1、代码演示
embedding = nn.Embedding(10,3)
print(embedding)
input = torch.LongTensor([[1,2,3,4],[4,3,2,9]])
embedding(input)
2、构建Embeddings类来实现文本嵌入层
# 构建Embedding类来实现文本嵌入层
class Embeddings(nn.Module):
def __init__(self,d_model,vocab):
"""
:param d_model: 词嵌入的维度
:param vocab: 词表的大小
"""
super(Embeddings,self).__init__()
self.lut = nn.Embedding(vocab,d_model)
self.d_model = d_model
def forward(self,x):
"""
:param x: 因为Embedding层是首层，所以代表输入给模型的文本通过词汇映射后的张量
:return:
"""
return self.lut(x) * math.sqrt(self.d_model)
x = Variable(torch.LongTensor([[100,2,42,508],[491,998,1,221]]))
emb = Embeddings(512,1000)
embr = emb(x)
print(embr.shape) # torch.Size([2, 4, 512])
print(embr)
print(embr[0][0].shape) # torch.Size([512])
