输入和输出到Pytorch中的LSTMS

发布于 2025-01-18 10:17:37 字数 172 浏览 1 评论 0原文

我想在Pytorch中实现LSTM，因为我的数据是时间序列数据，即视频框架以进行心率检测，我正在为LSTMS的输入和输出尺寸而苦苦挣扎。在考虑时间步骤，隐藏状态等时，在Pytorch中LSTMS的输入非常令人困惑我来自CNN的输出是“ 2批256帧”，现在是LSTMS的输入批次是2 功能= 256 输出也是256帧的2批次。

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彩虹直至黑白 2025-01-25 10:17:37

通常，顺序数据的输入形状采用(batch_size, seq_len, num_features)的形式。根据您的解释，我假设您的输入格式为 (2, 256)，其中 2 是批量大小，256 是标量（一维张量）的序列长度。因此，您应该通过 inputs.unsqueeze(2) 将输入重塑为 (2, 256, 1)。

要声明和使用 LSTM 模型，只需尝试

from torch import nn

model = nn.LSTM(
    input_size=1, # 1-dimensional features
    batch_first=True, # batch is the first (zero-th) dimension
    hidden_size=some_hidden_size, # maybe 64, 128, etc.
    num_layers=some_num_layers, # maybe 1 or 2
    proj_size=1, # output should also be 1-dimensional
)
outputs, (hidden_state, cell_state) = model(inputs)

Generally, the input shape of sequential data takes the form (batch_size, seq_len, num_features). Based on your explanation, I assume your input is of the form (2, 256), where 2 is the batch size and 256 is the sequence length of scalars (1-dimensional tensor). Therefore, you should reshape your input to be (2, 256, 1) by inputs.unsqueeze(2).

To declare and use an LSTM model, simply try

from torch import nn

model = nn.LSTM(
    input_size=1, # 1-dimensional features
    batch_first=True, # batch is the first (zero-th) dimension
    hidden_size=some_hidden_size, # maybe 64, 128, etc.
    num_layers=some_num_layers, # maybe 1 or 2
    proj_size=1, # output should also be 1-dimensional
)
outputs, (hidden_state, cell_state) = model(inputs)

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