def train(data):
print "Training model..."
data.show_data_summary()
save_data_name = data.model_dir + ".dset"
data.save(save_data_name)
model = SeqModel(data)
if data.HP_gpu:
model.cuda()
if data.optimizer.lower() == "sgd":
optimizer = optim.SGD(model.parameters(),
lr=data.HP_lr,
momentum=data.HP_momentum,
weight_decay=data.HP_l2)
elif data.optimizer.lower() == "adagrad":
optimizer = optim.Adagrad(model.parameters(),
lr=data.HP_lr,
weight_decay=data.HP_l2)
elif data.optimizer.lower() == "adadelta":
optimizer = optim.Adadelta(model.parameters(),
lr=data.HP_lr,
weight_decay=data.HP_l2)
elif data.optimizer.lower() == "rmsprop":
optimizer = optim.RMSprop(model.parameters(),
lr=data.HP_lr,
weight_decay=data.HP_l2)
elif data.optimizer.lower() == "adam":
optimizer = optim.Adam(model.parameters(),
lr=data.HP_lr,
weight_decay=data.HP_l2)
else:
print("Optimizer illegal: %s" % (data.optimizer))
exit(0)
optimizer_wc = optim.SGD(model.word_hidden.wordrep.w.parameters(),
lr=data.HP_lr,
momentum=data.HP_momentum,
weight_decay=data.HP_l2)
best_dev = -10
## start training
for idx in range(data.HP_iteration):
epoch_start = time.time()
temp_start = epoch_start
print("Epoch: %s/%s" % (idx, data.HP_iteration))
if data.optimizer == "SGD":
optimizer = lr_decay(optimizer, idx, data.HP_lr_decay, data.HP_lr)
optimizer_wc = lr_decay(optimizer_wc, idx, data.HP_lr_decay,
data.HP_lr)
instance_count = 0
sample_id = 0
sample_loss = 0
total_loss = 0
right_token = 0
whole_token = 0
random.shuffle(data.train_Ids)
## set model in train model
model.train()
model.zero_grad()
batch_size = data.HP_batch_size
batch_id = 0
train_num = len(data.train_Ids)
total_batch = train_num // batch_size + 1
for batch_id in range(total_batch):
start = batch_id * batch_size
end = (batch_id + 1) * batch_size
if end > train_num:
end = train_num
instance = data.train_Ids[start:end]
if not instance:
continue
batch_word, batch_features, batch_wordlen, batch_wordrecover, batch_char, batch_charlen, batch_charrecover, batch_label, batch_trans, trans_seq_lengths, trans_seq_recover, mask = batchify_with_label(
instance, data.HP_gpu)
instance_count += 1
loss, tag_seq, wc_loss = model.neg_log_likelihood_loss(
batch_word, batch_features, batch_wordlen, batch_char,
batch_charlen, batch_charrecover, batch_label, mask,
batch_trans, trans_seq_lengths, trans_seq_recover)
right, whole = predict_check(tag_seq, batch_label, mask)
right_token += right
whole_token += whole
sample_loss += loss.data[0]
total_loss += loss.data[0]
if end % 500 == 0:
temp_time = time.time()
temp_cost = temp_time - temp_start
temp_start = temp_time
print(
" Instance: %s; Time: %.2fs; loss: %.4f; acc: %s/%s=%.4f"
% (end, temp_cost, sample_loss, right_token, whole_token,
(right_token + 0.) / whole_token))
sys.stdout.flush()
sample_loss = 0
for param in model.word_hidden.wordrep.w.parameters():
param.requires_grad = False
loss.backward(retain_graph=True)
optimizer.step()
model.zero_grad()
for param in model.word_hidden.wordrep.w.parameters():
param.requires_grad = True
wc_loss.backward()
optimizer_wc.step()
model.zero_grad()
temp_time = time.time()
temp_cost = temp_time - temp_start
print(" Instance: %s; Time: %.2fs; loss: %.4f; acc: %s/%s=%.4f" %
(end, temp_cost, sample_loss, right_token, whole_token,
(right_token + 0.) / whole_token))
epoch_finish = time.time()
epoch_cost = epoch_finish - epoch_start
print(
"Epoch: %s training finished. Time: %.2fs, speed: %.2fst/s, total loss: %s"
