def train_eval(self):
train_loader = DataLoader(self.train_set,
batch_size=self.args.batch_size,
shuffle=True)
self.args.max_step = self.args.epoch * (len(train_loader) //
self.args.update_step)
print('max step:', self.args.max_step)
optimizer = Optimizer(
filter(lambda p: p.requires_grad, self.model.parameters()), args)
best_dev_metric, best_test_metric = dict(), dict()
patient = 0
for ep in range(1, 1 + self.args.epoch):
train_loss = 0.
self.model.train()
t1 = time.time()
train_head_acc, train_rel_acc, train_total_head = 0, 0, 0
for i, batcher in enumerate(train_loader):
batch = batch_variable(batcher, self.vocabs)
batch.to_device(self.args.device)
head_score, rel_score = self.model(batch.wd_ids, batch.ch_ids,
batch.tag_ids)
loss = self.calc_loss(head_score, rel_score, batch.head_ids,
batch.rel_ids, batch.wd_ids.gt(0))
loss_val = loss.data.item()
train_loss += loss_val
head_acc, rel_acc, total_head = self.calc_acc(
head_score, rel_score, batch.head_ids, batch.rel_ids)
train_head_acc += head_acc
train_rel_acc += rel_acc
train_total_head += total_head
if self.args.update_step > 1:
loss = loss / self.args.update_step
loss.backward()
if (i + 1) % self.args.update_step == 0 or (
i == self.args.max_step - 1):
nn_utils.clip_grad_norm_(filter(lambda p: p.requires_grad,
self.model.parameters()),
max_norm=self.args.grad_clip)
optimizer.step()
self.model.zero_grad()
logger.info(
'[Epoch %d] Iter%d time cost: %.2fs, lr: %.6f, train loss: %.3f, head acc: %.3f, rel acc: %.3f'
% (ep, i + 1, (time.time() - t1), optimizer.get_lr(),
loss_val, train_head_acc / train_total_head,
train_rel_acc / train_total_head))
dev_metric = self.evaluate('dev')
if dev_metric['uf'] > best_dev_metric.get('uf', 0):
best_dev_metric = dev_metric
test_metric = self.evaluate('test')
if test_metric['uf'] > best_test_metric.get('uf', 0):
# check_point = {'model': self.model.state_dict(), 'settings': args}
# torch.save(check_point, self.args.model_chkp)
best_test_metric = test_metric
patient = 0
else:
patient += 1
logger.info(
'[Epoch %d] train loss: %.4f, lr: %f, patient: %d, dev_metric: %s, test_metric: %s'
% (ep, train_loss, optimizer.get_lr(), patient,
best_dev_metric, best_test_metric))
# if patient == (self.args.patient // 2 + 1): # 训练一定epoch, dev性能不上升, decay lr
# optimizer.lr_decay(0.95)
if patient >= self.args.patient: # early stopping
break
logger.info('Final Metric: %s' % best_test_metric)
def train_eval(self):
train_loader = DataLoader(
self.train_set, batch_size=self.args.batch_size, shuffle=True)
self.args.max_step = self.args.epoch * \
(len(train_loader) // self.args.update_step)
print('max step:', self.args.max_step)
optimizer = Optimizer(
filter(lambda p: p.requires_grad, self.model.parameters()), args)
best_dev_metric, best_test_metric = dict(), dict()
patient = 0
for ep in range(1, 1 + self.args.epoch):
train_loss = 0.
