def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', '-g', type=int, default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--unit', '-u', type=int, default=650,
help='Number of LSTM units in each layer')
parser.add_argument('--layer', type=int, default=2)
parser.add_argument('--vocab', required=True)
parser.add_argument('--new-vocab', required=True)
parser.add_argument('--resume')
parser.add_argument('--suffix', required=True)
args = parser.parse_args()
print(json.dumps(args.__dict__, indent=2))
vocab = json.load(open(args.new_vocab))
n_vocab = len(vocab)
print('vocab is loaded', args.new_vocab)
print('vocab =', n_vocab)
new_vocab = vocab
vocab = json.load(open(args.vocab))
n_vocab = len(vocab)
print('old vocab is loaded', args.vocab)
print('old vocab =', n_vocab)
old_vocab = vocab
model = nets.BiLanguageModel(
n_vocab, args.unit, args.layer, 0.)
print('load')
chainer.serializers.load_npz(args.resume, model)
print('alter')
model = alter_embedding(model, old_vocab, new_vocab)
print('save')
chainer.serializers.save_npz(
args.resume + '.' + args.suffix, model)
def train(dir="datasets", print_log=False):
chainer.CHAINER_SEED = args.seed
numpy.random.seed(args.seed)
vocab = None
# Load a dataset
if args.dataset == 'dbpedia':
train, test, vocab = text_datasets.get_dbpedia(vocab=vocab)
elif args.dataset.startswith('imdb.'):
train, test, vocab = text_datasets.get_imdb(
fine_grained=args.dataset.endswith('.fine'), vocab=vocab)
elif args.dataset in [
'TREC', 'stsa.binary', 'stsa.fine', 'custrev', 'mpqa',
'rt-polarity', 'subj', 'toxic'
]:
train, test, real_test, vocab = text_datasets.read_text_dataset(
args.dataset, vocab=None, dir=dir)
#train, test, vocab = text_datasets.get_other_text_dataset(
# args.dataset, vocab=vocab)
#if args.validation:
# real_test = test
# dataset_pairs = chainer.datasets.get_cross_validation_datasets_random(
# train, 10, seed=777)
# train, test = dataset_pairs[0]
print('# train data: {}'.format(len(train)))
print('# test data: {}'.format(len(test)))
print('# vocab: {}'.format(len(vocab)))
n_class = len(set([int(d[1]) for d in train]))
print('# class: {}'.format(n_class))
chainer.CHAINER_SEED = args.seed
numpy.random.seed(args.seed)
train = UnkDropout(train, vocab['<unk>'], 0.01)
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
test_iter = chainer.iterators.SerialIterator(test,
args.batchsize,
repeat=False,
shuffle=False)
# Setup a model
chainer.CHAINER_SEED = args.seed
numpy.random.seed(args.seed)
if args.model == 'rnn':
Encoder = class_nets.RNNEncoder
elif args.model == 'cnn':
Encoder = class_nets.CNNEncoder
elif args.model == 'bow':
Encoder = class_nets.BOWMLPEncoder
encoder = Encoder(n_layers=args.layer,
n_vocab=len(vocab),
n_units=args.unit,
dropout=args.dropout)
model = class_nets.TextClassifier(encoder, n_class)
if args.bilm:
bilm = bilm_nets.BiLanguageModel(len(vocab), args.bilm_unit,
args.bilm_layer, args.bilm_dropout)
n_labels = len(set([int(v[1]) for v in test]))
print('# labels =', n_labels)
if not args.no_label:
print('add label')
bilm.add_label_condition_nets(n_labels, args.bilm_unit)
else:
print('not using label')
chainer.serializers.load_npz(args.bilm, bilm)
with model.encoder.init_scope():
initialW = numpy.array(model.encoder.embed.W.data)
del model.encoder.embed
model.encoder.embed = bilm_nets.PredictiveEmbed(len(vocab),
args.unit,
bilm,
args.dropout,
initialW=initialW)
model.encoder.use_predict_embed = True
model.encoder.embed.setup(mode=args.bilm_mode,
temp=args.bilm_temp,
word_lower_bound=0.,
gold_lower_bound=0.,
