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train_text_classifier.py
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train_text_classifier.py
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from __future__ import print_function
import argparse
import datetime
import json
import os
import numpy
import cupy
import nets as bilm_nets
import chainer
from chainer import training
from chainer.training import extensions
from evaluator import MicroEvaluator
from text_classification import nets as class_nets
from text_classification.nlp_utils import convert_seq
from text_classification import text_datasets
import args_of_text_classifier
from utils import UnkDropout, Outer
class DottableDict(dict):
def __init__(self, *args, **kwargs):
dict.__init__(self, *args, **kwargs)
self.__dict__ = self
def allowDotting(self, state=True):
if state:
self.__dict__ = self
else:
self.__dict__ = dict()
"""load global parameters"""
with open("global.config", "r", encoding='utf-8') as f:
args = DottableDict(json.load(f))
def main():
print(json.dumps(args.__dict__, indent=2))
train(dir="aug_data", print_log=True)
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']:
train, test, real_test, vocab = text_datasets.read_text_dataset(
args.dataset, vocab=None, dir=dir)
n_class = len(set([int(d[1]) for d in train]))
## str.format() uses '{}' and ':' to replace '%'
print(' # train data: {}'.format(len(train)))
print(' # test data: {}'.format(len(test)))
print(' # vocab: {}'.format(len(vocab)))
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_units, 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)
## Setup 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']), trigger=record_trigger)
else:
trainer.extend(extensions.PrintReport(
['main/accuracy', 'validation/main/accuracy',
'test/main/accuracy'], out=out), trigger=record_trigger)
## 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()
return float(out[-1])
if __name__ == '__main__':
main()