mindspore.nn.RNNCell

class mindspore.nn.RNNCell(input_size: int, hidden_size: int, has_bias: bool = True, nonlinearity: str = 'tanh')[source]

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:

  • hx’ (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 CPU

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)