def main():
parser = argparse.ArgumentParser(
description='Tuning with bi-directional RNN-CNN-CRF')
parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU'],
help='architecture of rnn',
required=True)
parser.add_argument('--num_epochs',
type=int,
default=100,
help='Number of training epochs')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--hidden_size',
type=int,
default=128,
help='Number of hidden units in RNN')
parser.add_argument('--tag_space',
type=int,
default=0,
help='Dimension of tag space')
parser.add_argument('--num_layers',
type=int,
default=1,
help='Number of layers of RNN')
parser.add_argument('--num_filters',
type=int,
default=30,
help='Number of filters in CNN')
parser.add_argument('--char_dim',
type=int,
default=30,
help='Dimension of Character embeddings')
parser.add_argument('--learning_rate',
type=float,
default=0.015,
help='Learning rate')
parser.add_argument('--decay_rate',
type=float,
default=0.1,
help='Decay rate of learning rate')
parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for regularization')
parser.add_argument('--dropout',
choices=['std', 'variational'],
help='type of dropout',
required=True)
parser.add_argument('--p_rnn',
nargs=2,
type=float,
required=True,
help='dropout rate for RNN')
parser.add_argument('--p_in',
type=float,
default=0.33,
help='dropout rate for input embeddings')
parser.add_argument('--p_out',
type=float,
default=0.33,
help='dropout rate for output layer')
parser.add_argument('--bigram',
action='store_true',
help='bi-gram parameter for CRF')
parser.add_argument('--schedule',
type=int,
help='schedule for learning rate decay')
parser.add_argument('--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
parser.add_argument('--embedding',
choices=['glove', 'senna', 'sskip', 'polyglot'],
help='Embedding for words',
required=True)
parser.add_argument('--embedding_dict', help='path for embedding dict')
parser.add_argument(
'--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
parser.add_argument(
'--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
parser.add_argument(
'--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args = parser.parse_args()
logger = get_logger("NERCRF")
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
num_filters = args.num_filters
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
gamma = args.gamma
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
bigram = args.bigram
embedding = args.embedding
embedding_path = args.embedding_dict
embedd_dict, embedd_dim = utils.load_embedding_dict(
embedding, embedding_path)
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("data/alphabets/ner_crf/", train_path, data_paths=[dev_path, test_path],
embedd_dict=embedd_dict, max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Chunk Alphabet Size: %d" % chunk_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = conll03_data.read_data_to_variable(train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=use_gpu)
num_data = sum(data_train[1])
num_labels = ner_alphabet.size()
data_dev = conll03_data.read_data_to_variable(dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=use_gpu,
volatile=True)
data_test = conll03_data.read_data_to_variable(test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=use_gpu,
volatile=True)
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conll03_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = args.num_layers
tag_space = args.tag_space
initializer = nn.init.xavier_uniform
if args.dropout == 'std':
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
else:
network = BiVarRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
if use_gpu:
network.cuda()
lr = learning_rate
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
logger.info(
"Network: %s, num_layer=%d, hidden=%d, filter=%d, tag_space=%d, crf=%s"
% (mode, num_layers, hidden_size, num_filters, tag_space,
'bigram' if bigram else 'unigram'))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, unk replace: %.2f)"
% (gamma, num_data, batch_size, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
num_batches = num_data / batch_size + 1
dev_f1 = 0.0
dev_acc = 0.0
dev_precision = 0.0
dev_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, mode, args.dropout, lr, decay_rate, schedule))
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths = conll03_data.get_batch_variable(
