class torch.nn.RNNCell(input_size, hidden_size, bias=True, nonlinearity='tanh', device=None, dtype=None)[source]

An Elman RNN cell with tanh or ReLU non-linearity.

h=tanh(Wihx+bih+Whhh+bhh)h' = \tanh(W_{ih} x + b_{ih} + W_{hh} h + b_{hh})

If nonlinearity is ‘relu’, then ReLU is used in place of tanh.

  • 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

  • nonlinearity – The non-linearity to use. Can be either 'tanh' or 'relu'. Default: 'tanh'

Inputs: input, hidden
  • input: tensor containing input features

  • hidden: tensor containing the initial hidden state Defaults to zero if not provided.

Outputs: h’
  • h’ of shape (batch, hidden_size): 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.

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

  • ~RNNCell.weight_hh (torch.Tensor) – the learnable hidden-hidden weights, of shape (hidden_size, hidden_size)

  • ~RNNCell.bias_ih – the learnable input-hidden bias, of shape (hidden_size)

  • ~RNNCell.bias_hh – the learnable hidden-hidden bias, of shape (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}}


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


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