RNNCell¶
- class RNNCell(input_size, hidden_size, bias=True, nonlinearity='tanh')[源代码]¶
An Elman RNN cell with tanh or ReLU non-linearity.
\[h' = \tanh(W_{ih} x + b_{ih} + W_{hh} h + b_{hh})\]If
nonlinearity
is ‘relu’, then ReLU is used in place of tanh.- 参数
input_size (
int
) – The number of expected features in the input xhidden_size (
int
) – The number of features in the hidden state hbias (
bool
) – IfFalse
, then the layer does not use bias weights b_ih and b_hh. Default:True
nonlinearity (
str
) – The non-linearity to use. Can be either'tanh'
or'relu'
. Default:'tanh'
- Inputs: input, hidden
input of shape (batch, input_size): tensor containing input features
hidden of shape (batch, hidden_size): tensor containing the initial hidden state for each element in the batch. Defaults to zero if not provided.
- Outputs: h’
h’ of shape (batch, hidden_size): tensor containing the next hidden state for each element in the batch
- 形状:
Input1: \((N, H_{in})\) tensor containing input features where \(H_{in}\) = input_size
Input2: \((N, H_{out})\) tensor containing the initial hidden state for each element in the batch where \(H_{out}\) = hidden_size Defaults to zero if not provided.
Output: \((N, H_{out})\) tensor containing the next hidden state for each element in the batch
实际案例
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) print(out.numpy().shape)
输出:
(3, 20)