data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 100 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total, time.time() - start_time))
# evaluate performance on dev data
network.eval()
tmp_filename = 'tmp/%s_dev%d' % (str(uid), epoch)
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_variable(data_dev, batch_size):
word, char, pos, chunk, labels, masks, lengths = batch
preds, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate(tmp_filename)
print(
'dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%' %
(acc, precision, recall, f1))
if dev_f1 < f1:
dev_f1 = f1
dev_acc = acc
dev_precision = precision
dev_recall = recall
best_epoch = epoch
# evaluate on test data when better performance detected
tmp_filename = 'tmp/%s_test%d' % (str(uid), epoch)
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_variable(
data_test, batch_size):
word, char, pos, chunk, labels, masks, lengths = batch
preds, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
test_acc, test_precision, test_recall, test_f1 = evaluate(
tmp_filename)
print(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
print(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))
if epoch % schedule == 0:
lr = learning_rate / (1.0 + epoch * decay_rate)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
def main():
parser = argparse.ArgumentParser(
description='Tuning with bi-directional RNN-CNN-CRF')
parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU'],
help='architecture of rnn',
required=True)
parser.add_argument('--num_epochs',
type=int,
default=1000,
help='Number of training epochs')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--hidden_size',
type=int,
default=128,
help='Number of hidden units in RNN')
parser.add_argument('--num_filters',
type=int,
default=30,
help='Number of filters in CNN')
parser.add_argument('--char_dim',
type=int,
default=30,
help='Dimension of Character embeddings')
parser.add_argument('--learning_rate',
type=float,
default=0.01,
help='Learning rate')
parser.add_argument('--decay_rate',
type=float,
default=0.1,
help='Decay rate of learning rate')
parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for regularization')
parser.add_argument('--dropout',
choices=['std', 'variational'],
help='type of dropout',
required=True)
parser.add_argument('--p', type=float, default=0.5, help='dropout rate')
parser.add_argument('--bigram',
action='store_true',
help='bi-gram parameter for CRF')
parser.add_argument('--schedule',
type=int,
help='schedule for learning rate decay')
parser.add_argument('--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
parser.add_argument(
'--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
parser.add_argument(
'--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
parser.add_argument(
'--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args = parser.parse_args()
logger = get_logger("POSCRFTagger")
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
num_filters = args.num_filters
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
gamma = args.gamma
schedule = args.schedule
p = args.p
unk_replace = args.unk_replace
bigram = args.bigram
embedd_dict, embedd_dim = utils.load_embedding_dict(
'glove', "data/glove/glove.6B/glove.6B.100d.gz")
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, pos_alphabet, \
type_alphabet = conllx_data.create_alphabets("data/alphabets/pos_crf/", train_path,
data_paths=[dev_path, test_path],
max_vocabulary_size=50000, embedd_dict=embedd_dict)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = conllx_data.read_data_to_variable(train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu)
# data_train = conllx_data.read_data(train_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet)
# num_data = sum([len(bucket) for bucket in data_train])
num_data = sum(data_train[1])
num_labels = pos_alphabet.size()
data_dev = conllx_data.read_data_to_variable(dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True)
data_test = conllx_data.read_data_to_variable(test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conllx_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = 1
if args.dropout == 'std':
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
embedd_word=word_table,
p_rnn=p,
bigram=bigram)
else:
raise NotImplementedError
if use_gpu:
network.cuda()
lr = learning_rate
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma)
logger.info("Network: %s, num_layer=%d, hidden=%d, filter=%d, crf=%s" %
