def train(data):
print("Training model...")
data.show_data_summary()
save_data_name = data.model_dir + ".dset"
data.save(save_data_name)
if data.sentence_classification:
model = SentClassifier(data)
else:
model = SeqLabel(data)
# loss_function = nn.NLLLoss()
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(1)
best_dev = -10
# data.HP_iteration = 1
## 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)
instance_count = 0
sample_id = 0
sample_loss = 0
total_loss = 0
right_token = 0
whole_token = 0
random.shuffle(data.train_Ids)
print("Shuffle: first input word list:", data.train_Ids[0][0])
## 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, mask = batchify_with_label(
instance, data.HP_gpu, True, data.sentence_classification)
instance_count += 1
loss, tag_seq = model.neg_log_likelihood_loss(
batch_word, batch_features, batch_wordlen, batch_char,
batch_charlen, batch_charrecover, batch_label, mask)
right, whole = predict_check(tag_seq, batch_label, mask,
data.sentence_classification)
right_token += right
whole_token += whole
# print("loss:",loss.item())
sample_loss += loss.item()
total_loss += loss.item()
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))
if sample_loss > 1e8 or str(sample_loss) == "nan":
print(
"ERROR: LOSS EXPLOSION (>1e8) ! PLEASE SET PROPER PARAMETERS AND STRUCTURE! EXIT...."
)
exit(1)
sys.stdout.flush()
sample_loss = 0
loss.backward()
optimizer.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))
print("totalloss:", total_loss)
if total_loss > 1e8 or str(total_loss) == "nan":
print(
"ERROR: LOSS EXPLOSION (>1e8) ! PLEASE SET PROPER PARAMETERS AND STRUCTURE! EXIT...."
)
exit(1)
# 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...")
data.show_data_summary()
save_data_name = data.model_dir +".dset"
data.save(save_data_name)
if data.sentence_classification:
model = SentClassifier(data)
else:
model = SeqLabel(data)
print (model)
# loss_function = nn.NLLLoss()
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(1)
best_dev = -10
# data.HP_iteration = 1
## 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)
instance_count = 0
sample_id = 0
sample_loss = 0
total_loss = 0
right_token = 0
whole_token = 0
train_data = list(zip(data.train_Ids, data.train_texts))
random.shuffle(train_data)
data.train_Ids, data.train_texts = zip(*train_data)
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]
instance_texts = data.train_texts[start:end]
if not instance:
continue
batch_word, batch_features, batch_wordlen, batch_wordrecover, batch_char, batch_charlen, batch_charrecover, batch_label, mask, batch_word_text = batchify_with_label(instance, instance_texts , data.HP_gpu, True, data.sentence_classification)
instance_count += 1
loss, tag_seq = model.neg_log_likelihood_loss(batch_word, batch_features, batch_wordlen, batch_char, batch_charlen, batch_charrecover, batch_label, mask, batch_word_text)
right, whole = predict_check(tag_seq, batch_label, mask, data.sentence_classification)
right_token += right
whole_token += whole
# print("loss:",loss.item())
sample_loss += loss.item()
total_loss += loss.item()
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))
if sample_loss > 1e8 or str(sample_loss) == "nan":
print("ERROR: LOSS EXPLOSION (>1e8) ! PLEASE SET PROPER PARAMETERS AND STRUCTURE! EXIT....")
# exit(1)
sys.stdout.flush()
sample_loss = 0
loss.backward()
optimizer.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))
print("totalloss:", total_loss)
if total_loss > 1e8 or str(total_loss) == "nan":
print("ERROR: LOSS EXPLOSION (>1e8) ! PLEASE SET PROPER PARAMETERS AND STRUCTURE! EXIT....")
#exit(1)
# continue
speed, acc, p, r, f, pred_results, pred_scores = evaluate(data, model, "dev")
dev_finish = time.time()
dev_cost = dev_finish - epoch_finish
if data.optimize_with_evalb:
with tempfile.NamedTemporaryFile("w",delete=False) as f_decode:
if data.decode_dir is None:
data.decode_dir = f_decode.name
decoded_st_dir = f_decode.name
data.write_decoded_results(pred_results, 'dev')
command = ["PYTHONPATH="+data.tree2labels,"python",
data.evaluate," --input ",decoded_st_dir," --gold ",data.gold_dev_trees," --evalb ",data.evalb,">",f_decode.name+".out"]
os.system(" ".join(command))
f_decode = open(f_decode.name+".out","r")
current_score = float([l for l in f_decode.read().split("\n")
if l.startswith("Bracketing FMeasure")][0].split("=")[1])
print ("Current Score (from EVALB)", current_score, "Previous best dev (from EVALB)", best_dev)
elif data.optimize_with_las:
with tempfile.NamedTemporaryFile("w",delete=False) as f_decode:
if data.decode_dir is None:
data.decode_dir = f_decode.name
decoded_st_dir = f_decode.name
data.write_decoded_results(pred_results, 'dev')
#Transforming the output file into a CoNLL file
command = [#"PYTHONPATH="+abspath(join(dirname(__file__), data.dep2labels)),
"python",
data.dep2labels+os.sep+"decode_output_file.py",
"--input",
decoded_st_dir,
"--output",
f_decode.name+".out"
]
p = Popen(" ".join(command),stdout=subprocess.PIPE, shell=True)
out, err = p.communicate()
command = ["python",
data.conll_ud, f_decode.name+".out",
data.gold_dev_trees]#,">",f_decode.name+".out"]
p = Popen(" ".join(command),stdout=subprocess.PIPE, shell=True)
out, err = p.communicate()
out = out.decode("utf-8")
current_score = float(out.strip().split(":")[1])
print ("Current Score (from conll_ud)", current_score, "Previous best dev (from conll_ud)", best_dev)
else:
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 + ".model"
#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()