mindspore.nn.GRUCell

class mindspore.nn.GRUCell(input_size: int, hidden_size: int, has_bias: bool = True, dtype=mstype.float32)

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. $h$ is hidden state. $r$ is reset gate. $z$ is update gate. $n$ is n-th layer. 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 $b_{in}$ and $b_{hn}$. Default: True .

• dtype (mindspore.dtype) – Dtype of Parameters. Default: mstype.float32 .

Inputs:

• x (Tensor) - Tensor of shape $(batch\mathrm{\_}size,input\mathrm{\_}size)$ .

• hx (Tensor) - Tensor of data type mindspore.float32 and shape $(batch\mathrm{\_}size,hidden\mathrm{\_}size)$ .

Outputs:

• hx' (Tensor) - Tensor of shape $(batch\mathrm{\_}size,hidden\mathrm{\_}size)$ .

Raises

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

• TypeError – If has_bias is not a bool.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> import numpy as np
>>> net = ms.nn.GRUCell(10, 16)
>>> x = ms.Tensor(np.ones([5, 3, 10]).astype(np.float32))
>>> hx = ms.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)