(mode, num_layers, hidden_size, num_filters,
'bigram' if bigram else 'unigram'))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, dropout: %.2f, unk replace: %.2f)"
% (gamma, num_data, batch_size, p, unk_replace))
num_batches = num_data / batch_size + 1
dev_correct = 0.0
best_epoch = 0
test_correct = 0.0
test_total = 0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, mode, args.dropout, lr, decay_rate, schedule))
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, labels, _, _, masks, lengths = conllx_data.get_batch_variable(
data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 100 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total, time.time() - start_time))
# evaluate performance on dev data
network.eval()
dev_corr = 0.0
dev_total = 0
for batch in conllx_data.iterate_batch_variable(data_dev, batch_size):
word, char, labels, _, _, masks, lengths = batch
preds, corr = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
num_tokens = masks.data.sum()
dev_corr += corr
dev_total += num_tokens
print('dev corr: %d, total: %d, acc: %.2f%%' %
(dev_corr, dev_total, dev_corr * 100 / dev_total))
if dev_correct < dev_corr:
dev_correct = dev_corr
best_epoch = epoch
# evaluate on test data when better performance detected
test_corr = 0.0
test_total = 0
for batch in conllx_data.iterate_batch_variable(
data_test, batch_size):
word, char, labels, _, _, masks, lengths = batch
preds, corr = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
num_tokens = masks.data.sum()
test_corr += corr
test_total += num_tokens
test_correct = test_corr
print("best dev corr: %d, total: %d, acc: %.2f%% (epoch: %d)" %
(dev_correct, dev_total, dev_correct * 100 / dev_total,
best_epoch))
print("best test corr: %d, total: %d, acc: %.2f%% (epoch: %d)" %
(test_correct, test_total, test_correct * 100 / test_total,
best_epoch))
if epoch % schedule == 0:
lr = learning_rate / (1.0 + epoch * decay_rate)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
def main():
# Arguments parser
parser = argparse.ArgumentParser(
description='Tuning with DNN Model for NER')
# Model Hyperparameters
parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU'],
help='architecture of rnn',
default='LSTM')
parser.add_argument('--encoder_mode',
choices=['cnn', 'lstm'],
help='Encoder type for sentence encoding',
default='lstm')
parser.add_argument('--char_method',
choices=['cnn', 'lstm'],
help='Method to create character-level embeddings',
required=True)
parser.add_argument(
'--hidden_size',
type=int,
default=128,
help='Number of hidden units in RNN for sentence level')
parser.add_argument('--char_hidden_size',
type=int,
default=30,
help='Output character-level embeddings size')
parser.add_argument('--char_dim',
type=int,
default=30,
help='Dimension of Character embeddings')
parser.add_argument('--tag_space',
type=int,
default=0,
help='Dimension of tag space')
parser.add_argument('--num_layers',
type=int,
default=1,
help='Number of layers of RNN')
parser.add_argument('--dropout',
choices=['std', 'weight_drop'],
help='Dropout method',
default='weight_drop')
parser.add_argument('--p_em',
type=float,
default=0.33,
help='dropout rate for input embeddings')
parser.add_argument('--p_in',
type=float,
default=0.33,
help='dropout rate for input of RNN model')
parser.add_argument('--p_rnn',
nargs=2,
type=float,
required=True,
help='dropout rate for RNN')
parser.add_argument('--p_out',
type=float,
default=0.33,
help='dropout rate for output layer')
parser.add_argument('--bigram',
action='store_true',
help='bi-gram parameter for CRF')
# Data loading and storing params
parser.add_argument('--embedding_dict', help='path for embedding dict')
parser.add_argument('--dataset_name',
type=str,
default='alexa',
help='Which dataset to use')
parser.add_argument('--train',
type=str,
required=True,
help='Path of train set')
parser.add_argument('--dev',
type=str,
required=True,
help='Path of dev set')
parser.add_argument('--test',
type=str,
required=True,
help='Path of test set')
parser.add_argument('--results_folder',
type=str,
default='results',
help='The folder to store results')
parser.add_argument('--tmp_folder',
type=str,
default='tmp',
help='The folder to store tmp files')
parser.add_argument('--alphabets_folder',
type=str,
default='data/alphabets',
help='The folder to store alphabets files')
parser.add_argument('--result_file_name',
type=str,
