mindspore.nn.RNNCell

class mindspore.nn.RNNCell(input_size: int, hidden_size: int, has_bias: bool = True, nonlinearity: str = 'tanh', dtype=mstype.float32)

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

$$h_t=\tanh (W_{ih}{x}_t+b_{ih}+W_{hh}{h}_{(t-1)}+b_{hh})$$

Here $h_t$ is the hidden state at time t, $x_t$ is the input at time t, and $h_{(t-1)}$ is the hidden state of the previous layer at time $t-1$ or the initial hidden state at time 0. If nonlinearity is relu, then relu is used instead of tanh.

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 .

• nonlinearity (str) – The non-linearity to use. Can be either "tanh" or "relu" . Default: "tanh" .

• 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 or hidden_size is not an int or not greater than 0.

• TypeError – If has_bias is not a bool.

• ValueError – If nonlinearity is not in ['tanh', 'relu'].

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> import numpy as np
>>> net = ms.nn.RNNCell(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)