% (idx, epoch_cost, train_num / epoch_cost, total_loss))
# continue
speed, acc, p, r, f, _, _ = evaluate(data, model, "dev")
dev_finish = time.time()
dev_cost = dev_finish - epoch_finish
if data.seg:
current_score = f
print(
"Dev: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f"
% (dev_cost, speed, acc, p, r, f))
else:
current_score = acc
print("Dev: time: %.2fs speed: %.2fst/s; acc: %.4f" %
(dev_cost, speed, acc))
if current_score > best_dev:
if data.seg:
print "Exceed previous best f score:", best_dev
else:
print "Exceed previous best acc score:", best_dev
model_name = data.model_dir + '.' + str(idx) + ".model"
print "Save current best model in file:", model_name
torch.save(model.state_dict(), model_name)
best_dev = current_score
# ## decode test
speed, acc, p, r, f, _, _ = evaluate(data, model, "test")
test_finish = time.time()
test_cost = test_finish - dev_finish
if data.seg:
print(
"Test: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f"
% (test_cost, speed, acc, p, r, f))
else:
print("Test: time: %.2fs, speed: %.2fst/s; acc: %.4f" %
(test_cost, speed, acc))
gc.collect()
def train(data):
print "Training model..."
model = SeqModel(data)
print "model:{}".format(model)
if data.gpu:
model.cuda()
if data.optimizer.lower() == "sgd":
optimizer = optim.SGD(model.parameters(),
lr=data.lr,
momentum=data.momentum,
weight_decay=data.l2)
if data.use_mapping:
optimizer_wc = optim.SGD(model.word_hidden.wordrep.w.parameters(),
lr=data.lr,
momentum=data.momentum,
weight_decay=data.l2)
elif data.optimizer.lower() == "adam":
optimizer = optim.Adam(model.parameters(),
lr=data.lr,
weight_decay=data.l2)
if data.use_mapping:
optimizer_wc = optim.Adam(model.word_hidden.wordrep.w.parameters(),
lr=data.lr,
weight_decay=data.l2)
else:
print("Optimizer illegal: %s , use sgd or adam." % data.optimizer)
exit(0)
best_dev = -10
best_dev_epoch = -1
best_test = -10
best_test_epoch = -1
# start training
for idx in range(data.iteration):
epoch_start = time.time()
temp_start = epoch_start
print("Epoch: %s/%s" % (idx + 1, data.iteration))
if data.optimizer == "SGD":
optimizer = lr_decay(optimizer, idx, data.lr_decay, data.lr)
if data.use_mapping:
optimizer_wc = lr_decay(optimizer_wc, idx, data.lr_decay,
data.lr)
instance_count = 0
sample_id = 0
#
sample_loss = 0
sample_mapping_loss = 0
total_loss = 0
total_mapping_loss = 0
right_token = 0
whole_token = 0
random.shuffle(data.train_Ids)
# set model in train mode
model.train()
model.zero_grad()
batch_size = data.batch_size
batch_id = 0
train_num = len(data.train_Ids)
total_batch = train_num // batch_size + 1
for batch_id in range(total_batch):
start = batch_id * batch_size
end = (batch_id + 1) * batch_size
if end > train_num:
end = train_num
instance = data.train_Ids[start:end]
if not instance:
continue
batch_word, batch_features, batch_wordlen, batch_wordrecover, batch_char, batch_charlen, batch_charrecover, batch_label, batch_trans, trans_seq_lengths, trans_seq_recover, mask = batchify_with_label(
instance, data.gpu)
instance_count += 1
loss, tag_seq, wc_loss = model.neg_log_likelihood_loss(
batch_word, batch_features, batch_wordlen, batch_char,
batch_charlen, batch_charrecover, batch_label, mask,
batch_trans, trans_seq_lengths, trans_seq_recover)
right, whole = predict_check(tag_seq, batch_label, mask)
right_token += right
whole_token += whole
#sample_loss += loss.data[0]
sample_loss += loss.data.item()
if data.use_mapping:
sample_mapping_loss += wc_loss.data[0]
