plot_config(args)
heads, tails = loader.heads_tails()
head_idx, tail_idx, head_cache, tail_cache, head_pos, tail_pos = loader.get_cache_list(
)
caches = [head_idx, tail_idx, head_cache, tail_cache, head_pos, tail_pos]
train_data = [torch.LongTensor(vec) for vec in train_data]
valid_data = [torch.LongTensor(vec) for vec in valid_data]
test_data = [torch.LongTensor(vec) for vec in test_data]
tester_val = lambda: model.test_link(valid_data, n_ent, heads, tails, args.
filter)
tester_tst = lambda: model.test_link(test_data, n_ent, heads, tails, args.
filter)
corrupter = BernCorrupter(train_data, n_ent, n_rel)
model = BaseModel(n_ent, n_rel, args)
if args.load:
model.load(os.path.join(args.task_dir, args.model + '.mdl'))
tester_val()
tester_tst()
best_str = model.train(train_data, caches, corrupter, tester_val,
tester_tst)
with open(args.perf_file, 'a') as f:
print('Training finished and best performance:', best_str)
f.write('best_performance: ' + best_str)
