RNN¶

class RNN(*args, **kwargs)[source]¶

Applies a multi-layer Elman RNN with \(\tanh\) or \(\text{ReLU}\) non-linearity to an input sequence.

For each element in the input sequence, each layer computes the following function:

\[h_t = \tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh})\]

where \(h_t\) 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 \(\text{ReLU}\) is used instead of \(\tanh\).

Parameters

input_size (int) – The number of expected features in the input x.
hidden_size (int) – The number of features in the hidden state h.
num_layers (int) – Number of recurrent layers. E.g., setting num_layers=2 would mean stacking two RNNs together to form a stacked RNN, with the second RNN taking in outputs of the first RNN and computing the final results. Default: 1.
nonlinearity (str) – The non-linearity to use. Can be either 'tanh' or 'relu'. Default: 'tanh'.
bias (bool) – If False, then the layer does not use bias weights b_ih and b_hh. Default: True.
batch_first (bool) – If True, then the input and output tensors are provided as (batch, seq, feature) instead of (seq, batch, feature). Note that this does not apply to hidden or cell states. See the Inputs/Outputs sections below for details. Default: False.
dropout (float) – If non-zero, introduces a Dropout layer on the outputs of each RNN layer except the last layer, with dropout probability equal to dropout. Default: 0.
bidirectional (bool) – If True, becomes a bidirectional RNN. Default: False.

Shape:

Inputs: input, h_0

input: \((L, N, H_{in})\) when batch_first=False or \((N, L, H_{in})\)
when batch_first=True. Containing the features of the input sequence.

h_0: \((D * \text{num\_layers}, N, H_{out})\). Containing the initial hidden
state for each element in the batch. Defaults to zeros if not provided.

where:

\[\begin{split}\begin{aligned} N ={} & \text{batch size} \\ L ={} & \text{sequence length} \\ D ={} & 2 \text{ if bidirectional=True otherwise } 1 \\ H_{in} ={} & \text{input\_size} \\ H_{out} ={} & \text{hidden\_size} \end{aligned}\end{split}\]
Outputs: output, h_n

output: \((L, N, D * H_{out})\) when batch_first=False or \((N, L, D * H_{out})\) when batch_first=True.
Containing the output features (h_t) from the last layer of the RNN, for each t.

h_n: \((D * \text{num\_layers}, N, H_{out})\). Containing the final hidden state for each element in the batch.

Examples

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

m = M.RNN(10,20,2,batch_first=False,nonlinearity="relu",bias=True,bidirectional=True)
inp = mge.tensor(np.random.randn(6, 30, 10), dtype=np.float32)
hx = mge.tensor(np.random.randn(4, 30, 20), dtype=np.float32)
out, hn = m(inp, hx)
print(out.numpy().shape)

Outputs:

(6, 30, 40)

LocalResponseNorm

RNNCell