torch / nn / torch.nn
LSTMCell¶
-
class
torch.nn.
LSTMCell
(input_size: int, hidden_size: int, bias: bool = True)[source]¶ A long short-term memory (LSTM) 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, (h_0, c_0)
input of shape (batch, input_size): tensor containing input features
h_0 of shape (batch, hidden_size): tensor containing the initial hidden state for each element in the batch.
c_0 of shape (batch, hidden_size): tensor containing the initial cell state for each element in the batch.
If (h_0, c_0) is not provided, both h_0 and c_0 default to zero.
- Outputs: (h_1, c_1)
h_1 of shape (batch, hidden_size): tensor containing the next hidden state for each element in the batch
c_1 of shape (batch, hidden_size): tensor containing the next cell state for each element in the batch
- Variables
~LSTMCell.weight_ih – the learnable input-hidden weights, of shape (4*hidden_size, input_size)
~LSTMCell.weight_hh – the learnable hidden-hidden weights, of shape (4*hidden_size, hidden_size)
~LSTMCell.bias_ih – the learnable input-hidden bias, of shape (4*hidden_size)
~LSTMCell.bias_hh – the learnable hidden-hidden bias, of shape (4*hidden_size)
Note
All the weights and biases are initialized from where
Examples:
>>> rnn = nn.LSTMCell(10, 20) >>> input = torch.randn(3, 10) >>> hx = torch.randn(3, 20) >>> cx = torch.randn(3, 20) >>> output = [] >>> for i in range(6): hx, cx = rnn(input[i], (hx, cx)) output.append(hx)
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