def main():
parser = argparse.ArgumentParser(
description='Tuning with bi-directional RNN-CNN')
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('--learning_rate',
type=float,
default=0.1,
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('--schedule',
type=int,
help='schedule for learning rate decay')
parser.add_argument('--output_prediction',
action='store_true',
help='Output predictions to temp files')
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("POSTagger")
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
output_predict = args.output_prediction
embedd_dict, embedd_dim = utils.load_word_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/", 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)
data_test = conllx_data.read_data_to_variable(test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu)
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 = 30
window = 3
num_layers = 1
if args.dropout == 'std':
network = BiRecurrentConv(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)
else:
network = BiVarRecurrentConv(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)
if use_gpu:
network.cuda()
lr = learning_rate
# optim = Adam(network.parameters(), lr=lr, betas=(0.9, 0.9), weight_decay=gamma)
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
logger.info("Network: %s, num_layer=%d, hidden=%d, filter=%d" %
(mode, num_layers, hidden_size, num_filters))
logger.info(
"training: l2: %f, (#training data: %d, batch: %d, dropout: %.2f)" %
(gamma, num_data, batch_size, p))
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 (%d)): ' %
(epoch, mode, args.dropout, lr, decay_rate, schedule))
train_err = 0.
train_corr = 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)
optim.zero_grad()
loss, corr, _ = network.loss(
word,
char,
labels,
mask=masks,
leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
loss.backward()
optim.step()
num_tokens = masks.data.sum()
train_err += loss.data[0] * num_tokens
train_corr += corr.data[0]
train_total += num_tokens
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)
log_info = 'train: %d/%d loss: %.4f, acc: %.2f%%, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total,
train_corr * 100 / train_total, time_left)
sys.stdout.write(log_info)
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, acc: %.2f%%, time: %.2fs' %
(epoch * num_batches, train_err / train_total,
train_corr * 100 / 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
_, corr, preds = network.loss(
word,
char,
labels,
mask=masks,
leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
num_tokens = masks.data.sum()
dev_corr += corr.data[0]
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
_, corr, preds = network.loss(
word,
char,
labels,
mask=masks,
leading_symbolic=conllx_data.NUM_SYMBOLIC_TAGS)
num_tokens = masks.data.sum()
test_corr += corr.data[0]
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 in schedule:
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')
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=0.1,
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('--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("NER")
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
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/", 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 list(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 = BiRecurrentConv(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,
initializer=initializer)
else:
network = BiVarRecurrentConv(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,
initializer=initializer)
if use_gpu:
network.cuda()
lr = learning_rate
# optim = Adam(network.parameters(), lr=lr, betas=(0.9, 0.9), weight_decay=gamma)
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" %
(mode, num_layers, hidden_size, num_filters, tag_space))
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_corr = 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, corr, _ = network.loss(
word,
char,
labels,
mask=masks,
length=lengths,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
loss.backward()
optim.step()
num_tokens = masks.data.sum()
train_err += loss.data[0] * num_tokens
train_corr += corr.data[0]
train_total += num_tokens
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, acc: %.2f%%, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total,
train_corr * 100 / 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, acc: %.2f%%, time: %.2fs' %
(num_batches, train_err / train_total,
train_corr * 100 / 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.loss(
word,
char,
labels,
mask=masks,
length=lengths,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.data.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.loss(
word,
char,
labels,
mask=masks,
length=lengths,
leading_symbolic=conll03_data.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.data.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')
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('--num_layers',
type=int,
default=2,
help='Number of layers')
parser.add_argument('--batch_size',
type=int,
default=16,
help='Number of sentences in each batch')
