oneflow.nn.LSTMCell¶
-
class
oneflow.nn.
LSTMCell
(input_size: int, hidden_size: int, bias: bool = True, device=None, dtype=None)¶ A long short-term memory (LSTM) cell.
\[\begin{split}\begin{array}{ll} i = \sigma(W_{ii} x + b_{ii} + W_{hi} h + b_{hi}) \\ f = \sigma(W_{if} x + b_{if} + W_{hf} h + b_{hf}) \\ g = \tanh(W_{ig} x + b_{ig} + W_{hg} h + b_{hg}) \\ o = \sigma(W_{io} x + b_{io} + W_{ho} h + b_{ho}) \\ c' = f * c + i * g \\ h' = o * \tanh(c') \\ \end{array}\end{split}\]where \(\sigma\) is the sigmoid function, and \(*\) is the Hadamard product.
The interface is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/1.10/generated/torch.nn.LSTMCell.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
- Inputs: input, (h_0, c_0)
input of shape (batch, input_size) or (input_size): tensor containing input features
h_0 of shape (batch, hidden_size) or (hidden_size): tensor containing the initial hidden state
c_0 of shape (batch, hidden_size) or (hidden_size): tensor containing the initial cell state
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) or (hidden_size): tensor containing the next hidden state
c_1 of shape (batch, hidden_size) or (hidden_size): tensor containing the next cell state
-
weight_ih
¶ the learnable input-hidden weights, of shape (4*hidden_size, input_size)
-
weight_hh
¶ the learnable hidden-hidden weights, of shape (4*hidden_size, hidden_size)
-
bias_ih
¶ the learnable input-hidden bias, of shape (4*hidden_size)
-
bias_hh
¶ the learnable hidden-hidden bias, of shape (4*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.LSTMCell(10, 20) # (input_size, hidden_size) >>> input = flow.randn(2, 3, 10) # (time_steps, batch, input_size) >>> hx = flow.randn(3, 20) # (batch, hidden_size) >>> cx = flow.randn(3, 20) >>> hx, cx = rnn(input[0], (hx, cx)) >>> hx.size() oneflow.Size([3, 20])