class torch.nn.GRUCell(input_size, hidden_size, bias=True, device=None, dtype=None)[source]

A gated recurrent unit (GRU) cell.

r=σ(Wirx+bir+Whrh+bhr)z=σ(Wizx+biz+Whzh+bhz)n=tanh(Winx+bin+r(Whnh+bhn))h=(1z)n+zh\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 \odot (W_{hn} h + b_{hn})) \\ h' = (1 - z) \odot n + z \odot h \end{array}

where σ\sigma is the sigmoid function, and \odot is the Hadamard product.

  • input_size (int) – The number of expected features in the input x

  • hidden_size (int) – The number of features in the hidden state h

  • bias (bool) – 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

  • input: (N,Hin)(N, H_{in}) or (Hin)(H_{in}) tensor containing input features where HinH_{in} = input_size.

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

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

  • weight_ih (torch.Tensor) – the learnable input-hidden weights, of shape (3*hidden_size, input_size)

  • weight_hh (torch.Tensor) – 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)


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}}

On certain ROCm devices, when using float16 inputs this module will use different precision for backward.


>>> rnn = nn.GRUCell(10, 20)
>>> input = torch.randn(6, 3, 10)
>>> hx = torch.randn(3, 20)
>>> output = []
>>> for i in range(6):
...     hx = rnn(input[i], hx)
...     output.append(hx)


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources