def train(self, dataset):
self.logger.info("Start training...")
best_score, no_imprv_epoch, cur_step = -np.inf, 0, 0
for epoch in range(1, self.cfg.epochs + 1):
self.logger.info('Epoch {}/{}:'.format(epoch, self.cfg.epochs))
prog = Progbar(target=dataset.num_batches)
for i in range(dataset.num_batches):
cur_step += 1
data = dataset.next_batch()
feed_dict = self._get_feed_dict(data, training=True)
_, train_loss = self.sess.run([self.train_op, self.loss],
feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(cur_step)),
("Train Loss", train_loss)])
# evaluate
score = self.evaluate_data(dataset.dev_batches(), name="dev")
self.evaluate_data(dataset.test_batches(), name="test")
if score > best_score:
best_score, no_imprv_epoch = score, 0
self.save_session(epoch)
self.logger.info(
' -- new BEST score on dev dataset: {:04.2f}'.format(
best_score))
else:
no_imprv_epoch += 1
if self.cfg.no_imprv_tolerance is not None and no_imprv_epoch >= self.cfg.no_imprv_tolerance:
self.logger.info(
'early stop at {}th epoch without improvement'.format(
epoch))
self.logger.info(
'best score on dev set: {}'.format(best_score))
break
def train(self, dataset):
self.logger.info("Start training...")
best_f1, no_imprv_epoch, init_lr, lr, cur_step = -np.inf, 0, self.cfg.lr, self.cfg.lr, 0
for epoch in range(1, self.cfg.epochs + 1):
self.logger.info("Epoch {}/{}:".format(epoch, self.cfg.epochs))
prog = Progbar(target=dataset.get_num_batches())
for i, data in enumerate(dataset.get_data_batches()):
cur_step += 1
feed_dict = self._get_feed_dict(data, is_train=True, lr=lr)
_, train_loss = self.sess.run([self.train_op, self.loss], feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(cur_step)), ("Train Loss", train_loss)])
# learning rate decay
if self.cfg.use_lr_decay:
if self.cfg.decay_step:
lr = max(init_lr / (1.0 + self.cfg.lr_decay * epoch / self.cfg.decay_step), self.cfg.minimal_lr)
# evaluate
score = self.evaluate(dataset.get_data_batches("dev"), name="dev")
self.evaluate(dataset.get_data_batches("test"), name="test")
if score["FB1"] > best_f1:
best_f1, no_imprv_epoch = score["FB1"], 0
self.save_session(epoch)
self.logger.info(" -- new BEST score on dev dataset: {:04.2f}".format(best_f1))
else:
no_imprv_epoch += 1
if self.cfg.no_imprv_tolerance is not None and no_imprv_epoch >= self.cfg.no_imprv_tolerance:
self.logger.info("early stop at {}th epoch without improvement".format(epoch))
self.logger.info("best score on dev set: {}".format(best_f1))
break
def train(self, train_words, train_chars, train_labels, test_words,
test_chars, test_labels):
global_test_acc, global_step, lr = 0.0, 0, self.cfg.lr
num_batches = math.ceil(float(len(train_words) / self.cfg.batch_size))
self.logger.info("start training...")