gumbel=args.bilm_gumbel,
residual=args.bilm_residual,
wordwise=args.bilm_wordwise,
add_original=args.bilm_add_original,
augment_ratio=args.bilm_ratio,
ignore_unk=vocab['<unk>'])
if args.gpu >= 0:
# Make a specified GPU current
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu() # Copy the model to the GPU
model.xp.random.seed(args.seed)
chainer.CHAINER_SEED = args.seed
numpy.random.seed(args.seed)
# Setup an optimizer
optimizer = chainer.optimizers.Adam(args.learning_rate)
optimizer.setup(model)
# Set up a trainer
updater = training.StandardUpdater(train_iter,
optimizer,
converter=convert_seq,
device=args.gpu)
from triggers import FailMaxValueTrigger
stop_trigger = FailMaxValueTrigger(key='validation/main/accuracy',
trigger=(1, 'epoch'),
n_times=args.stop_epoch,
max_trigger=args.epoch)
trainer = training.Trainer(updater, stop_trigger, out=args.out)
# Evaluate the model with the test dataset for each epoch
# VALIDATION SET
trainer.extend(
MicroEvaluator(test_iter,
model,
converter=convert_seq,
device=args.gpu))
if args.validation:
real_test_iter = chainer.iterators.SerialIterator(real_test,
args.batchsize,
repeat=False,
shuffle=False)
eval_on_real_test = MicroEvaluator(real_test_iter,
model,
converter=convert_seq,
device=args.gpu)
eval_on_real_test.default_name = 'test'
trainer.extend(eval_on_real_test)
# Take a best snapshot
record_trigger = training.triggers.MaxValueTrigger(
'validation/main/accuracy', (1, 'epoch'))
if args.save_model:
trainer.extend(extensions.snapshot_object(model, 'best_model.npz'),
trigger=record_trigger)
# Write a log of evaluation statistics for each epoch
out = Outer()
trainer.extend(extensions.LogReport())
if print_log:
trainer.extend(
extensions.PrintReport([
'epoch',
'main/loss',
'validation/main/loss',
'main/accuracy',
'validation/main/accuracy',
'test/main/loss',
'test/main/accuracy',
# 'elapsed_time']))
'elapsed_time'
]),
trigger=record_trigger)
else:
trainer.extend(extensions.PrintReport([
'main/accuracy', 'validation/main/accuracy', 'test/main/accuracy'
],
out=out),
trigger=record_trigger)
# Print a progress bar to stdout
#trainer.extend(extensions.ProgressBar())
# Run the training
trainer.run()
# free all unused memory blocks “cached” in the memory pool
mempool = cupy.get_default_memory_pool()
mempool.free_all_blocks()
#print("val_acc:{}, test_acc:{}\n", out[-2], out[-1])
return float(out[-1])
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu',
'-g',
type=int,
default=-1,
help='GPU ID (negative value indicates CPU)')
parser.add_argument('--out',
'-o',
default='result',
help='Directory to output the result')
parser.add_argument('--batchsize',
'-b',
type=int,
default=32,
help='Number of examples in each mini-batch')
parser.add_argument('--epoch',
'-e',
type=int,
default=5,
help='Number of sweeps over the dataset to train')
parser.add_argument('--gradclip',
'-c',
type=float,
default=10,
help='Gradient norm threshold to clip')
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--unit',
'-u',
type=int,
default=1024,
help='Number of LSTM units in each layer')
parser.add_argument('--layer', type=int, default=2)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--vocab', required=True)
parser.add_argument('--train-path', '--train')
parser.add_argument('--valid-path', '--valid')
parser.add_argument('--resume')
parser.add_argument('--labeled-dataset',
'-ldata',
default=None,
choices=[
'dbpedia', 'imdb.binary', 'imdb.fine', 'TREC',
'stsa.binary', 'stsa.fine', 'custrev', 'mpqa',
'rt-polarity', 'subj'
],
help='Name of dataset.')