default='hyperparameters_tuning',
help='File name to store some results')
parser.add_argument('--result_file_path',
type=str,
default='results/hyperparameters_tuning',
help='File name to store some results')
# Training parameters
parser.add_argument('--cuda',
action='store_true',
help='whether using GPU')
parser.add_argument('--num_epochs',
type=int,
default=100,
help='Number of training epochs')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--learning_rate',
type=float,
default=0.001,
help='Base learning rate')
parser.add_argument('--decay_rate',
type=float,
default=0.95,
help='Decay rate of learning rate')
parser.add_argument('--schedule',
type=int,
default=3,
help='schedule for learning rate decay')
parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for l2 regularization')
parser.add_argument('--max_norm',
type=float,
default=1.,
help='Max norm for gradients')
parser.add_argument('--gpu_id',
type=int,
nargs='+',
required=True,
help='which gpu to use for training')
# Misc
parser.add_argument('--embedding',
choices=['glove', 'senna', 'alexa'],
help='Embedding for words',
required=True)
parser.add_argument('--restore',
action='store_true',
help='whether restore from stored parameters')
parser.add_argument('--save_checkpoint',
type=str,
default='',
help='the path to save the model')
parser.add_argument('--o_tag',
type=str,
default='O',
help='The default tag for outside tag')
parser.add_argument('--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
parser.add_argument('--evaluate_raw_format',
action='store_true',
help='The tagging format for evaluation')
args = parser.parse_args()
logger = get_logger("NERCRF")
# rename the parameters
mode = args.mode
encoder_mode = args.encoder_mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
char_hidden_size = args.char_hidden_size
char_method = args.char_method
learning_rate = args.learning_rate
momentum = 0.9
decay_rate = args.decay_rate
gamma = args.gamma
max_norm = args.max_norm
schedule = args.schedule
dropout = args.dropout
p_em = args.p_em
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
bigram = args.bigram
embedding = args.embedding
embedding_path = args.embedding_dict
dataset_name = args.dataset_name
result_file_name = args.result_file_name
evaluate_raw_format = args.evaluate_raw_format
o_tag = args.o_tag
restore = args.restore
save_checkpoint = args.save_checkpoint
gpu_id = args.gpu_id
results_folder = args.results_folder
tmp_folder = args.tmp_folder
alphabets_folder = args.alphabets_folder
use_elmo = False
p_em_vec = 0.
result_file_path = args.result_file_path
score_file = "%s/score_gpu_%s" % (tmp_folder, '-'.join(map(str, gpu_id)))
if not os.path.exists(results_folder):
os.makedirs(results_folder)
if not os.path.exists(tmp_folder):
os.makedirs(tmp_folder)
if not os.path.exists(alphabets_folder):
os.makedirs(alphabets_folder)
embedd_dict, embedd_dim = utils.load_embedding_dict(
embedding, embedding_path)
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, ner_alphabet = conll03_data.create_alphabets(
"{}/{}/".format(alphabets_folder, dataset_name),
train_path,
data_paths=[dev_path, test_path],
embedd_dict=embedd_dict,
max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
logger.info("Reading Data")
device = torch.device('cuda') if args.cuda else torch.device('cpu')
print(device)
data_train = conll03_data.read_data_to_tensor(train_path,
word_alphabet,
char_alphabet,
ner_alphabet,
device=device)
num_data = sum(data_train[1])
num_labels = ner_alphabet.size()
data_dev = conll03_data.read_data_to_tensor(dev_path,
word_alphabet,
char_alphabet,
ner_alphabet,
device=device)
data_test = conll03_data.read_data_to_tensor(test_path,
word_alphabet,
char_alphabet,
ner_alphabet,
device=device)
writer = CoNLL03Writer(word_alphabet, char_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[conll03_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in embedd_dict:
embedding = embedd_dict[word]
elif word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = args.num_layers
tag_space = args.tag_space
initializer = nn.init.xavier_uniform_
if args.dropout == 'std':
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
char_hidden_size,
window,
mode,
encoder_mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
use_elmo=use_elmo,
p_em_vec=p_em_vec,
p_em=p_em,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
elif args.dropout == 'var':
network = BiVarRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
char_hidden_size,
window,
mode,
encoder_mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
use_elmo=use_elmo,
p_em_vec=p_em_vec,
p_em=p_em,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
else:
network = BiWeightDropRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
char_hidden_size,
window,
mode,
encoder_mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=word_table,
p_em=p_em,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
bigram=bigram,
initializer=initializer)
network = network.to(device)
lr = learning_rate
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
# optim = Adam(network.parameters(), lr=lr, weight_decay=gamma, amsgrad=True)
nn.utils.clip_grad_norm_(network.parameters(), max_norm)
logger.info("Network: %s, encoder_mode=%s, num_layer=%d, hidden=%d, char_hidden_size=%d, char_method=%s, tag_space=%d, crf=%s" % \
(mode, encoder_mode, num_layers, hidden_size, char_hidden_size, char_method, tag_space, 'bigram' if bigram else 'unigram'))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, unk replace: %.2f)"
% (gamma, num_data, batch_size, unk_replace))
logger.info("dropout(in, out, rnn): (%.2f, %.2f, %s)" %
(p_in, p_out, p_rnn))
num_batches = num_data // batch_size + 1
dev_f1 = 0.0
dev_acc = 0.0
dev_precision = 0.0
dev_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
best_test_f1 = 0.0
best_test_acc = 0.0
best_test_precision = 0.0
best_test_recall = 0.0
best_test_epoch = 0.0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, mode, args.dropout, lr, decay_rate, schedule))
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
_, word, char, labels, masks, lengths = conll03_data.get_batch_tensor(
data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(_, word, char, labels, mask=masks)
loss.backward()
optim.step()
with torch.no_grad():
num_inst = word.size(0)
train_err += loss * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 20 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total, time.time() - start_time))
# evaluate performance on dev data
with torch.no_grad():
network.eval()
tmp_filename = '%s/gpu_%s_dev' % (tmp_folder, '-'.join(
map(str, gpu_id)))
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_tensor(
data_dev, batch_size):
_, word, char, labels, masks, lengths = batch
preds, _ = network.decode(
_,
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.cpu().numpy(),
preds.cpu().numpy(),
labels.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate(tmp_filename, score_file,
evaluate_raw_format, o_tag)
print(
'dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%'
% (acc, precision, recall, f1))
if dev_f1 < f1:
dev_f1 = f1
dev_acc = acc
dev_precision = precision
dev_recall = recall
best_epoch = epoch
# evaluate on test data when better performance detected
tmp_filename = '%s/gpu_%s_test' % (tmp_folder, '-'.join(
map(str, gpu_id)))
writer.start(tmp_filename)
for batch in conll03_data.iterate_batch_tensor(
data_test, batch_size):
_, word, char, labels, masks, lengths = batch
preds, _ = network.decode(
_,
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.cpu().numpy(),
preds.cpu().numpy(),
labels.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
test_acc, test_precision, test_recall, test_f1 = evaluate(
tmp_filename, score_file, evaluate_raw_format, o_tag)
if best_test_f1 < test_f1:
best_test_acc, best_test_precision, best_test_recall, best_test_f1 = test_acc, test_precision, test_recall, test_f1
best_test_epoch = epoch
print(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
print(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))
print(
"overall best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (best_test_acc, best_test_precision, best_test_recall,
best_test_f1, best_test_epoch))
if epoch % schedule == 0:
lr = learning_rate / (1.0 + epoch * decay_rate)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
with open(result_file_path, 'a') as ofile:
ofile.write(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
ofile.write(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))
ofile.write(
"overall best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)\n\n"
% (best_test_acc, best_test_precision, best_test_recall,
best_test_f1, best_test_epoch))
print('Training finished!')
def main():
embedding = 'glove'
embedding_path = '/media/xianyang/OS/workspace/ner/glove.6B/glove.6B.100d.txt'
