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Specifications and Common Mistakes

- Specifications and Common Mistakes:

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mindspore.nn.RNNCell

class mindspore.nn.RNNCell(input_size: int, hidden_size: int, has_bias: bool =  True, nonlinearity: str =  "tanh")

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.

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 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

Examples

>>> net = nn.RNNCell(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)