class oneflow.nn.GRU(*args, **kwargs)

Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.

For each element in the input sequence, each layer computes the following


\[\begin{split}\begin{array}{ll} r_t = \sigma(W_{ir} x_t + b_{ir} + W_{hr} h_{(t-1)} + b_{hr}) \\ z_t = \sigma(W_{iz} x_t + b_{iz} + W_{hz} h_{(t-1)} + b_{hz}) \\ n_t = \\tanh(W_{in} x_t + b_{in} + r_t * (W_{hn} h_{(t-1)}+ b_{hn})) \\ h_t = (1 - z_t) * n_t + z_t * h_{(t-1)} \end{array}\end{split}\]

where \(h_t\) is the hidden state at time t, \(x_t\) is the input at time t, \(h_{(t-1)}\) is the hidden state of the layer at time t-1 or the initial hidden state at time 0, and \(r_t\), \(z_t\), \(n_t\) are the reset, update, and new gates, respectively. \(\sigma\) is the sigmoid function, and \(*\) is the Hadamard product.

In a multilayer GRU, the input \(x^{(l)}_t\) of the \(l\) -th layer (\(l >= 2\)) is the hidden state \(h^{(l-1)}_t\) of the previous layer multiplied by dropout \(\delta^{(l-1)}_t\) where each \(\delta^{(l-1)}_t\) is a Bernoulli random variable which is \(0\) with probability dropout.

The interface is consistent with PyTorch. The documentation is referenced from:

  • num_layers – Number of recurrent layers. E.g., setting num_layers=2 would mean stacking two GRUs together to form a stacked GRU, with the second GRU taking in outputs of the first GRU and computing the final results. Default: 1

  • bias – If False, then the layer does not use bias weights b_ih and b_hh. Default: True

  • batch_first – If True, then the input and output tensors are provided as (batch, seq, feature) instead of (seq, batch, feature). Note that this does not apply to hidden or cell states. See the Inputs/Outputs sections below for details. Default: False

  • dropout – If non-zero, introduces a Dropout layer on the outputs of each GRU layer except the last layer, with dropout probability equal to dropout. Default: 0

  • bidirectional – If True, becomes a bidirectional GRU. Default: False

Inputs: input, h_0
  • input: tensor of shape \((L, N, H_{in})\) when batch_first=False or \((N, L, H_{in})\) when batch_first=True containing the features of the input sequence.

  • h_0: tensor of shape \((D * \text{num\_layers}, N, H_{out})\) containing the initial hidden state for each element in the batch. Defaults to zeros if not provided.


\[\begin{split}\begin{aligned} N ={} & \text{batch size} \\ L ={} & \text{sequence length} \\ D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\ H_{in} ={} & \text{input\_size} \\ H_{out} ={} & \text{hidden\_size} \end{aligned}\end{split}\]
Outputs: output, h_n
  • output: tensor of shape \((L, N, D * H_{out})\) when batch_first=False or \((N, L, D * H_{out})\) when batch_first=True containing the output features (h_t) from the last layer of the GRU, for each t. If a

  • h_n: tensor of shape \((D * \text{num\_layers}, N, H_{out})\) containing the final hidden state for each element in the batch.


the learnable input-hidden weights of the \(\text{k}^{th}\) layer (W_ir|W_iz|W_in), of shape (3*hidden_size, input_size) for k = 0. Otherwise, the shape is (3*hidden_size, num_directions * hidden_size)


the learnable hidden-hidden weights of the \(\text{k}^{th}\) layer (W_hr|W_hz|W_hn), of shape (3*hidden_size, hidden_size)


the learnable input-hidden bias of the \(\text{k}^{th}\) layer (b_ir|b_iz|b_in), of shape (3*hidden_size)


the learnable hidden-hidden bias of the \(\text{k}^{th}\) layer (b_hr|b_hz|b_hn), of shape (3*hidden_size)


All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where \(k = \frac{1}{\text{hidden\_size}}\)


For bidirectional GRUs, forward and backward are directions 0 and 1 respectively. Example of splitting the output layers when batch_first=False: output.view(seq_len, batch, num_directions, hidden_size).

For example:

>>> import oneflow as flow
>>> import numpy as np
>>> rnn = flow.nn.GRU(10, 20, 2)
>>> input = flow.tensor(np.random.randn(5, 3, 10), dtype=flow.float32)
>>> h0 = flow.tensor(np.random.randn(2, 3, 20), dtype=flow.float32)
>>> output, hn = rnn(input, h0)
>>> output.size()
oneflow.Size([5, 3, 20])