self.layer2 = torch.nn.TransformerEncoderLayer(d_model=LSTM_hidden_size, nhead=Attention_head)这句代码的意思
时间: 2023-02-25 08:59:00 浏览: 54
这句代码定义了一个 TransformerEncoderLayer 层,并将其命名为 self.layer2。它的参数如下:
- d_model:TransformerEncoderLayer 层的模型维度,值为 LSTM_hidden_size。
- nhead:TransformerEncoderLayer 层的注意力头数量,值为 Attention_head。
这个 TransformerEncoderLayer 层是 PyTorch 中的一个编码器层,它可以在 Transformer 模型中用于对序列数据进行编码。
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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.
def setup_layers(self): self.lstm = torch.nn.LSTM( input_size = self.lstm_inputsize, hidden_size = self.lstm_hiddensize, num_layers = self.lstm_layers, batch_first=True, dropout=(0 if self.lstm_layers == 1 else self.lstm_dropout), bidirectional=False )
这段代码使用PyTorch中的`nn.LSTM`构建了一个LSTM层,具体参数如下:
- `input_size`:输入特征的维度。
- `hidden_size`:隐藏状态的维度。
- `num_layers`:LSTM层数。
- `batch_first`:如果为True,则输入和输出张量的形状为(batch_size, seq_length, feature_dims);否则,形状为(seq_length, batch_size, feature_dims)。
- `dropout`:如果`num_layers=1`,则不使用dropout;否则,将应用dropout并指定丢弃概率。
- `bidirectional`:是否使用双向LSTM。
需要注意的是,这段代码构建的是单向LSTM,如果需要构建双向LSTM,需要将`bidirectional`参数设置为True,如下所示:
```python
self.lstm = torch.nn.LSTM(
input_size=self.lstm_inputsize,
hidden_size=self.lstm_hiddensize,
num_layers=self.lstm_layers,
batch_first=True,
dropout=(0 if self.lstm_layers == 1 else self.lstm_dropout),
bidirectional=True
)
```