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = conll03_data.create_alphabets("/media/xianyang/OS/workspace/ner/NeuroNLP2/data/alphabets/ner_crf/", None)
char_dim = 30
num_filters = 30
window = 3
mode = 'LSTM'
hidden_size = 256
num_layers = 1
num_labels = ner_alphabet.size()
tag_space = 128
p = 0.5
bigram = True
embedd_dim = 100
use_gpu = False
print(len(word_alphabet.get_content()['instances']))
print(ner_alphabet.get_content())
# writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet, chunk_alphabet, ner_alphabet)
network = BiRecurrentConvCRF(embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
embedd_word=None,
p_rnn=p,
bigram=bigram)
network.load_state_dict(torch.load('temp/23df51_model45'))
ner_alphabet.add('B-VEH')
ner_alphabet.add('I-VEH')
ner_alphabet.add('B-WEA')
ner_alphabet.add('I-WEA')
num_new_word = 0
with open('temp/target.train.conll', 'r') as f:
sents = []
sent_buffer = []
for line in f:
if len(line) <= 1:
sents.append(sent_buffer)
sent_buffer = []
else:
id, word, _, _, ner = line.strip().split()
if word_alphabet.get_index(word) == 0:
word_alphabet.add(word)
num_new_word += 1
sent_buffer.append((word_alphabet.get_index(word),
ner_alphabet.get_index(ner)))
print(len(word_alphabet.get_content()['instances']))
print(ner_alphabet.get_content())
init_embed = network.word_embedd.weight.data
init_embed = np.concatenate(
(init_embed, np.zeros((num_new_word, embedd_dim))), axis=0)
network.word_embedd = Embedding(word_alphabet.size(), embedd_dim,
torch.from_numpy(init_embed))
old_crf = network.crf
new_crf = ChainCRF(tag_space, ner_alphabet.size(), bigram=bigram)
trans_matrix = np.zeros((new_crf.num_labels, old_crf.num_labels))
for i in range(old_crf.num_labels):
trans_matrix[i, i] = 1
new_crf.state_nn.weight.data = torch.FloatTensor(
np.dot(trans_matrix, old_crf.state_nn.weight.data))
network.crf = new_crf
target_train_data = conll03_data.read_data_to_variable(
'temp/target.train.conll',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
target_dev_data = conll03_data.read_data_to_variable(
'temp/target.dev.conll',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
target_test_data = conll03_data.read_data_to_variable(
'temp/target.test.conll',
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet,
use_gpu=False,
volatile=False)
num_epoch = 50
batch_size = 32
num_data = sum(target_train_data[1])
num_batches = num_data / batch_size + 1
unk_replace = 0.0
# optim = SGD(network.parameters(), lr=0.001, momentum=0.9, weight_decay=0.0, nesterov=True)
optim = Adam(network.parameters(), lr=1e-3)
for epoch in range(1, num_epoch + 1):
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths = conll03_data.get_batch_variable(
target_train_data, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks)
loss.backward()
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
if batch % 20 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d loss: %.4f, time: %.2fs' % (
num_batches, train_err / train_total,
time.time() - start_time)
print(log_info)
num_back = len(log_info)
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
os.system('rm temp/output.txt')
writer.start('temp/output.txt')
network.eval()
for batch in conll03_data.iterate_batch_variable(
target_dev_data, batch_size):
word, char, pos, chunk, labels, masks, lengths, _ = batch
preds, _, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate('temp/output.txt')
log_info = 'dev: %f %f %f %f' % (acc, precision, recall, f1)
print(log_info)
if epoch % 10 == 0:
writer = CoNLL03Writer(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
os.system('rm temp/output.txt')
writer.start('temp/output.txt')
network.eval()
for batch in conll03_data.iterate_batch_variable(
target_test_data, batch_size):
word, char, pos, chunk, labels, masks, lengths, _ = batch
preds, _, _ = network.decode(
word,
char,
target=labels,
mask=masks,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate('temp/output.txt')
log_info = 'test: %f %f %f %f' % (acc, precision, recall, f1)
print(log_info)
torch.save(network, 'temp/tuned_0905.pt')
alphabet_directory = '0905_alphabet/'
word_alphabet.save(alphabet_directory)
char_alphabet.save(alphabet_directory)
pos_alphabet.save(alphabet_directory)
chunk_alphabet.save(alphabet_directory)
ner_alphabet.save(alphabet_directory)
def main():
parser = argparse.ArgumentParser(description='Tuning with bi-directional RNN-CNN-CRF')
