def train(data):
print("Training model...")
model = SeqModel(data)
if data.use_cuda:
model.cuda()
parameters = filter(lambda p: p.requires_grad, model.parameters())
if data.optimizer == "SGD":
optimizer = optim.SGD(parameters,
lr=data.lr,
momentum=data.momentum,
weight_decay=data.weight_decay)
elif data.optimizer == "Adagrad":
optimizer = optim.Adagrad(parameters,
lr=data.lr,
weight_decay=data.weight_decay)
elif data.optimizer == "Adam":
optimizer = optim.Adam(parameters,
lr=data.lr,
weight_decay=data.weight_decay)
else:
print("Optimizer Error: {} optimizer is not support.".format(
data.optimizer))
for idx in range(data.iter):
epoch_start = temp_start = time.time()
train_num = len(data.train_text)
if train_num % data.batch_size == 0:
batch_block = train_num // data.batch_size
else:
batch_block = train_num // data.batch_size + 1
correct = total = total_loss = 0
random.shuffle(data.train_idx)
for block in range(batch_block):
left = block * data.batch_size
right = left + data.batch_size
if right > train_num:
right = train_num
instance = data.train_idx[left:right]
batch_word, batch_word_len, word_recover, batch_char, batch_char_len, char_recover, batch_label, mask = generate_batch(
instance, data.use_cuda)
loss, seq = model.forward(batch_word, batch_word_len, batch_char,
batch_char_len, char_recover,
batch_label, mask)
right_token, total_token = predict_check(seq, batch_label, mask)
correct += right_token
total += total_token
total_loss += loss.data[0]
loss.backward()
optimizer.step()
model.zero_grad()
epoch_end = time.time()
print("Epoch:{}. Time:{}. Loss:{}. acc:{}".format(
idx, epoch_end - epoch_start, total_loss, correct / total))
evaluate(data, model, "dev", idx)
evaluate(data, model, "test", idx)
print("Finish.")
def train(data):
print("Training model...")
data.show_data_summary()
model = SeqModel(data)
if data.use_cuda:
model.cuda()
parameters = filter(lambda p: p.requires_grad, model.parameters())
if data.optimizer == "SGD":
optimizer = optim.SGD(parameters,
lr=data.lr,
momentum=data.momentum,
weight_decay=data.weight_decay)
elif data.optimizer == "Adagrad":
optimizer = optim.Adagrad(parameters,
lr=data.lr,
weight_decay=data.weight_decay)
elif data.optimizer == "Adam":
optimizer = optim.Adam(parameters,
lr=data.lr,
weight_decay=data.weight_decay)
else:
print("Optimizer Error: {} optimizer is not support.".format(
data.optimizer))
# if data.out_dict:
# external_dict(data.out_dict, data.train_file)
# external_dict(data.out_dict, data.dev_file)
# external = external_dict(data.out_dict, data.test_file)
# external_dict(data.out_dict,data.oov_file)
# #print(len(external))
# #with open('../data/ali_7k/external_dict','w',encoding='utf-8') as fout:
# # for e in external:
# # fout.write(e+'\n')
for idx in range(data.iter):
# data.mask_entity(data.iter, idx)
epoch_start = temp_start = time.time()
# if data.optimizer == "SGD":
# optimizer = lr_decay(optimizer,idx,data.lr_decay,data.lr)
train_num = len(data.train_text)
if train_num % data.batch_size == 0:
batch_block = train_num // data.batch_size
else:
batch_block = train_num // data.batch_size + 1
correct = total = total_loss = 0
random.shuffle(data.train_idx)
model.train()
model.zero_grad()
for block in range(batch_block):
left = block * data.batch_size
right = left + data.batch_size
if right > train_num:
right = train_num
instance = data.train_idx[left:right]
batch_word, batch_feat, batch_word_len, word_recover, batch_char, batch_char_len, char_recover, batch_label, mask, batch_bert \
= generate_batch(instance, data.use_cuda, data.use_bert)
batch_dict = None
if data.out_dict:
batch_dict = generate_dict_feature(instance, data,
data.use_cuda)
loss, seq = model.neg_log_likehood(batch_word, batch_feat,
batch_word_len, batch_char,
batch_char_len, char_recover,
batch_label, mask, batch_dict,
batch_bert)
right_token, total_token = predict_check(seq, batch_label, mask)
correct += right_token
total += total_token
total_loss += loss.item()
loss.backward()
optimizer.step()
model.zero_grad()
epoch_end = time.time()
print("Epoch:{}. Time:{}. Loss:{}. acc:{}".format(
idx, epoch_end - epoch_start, total_loss, correct / total))
# torch.save(model,data.model_save_dir+'/model'+str(idx)+'.pkl')
evaluate(data, model, "dev", idx)
evaluate(data, model, "test", idx)
if data.oov_file is not None:
evaluate(data, model, "oov", idx)
print("Finish.")