self.model.train()
t1 = time.time()
train_right, train_pred, train_gold = 0, 0, 0
for i, batcher in enumerate(train_loader):
batch = batch_variable(batcher, self.vocabs)
batch.to_device(self.args.device)
pred_score = self.model(
batch.wd_ids, batch.ch_ids, batch.tag_ids, batch.bert_inps)
loss = self.calc_loss(pred_score, batch.ner_ids)
loss_val = loss.data.item()
train_loss += loss_val
sent_lens = batch.wd_ids.gt(0).sum(dim=1)
gold_res = self.ner_gold(
batch.ner_ids, sent_lens, self.vocabs['ner'])
pred_res = self.ner_pred(
pred_score, sent_lens, self.vocabs['ner'])
nb_right, nb_pred, nb_gold = self.calc_acc(
pred_res, gold_res, return_prf=False)
train_right += nb_right
train_pred += nb_pred
train_gold += nb_gold
train_p, train_r, train_f = self.calc_prf(
train_right, train_pred, train_gold)
if self.args.update_step > 1:
loss = loss / self.args.update_step
loss.backward()
if (i + 1) % self.args.update_step == 0 or (i == self.args.max_step - 1):
nn_utils.clip_grad_norm_(filter(lambda p: p.requires_grad, self.model.parameters()),
max_norm=self.args.grad_clip)
optimizer.step()
self.model.zero_grad()
logger.info('[Epoch %d] Iter%d time cost: %.2fs, lr: %.6f, train loss: %.3f, P: %.3f, R: %.3f, F: %.3f' % (
ep, i + 1, (time.time() - t1), optimizer.get_lr(), loss_val, train_p, train_r, train_f))
dev_metric = self.evaluate('dev')
if dev_metric['f'] > best_dev_metric.get('f', 0):
best_dev_metric = dev_metric
test_metric = self.evaluate('test')
if test_metric['f'] > best_test_metric.get('f', 0):
# check_point = {'model': self.model.state_dict(), 'settings': args}
# torch.save(check_point, self.args.model_chkp)
best_test_metric = test_metric
patient = 0
else:
patient += 1
logger.info('[Epoch %d] train loss: %.4f, lr: %f, patient: %d, dev_metric: %s, test_metric: %s' % (
ep, train_loss, optimizer.get_lr(), patient, best_dev_metric, best_test_metric))
# if patient >= (self.args.patient // 2 + 1): # 训练一定epoch, dev性能不上升, decay lr
# optimizer.lr_decay(0.95)
if patient >= self.args.patient: # early stopping
break
logger.info('Final Metric: %s' % best_test_metric)
def train(model, train_data, dev_data, test_data, args, word_vocab,
extwd_vocab, lbl_vocab):
args.max_step = args.epoch * ((len(train_data) + args.batch_size - 1) //
(args.batch_size * args.update_steps))
optimizer = Optimizer(
filter(lambda p: p.requires_grad, lni_model.parameters()), args)
best_dev_acc, best_test_acc = 0, 0
patient = 0
for ep in range(1, 1 + args.epoch):
model.train()
train_loss = 0.
start_time = time.time()
for i, batch_data in enumerate(
batch_iter(train_data, args.batch_size, True)):
batcher = batch_variable(batch_data, word_vocab, extwd_vocab,
lbl_vocab)
batcher = (x.to(args.device) for x in batcher)
sent1, sent2, extsent1, extsent2, gold_lbl = batcher
pred = model((sent1, sent2), (extsent1, extsent2))
loss = criterion(pred, gold_lbl)
if args.update_steps > 1:
loss = loss / args.update_steps
loss_val = loss.data.item()
train_loss += loss_val
loss.backward()
if (i + 1) % args.update_steps == 0 or (i == args.max_step - 1):
nn.utils.clip_grad_norm_(filter(lambda p: p.requires_grad,
model.parameters()),
max_norm=args.grad_clip)
optimizer.step()
model.zero_grad()
train_acc = calc_acc(pred, gold_lbl) / len(batch_data)
logger.info(
'Iter%d time cost: %.2fs, lr: %.8f, train loss: %.3f, train acc: %.3f'
% (i + 1, (time.time() - start_time), optimizer.get_lr(),
loss_val, train_acc))
train_loss /= len(train_data)
dev_acc = eval(model, dev_data, args, word_vocab, extwd_vocab,
lbl_vocab)
logger.info('[Epoch %d] train loss: %.3f, lr: %f, DEV ACC: %.3f' %
(ep, train_loss, optimizer.get_lr(), dev_acc))
if dev_acc > best_dev_acc:
patient = 0
best_dev_acc = dev_acc
test_acc = eval(model, test_data, args, word_vocab, extwd_vocab,
lbl_vocab)
logger.info('Test ACC: %.3f' % test_acc)
if test_acc > best_test_acc:
best_test_acc = test_acc
else:
patient += 1
if patient > args.patient:
break
logger.info('Final Test ACC: %.3f' % best_test_acc)