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Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

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- Minor inconsistencies between the UI and descriptions.

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mindspore.nn.GRUCell

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

A GRU(Gated Recurrent Unit) cell.

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

Here $\sigma$ is the sigmoid function, and $\ast$ 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)