#total_loss += loss.data[0]
total_loss += loss.data.item()
if data.use_mapping:
total_mapping_loss += wc_loss.data[0]
if batch_id % data.show_loss_per_batch == 0:
temp_time = time.time()
temp_cost = temp_time - temp_start
temp_start = temp_time
if data.use_mapping:
print(
" Instance: %s; Time: %.2fs; loss: %.4f; acc: %s/%s=%.4f"
% (batch_id, temp_cost, sample_loss, right_token,
whole_token, (right_token + 0.) / whole_token))
else:
print(
" Instance: %s; Time: %.2fs; loss: %.4f;mapping_loss: %.4f; acc: %s/%s=%.4f"
% (batch_id, temp_cost, sample_loss,
sample_mapping_loss, right_token, whole_token,
(right_token + 0.) / whole_token))
sys.stdout.flush()
sample_loss = 0
sample_mapping_loss = 0
if data.use_trans and data.use_mapping:
for param in model.word_hidden.wordrep.w.parameters():
param.requires_grad = False
loss.backward(retain_graph=True)
if data.clip != None:
torch.nn.utils.clip_grad_norm(model.parameters(),
data.clip)
optimizer.step()
model.zero_grad()
for param in model.word_hidden.wordrep.w.parameters():
param.requires_grad = True
wc_loss.backward()
optimizer_wc.step()
model.zero_grad()
else:
loss.backward()
# torch.nn.utils.clip_grad_norm(model.parameters(), data.clip)
optimizer.step()
model.zero_grad()
temp_time = time.time()
temp_cost = temp_time - temp_start
if data.use_mapping:
print(
" Instance: %s; Time: %.2fs; loss: %.4f; acc: %s/%s=%.4f" %
(batch_id, temp_cost, sample_loss, right_token, whole_token,
(right_token + 0.) / whole_token))
else:
print(
" Instance: %s; Time: %.2fs; loss: %.4f;mapping_loss: %.4f; acc: %s/%s=%.4f"
% (batch_id, temp_cost, sample_loss, sample_mapping_loss,
right_token, whole_token, (right_token + 0.) / whole_token))
epoch_finish = time.time()
epoch_cost = epoch_finish - epoch_start
if data.use_mapping:
print(
"Epoch: %s training finished. Time: %.2fs, speed: %.2fst/s, total loss: %s,total mapping loss: %s"
% (idx + 1, epoch_cost, train_num / epoch_cost, total_loss,
total_mapping_loss))
else:
print(
"Epoch: %s training finished. Time: %.2fs, speed: %.2fst/s, total loss: %s"
% (idx + 1, epoch_cost, train_num / epoch_cost, total_loss))
# continue
speed, acc, p, r, f, _, _ = evaluate(data, model, "dev", data.nbest)
dev_finish = time.time()
dev_cost = dev_finish - epoch_finish
if data.seg:
current_score = f
print(
"Dev: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f"
% (dev_cost, speed, acc, p, r, f))
else:
current_score = acc
print("Dev: time: %.2fs speed: %.2fst/s; acc: %.4f" %
(dev_cost, speed, acc))
if current_score > best_dev:
if data.seg:
print "Exceed previous best f score:", best_dev
else:
print "Exceed previous best acc score:", best_dev
if data.save_model:
model_name = data.model_dir + data.state_training_name + '.' + str(
current_score)[2:-1]
print "Save current best model in file:", model_name
torch.save(model.state_dict(), model_name)
best_dev = current_score
best_dev_epoch = idx
# ## decode test
speed, acc, p, r, f, _, _ = evaluate(data, model, "test", data.nbest)
if f > best_test:
best_test = f
best_test_epoch = idx
test_finish = time.time()
test_cost = test_finish - dev_finish
if data.seg:
print(
"Test: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f"
% (test_cost, speed, acc, p, r, f))
else:
print("Test: time: %.2fs, speed: %.2fst/s; acc: %.4f" %
(test_cost, speed, acc))
print('best_dev_score: %.4f, best_dev_epoch:%d' %
(best_dev, best_dev_epoch))
print('best_test_score: %.4f, best_test_epoch:%d' %
(best_test, best_test_epoch))
gc.collect()
