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.

Parameters

• input_size (int) – The number of expected features in the input x
• hidden_size (int) – The number of features in the hidden state h
• bias (bool) – If False, then the layer does not use bias weights b_ih and b_hh. Default: True
• nonlinearity (str) – 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.

Variables

• weight_ih (torch.Tensor) – the learnable input-hidden weights, of shape (hidden_size, input_size)
• weight_hh (torch.Tensor) – 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 $k = \frac{1}{\text{hidden\_size}}$

Examples:

>>> rnn = nn.RNNCell(10, 20)
>>> input = torch.randn(6, 3, 10)
>>> hx = torch.randn(3, 20)
>>> output = []
>>> for i in range(6):
...     hx = rnn(input[i], hx)
...     output.append(hx)