for epoch in range(1, self.cfg.epochs + 1):
self.logger.info("Epoch {}/{}:".format(epoch, self.cfg.epochs))
train_words, train_chars, train_labels = sklearn.utils.shuffle(
train_words, train_chars, train_labels)
prog = Progbar(target=num_batches)
for i, (b_words, b_seq_len, b_chars, b_char_seq_len,
b_labels) in enumerate(
batch_iter(train_words, train_chars, train_labels,
self.cfg.batch_size)):
global_step += 1
batch_labels = []
for j in range(self.num_classifier):
ecoc_array = self.nary_ecoc[:, j]
b_lbs = remap_labels(b_labels.copy(), ecoc_array)
b_lbs = dense_to_one_hot(b_lbs, self.num_class)
batch_labels.append(b_lbs)
feed_dict = self.get_feed_dict(b_words,
b_seq_len,
b_chars,
b_char_seq_len,
batch_labels,
lr=lr,
training=True)
_, pred_labels, loss = self.sess.run(
[self.train_op, self.pred_labels, self.loss],
feed_dict=feed_dict)
acc = compute_ensemble_accuracy(pred_labels, self.nary_ecoc,
b_labels)
prog.update(i + 1, [("Global Step", global_step),
("Train Loss", loss),
("Train Acc", acc * 100)])
accuracy, _ = self.test(test_words,
test_chars,
test_labels,
batch_size=200,
print_info=True,
restore=False)
if accuracy > global_test_acc:
global_test_acc = accuracy
self.save_session(epoch)
lr = self.cfg.lr / (1 + epoch * self.cfg.lr_decay)
def train_epoch(self, train_set, valid_data, epoch):
num_batches = len(train_set)
prog = Progbar(target=num_batches)
total_cost, total_samples = 0, 0
for i, batch in enumerate(train_set):
feed_dict = self._get_feed_dict(batch, is_train=True, keep_prob=self.cfg["keep_prob"], lr=self.cfg["lr"])
_, train_loss, summary = self.sess.run([self.train_op, self.loss, self.summary], feed_dict=feed_dict)
cur_step = (epoch - 1) * num_batches + (i + 1)
total_cost += train_loss
total_samples += np.array(batch["words"]).shape[0]
prog.update(i + 1, [("Global Step", int(cur_step)), ("Train Loss", train_loss),
("Perplexity", np.exp(total_cost / total_samples))])
self.train_writer.add_summary(summary, cur_step)
if i % 100 == 0:
valid_feed_dict = self._get_feed_dict(valid_data)
valid_summary = self.sess.run(self.summary, feed_dict=valid_feed_dict)
self.test_writer.add_summary(valid_summary, cur_step)
def train(self, train_dataset, test_dataset):
global_test_acc = 0.0
global_step = 0
test_imgs, test_labels = test_dataset.images, test_dataset.labels
self.logger.info("start training...")
for epoch in range(1, self.epochs + 1):
self.logger.info("Epoch {}/{}:".format(epoch, self.epochs))
num_batches = train_dataset.num_examples // self.batch_size
prog = Progbar(target=num_batches)
prog.update(0, [("Global Step", 0), ("Train Loss", 0.0), ("Train Acc", 0.0), ("Test Loss", 0.0),
("Test Acc", 0.0)])
for i in range(num_batches):
global_step += 1
train_imgs, train_labels = train_dataset.next_batch(self.batch_size)
feed_dict = {self.inputs: train_imgs, self.labels: train_labels, self.training: True}
_, loss, acc = self.sess.run([self.train_op, self.cost, self.accuracy], feed_dict=feed_dict)
if global_step % 100 == 0:
feed_dict = {self.inputs: test_imgs, self.labels: test_labels, self.training: False}
test_loss, test_acc = self.sess.run([self.cost, self.accuracy], feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(global_step)), ("Train Loss", loss), ("Train Acc", acc),
("Test Loss", test_loss), ("Test Acc", test_acc)])
if test_acc > global_test_acc:
global_test_acc = test_acc
self.save_session(global_step)
else:
prog.update(i + 1, [("Global Step", int(global_step)), ("Train Loss", loss), ("Train Acc", acc)])
feed_dict = {self.inputs: test_imgs, self.labels: test_labels, self.training: False}
test_loss, test_acc = self.sess.run([self.cost, self.accuracy], feed_dict=feed_dict)
self.logger.info("Epoch: {}, Global Step: {}, Test Loss: {}, Test Accuracy: {}".format(
epoch, global_step, test_loss, test_acc))
def train(self, label_dataset, partial_dataset, unlabeled_dataset):
best_f1, no_imprv_epoch, lr, cur_step = -np.inf, 0, self.cfg.lr, 0
loss_weight = self._compute_loss_weights(label_dataset.get_dataset_size(), partial_dataset.get_dataset_size(),
unlabeled_dataset.get_dataset_size(), method=self.cfg.loss_method)
self.logger.info("Start training...")
for epoch in range(1, self.cfg.epochs + 1):
self.logger.info("Epoch {}/{}:".format(epoch, self.cfg.epochs))
batches = self._create_batches(label_dataset.get_num_batches(), partial_dataset.get_num_batches(),
unlabeled_dataset.get_num_batches())
prog = Progbar(target=len(batches))
prog.update(0, [("Global Step", int(cur_step)), ("Label Loss", 0.0), ("Partial Loss", 0.0),
("AE Loss", 0.0), ("AE Acc", 0.0)])
for i, batch_name in enumerate(batches):
cur_step += 1
if batch_name == "label":
data = label_dataset.get_next_train_batch()
feed_dict = self._get_feed_dict(data, loss_weight=loss_weight[0], is_train=True, lr=lr)
_, cost = self.sess.run([self.train_op, self.loss], feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(cur_step)), ("Label Loss", cost)])
if batch_name == "partial":
data = partial_dataset.get_next_train_batch()
feed_dict = self._get_feed_dict(data, loss_weight=loss_weight[1], is_train=True, lr=lr)
_, cost = self.sess.run([self.train_op, self.loss], feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(cur_step)), ("Partial Loss", cost)])
if batch_name == "unlabeled":
data = unlabeled_dataset.get_next_train_batch()
feed_dict = self._get_feed_dict(data, loss_weight=loss_weight[2], is_train=True, lr=lr)
_, cost, acc = self.sess.run([self.ae_train_op, self.ae_loss, self.ae_acc], feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(cur_step)), ("AE Loss", cost), ("AE Acc", acc)])
# learning rate decay
if self.cfg.use_lr_decay:
if self.cfg.decay_step:
lr = max(self.cfg.lr / (1.0 + self.cfg.lr_decay * epoch / self.cfg.decay_step), self.cfg.minimal_lr)
self.evaluate(label_dataset.get_data_batches("dev"), name="dev")
score = self.evaluate(label_dataset.get_data_batches("test"), name="test")
if score["FB1"] > best_f1:
best_f1, no_imprv_epoch = score["FB1"], 0
self.save_session(epoch)
self.logger.info(" -- new BEST score on test dataset: {:04.2f}".format(best_f1))
else:
no_imprv_epoch += 1
if self.cfg.no_imprv_tolerance is not None and no_imprv_epoch >= self.cfg.no_imprv_tolerance:
self.logger.info("early stop at {}th epoch without improvement".format(epoch))
self.logger.info("best score on test set: {}".format(best_f1))
break
def train(self, x_train, y_train, x_test, y_test, batch_size=200, epochs=10):
x_train, x_test = self.normalize(x_train, x_test)
y_train = keras.utils.to_categorical(y_train, self.num_class)
y_test = keras.utils.to_categorical(y_test, self.num_class)
self.logger.info("data augmentation...")
datagen = ImageDataGenerator(featurewise_center=True, samplewise_center=False, horizontal_flip=True, cval=0.0,
featurewise_std_normalization=False, preprocessing_function=None, rescale=None,
samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06,
rotation_range=15, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.0,
zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', vertical_flip=False,
data_format="channels_last")
datagen.fit(x_train)
x_aug, y_aug = x_train.copy(), y_train.copy()
x_aug = datagen.flow(x_aug, np.zeros(x_train.shape[0]), batch_size=x_train.shape[0], shuffle=False).next()[0]
x_train, y_train = np.concatenate((x_train, x_aug)), np.concatenate((y_train, y_aug))
self.logger.info("start training...")
