def train(data, save_model_dir, seg=True):
model = SeqModel(data)
# print "finished built model."
parameters = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.SGD(parameters, lr=data.HP_lr, momentum=data.HP_momentum)
## 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))
# random.shuffle(data.index_data)
## set model in train model
model.train()
model.zero_grad()
batch_size = 1 ## current only support batch size = 1 to compulate and accumulate to data.HP_batch_size update weights
train_num = len(data.index_data)
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.index_data[start:end]
if not instance:
continue
gaz_list, batch_word, batch_entity, batch_gloss, batch_label, mask = batchify_with_label(instance, data.HP_gpu)
loss,predict = model.neg_log_likelihood_loss(gaz_list,batch_word,batch_entity,batch_gloss,batch_label, mask)
# print(predict)
print("Batch_Id:",batch_id," Loss:",loss)
loss.backward()
optimizer.step()
model.zero_grad()
predict_check(predict, batch_label, mask)
evaluate_result(predict, batch_label, mask)
print("\n")
def train(data, save_model_dir,save_data_set, seg=True):
print ("Training model...")
data.show_data_summary()
#save_data_name = save_data_set
#save_data_setting(data, save_data_name)
model = SeqModel(data)
print ("finished built model.")
loss_function = nn.NLLLoss()
parameters = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.SGD(parameters, lr=data.HP_lr, momentum=data.HP_momentum)
best_dev = -1
data.HP_iteration = 100
## start training data.HP_iteration
for idx in range(data.HP_iteration):
epoch_start = time.time()
temp_start = epoch_start
print("Epoch: %s/%s" %(idx,data.HP_iteration))
optimizer = lr_decay(optimizer, idx, data.HP_lr_decay, data.HP_lr)
instance_count = 0
sample_id = 0
sample_loss = 0
batch_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 = 10 ## current only support batch size = 1 to compulate and accumulate to data.HP_batch_size update weights
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
gaz_list, batch_word, batch_biword, batch_wordlen, batch_wordrecover, batch_char, batch_charlen, batch_charrecover, batch_label, mask = batchify_with_label(instance, data.HP_gpu)
# print "gaz_list:",gaz_list
# exit(0)
instance_count += 1
loss, tag_seq = model.neg_log_likelihood_loss(gaz_list, batch_word, batch_biword, batch_wordlen, batch_char, batch_charlen, batch_charrecover, batch_label, mask)
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]
batch_loss += loss
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
if end%data.HP_batch_size == 0:
batch_loss.backward()
optimizer.step()
model.zero_grad()
batch_loss = 0
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))
# exit(0)
# continue
speed, acc, p, r, f, _ = evaluate(data, model, "dev")
dev_finish = time.time()
dev_cost = dev_finish - epoch_finish
if 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 seg:
print ("Exceed previous best f score:", best_dev)
else:
print ("Exceed previous best acc score:", best_dev)
model_name = save_model_dir +'.'+ str(idx) + ".model"
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 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, save_model_dir, seg=True):
print "Training model..."
data.show_data_summary()
save_data_name = save_model_dir + ".dset"
save_data_setting(data, save_data_name)
model = SeqModel(data)
print "finished built model."
loss_function = nn.NLLLoss()
parameters = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.SGD(parameters, lr=data.HP_lr, momentum=data.HP_momentum)
best_dev = -1
data.HP_iteration = 100
## 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))
optimizer = lr_decay(optimizer, idx, data.HP_lr_decay, data.HP_lr)
instance_count = 0
sample_id = 0
sample_loss = 0
batch_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 = 1 ## current only support batch size = 1 to compulate and accumulate to data.HP_batch_size update weights
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
gaz_list, batch_word, batch_biword, batch_wordlen, batch_wordrecover, batch_char, batch_charlen, batch_charrecover, batch_label, mask = batchify_with_label(
instance, data.HP_gpu)
# print "gaz_list:",gaz_list
# exit(0)
instance_count += 1
loss, tag_seq = model.neg_log_likelihood_loss(
gaz_list, batch_word, batch_biword, batch_wordlen, batch_char,
batch_charlen, batch_charrecover, batch_label, mask)
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]
batch_loss += loss
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
if end % data.HP_batch_size == 0:
batch_loss.backward()
optimizer.step()
model.zero_grad()
batch_loss = 0
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))
# exit(0)
# continue
speed, acc, p, r, f, _ = evaluate(data, model, "dev")
dev_finish = time.time()
dev_cost = dev_finish - epoch_finish
if 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 seg:
print "Exceed previous best f score:", best_dev
else:
print "Exceed previous best acc score:", best_dev
model_name = save_model_dir + '.' + str(idx) + ".model"
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 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, save_model_dir, seg=True):
print "Training model..."