parser.add_argument('--no-label', action='store_true')
parser.add_argument('--validation', action='store_true')
args = parser.parse_args()
print(json.dumps(args.__dict__, indent=2))
vocab = json.load(open(args.vocab))
if args.labeled_dataset:
if args.labeled_dataset == 'dbpedia':
train, valid, _ = text_datasets.get_dbpedia(vocab=vocab)
elif args.labeled_dataset.startswith('imdb.'):
train, valid, _ = text_datasets.get_imdb(
fine_grained=args.labeled_dataset.endswith('.fine'),
vocab=vocab)
elif args.labeled_dataset in [
'TREC', 'stsa.binary', 'stsa.fine', 'custrev', 'mpqa',
'rt-polarity', 'subj'
]:
train, valid, _ = text_datasets.get_other_text_dataset(
args.labeled_dataset, vocab=vocab)
if args.validation:
train, valid = \
chainer.datasets.get_cross_validation_datasets_random(
train, 10, seed=777)[0]
else:
print('do not use test dataset. pls use validation split.')
else:
train = chain_utils.SequenceChainDataset(args.train_path,
vocab,
chain_length=1)
valid = chain_utils.SequenceChainDataset(args.valid_path,
vocab,
chain_length=1)
print('#train =', len(train))
print('#valid =', len(valid))
print('#vocab =', len(vocab))
# Create the dataset iterators
train_iter = chainer.iterators.SerialIterator(train, args.batchsize)
valid_iter = chainer.iterators.SerialIterator(valid,
args.batchsize,
repeat=False,
shuffle=False)
# Prepare an biRNNLM model
model = nets.BiLanguageModel(len(vocab), args.unit, args.layer,
args.dropout)
if args.resume:
print('load {}'.format(args.resume))
chainer.serializers.load_npz(args.resume, model)
if args.labeled_dataset and not args.no_label:
n_labels = len(set([int(v[1]) for v in valid]))
print('# labels =', n_labels)
model.add_label_condition_nets(n_labels, args.unit)
if args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu()
# Set up an optimizer
optimizer = chainer.optimizers.Adam(alpha=args.lr)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(args.gradclip))
iter_per_epoch = len(train) // args.batchsize
print('{} iters per epoch'.format(iter_per_epoch))
if iter_per_epoch >= 10000:
log_trigger = (iter_per_epoch // 100, 'iteration')
eval_trigger = (log_trigger[0] * 50, 'iteration') # every half epoch
else:
log_trigger = (iter_per_epoch // 2, 'iteration')
eval_trigger = (log_trigger[0] * 2, 'iteration') # every epoch
print('log and eval are scheduled at every {} and {}'.format(
log_trigger, eval_trigger))
if args.labeled_dataset:
updater = training.StandardUpdater(
train_iter,
optimizer,
converter=convert_seq,
device=args.gpu,
loss_func=model.calculate_loss_with_labels)
trainer = training.Trainer(updater, (args.epoch, 'epoch'),
out=args.out)
trainer.extend(extensions.Evaluator(
valid_iter,
model,
converter=convert_seq,
device=args.gpu,
eval_func=model.calculate_loss_with_labels),
trigger=eval_trigger)
else:
updater = training.StandardUpdater(
train_iter,
optimizer,
converter=chain_utils.convert_sequence_chain,
device=args.gpu,
loss_func=model.calculate_loss)
trainer = training.Trainer(updater, (args.epoch, 'epoch'),
out=args.out)
trainer.extend(extensions.Evaluator(
valid_iter,
model,
converter=chain_utils.convert_sequence_chain,
device=args.gpu,
eval_func=model.calculate_loss),
trigger=eval_trigger)
record_trigger = training.triggers.MinValueTrigger('validation/main/perp',
trigger=eval_trigger)
trainer.extend(extensions.snapshot_object(model, 'best_model.npz'),
trigger=record_trigger)
trainer.extend(extensions.LogReport(trigger=log_trigger),
trigger=log_trigger)
keys = [
'epoch', 'iteration', 'main/perp', 'validation/main/perp',
'elapsed_time'
]
trainer.extend(extensions.PrintReport(keys), trigger=log_trigger)
trainer.extend(extensions.ProgressBar(update_interval=50))
print('iter/epoch', iter_per_epoch)
print('Training start')
trainer.run()