global_step, lr, global_test_acc = 0, self.learning_rate, 0.0
num_batches = x_train.shape[0] // batch_size
for epoch in range(1, epochs + 1):
self.logger.info("Epoch {}/{}:".format(epoch, epochs))
x_train, y_train = utils.shuffle(x_train, y_train, random_state=0) # shuffle training dataset
prog = Progbar(target=num_batches)
prog.update(0, [("Global Step", 0), ("Train Loss", 0.0), ("Train Acc", 0.0), ("Test Loss", 0.0),
("Test Acc", 0.0)])
for i, (batch_imgs, batch_labels) in enumerate(batch_dataset(x_train, y_train, batch_size)):
global_step += 1
feed_dict = {self.inputs: batch_imgs, self.labels: batch_labels, self.training: True, self.lr: lr}
_, loss, acc = self.sess.run([self.train_op, self.cost, self.accuracy], feed_dict=feed_dict)
if global_step % 200 == 0:
feed_dict = {self.inputs: x_test, self.labels: y_test, self.training: False}
test_loss, test_acc = self.sess.run([self.cost, self.accuracy], feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(global_step)), ("Train Loss", loss), ("Train Acc", acc),
("Test Loss", test_loss), ("Test Acc", test_acc)])
if test_acc > global_test_acc:
global_test_acc = test_acc
self.save_session(global_step)
else:
prog.update(i + 1, [("Global Step", int(global_step)), ("Train Loss", loss), ("Train Acc", acc)])
if epoch > 10:
lr = self.learning_rate / (1 + (epoch - 10) * self.lr_decay)
feed_dict = {self.inputs: x_test, self.labels: y_test, self.training: False}
test_loss, test_acc = self.sess.run([self.cost, self.accuracy], feed_dict=feed_dict)
self.logger.info("Epoch: {}, Global Step: {}, Test Loss: {}, Test Accuracy: {}".format(
epoch, global_step, test_loss, test_acc))
def train(self, src_dataset, tgt_dataset):
self.logger.info("Start training...")
best_score, no_imprv_epoch, src_lr, tgt_lr, cur_step = -np.inf, 0, self.cfg.lr, self.cfg.lr, 0
for epoch in range(1, self.cfg.epochs + 1):
self.logger.info("Epoch {}/{}:".format(epoch, self.cfg.epochs))
batches = self._arrange_batches(src_dataset.num_batches, tgt_dataset.num_batches, self.cfg.mix_rate)
prog = Progbar(target=len(batches))
prog.update(0, [("Global Step", int(cur_step)), ("Source Train Loss", 0.0), ("Target Train Loss", 0.0)])
for i, batch_name in enumerate(batches):
cur_step += 1
if batch_name == "src":
data = src_dataset.next_batch()
domain_labels = [[1, 0]] * data["batch_size"]
feed_dict = self._get_feed_dict(src_data=data, tgt_data=None, domain_labels=domain_labels,
training=True)
_, src_cost = self.sess.run([self.src_train_op, self.src_loss], feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(cur_step)), ("Source Train Loss", src_cost)])
else: # "tgt"
data = tgt_dataset.next_batch()
domain_labels = [[0, 1]] * data["batch_size"]
feed_dict = self._get_feed_dict(src_data=None, tgt_data=data, domain_labels=domain_labels,
training=True)
_, tgt_cost = self.sess.run([self.tgt_train_op, self.tgt_loss], feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(cur_step)), ("Target Train Loss", tgt_cost)])
score = self.evaluate_data(tgt_dataset.dev_batches(), "target_dev", resource="target")
self.evaluate_data(tgt_dataset.test_batches(), "target_test", resource="target")
if score > best_score:
best_score, no_imprv_epoch = score, 0
self.save_session(epoch)
self.logger.info(' -- new BEST score on target dev dataset: {:04.2f}'.format(best_score))
else:
no_imprv_epoch += 1
if self.cfg.no_imprv_tolerance is not None and no_imprv_epoch >= self.cfg.no_imprv_tolerance:
self.logger.info('early stop at {}th epoch without improvement'.format(epoch))
self.logger.info('best score on target dev set: {}'.format(best_score))
break
def _train_epoch(
self,
dataloader_train,
dataloader_dev,
optimizer,
criterion,
eval_every,
train_step,
best_score,
best_loss,
use_prog_bar=False,
):
prog = Progbar(len(dataloader_train))
tr_loss = 0
for batch_idx, batch in enumerate(dataloader_train):
torch.cuda.empty_cache()
self.scheduler.step()
train_step += 1
optimizer.zero_grad()
(sents, target, doc_encoding) = batch
if not self.binary_class:
target = target.squeeze(1)
if self.use_doc_encoding: # Capsule based models
(
preds,
word_attention_scores,
sent_attention_scores,
rec_loss,
) = self.model(sents, doc_encoding)
else: # Other models
(
preds,
word_attention_scores,
sent_attention_scores,
rec_loss,
) = self.model(
sents
) # rec loss defaults to 0 for non-CapsNet models
if torch.cuda.device_count() > 1:
rec_loss = rec_loss.mean()
loss = criterion(preds, target)
loss += rec_loss
tr_loss += loss.item()
# if APEX_AVAILABLE:
# with amp.scale_loss(loss, optimizer) as scaled_loss:
# scaled_loss.backward()
# else:
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 5)
optimizer.step()
if use_prog_bar:
prog.update(
batch_idx + 1,
values=[("train loss", loss.item()), ("recon loss", rec_loss)],
)
torch.cuda.empty_cache()
if train_step % eval_every == 0:
best_score, best_loss = self._eval_model(
dataloader_train, dataloader_dev, best_score, best_loss, train_step
)
return best_score, best_loss, train_step
def train(self, src_dataset, tgt_dataset):
self.logger.info("Start training...")
best_f1, no_imprv_epoch, src_lr, tgt_lr, cur_step = -np.inf, 0, self.cfg.lr, self.cfg.lr, 0
for epoch in range(1, self.cfg.epochs + 1):
self.logger.info("Epoch {}/{}:".format(epoch, self.cfg.epochs))
batches = self._arrange_batches(src_dataset, tgt_dataset,
self.cfg.mix_rate,
self.cfg.train_ratio)
prog = Progbar(target=len(batches))
prog.update(0, [("Global Step", int(cur_step)),
("Source Train Loss", 0.0),
("Target Train Loss", 0.0)])
for i, batch_name in enumerate(batches):
cur_step += 1
if batch_name == "src":
data = src_dataset.get_next_train_batch()
domain_labels = [[1, 0]] * data["batch_size"]
feed_dict = self._get_feed_dict(
src_data=data,
tgt_data=None,
domain_labels=domain_labels,
is_train=True,
src_lr=src_lr)
_, src_cost = self.sess.run(
[self.src_train_op, self.src_loss],
feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(cur_step)),
("Source Train Loss", src_cost)])
else: # "tgt"
data = tgt_dataset.get_next_train_batch()
domain_labels = [[0, 1]] * data["batch_size"]
feed_dict = self._get_feed_dict(
src_data=None,
tgt_data=data,
domain_labels=domain_labels,
is_train=True,
tgt_lr=tgt_lr)
_, tgt_cost = self.sess.run(
[self.tgt_train_op, self.tgt_loss],
feed_dict=feed_dict)
prog.update(i + 1, [("Global Step", int(cur_step)),
("Target Train Loss", tgt_cost)])
if self.cfg.use_lr_decay: # learning rate decay
src_lr = max(self.cfg.lr / (1.0 + self.cfg.lr_decay * epoch),
self.cfg.minimal_lr)
if epoch % self.cfg.decay_step == 0:
tgt_lr = max(
self.cfg.lr /
(1.0 +
self.cfg.lr_decay * epoch / self.cfg.decay_step),
self.cfg.minimal_lr)
if not self.cfg.dev_for_train:
self.evaluate(tgt_dataset.get_data_batches("dev"),
"target_dev",
self._tgt_predict_op,
rev_word_dict=self.rev_tw_dict,
rev_label_dict=self.rev_tl_dict)
score = self.evaluate(tgt_dataset.get_data_batches("test"),
"target_test",
self._tgt_predict_op,
rev_word_dict=self.rev_tw_dict,
rev_label_dict=self.rev_tl_dict)
if score["FB1"] > best_f1:
best_f1, no_imprv_epoch = score["FB1"], 0
self.save_session(epoch)
self.logger.info(
' -- new BEST score on target test dataset: {:04.2f}'.