data.show_data_summary()
save_data_name = save_model_dir + ".dset"
save_data_setting(data, save_data_name)
model = SeqModel(data)
# model = torch.nn.DataParallel(model, device_ids=[1, 2, 3, 0]) ## catner
loss_function = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr = data.HP_lr, momentum = data.HP_momentum)
# optimizer = optim.Adam(model.parameters(), lr = data.HP_lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
# best_dev = -1
best_test = -1
data.HP_iteration = 100
vis = visdom.Visdom()
losses = []
all_F = [[0, 0, 0]]
## 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))
optimizer = lr_decay(optimizer, idx, data.HP_lr_decay, data.HP_lr) ### catner udpate 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_wordlen, batch_wordrecover, batch_char, batch_charlen, batch_charrecover, batch_label, mask = batchify_with_label(instance, data.HP_gpu)
instance_count += 1
loss, tag_seq = model.neg_log_likelihood_loss(batch_word, batch_wordlen, batch_char, batch_charlen, batch_charrecover, batch_label, mask)
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()
losses.append(sample_loss / 50.0)
vis.line(np.array(losses), X=np.array([i for i in range(len(losses))]),
win='loss', opts={'title': 'loss', 'legend': ['loss']})
sample_loss = 0
loss.backward()
if data.HP_clip:
torch.nn.utils.clip_grad_norm(model.parameters(), 50.0)
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))
# continue
speed, acc, p, r, f_train, _ = evaluate(data, model, "train")
speed, acc, p, r, f_dev, _ = evaluate(data, model, "dev")
dev_finish = time.time()
dev_cost = dev_finish - epoch_finish
if seg:
# current_score = f_dev
print("Dev: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f"%(dev_cost, speed, acc, p, r, f_dev))
else:
# current_score = acc
print("Dev: time: %.2fs speed: %.2fst/s; acc: %.4f"%(dev_cost, speed, acc))
# if current_score > best_dev:
# if seg:
# print "Exceed previous best f score:", best_dev
# else:
# print "Exceed previous best acc score:", best_dev
# model_name = save_model_dir +'.'+ str(idx) + ".model"
# torch.save(model.state_dict(), model_name)
# best_dev = current_score
# ## decode test
speed, acc, p, r, f_test, _ = evaluate(data, model, "test")
test_finish = time.time()
test_cost = test_finish - dev_finish
if seg:
current_score = f_test
print("Test: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f"%(test_cost, speed, acc, p, r, f_test))
else:
current_score = acc
print("Test: time: %.2fs, speed: %.2fst/s; acc: %.4f"%(test_cost, speed, acc))
if current_score > best_test:
if seg:
print("Exceed previous best f score:", best_test)
else:
print ("Exceed previous best acc score:", best_test)
model_name = save_model_dir +'/model'+ str(idx)
torch.save(model.state_dict(), model_name)
best_test = current_score
with open(save_model_dir + '/eval' + str(idx) + ".txt", 'wb') as f:
if seg:
f.write("acc: %.4f, p: %.4f, r: %.4f, f: %.4f" % (acc, p, r, f_test))
else:
f.write("acc: %.4f" % acc)
if seg:
print("Current best f score:", best_test)
else:
print("Current best acc score:", best_test)
all_F.append([f_train*100.0, f_dev*100.0, f_test*100.0])
Fwin = 'F-score of {train, dev, test}'
vis.line(np.array(all_F), win=Fwin,
X=np.array([i for i in range(len(all_F))]),
opts={'title': Fwin, 'legend': ['train', 'dev', 'test']})
gc.collect()
def train(data,
save_model_dir,
seg=True,
ori_model_dir=None,
use_attn=False,
use_w2c=False):
logger.info(("Training model..."))
data.show_data_summary()
save_data_name = save_model_dir + ".dset"
# save_data_setting(data, save_data_name)
model = SeqModel(data, use_attn=use_attn, use_w2c=use_w2c)
'''i added'''
if data.HP_gpu:
model = model.cuda()
logger.info(("finished built model."))
