Source code for models.TransE

import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
from Model import *
from torch.autograd import Variable

[docs]class TransE(Model): r"""THis class implements TransE model based on :class:`models.Model` Args: config: configuration information Shape: - Output: loss value which is a scalar """ def __init__(self, config): super(TransE,self).__init__(config) self.ent_embeddings=nn.Embedding(config.entTotal,config.hidden_size) self.rel_embeddings=nn.Embedding(config.relTotal,config.hidden_size) self.init_weights()
[docs] def init_weights(self): r"""Init entity and relation embeddings. In this model, we use ``xaview_uniform`` """ nn.init.xavier_uniform( nn.init.xavier_uniform(
def _calc(self,h,t,r): r"""The definition of loss function """ return torch.abs(h + r - t)
[docs] def loss_func(self,p_score,n_score): r"""This method calculatest the pairwise margin-based loss Args: p_score (float): the score for positive triple n_score (float): the score for negative triple Returns: float: the margin-based loss for this pair """ criterion = nn.MarginRankingLoss(self.config.margin, False).cuda() y = Variable(torch.Tensor([-1])).cuda() loss = criterion(p_score,n_score,y) return loss
def forward(self): pos_h,pos_t,pos_r=self.get_postive_instance() neg_h,neg_t,neg_r=self.get_negtive_instance() p_h=self.ent_embeddings(pos_h) p_t=self.ent_embeddings(pos_t) p_r=self.rel_embeddings(pos_r) n_h=self.ent_embeddings(neg_h) n_t=self.ent_embeddings(neg_t) n_r=self.rel_embeddings(neg_r) _p_score = self._calc(p_h, p_t, p_r) _n_score = self._calc(n_h, n_t, n_r) p_score=torch.sum(_p_score,1) n_score=torch.sum(_n_score,1) loss=self.loss_func(p_score, n_score) return loss
[docs] def predict(self, predict_h, predict_t, predict_r): r"""This method predictss the score for testting sample Args: predict_h: head entity of triple predict_t: tail entity of triple predict_r: relaiton of triple Returns: float: the score for testing triple """ p_h=self.ent_embeddings(Variable(torch.from_numpy(predict_h)).cuda()) p_t=self.ent_embeddings(Variable(torch.from_numpy(predict_t)).cuda()) p_r=self.rel_embeddings(Variable(torch.from_numpy(predict_r)).cuda()) _p_score = self._calc(p_h, p_t, p_r) p_score=torch.sum(_p_score,1) return p_score.cpu()