oneflow.nn.GRUCell

class oneflow.nn.GRUCell(input_size: int, hidden_size: int, bias: bool = True, device=None, dtype=None)

A gated recurrent unit (GRU) cell

\[\begin{split}\begin{array}{ll} r = \sigma(W_{ir} x + b_{ir} + W_{hr} h + b_{hr}) \\ z = \sigma(W_{iz} x + b_{iz} + W_{hz} h + b_{hz}) \\ n = \tanh(W_{in} x + b_{in} + r * (W_{hn} h + b_{hn})) \\ h' = (1 - z) * n + z * h \end{array}\end{split}\]

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.GRUCell.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, 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: \((N, H_{in})\) or \((H_{in})\) tensor containing input features where \(H_{in}\) = input_size.

  • hidden: \((N, H_{out})\) or \((H_{out})\) tensor containing the initial hidden state where \(H_{out}\) = hidden_size. Defaults to zero if not provided.

  • output: \((N, H_{out})\) or \((H_{out})\) tensor containing the next hidden state.

weight_ih

the learnable input-hidden weights, of shape (3*hidden_size, input_size)

weight_hh

the learnable hidden-hidden weights, of shape (3*hidden_size, hidden_size)

bias_ih

the learnable input-hidden bias, of shape (3*hidden_size)

bias_hh

the learnable hidden-hidden bias, of shape (3*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.GRUCell(10, 20)
>>> input = flow.randn(6, 3, 10)
>>> hx = flow.randn(3, 20)
>>> hx = rnn(input[0], hx)
>>> hx.size()
oneflow.Size([3, 20])