# model.load_state_dict(torch.load(ori_model_dir))
loss_function = nn.NLLLoss()
parameters = [p for p in model.parameters() if p.requires_grad]
sgd_optimizer = optim.SGD(parameters,
lr=data.HP_lr,
momentum=data.HP_momentum)
# optimizer = optim.Adam(parameters)
best_dev = -1
data.HP_iteration = 100
## start training
for idx in range(data.HP_iteration):
# idx = idx + 6
epoch_start = time.time()
temp_start = epoch_start
logger.info((("Epoch: %s/%s" % (idx, data.HP_iteration))))
# if (idx < 5):
# optimizer = optim.Adam(parameters)
# else:
# optimizer = lr_decay(sgd_optimizer, idx + 5, data.HP_lr_decay, data.HP_lr)
optimizer = lr_decay(sgd_optimizer, idx, data.HP_lr_decay, data.HP_lr)
instance_count = 0
sample_id = 0
sample_loss = 0
batch_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 = 1 ## current only support batch size = 1 to compulate and accumulate to data.HP_batch_size update weights
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
gaz_list, batch_word, batch_biword, batch_wordlen, batch_wordrecover, batch_char, batch_charlen, batch_charrecover, batch_label, mask = batchify_with_label(
instance, data.HP_gpu)
# logger.info(( "gaz_list:",gaz_list))
# logger.info(())
# exit(0)
instance_count += 1
loss, tag_seq = model.neg_log_likelihood_loss(
gaz_list, batch_word, batch_biword, batch_wordlen, batch_char,
batch_charlen, batch_charrecover, batch_label, mask)
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]
batch_loss += loss
# originally 500
if end % 500 == 0:
temp_time = time.time()
temp_cost = temp_time - temp_start
temp_start = temp_time
logger.info(((
" 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
if end % data.HP_batch_size == 0:
batch_loss.backward()
optimizer.step()
model.zero_grad()
batch_loss = 0
temp_time = time.time()
temp_cost = temp_time - temp_start
logger.info(
((" 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
logger.info(((
"Epoch: %s training finished. Time: %.2fs, speed: %.2fst/s, total loss: %s"
% (idx, epoch_cost, train_num / epoch_cost, total_loss))))
# exit(0)
# continue
speed, acc, p, r, f, _ = evaluate(data, model, "dev")
dev_finish = time.time()
dev_cost = dev_finish - epoch_finish
if seg:
current_score = f
logger.info(((
"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
logger.info((("Dev: time: %.2fs speed: %.2fst/s; acc: %.4f" %
(dev_cost, speed, acc))))
if current_score > best_dev:
if seg:
logger.info(("Exceed previous best f score:", best_dev))
else:
logger.info(("Exceed previous best acc score:", best_dev))
if current_score > best_dev or idx == data.HP_iteration - 1:
model_name = save_model_dir + '.' + str(idx) + ".model"
torch.save(model.state_dict(), model_name)
logger.info(("model name: " + model_name))
if idx > 3:
save_data_setting(data,
save_data_name + '.' + str(idx) + '.dset')
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 seg:
# logger.info((("Test: time: %.2fs, speed: %.2fst/s; acc: %.4f, p: %.4f, r: %.4f, f: %.4f"%(test_cost, speed, acc, p, r, f))))
# else:
# logger.info((("Test: time: %.2fs, speed: %.2fst/s; acc: %.4f"%(test_cost, speed, acc))))
gc.collect()
# -*- coding: utf-8 -*-