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- import numpy
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- from . import BaseModel, register_model
-
- """
- u_embedding: Embedding for center word.
- v_embedding: Embedding for neighbor words.
- """
-
-
- @register_model('HERec')
- @register_model('Metapath2vec')
- class SkipGram(BaseModel):
- @classmethod
- def build_model_from_args(cls, args, hg):
- return cls(hg.num_nodes(), args.dim)
-
- def __init__(self, num_nodes, dim):
- super(SkipGram, self).__init__()
- self.embedding_dim = dim
-
- self.u_embeddings = nn.Embedding(num_nodes, self.embedding_dim,
- sparse=True)
-
- self.v_embeddings = nn.Embedding(num_nodes, self.embedding_dim,
- sparse=True)
-
- initrange = 1.0 / self.embedding_dim
- nn.init.uniform_(self.u_embeddings.weight.data, -initrange, initrange)
- nn.init.constant_(self.v_embeddings.weight.data, 0)
-
- def forward(self, pos_u, pos_v, neg_v):
- emb_u = self.u_embeddings(pos_u)
- emb_v = self.v_embeddings(pos_v)
- emb_neg_v = self.v_embeddings(neg_v)
-
- score = torch.sum(torch.mul(emb_u, emb_v), dim=1)
- score = torch.clamp(score, max=10, min=-10)
- score = -F.logsigmoid(score)
-
- neg_score = torch.bmm(emb_neg_v, emb_u.unsqueeze(2)).squeeze()
- neg_score = torch.clamp(neg_score, max=10, min=-10)
- neg_score = -torch.sum(F.logsigmoid(-neg_score), dim=1)
-
- return torch.mean(score + neg_score)
-
- def save_embedding(self, file_name):
- numpy.save(file_name, self.u_embeddings.weight.cpu().data.numpy())
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