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])