class RNNCell(input_size, hidden_size, bias=True, nonlinearity='tanh')[源代码]

一个使用 tanh 或 ReLU 非线性的 Elman RNN 单元

\[h' = \tanh(W_{ih} x + b_{ih} + W_{hh} h + b_{hh})\]

如果 nonlinearity‘relu’ ,那么会使用 ReLU 代替 tanh。

  • input_size (int) – The number of expected features in the input x.

  • hidden_size (int) – The number of features in the hidden state h.

  • bias (bool) – If False, then the layer does not use bias weights b_ih and b_hh. Default: True.

  • nonlinearity (str) – 需要使用的非线性函数。可以是 'tanh''relu' 。默认值: 'tanh'

  • 输入:input,hidden

    input: (batch, input_size). Tensor containing input features. hidden: (batch, hidden_size). Tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided.

  • 输出: h’

    h’: (batch, hidden_size). Tensor containing the next hidden state for each element in the batch.


import numpy as np
import megengine as mge
import megengine.module as M

m = M.RNNCell(10, 20)
inp = mge.tensor(np.random.randn(3, 10), dtype=np.float32)
hx = mge.tensor(np.random.randn(3, 20), dtype=np.float32)
out = m(inp, hx)


(3, 20)