mindspore.nn.RNNCell
- class mindspore.nn.RNNCell(input_size: int, hidden_size: int, has_bias: bool = True, nonlinearity: str = 'tanh', dtype=mstype.float32)[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"
.dtype (
mindspore.dtype
) – Dtype of Parameters. Default:mstype.float32
.
- Inputs:
x (Tensor) - Tensor of shape \((batch\_size, input\_size)\) .
hx (Tensor) - Tensor of data type mindspore.float32 and shape \((batch\_size, hidden\_size)\) .
- 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
>>> 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)