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# GRUCell¶

class torch.nn.GRUCell(input_size, hidden_size, bias=True)[source]

A gated recurrent unit (GRU) cell

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

where $\sigma$ is the sigmoid function, and $*$ is the Hadamard product.

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 of shape (batch, input_size): tensor containing input features

• hidden of shape (batch, hidden_size): tensor containing the initial hidden state for each element in the batch. 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

Shape:
• Input1: $(N, H_{in})$ tensor containing input features where $H_{in}$ = input_size

• Input2: $(N, H_{out})$ tensor containing the initial hidden state for each element in the batch where $H_{out}$ = hidden_size Defaults to zero if not provided.

• Output: $(N, H_{out})$ tensor containing the next hidden state for each element in the batch

Variables
• ~GRUCell.weight_ih – the learnable input-hidden weights, of shape (3*hidden_size, input_size)

• ~GRUCell.weight_hh – the learnable hidden-hidden weights, of shape (3*hidden_size, hidden_size)

• ~GRUCell.bias_ih – the learnable input-hidden bias, of shape (3*hidden_size)

• ~GRUCell.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 $k = \frac{1}{\text{hidden\_size}}$

Examples:

>>> 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)


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