model.zero_grad()
pos, neg, pos_h_e, pos_t_e, neg_h_e, neg_t_e = model(
pos_h_batch, pos_t_batch, pos_r_batch, neg_h_batch,
neg_t_batch, neg_r_batch)
if args.loss_type == 0:
losses = loss_function(pos, neg, margin)
else:
losses = loss_function(pos, neg)
ent_embeddings = model.ent_embeddings(
torch.cat([pos_h_batch, pos_t_batch, neg_h_batch,
neg_t_batch]))
rel_embeddings = model.rel_embeddings(
torch.cat([pos_r_batch, neg_r_batch]))
losses = losses + loss.normLoss(ent_embeddings) + loss.normLoss(
rel_embeddings) + loss.normLoss(pos_h_e) + loss.normLoss(
pos_t_e) + loss.normLoss(neg_h_e) + loss.normLoss(neg_t_e)
losses.backward()
optimizer.step()
total_loss += losses.data
agent.append(trainCurve, epoch, total_loss[0])
if epoch % 10 == 0:
now_time = time.time()
print(now_time - start_time)
print("Train total loss: %d %f" % (epoch, total_loss[0]))
if epoch % 10 == 0:
pos_h_batch, pos_t_batch, pos_r_batch,
pos_time_batch, neg_h_batch, neg_t_batch,
neg_r_batch, neg_time_batch)
if args.loss_type == 0:
losses = loss_function(pos, neg, margin)
else:
losses = F.cross_entropy(pos, neg)
ent_embeddings = model.ent_embeddings(
torch.cat(
[pos_h_batch, pos_t_batch, neg_h_batch, neg_t_batch]))
rseq_embeddings = model.get_rseq(
torch.cat([pos_r_batch, neg_r_batch]),
torch.cat([pos_time_batch, neg_time_batch]))
losses = losses + loss.normLoss(
ent_embeddings) + loss.normLoss(rseq_embeddings)
losses.backward()
optimizer.step()
total_loss += losses.data
average_loss = int(total_loss[0]) / len(trainBatchList)
for_loss.append(average_loss)
if (epoch) % 5 == 0:
now_time = time.time()
print(now_time - start_time)
print("Train total loss: %d %f" % (epoch, total_loss[0]))
if config.early_stopping_round > 0:
if epoch == 0:
neg_r_batch = autograd.Variable(longTensor(neg_r_batch))
model.zero_grad()
pos, neg = model(pos_h_batch, pos_t_batch, pos_r_batch,
neg_h_batch, neg_t_batch, neg_r_batch)
if args.loss_type == 0:
losses = loss_function(pos, neg, margin)
else:
losses = loss_function(pos, neg)
ent_embeddings = model.ent_embeddings(
torch.cat([pos_h_batch, pos_t_batch, neg_h_batch,
neg_t_batch]))
rel_embeddings = model.rel_embeddings(
torch.cat([pos_r_batch, neg_r_batch]))
losses = losses + loss.normLoss(ent_embeddings) + loss.normLoss(
rel_embeddings)
losses.backward()
optimizer.step()
total_loss += losses.data
agent.append(trainCurve, epoch, total_loss[0])
if epoch % 10 == 0:
now_time = time.time()
print(now_time - start_time)
print("Train total loss: %d %f" % (epoch, total_loss[0]))
if epoch % 10 == 0:
if config.filter == True:
losses = loss_function(
pos, neg, margin
) #margin is applied to every (pos_distance, neg_distance) and summed
else:
losses = loss_function(pos, neg)
#check shape
#print("ENT EMBEDDING SHAPE: " , ent_embeddings.shape)
#print("REL EMBEDDING SHAPE: " , rel_embeddings.shape)
#losses = losses #+ loss.normLoss(ent_embeddings) + loss.normLoss(rel_embeddings) #Are these regularization? #What are these?
#losses += loss.normLoss(ent_embeddings)
if args.norm_reg_rel == 1:
ent_embeddings = model.ent_embeddings(
torch.cat(
[pos_h_batch, pos_t_batch, neg_h_batch, neg_t_batch]))
#rel_embeddings = model.vvrel_embedding_func(torch.cat([pos_r_batch]))
losses += loss.normLoss(
ent_embeddings) #+ loss.normLoss(ent_embeddings)
backward_tim = time.time()
losses.backward()
optimizer.step()
total_loss += losses.data
#print("backward time :", time.time()-backward_tim)
#Normalize the rand vecs and put them back
if args.transH_test == 0:
if args.norm_mat == 1:
model.normalize_all_rows()
if args.norm_ent == 1:
model.normalize_ent_embeddings()
if args.norm_rel == 1:
model.normalize_vvrel_embeddings()
else: