| import logging |
| import torch |
| import torch.nn as nn |
|
|
|
|
| class DistMult(nn.Module): |
| def __init__(self, params): |
| super(DistMult, self).__init__() |
| self.params = params |
| self.ent_embeddings = nn.Embedding(self.params.total_ent, self.params.embedding_dim, max_norm=1) |
| self.rel_embeddings = nn.Embedding(self.params.total_rel, self.params.embedding_dim) |
|
|
| |
| self.criterion = nn.MarginRankingLoss(self.params.margin, reduction='sum') |
|
|
| self.init_weights() |
|
|
| logging.info('Initialized the model successfully!') |
|
|
| def init_weights(self): |
| nn.init.xavier_uniform_(self.ent_embeddings.weight.data) |
| nn.init.xavier_uniform_(self.rel_embeddings.weight.data) |
|
|
| def get_score(self, h, t, r): |
| return - torch.sum(h * t * r, -1) |
|
|
| def forward(self, batch_h, batch_t, batch_r, batch_y): |
| h = self.ent_embeddings(batch_h) |
| t = self.ent_embeddings(batch_t) |
| r = self.rel_embeddings(batch_r) |
|
|
| score = self.get_score(h, t, r) |
|
|
| pos_score = score[0: int(len(score) / 2)] |
| neg_score = score[int(len(score) / 2): len(score)] |
|
|
| loss = self.criterion(pos_score, neg_score, torch.Tensor([-1]).to(self.params.device)) |
| |
| return loss, pos_score, neg_score |
|
|