parser.add_argument('--mode', choices=['RNN', 'LSTM', 'GRU'], help='architecture of rnn', required=True)
parser.add_argument('--num_epochs', type=int, default=100, help='Number of training epochs')
parser.add_argument('--batch_size', type=int, default=16, help='Number of sentences in each batch')
parser.add_argument('--hidden_size', type=int, default=128, help='Number of hidden units in RNN')
parser.add_argument('--tag_space', type=int, default=0, help='Dimension of tag space')
parser.add_argument('--num_filters', type=int, default=30, help='Number of filters in CNN')
parser.add_argument('--char_dim', type=int, default=30, help='Dimension of Character embeddings')
parser.add_argument('--learning_rate', type=float, default=0.015, help='Learning rate')
parser.add_argument('--alpha', type=float, default=0.1, help='alpha of rmsprop')
parser.add_argument('--momentum', type=float, default=0, help='momentum')
parser.add_argument('--lr_decay', type=float, default=0, help='Decay rate of learning rate')
parser.add_argument('--gamma', type=float, default=0.0, help='weight for regularization')
parser.add_argument('--dropout', choices=['std', 'variational'], help='type of dropout', required=True)
parser.add_argument('--p', type=float, default=0.5, help='dropout rate')
parser.add_argument('--bigram', action='store_true', help='bi-gram parameter for CRF')
parser.add_argument('--schedule', type=int, help='schedule for learning rate decay')
parser.add_argument('--unk_replace', type=float, default=0., help='The rate to replace a singleton word with UNK')
parser.add_argument('--embedding', choices=['word2vec', 'glove', 'senna', 'sskip', 'polyglot', 'elmo'], help='Embedding for words', required=True)
parser.add_argument('--embedding_dict', help='path for embedding dict')
parser.add_argument('--elmo_option', help='path for ELMo option file')
parser.add_argument('--elmo_weight', help='path for ELMo weight file')
parser.add_argument('--elmo_cuda', help='assign GPU for ELMo embedding task')
parser.add_argument('--attention', choices=['none', 'mlp', 'fine'], help='attetion mode', required=True)
parser.add_argument('--data_reduce', help='data size reduce, value is keeping rate', default=1.0)
parser.add_argument('--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
parser.add_argument('--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
parser.add_argument('--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args = parser.parse_args()
logger = get_logger("NERCRF")
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
num_filters = args.num_filters
learning_rate = args.learning_rate
alpha = args.alpha
momentum = args.momentum
lr_decay = args.lr_decay
gamma = args.gamma
schedule = args.schedule
p = args.p
unk_replace = args.unk_replace
bigram = args.bigram
embedding = args.embedding
embedding_path = args.embedding_dict
elmo_option = args.elmo_option
elmo_weight = args.elmo_weight
elmo_cuda = int(args.elmo_cuda)
attention_mode = args.attention
data_reduce = float(args.data_reduce)
embedd_dict, embedd_dim = utils.load_embedding_dict(embedding, embedding_path)
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, pos_alphabet, \
chunk_alphabet, ner_alphabet = bionlp_data.create_alphabets(os.path.join(Path(train_path).parent.abspath(
), "alphabets"), train_path, data_paths=[dev_path, test_path], embedd_dict=embedd_dict, max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Chunk Alphabet Size: %d" % chunk_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
if embedding == 'elmo':
logger.info("Loading ELMo Embedder")
ee = ElmoEmbedder(options_file=elmo_option, weight_file=elmo_weight, cuda_device=elmo_cuda)
else:
ee = None
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = bionlp_data.read_data_to_variable(train_path, word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet, use_gpu=use_gpu,
elmo_ee=ee, data_reduce=data_reduce)
num_data = sum(data_train[1])
num_labels = ner_alphabet.size()
data_dev = bionlp_data.read_data_to_variable(dev_path, word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet, use_gpu=use_gpu, volatile=True,
elmo_ee=ee)
data_test = bionlp_data.read_data_to_variable(test_path, word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet, use_gpu=use_gpu, volatile=True,
elmo_ee=ee)
writer = BioNLPWriter(word_alphabet, char_alphabet, pos_alphabet, chunk_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[bionlp_data.UNK_ID, :] = np.random.uniform(-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if not embedd_dict == None and word in embedd_dict:
