super(Net, self).__init__() self.params = params self.embedding = nn.Embedding(params.num_class, params.embedding_dim)
时间: 2024-05-20 12:13:00 浏览: 6
这是一个 Python 类的构造函数,用于初始化该类的实例对象。在这个类中,首先调用了父类(即 nn.Module)的构造函数以初始化该类的基本属性。接着将传入的参数(params)赋值给该类的一个属性(self.params),以便在后续方法中使用。然后使用 PyTorch 中的 nn.Embedding 方法创建一个 Embedding 层,该层将输入的数字(即类别)转换为对应的向量表示。其中,params.num_class 表示类别数,params.embedding_dim 表示每个向量的维度。
相关问题
把这段代码里的location_embedding_dim去掉class my_GAT(torch.nn.Module): def __init__(self, in_channels, out_channels, n_heads, location_embedding_dim, filters_1, filters_2, dropout): super(my_GAT, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.n_heads = n_heads location_embedding_dim = 0 self.filters_1 = filters_1 self.filters_2 = filters_2 self.dropout = dropout self.location_embedding_dim = location_embedding_dim self.setup_layers() def setup_layers(self): self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1) self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads + self.location_embedding_dim, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False) def forward(self, edge_indices, features, location_embedding): features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_1(features, edge_indices) features = torch.nn.functional.relu(features) features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training) features = torch.cat((features, location_embedding), dim=-1) features = self.GAT_2(features, edge_indices) return features
这段代码里的location_embedding_dim是一个参数,它表示位置嵌入的维度。如果你想去掉它,可以把它的值设为0,然后在构造函数中把self.location_embedding_dim也设为0即可。具体来说,修改后的代码如下:
```
class my_GAT(torch.nn.Module):
def __init__(self, in_channels, out_channels, n_heads, filters_1, filters_2, dropout):
super(my_GAT, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.n_heads = n_heads
self.filters_1 = filters_1
self.filters_2 = filters_2
self.dropout = dropout
self.location_embedding_dim = 0
self.setup_layers()
def setup_layers(self):
self.GAT_1 = GATConv(in_channels=self.in_channels,out_channels=self.filters_1, heads=self.n_heads, dropout=0.1)
self.GAT_2 = GATConv(in_channels=self.filters_1 * self.n_heads, out_channels=self.out_channels, heads=self.n_heads, dropout=0.1, concat=False)
def forward(self, edge_indices, features, location_embedding):
features = self.GAT_1(features, edge_indices)
features = torch.nn.functional.relu(features)
features = torch.nn.functional.dropout(features, p=self.dropout, training=self.training)
features = self.GAT_2(features, edge_indices)
return features
```
class Encoder(nn.Module): def __init__(self,encoder_embedding_num,encoder_hidden_num,en_corpus_len): super().__init__() self.embedding = nn.Embedding(en_corpus_len,encoder_embedding_num) self.lstm = nn.LSTM(encoder_embedding_num,encoder_hidden_num,batch_first=True) def forward(self,en_index): en_embedding = self.embedding(en_index) _,encoder_hidden =self.lstm(en_embedding) return encoder_hidden解释每行代码的含义
- `class Encoder(nn.Module):` 定义一个名为Encoder的类,继承自nn.Module。
- `def __init__(self,encoder_embedding_num,encoder_hidden_num,en_corpus_len):` 定义Encoder类的初始化函数,传入三个参数:encoder_embedding_num(编码器嵌入层的维度),encoder_hidden_num(编码器LSTM隐藏层的维度)和en_corpus_len(英文语料库的长度)。
- `super().__init__()` 调用父类nn.Module的初始化函数。
- `self.embedding = nn.Embedding(en_corpus_len,encoder_embedding_num)` 定义编码器的嵌入层,使用nn.Embedding类,将英文语料库的长度和编码器嵌入层的维度作为参数传入。
- `self.lstm = nn.LSTM(encoder_embedding_num,encoder_hidden_num,batch_first=True)` 定义编码器的LSTM层,使用nn.LSTM类,将编码器嵌入层的维度和编码器LSTM隐藏层的维度作为参数传入,并设置batch_first参数为True,表示输入数据的第一维是batch_size。
- `def forward(self,en_index):` 定义Encoder类的前向传播函数,传入一个参数en_index(英文句子的索引序列)。
- `en_embedding = self.embedding(en_index)` 将英文句子的索引序列通过嵌入层转换为嵌入向量。
- `_,encoder_hidden =self.lstm(en_embedding)` 将嵌入向量输入到编码器LSTM层中,获取编码器的最后一个时间步的隐藏状态encoder_hidden。
- `return encoder_hidden` 返回编码器的最后一个时间步的隐藏状态encoder_hidden。