self.lstm=nn.LSTM(input_size=input_size,hidden_size=hidden_size,num_layers=num_layers,batch_first=True)
时间: 2024-06-09 22:09:08 浏览: 26
这段代码是在构建一个LSTM模型,其中包含了输入层、隐藏层、以及LSTM的层数。具体来说,input_size是输入层的大小,hidden_size表示隐藏层的大小,num_layers表示LSTM的层数,batch_first为True表示输入的数据格式为(batch_size, sequence_length, input_size)。这段代码实现了一个基本的LSTM模型,可以用于各种序列数据的处理。
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self.rnn=nn.LSTM(input_size=1,hidden_size=32,num_layers=1)
这是一个使用 PyTorch 框架中的 nn 模块定义的 LSTM 神经网络,其中 input_size 为输入数据的特征数,hidden_size 为隐藏层的神经元数,num_layers 为 LSTM 层的数量。
self.lstm = torch.nn.LSTM( input_size = 224, hidden_size = 128, num_layer = 2,
batch_first = True)
This code initializes an LSTM module in PyTorch with the following parameters:
- `input_size`: The size of the input tensor at each time step. Here, it is 224.
- `hidden_size`: The number of features in the hidden state at each time step. Here, it is set to 128.
- `num_layers`: The number of recurrent layers in the LSTM module. Here, it is set to 2.
- `batch_first`: Whether the input and output tensors should have batch size as their first dimension. Here, it is set to `True`.
Overall, this code creates an LSTM module with two layers and a hidden state size of 128, which can process input sequences of length 224 with batch size as the first dimension.