parser.add_argument('--bidirectional', default=True)
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=0.1,
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('--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('--data_path')
parser.add_argument('--modelname',
default="ASR_ERR_LSTM.json.pth.tar",
help='model name')
parser.add_argument('--task',
default="MEDIA",
help='task name : MEDIA or ATIS')
parser.add_argument('--optim',
default="SGD",
help=' Optimizer : SGD or ADAM')
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()
tim = datetime.now().strftime("%Y%m%d-%H%M%S")
log_file = '%s/log/log_model_%s_mode_%s_num_epochs_%d_batch_size_%d_hidden_size_%d_num_layers_%d_optim_%s_lr_%f_tag_space_%s.txt' % (
args.data_path, args.modelname, args.mode, args.num_epochs,
args.batch_size, args.hidden_size, args.num_layers, args.optim,
args.learning_rate, str(args.tag_space))
logger = get_logger("SLU_BLSTM", log_file)
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
data_path = args.data_path
bidirectional = args.bidirectional
p = args.p
unk_replace = args.unk_replace
# embedding = args.embedding
# embedding_path = args.embedding_dict
feat_vec_dim = data_loader.get_feature_dim(train_path)
out_path = args.data_path
logger.info("Creating Alphabets")
word_alphabet, char_alphabet, target_alphabet = data_loader.create_alphabets(
'%s/data_dic' % (data_path),
train_path,
data_paths=[dev_path, test_path],
max_vocabulary_size=50000)
logger.info("Word feature_vec_dim: %d" % feat_vec_dim)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("Target Alphabet Size: %d" % target_alphabet.size())
logger.info("Bidirectionnal %s" % bidirectional)
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = data_loader.read_data_to_variable(train_path,
word_alphabet,
char_alphabet,
target_alphabet,
use_gpu=use_gpu)
num_data = sum(data_train[1])
num_labels = target_alphabet.size()
data_dev = data_loader.read_data_to_variable(dev_path,
word_alphabet,
char_alphabet,
target_alphabet,
use_gpu=use_gpu,
volatile=True)
data_test = data_loader.read_data_to_variable(test_path,
word_alphabet,
char_alphabet,
target_alphabet,
use_gpu=use_gpu,
volatile=True)
writer = DataWriter(word_alphabet, char_alphabet, target_alphabet)
char_dim = args.char_dim
window = 3
num_layers = args.num_layers
tag_space = args.tag_space
print("num_labels ", word_alphabet.size())
if args.dropout == 'std':
network = BiRecurrentConv(feat_vec_dim,
word_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
tag_space=tag_space,
p_rnn=p,
bidirectional=bidirectional)
else:
network = BiVarRecurrentConv(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_rnn=p)
print(network)
if use_gpu:
network.cuda()
lr = learning_rate
if args.optim == "SGD":
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
else:
optim = Adam(network.parameters(),
lr=lr,
betas=(0.9, 0.9),
weight_decay=gamma)
logger.info(
"Network: %s, num_layer=%d, hidden=%d, filter=%d, tag_space=%d" %
(mode, num_layers, hidden_size, num_filters, tag_space))
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
model_path = ""
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_corr = 0.
train_total = 0.
start_time = time.time()
num_back = 0
network.train()
#batch=1
for batch in range(1, num_batches + 1):
#for batch_train in data_loader.iterate_batch_variable(data_train, batch_size):
word, features, sents, char, labels, masks, lengths = data_loader.get_batch_variable(
data_train, batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss, corr, _, _ = network.loss(
features,
char,
labels,
mask=masks,
length=lengths,
leading_symbolic=data_loader.NUM_SYMBOLIC_TAGS)
loss.backward()
optim.step()
num_tokens = masks.data.sum()
#train_err += loss.data * num_tokens
train_err += loss.data[0] * num_tokens
#train_corr += corr.data
train_corr += corr.data[0]
train_total += num_tokens
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, acc: %.2f%%, time left (estimated): %.2fs' % (
batch, num_batches, train_err / train_total,
train_corr * 100 / train_total, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
batch = batch + 1
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))
logger.info(
'train: %d loss: %.4f, time: %.2fs' %
(num_batches, train_err / train_total, time.time() - start_time))
print('train: %d loss: %.4f, acc: %.2f%%, time: %.2fs' %
(num_batches, train_err / train_total,
train_corr * 100 / train_total, time.time() - start_time))
loss_results = train_err / train_total
# evaluate performance on dev data
network.eval()
tmp_filename = '%s/predictions/dev_%s_num_layers_%s_%s.txt' % (
out_path, args.optim, str(args.num_layers), str(uid))
writer.start(tmp_filename)
all_target = []
all_preds = []
for batch in data_loader.iterate_batch_variable(data_dev, batch_size):
word, features, sents, char, labels, masks, lengths = batch
_, _, preds, probs = network.loss(
features,
char,
labels,
mask=masks,
length=lengths,
leading_symbolic=data_loader.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
sents.data.cpu().numpy(),
preds.data.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
