mindspore.nn.GRUCell

class mindspore.nn.GRUCell(input_size: int, hidden_size: int, has_bias: bool = True)[source]

A GRU(Gated Recurrent Unit) cell.

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

Here \(\sigma\) is the sigmoid function, and \(*\) is the Hadamard product. \(W, b\) are learnable weights between the output and the input in the formula. For instance, \(W_{ir}, b_{ir}\) are the weight and bias used to transform from input \(x\) to \(r\). Details can be found in paper Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation.

Parameters

  • input_size (int) – Number of features of input.

  • hidden_size (int) – Number of features of hidden layer.

  • has_bias (bool) – Whether the cell has bias b_ih and b_hh. Default: True.

Inputs:

  • x (Tensor) - Tensor of shape (batch_size, input_size).

  • hx (Tensor) - Tensor of data type mindspore.float32 and shape (batch_size, hidden_size). Data type of hx must be the same as x.

Outputs:

  • h’ (Tensor) - Tensor of shape (batch_size, hidden_size).

Raises

  • TypeError – If input_size, hidden_size is not an int.

  • TypeError – If has_bias is not a bool.

Supported Platforms:

Ascend GPU

Examples

>>> net = nn.GRUCell(10, 16)
>>> x = Tensor(np.ones([5, 3, 10]).astype(np.float32))
>>> hx = Tensor(np.ones([3, 16]).astype(np.float32))
>>> output = []
>>> for i in range(5):
>>>     hx = net(x[i], hx)
>>>     output.append(hx)
>>> print(output[0].shape)
(3, 16)