torch / nn / torch.nn
RNNCell¶
-
class
torch.nn.
RNNCell
(input_size: int, hidden_size: int, bias: bool = True, nonlinearity: str = 'tanh')[source]¶ An Elman RNN cell with tanh or ReLU non-linearity.
If
nonlinearity
is ‘relu’, then ReLU is used in place of tanh.- 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 of shape (batch, input_size): tensor containing input features
hidden of shape (batch, hidden_size): tensor containing the initial hidden state for each element in the batch. 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:
Input1: tensor containing input features where = input_size
Input2: tensor containing the initial hidden state for each element in the batch where = hidden_size Defaults to zero if not provided.
Output: tensor containing the next hidden state for each element in the batch
- Variables
~RNNCell.weight_ih – the learnable input-hidden weights, of shape (hidden_size, input_size)
~RNNCell.weight_hh – the learnable hidden-hidden weights, of shape (hidden_size, hidden_size)
~RNNCell.bias_ih – the learnable input-hidden bias, of shape (hidden_size)
~RNNCell.bias_hh – the learnable hidden-hidden bias, of shape (hidden_size)
Note
All the weights and biases are initialized from where
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)
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