# correct_tag, pred_tag=writer.tensor_to_list(preds.cpu().numpy(),labels.cpu().numpy(), lengths.cpu().numpy())
# all_target.extend(correct_tag)
# all_preds.extend(pred_tag)
writer.close()
# precision, recall,f1,acc=writer.evaluate(all_preds,all_target)
acc, precision, recall, f1 = evaluate(tmp_filename, data_path, "dev",
args.task, args.optim)
print(
'dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%' %
(acc, precision, recall, f1))
logger.info(
'dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%' %
(acc, precision, recall, f1))
if dev_acc < acc:
dev_f1 = f1
dev_acc = acc
dev_precision = precision
dev_recall = recall
best_epoch = epoch
# save best model
model_path = "%s/models/best_model_%s_mode_%s_num_epochs_%d_batch_size_%d_hidden_size_%d_num_layers_%d_bestdevacc_%f_bestepoch_%d_optim_%s_lr_%f_tag_space_%s" % (
args.data_path, args.modelname, mode, num_epochs, batch_size,
hidden_size, args.num_layers, dev_acc, best_epoch, args.optim,
args.learning_rate, str(tag_space))
torch.save(network, model_path)
# evaluate on test data when better performance detected
"""
tmp_filename = '%s/tmp/%s_test%d' % (data_path,tim, epoch)
writer.start(tmp_filename)
for batch in data_loader.iterate_batch_variable(data_test, batch_size):
word, features, sents, char, labels, masks, lengths = batch
_, _, preds,probs = network.loss(features, char, labels, mask=masks, length=lengths,
leading_symbolic=data_loader.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),sents.data.cpu().numpy(),
preds.data.cpu().numpy(), probs.data.cpu().numpy(), labels.data.cpu().numpy(), lengths.cpu().numpy())
writer.close()
test_acc, test_precision, test_recall, test_f1 = evaluate(tmp_filename, data_path,"test",tim)
"""
logger.info(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
# logger.info("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)
if args.optim == "SGD":
optim = SGD(network.parameters(),
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
else:
optim = Adam(network.parameters(),
lr=lr,
betas=(0.9, 0.9),
weight_decay=gamma)
# end epoch
# test evaluation
# load model
print("model path ", model_path)
network = torch.load(model_path)
if use_gpu:
network.cuda()
# mode eval
network.eval()
# evaluate on test dev when better performance detected
tmp_filename = '%s/predictions/dev_best_model_%s_mode_%s_num_epochs_%d_batch_size_%d_hidden_size_%d_num_layers_%d_bestdevacc_%f_bestF1_%f_bestepoch_%d_optim_%s_lr_%f_tag_space_%s' % (
out_path, args.modelname, mode, num_epochs, batch_size, hidden_size,
num_layers, dev_acc, dev_f1, best_epoch, args.optim,
args.learning_rate, tag_space)
#tmp_filename = '%s/predictions/dev_best_model_%s_mode_%s_num_epochs_%d_batch_size_%d_hidden_size_%d_num_layers_%d_bestdevacc_%f_bestepoch_%d_optim_%s_lr_%f_tag_space_%s' % (out_path,args.modelname,mode,num_epochs,batch_size,hidden_size,num_layers,dev_acc,best_epoch,args.optim,args.learning_rate,tag_space) #tmp_filename = '%s/predictions/dev_bestmodel_devacc_%f_epoch_%d' % (out_path,dev_acc, best_epoch)
writer.start(tmp_filename)
all_target = []
all_preds = []
for batch in data_loader.iterate_batch_variable(data_dev, batch_size):
word, features, sents, char, labels, masks, lengths = batch
_, _, preds, probs = network.loss(
features,
char,
labels,
mask=masks,
length=lengths,
leading_symbolic=data_loader.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
sents.data.cpu().numpy(),
preds.data.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
dev_acc, dev_precision, dev_recall, dev_f1 = evaluate(
tmp_filename, data_path, "dev", args.task, args.optim)
# evaluate on test data when better performance detected
tmp_filename = '%s/predictions/test_best_model_%s_mode_%s_num_epochs_%d_batch_size_%d_hidden_size_%d_num_layers_%d_bestdevacc_%f_bestF1_%f_bestepoch_%d_optim_%s_lr_%f_tag_space_%s' % (
out_path, args.modelname, mode, num_epochs, batch_size, hidden_size,
num_layers, dev_acc, dev_f1, best_epoch, args.optim,
args.learning_rate, tag_space)
#tmp_filename = '%s/predictions/test_best_model_%s_mode_%s_num_epochs_%d_batch_size_%d_hidden_size_%d_num_layers_%d_bestdevacc_%f_bestepoch_%d_optim_%s_lr_%f_tag_space_%s' % (out_path,args.modelname,mode,num_epochs,batch_size,hidden_size,num_layers,dev_acc,best_epoch, args.optim, args.learning_rate, tag_space)
writer.start(tmp_filename)
all_target = []
all_preds = []
for batch in data_loader.iterate_batch_variable(data_test, batch_size):
word, features, sents, char, labels, masks, lengths = batch
_, _, preds, probs = network.loss(
features,
char,
labels,
mask=masks,
length=lengths,
leading_symbolic=data_loader.NUM_SYMBOLIC_TAGS)
writer.write(word.data.cpu().numpy(),
sents.data.cpu().numpy(),
preds.data.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
test_acc, test_precision, test_recall, test_f1 = evaluate(
tmp_filename, data_path, "test", args.task, args.optim)
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))
logger.info(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (dev_acc, dev_precision, dev_recall, dev_f1, best_epoch))
logger.info(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (test_acc, test_precision, test_recall, test_f1, best_epoch))