torch中RNN模块详细接口参数解析
rnn = torch.nn.RNN(
input_size: int,
hidden_size: int,
num_layers: int = 1,
nonlinearity: str = 'tanh',
bias: bool = True,
batch_first: bool = False,
dropout: float = 0.0,
bidirectional: bool = False,
)input_size (int)：输入序列中每个时间步的特征维度。
hidden_size (int)：隐藏状态（记忆单元）的维度。
num_layers (int, 默认为1)：RNN 层的堆叠数量。
nonlinearity (str, 默认为'tanh')：激活函数的选择，可以是 'tanh' 或 'relu'。不过在标准 RNN 中通常使用 'tanh'。
bias (bool, 默认为True)：是否在计算中包含偏置项。
batch_first (bool, 默认为False)：如果设为 True，则输入和输出张量的第一个维度将被视为批次大小，而不是时间步长。即数据格式为 (batch_size, seq_len, input_size) 而不是 (seq_len, batch, input_size)。
dropout (float, 默认为0.0)：应用于隐层到隐层之间的失活率，用于正则化以防止过拟合。只有当 num_layers > 1 时才会生效。
bidirectional (bool, 默认为False)：若设置为 True，将会创建一个双向 RNN，这样模型可以同时处理过去和未来的上下文信息。
注意torch.nn.RNN 本身并不直接支持双向模式；要实现双向RNN，应使用 torch.nn.Bidirectional 包装器包裹一个单向RNN。
outputs, hn = rnn(...)outputs: Tensor 如果batch_first=True，则为则为 (batch_size, seq_len, num_directions * hidden_size)。否则 (seq_len, batch_size, num_directions * hidden_size)；
RNN 对输入序列每个时间步的输出。对于双向 RNN，num_directions 为2，输出是正向和反向隐藏状态的串联或拼接结果。
hn: Tensor (h_n 或 hidden)：形状(num_layers * num_directions, batch_size, hidden_size)
最后一个时间步的隐藏状态（或者在双向情况下，正向和反向隐藏状态）。等价 output[:, -1, :]
实例化一个单向的RNN单元
python
import torch.nn as nn
import torch
batch_size = 2
seq_len = 7
input_size = 5
hidden_size = 3
num_layers = 1
rnn = nn.RNN(input_size, hidden_size, num_layers, nonlinearity="tanh", batch_first=True)
# (batch_size, seq_len, input_size) 来两个样本为一批，每个样本在时序上分7步，每一步的维度是5
input = torch.randn(batch_size, seq_len, input_size)
# (num_layers, batch_size, hidden_size) torch源码默认全零，建议使用默认值
h0 = torch.randn(1, 2, 3)
# output = (batch_size, seq_len, hidden_size)
# hn = (num_layers, batch_size, hidden_size)
# hn 每一个样本最后一步的信息，等价 output[:,-1,:]
# ！！注意此处变量的维度大小都是基于本例计算的，并不是实际计算公式！！
output, hn = rnn(input, h0)
# print(output)
print(output.shape) # torch.Size([2, 7, 3])
# print(hn)
print(hn.shape) # torch.Size([1, 2, 3])
# print(output[:, -1, :])为了方便复习现将源码参数说明附在这里
class RNN(RNNBase):
r"""Applies a multi-layer Elman RNN with :math:`\tanh` or :math:`\text{ReLU}` non-linearity to an
input sequence.
For each element in the input sequence, each layer computes the following
function:
.. math::
h_t = \tanh(x_t W_{ih}^T + b_{ih} + h_{t-1}W_{hh}^T + b_{hh})
where :math:`h_t` is the hidden state at time `t`, :math:`x_t` is
the input at time `t`, and :math:`h_{(t-1)}` is the hidden state of the
previous layer at time `t-1` or the initial hidden state at time `0`.
If :attr:`nonlinearity` is ``'relu'``, then :math:`\text{ReLU}` is used instead of :math:`\tanh`.
Args:
input_size: The number of expected features in the input `x`
hidden_size: The number of features in the hidden state `h`
num_layers: 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: The non-linearity to use. Can be either ``'tanh'`` or ``'relu'``. Default: ``'tanh'``
bias: If ``False``, then the layer does not use bias weights `b_ih` and `b_hh`.
Default: ``True``
batch_first: 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: If non-zero, introduces a `Dropout` layer on the outputs of each
RNN layer except the last layer, with dropout probability equal to
:attr:`dropout`. Default: 0
bidirectional: If ``True``, becomes a bidirectional RNN. Default: ``False``
Inputs: input, h_0
* **input**: tensor of shape :math:`(L, H_{in})` for unbatched input,
:math:`(L, N, H_{in})` when ``batch_first=False`` or
:math:`(N, L, H_{in})` when ``batch_first=True`` containing the features of
the input sequence. The input can also be a packed variable length sequence.
See :func:`torch.nn.utils.rnn.pack_padded_sequence` or
:func:`torch.nn.utils.rnn.pack_sequence` for details.
* **h_0**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` for unbatched input or
:math:`(D * \text{num\_layers}, N, H_{out})` containing the initial hidden
state for the input sequence batch. Defaults to zeros if not provided.
where:
.. math::
\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}
Outputs: output, h_n
* **output**: tensor of shape :math:`(L, D * H_{out})` for unbatched input,
:math:`(L, N, D * H_{out})` when ``batch_first=False`` or
:math:`(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`. If a
:class:`torch.nn.utils.rnn.PackedSequence` has been given as the input, the output
will also be a packed sequence.
* **h_n**: tensor of shape :math:`(D * \text{num\_layers}, H_{out})` for unbatched input or
:math:`(D * \text{num\_layers}, N, H_{out})` containing the final hidden state
for each element in the batch.参考 torch.nn.RNN 源码接口文档。