GRU — PyTorch 2.7 documentation (original) (raw)

class torch.nn.GRU(input_size, hidden_size, num_layers=1, bias=True, batch_first=False, dropout=0.0, bidirectional=False, device=None, dtype=None)[source][source]¶

Apply a multi-layer gated recurrent unit (GRU) RNN to an input sequence. For each element in the input sequence, each layer computes the following function:

rt=σ(Wirxt+bir+Whrh(t−1)+bhr)zt=σ(Wizxt+biz+Whzh(t−1)+bhz)nt=tanh⁡(Winxt+bin+rt⊙(Whnh(t−1)+bhn))ht=(1−zt)⊙nt+zt⊙h(t−1)\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 \odot (W_{hn} h_{(t-1)}+ b_{hn})) \\ h_t = (1 - z_t) \odot n_t + z_t \odot h_{(t-1)} \end{array}

where hth_t is the hidden state at time t, xtx_t is the input at time t, h(t−1)h_{(t-1)} is the hidden state of the layer at time t-1 or the initial hidden state at time 0, and rtr_t,ztz_t, ntn_t are the reset, update, and new gates, respectively.σ\sigma is the sigmoid function, and ⊙\odot is the Hadamard product.

In a multilayer GRU, the input xt(l)x^{(l)}_t of the ll -th layer (l≥2l \ge 2) is the hidden state ht(l−1)h^{(l-1)}_t of the previous layer multiplied by dropout δt(l−1)\delta^{(l-1)}_t where each δt(l−1)\delta^{(l-1)}_t is a Bernoulli random variable which is 00 with probability dropout.

Parameters

• input_size – The number of expected features in the input x
• hidden_size – The number of features in the hidden state h
• num_layers – Number of recurrent layers. E.g., setting num_layers=2would 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 todropout. Default: 0
• bidirectional – If True, becomes a bidirectional GRU. Default: False

Inputs: input, h_0

• input: tensor of shape (L,Hin)(L, H_{in}) for unbatched input,(L,N,Hin)(L, N, H_{in}) when batch_first=False or(N,L,Hin)(N, L, H_{in}) when batch_first=True containing the features of the input sequence. The input can also be a packed variable length sequence. See torch.nn.utils.rnn.pack_padded_sequence() ortorch.nn.utils.rnn.pack_sequence() for details.
• h_0: tensor of shape (D∗num_layers,Hout)(D * \text{num\_layers}, H_{out}) or(D∗num_layers,N,Hout)(D * \text{num\_layers}, N, H_{out})containing the initial hidden state for the input sequence. Defaults to zeros if not provided.

where:

N=batch sizeL=sequence lengthD=2 if bidirectional=True otherwise 1Hin=input_sizeHout=hidden_size\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}

Outputs: output, h_n

• output: tensor of shape (L,D∗Hout)(L, D * H_{out}) for unbatched input,(L,N,D∗Hout)(L, N, D * H_{out}) when batch_first=False or(N,L,D∗Hout)(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 atorch.nn.utils.rnn.PackedSequence has been given as the input, the output will also be a packed sequence.
• h_n: tensor of shape (D∗num_layers,Hout)(D * \text{num\_layers}, H_{out}) or(D∗num_layers,N,Hout)(D * \text{num\_layers}, N, H_{out}) containing the final hidden state for the input sequence.

Variables

• weight_ih_l[k] – the learnable input-hidden weights of the kth\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)
• weight_hh_l[k] – the learnable hidden-hidden weights of the kth\text{k}^{th} layer (W_hr|W_hz|W_hn), of shape (3*hidden_size, hidden_size)
• bias_ih_l[k] – the learnable input-hidden bias of the kth\text{k}^{th} layer (b_ir|b_iz|b_in), of shape (3*hidden_size)
• bias_hh_l[k] – the learnable hidden-hidden bias of the kth\text{k}^{th} layer (b_hr|b_hz|b_hn), of shape (3*hidden_size)

Note

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

Note

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).

Note

batch_first argument is ignored for unbatched inputs.

Note

The calculation of new gate ntn_t subtly differs from the original paper and other frameworks. In the original implementation, the Hadamard product (⊙)(\odot) between rtr_t and the previous hidden state h(t−1)h_{(t-1)} is done before the multiplication with the weight matrixW and addition of bias:

nt=tanh⁡(Winxt+bin+Whn(rt⊙h(t−1))+bhn)\begin{aligned} n_t = \tanh(W_{in} x_t + b_{in} + W_{hn} ( r_t \odot h_{(t-1)} ) + b_{hn}) \end{aligned}

This is in contrast to PyTorch implementation, which is done after Whnh(t−1)W_{hn} h_{(t-1)}

nt=tanh⁡(Winxt+bin+rt⊙(Whnh(t−1)+bhn))\begin{aligned} n_t = \tanh(W_{in} x_t + b_{in} + r_t \odot (W_{hn} h_{(t-1)}+ b_{hn})) \end{aligned}

This implementation differs on purpose for efficiency.

Note

If the following conditions are satisfied: 1) cudnn is enabled, 2) input data is on the GPU 3) input data has dtype torch.float164) V100 GPU is used, 5) input data is not in PackedSequence format persistent algorithm can be selected to improve performance.

Examples:

rnn = nn.GRU(10, 20, 2) input = torch.randn(5, 3, 10) h0 = torch.randn(2, 3, 20) output, hn = rnn(input, h0)