GRUCell¶
-
class
torch.nn.
GRUCell
(input_size, hidden_size, bias=True, device=None, dtype=None)[source]¶ A gated recurrent unit (GRU) cell
where is the sigmoid function, and is the Hadamard product.
- 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
- Inputs: input, hidden
input : tensor containing input features
hidden : tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided.
- Outputs: h’
h’ : tensor containing the next hidden state for each element in the batch
- Shape:
input: or tensor containing input features where = input_size.
hidden: or tensor containing the initial hidden state where = hidden_size. Defaults to zero if not provided.
output: or tensor containing the next hidden state.
- Variables
~GRUCell.weight_ih (torch.Tensor) – the learnable input-hidden weights, of shape (3*hidden_size, input_size)
~GRUCell.weight_hh (torch.Tensor) – the learnable hidden-hidden weights, of shape (3*hidden_size, hidden_size)
~GRUCell.bias_ih – the learnable input-hidden bias, of shape (3*hidden_size)
~GRUCell.bias_hh – the learnable hidden-hidden bias, of shape (3*hidden_size)
Note
All the weights and biases are initialized from where
Examples:
>>> rnn = nn.GRUCell(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)