embedding = embedd_dict[word]
elif not embedd_dict == None and word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
char_dim = args.char_dim
window = 3
num_layers = 1
tag_space = args.tag_space
if args.dropout == 'std':
if attention_mode == 'none':
network = BiRecurrentConvCRF(embedd_dim, word_alphabet.size(),
char_dim, char_alphabet.size(),
num_filters, window,
mode, hidden_size, num_layers, num_labels,
tag_space=tag_space, embedd_word=word_table, p_in=p, p_rnn=p, bigram=bigram,
elmo=(embedding == 'elmo'))
else:
network = BiRecurrentConvAttentionCRF(embedd_dim, word_alphabet.size(),
char_dim, char_alphabet.size(),
num_filters, window,
mode, hidden_size, num_layers, num_labels,
tag_space=tag_space, embedd_word=word_table, p_in=p, p_rnn=p, bigram=bigram,
elmo=(embedding == 'elmo'), attention_mode=attention_mode)
else:
raise NotImplementedError
if use_gpu:
network.cuda()
lr = learning_rate
# optim = SGD(network.parameters(), lr=lr, momentum=momentum, weight_decay=gamma, nesterov=True)
optim = RMSprop(network.parameters(), lr=lr, alpha=alpha, momentum=momentum, weight_decay=gamma)
logger.info("Network: %s, num_layer=%d, hidden=%d, filter=%d, tag_space=%d, crf=%s" % (
mode, num_layers, hidden_size, num_filters, tag_space, 'bigram' if bigram else 'unigram'))
logger.info("training: l2: %f, (#training data: %d, batch: %d, dropout: %.2f, unk replace: %.2f)" % (
gamma, num_data, batch_size, p, unk_replace))
num_batches = num_data // batch_size + 1
dev_f1 = 0.0
dev_acc = 0.0
dev_precision = 0.0
dev_recall = 0.0
test_f1 = 0.0
test_acc = 0.0
test_precision = 0.0
test_recall = 0.0
best_epoch = 0
for epoch in range(1, num_epochs + 1):
print('Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): ' % (
epoch, mode, args.dropout, lr, lr_decay, schedule))
train_err = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
word, char, _, _, labels, masks, lengths, elmo_embedding = bionlp_data.get_batch_variable(data_train, batch_size,
unk_replace=unk_replace)
optim.zero_grad()
loss = network.loss(word, char, labels, mask=masks, elmo_word=elmo_embedding)
loss.backward()
clip_grad_norm(network.parameters(), 5.0)
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 100 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' % (num_batches, train_err / train_total, time.time() - start_time))
# evaluate performance on dev data
network.eval()
tmp_filename = 'tmp/%s_dev%d' % (str(uid), epoch)
writer.start(tmp_filename)
for batch in bionlp_data.iterate_batch_variable(data_dev, batch_size):
word, char, pos, chunk, labels, masks, lengths, elmo_embedding = batch
preds, _ = network.decode(word, char, target=labels, mask=masks,
leading_symbolic=bionlp_data.NUM_SYMBOLIC_TAGS, elmo_word=elmo_embedding)
writer.write(word.data.cpu().numpy(), pos.data.cpu().numpy(), chunk.data.cpu().numpy(),
preds.cpu().numpy(), labels.data.cpu().numpy(), lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate(tmp_filename)
print('dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%' % (acc, precision, recall, f1))
if dev_f1 < f1:
dev_f1 = f1
dev_acc = acc
dev_precision = precision
dev_recall = recall
best_epoch = epoch
# evaluate on test data when better performance detected
tmp_filename = 'tmp/%s_test%d' % (str(uid), epoch)
writer.start(tmp_filename)
for batch in bionlp_data.iterate_batch_variable(data_test, batch_size):
word, char, pos, chunk, labels, masks, lengths, elmo_embedding = batch
preds, _ = network.decode(word, char, target=labels, mask=masks,
leading_symbolic=bionlp_data.NUM_SYMBOLIC_TAGS, elmo_word=elmo_embedding)
writer.write(word.data.cpu().numpy(), pos.data.cpu().numpy(), chunk.data.cpu().numpy(),
preds.cpu().numpy(), labels.data.cpu().numpy(), lengths.cpu().numpy())
writer.close()
test_acc, test_precision, test_recall, test_f1 = evaluate(tmp_filename)
print("best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)" % (
dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
print("best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)" % (
test_acc, test_precision, test_recall, test_f1, best_epoch))
if epoch % schedule == 0:
# lr = learning_rate / (1.0 + epoch * lr_decay)
# optim = SGD(network.parameters(), lr=lr, momentum=momentum, weight_decay=gamma, nesterov=True)
lr = lr * lr_decay
optim.param_groups[0]['lr'] = lr