format(best_f1))
else:
no_imprv_epoch += 1
if self.cfg.no_imprv_tolerance is not None and no_imprv_epoch >= self.cfg.no_imprv_tolerance:
self.logger.info(
'early stop at {}th epoch without improvement'.format(
epoch))
self.logger.info(
'best score on target test set: {}'.format(best_f1))
break
def train(self, train_dataset, test_dataset):
global_test_acc = 0.0
global_step = 0
test_imgs, test_labels = test_dataset.images, test_dataset.labels
self.logger.info("start training...")
for epoch in range(1, self.epochs + 1):
self.logger.info("Epoch {}/{}:".format(epoch, self.epochs))
num_batches = train_dataset.num_examples // self.batch_size
prog = Progbar(target=num_batches)
prog.update(0, [("Global Step", 0), ("Train Loss", 0.0),
("Train Acc", 0.0), ("Test Loss", 0.0),
("Test Acc", 0.0)])
for i in range(num_batches):
global_step += 1
train_imgs, train_labels = train_dataset.next_batch(
self.batch_size)
b_labels = []
for j in range(self.num_classifier):
ecoc_array = self.nary_ecoc[:, j]
b_lbs = remap_labels(train_labels.copy(), ecoc_array)
b_lbs = dense_to_one_hot(b_lbs, self.num_class)
b_labels.append(b_lbs)
feed_dict = self._get_feed_dict(train_imgs, b_labels, True)
_, pred_labels, loss = self.sess.run(
[self.train_op, self.pred_labels, self.cost],
feed_dict=feed_dict)
acc = compute_ensemble_accuracy(pred_labels, self.nary_ecoc,
train_labels)
if global_step % 100 == 0:
y_labels = []
for j in range(self.num_classifier):
ecoc_array = self.nary_ecoc[:, j]
b_lbs = remap_labels(test_labels.copy(), ecoc_array)
b_lbs = dense_to_one_hot(b_lbs, self.num_class)
y_labels.append(b_lbs)
feed_dict = self._get_feed_dict(test_imgs, y_labels)
test_pred_labels, test_loss = self.sess.run(
[self.pred_labels, self.cost], feed_dict=feed_dict)
test_acc = compute_ensemble_accuracy(
test_pred_labels, self.nary_ecoc, test_labels)
prog.update(i + 1, [("Global Step", int(global_step)),
("Train Loss", loss),
("Train Acc", acc),
("Test Loss", test_loss),
("Test Acc", test_acc)])
if test_acc > global_test_acc:
global_test_acc = test_acc
self.save_session(global_step)
else:
prog.update(i + 1, [("Global Step", int(global_step)),
("Train Loss", loss),
("Train Acc", acc)])
y_labels = []
for j in range(self.num_classifier):
ecoc_array = self.nary_ecoc[:, j]
b_lbs = remap_labels(test_labels.copy(), ecoc_array)
b_lbs = dense_to_one_hot(b_lbs, self.num_class)
y_labels.append(b_lbs)
feed_dict = self._get_feed_dict(test_imgs, y_labels)
test_pred_labels, test_loss = self.sess.run(
[self.pred_labels, self.cost], feed_dict=feed_dict)
test_acc = compute_ensemble_accuracy(test_pred_labels,
self.nary_ecoc, test_labels)
self.logger.info(
"Epoch: {}, Global Step: {}, Test Loss: {}, Test Accuracy: {}".
format(epoch, global_step, test_loss, test_acc))
