LSTM中的input_size=3 num_layers=6 hidden_size=12是否代表每一个LSTM层会有12个LSTM单元
时间: 2024-04-28 16:27:03 浏览: 14
不完全正确。在LSTM中,隐藏状态的维度(即这里的hidden_size)指的是LSTM的输出维度,也就是每个时间步的输出向量的维度。每个LSTM单元(也就是LSTM中的一个节点)的维度并不是由hidden_size决定的,而是由输入数据的维度(即这里的input_size)和LSTM单元内部的参数数量决定的。LSTM单元内部包含3个门(输入门、遗忘门和输出门)以及对应的门控参数,每个门的输入是包括输入数据和前一个时间步的隐藏状态在内的向量,门控参数的维度则由输入数据的维度和隐藏状态维度决定,因此每个LSTM单元的维度是由这些门控参数的数量决定的。因此,num_layers=6 hidden_size=12意味着LSTM模型有6个LSTM层,每个LSTM层的隐藏状态的维度为12,但每个LSTM单元的维度并不是12。
相关问题
LSTM中的input_size=3 num_layers=1 hidden_size=12是否代表每一个LSTM层会有12个LSTM单元
在这种情况下,每个LSTM层确实有12个LSTM单元。在LSTM中,隐藏状态的维度(也称为隐藏单元或输出数量)由hidden_size参数定义。因此,当hidden_size=12时,每个LSTM层都将包含12个LSTM单元。请注意,在LSTM中,每个LSTM单元内部有多个神经元,但是hidden_size参数代表的是每个LSTM单元的输出维度,而不是LSTM单元内部神经元的数量。
nn.LSTM(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, bidirectional=bidirectional)
This is a PyTorch module that implements a Long Short-Term Memory (LSTM) layer. An LSTM is a type of recurrent neural network (RNN) that is designed to handle sequence data by maintaining a memory cell that can selectively forget or remember information over time.
The arguments for this module are:
- input_size: the number of expected features in the input
- hidden_size: the number of features in the hidden state/output
- num_layers: the number of recurrent layers (default is 1)
- bidirectional: if True, the LSTM will be bidirectional (default is False)
The input to this module is a tensor of shape (seq_len, batch, input_size), where seq_len is the length of the input sequence, batch is the batch size, and input_size is the number of expected features in the input.
The output of this module is a tensor of shape (seq_len, batch, num_directions * hidden_size), where num_directions is 2 if bidirectional=True, and 1 otherwise. The output represents the hidden state/output value for each element in the input sequence. The final hidden state is also returned as a tensor of shape (num_layers * num_directions, batch, hidden_size).
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