def test_train_bert_model(self):
preprocessor = SPMPreprocessor(
(self.train_data[0] + self.valid_data[0],
self.train_data[1] + self.valid_data[1]),
self.train_labels + self.valid_labels,
use_word=False,
use_char=False,
use_bert=True,
use_bert_model=True,
bert_vocab_file=self.bert_vocab_file,
max_len=10)
spm_model = BertSPM(num_class=self.num_class,
bert_config_file=self.bert_config_file,
bert_checkpoint_file=self.bert_model_file,
bert_trainable=True,
max_len=preprocessor.max_len).build_model()
spm_trainer = SPMTrainer(spm_model, preprocessor)
spm_trainer.train(self.train_data,
self.train_labels,
self.valid_data,
self.valid_labels,
batch_size=6,
epochs=2)
assert not os.path.exists(self.json_file)
assert not os.path.exists(self.weights_file)
def test_train_no_word(self):
preprocessor = SPMPreprocessor(
(self.train_data[0] + self.valid_data[0],
self.train_data[1] + self.valid_data[1]),
self.train_labels + self.valid_labels,
use_word=False,
use_char=True,
use_bert=True,
bert_vocab_file=self.bert_vocab_file,
char_embed_type='word2vec',
max_len=10)
self.num_class = preprocessor.num_class
self.char_embeddings = preprocessor.char_embeddings
self.char_vocab_size = preprocessor.char_vocab_size
self.char_embed_dim = preprocessor.char_embed_dim
spm_model = SiameseCNN(num_class=self.num_class,
use_word=False,
use_char=True,
char_embeddings=self.char_embeddings,
char_vocab_size=self.char_vocab_size,
char_embed_dim=self.char_embed_dim,
char_embed_trainable=False,
use_bert=True,
bert_config_file=self.bert_config_file,
bert_checkpoint_file=self.bert_model_file,
max_len=preprocessor.max_len).build_model()
spm_trainer = SPMTrainer(spm_model, preprocessor)
spm_trainer.train(self.train_data,
self.train_labels,
self.valid_data,
self.valid_labels,
batch_size=6,
epochs=2)
assert not os.path.exists(self.json_file)
assert not os.path.exists(self.weights_file)
class TestSPMTrainer:
test_file = os.path.join(os.path.dirname(__file__),
'../../../data/spm/webank/example.txt')
bert_vocab_file = os.path.join(
os.path.dirname(__file__),
'../../../data/embeddings/bert_sample_model/vocab.txt')
bert_config_file = os.path.join(
os.path.dirname(__file__),
'../../../data/embeddings/bert_sample_model/bert_config.json')
bert_model_file = os.path.join(
os.path.dirname(__file__),
'../../../data/embeddings/bert_sample_model/bert_model.ckpt')
def setup_class(self):
self.train_data, self.train_labels, self.valid_data, self.valid_labels = \
load_spm_data_and_labels(self.test_file, split_mode=1)
self.preprocessor = SPMPreprocessor(
(self.train_data[0] + self.valid_data[0],
self.train_data[1] + self.valid_data[1]),
self.train_labels + self.valid_labels,
use_word=True,
use_char=True,
bert_vocab_file=self.bert_vocab_file,
word_embed_type='word2vec',
char_embed_type='word2vec',
max_len=10)
self.num_class = self.preprocessor.num_class
self.char_embeddings = self.preprocessor.char_embeddings
self.char_vocab_size = self.preprocessor.char_vocab_size
self.char_embed_dim = self.preprocessor.char_embed_dim
self.word_embeddings = self.preprocessor.word_embeddings
self.word_vocab_size = self.preprocessor.word_vocab_size
self.word_embed_dim = self.preprocessor.word_embed_dim
self.checkpoint_dir = os.path.dirname(__file__)
self.spm_model = SiameseCNN(
num_class=self.num_class,
use_word=True,
word_embeddings=self.word_embeddings,
word_vocab_size=self.word_vocab_size,
word_embed_dim=self.word_embed_dim,
word_embed_trainable=False,
use_char=True,
char_embeddings=self.char_embeddings,
char_vocab_size=self.char_vocab_size,
char_embed_dim=self.char_embed_dim,
char_embed_trainable=False,
use_bert=False,
bert_config_file=self.bert_config_file,
bert_checkpoint_file=self.bert_model_file,
bert_trainable=True,
max_len=self.preprocessor.max_len,
max_word_len=self.preprocessor.max_word_len).build_model()
self.swa_model = SiameseCNN(
num_class=self.num_class,
use_word=True,
word_embeddings=self.word_embeddings,
word_vocab_size=self.word_vocab_size,
word_embed_dim=self.word_embed_dim,
word_embed_trainable=False,
use_char=True,
char_embeddings=self.char_embeddings,
char_vocab_size=self.char_vocab_size,
char_embed_dim=self.char_embed_dim,
char_embed_trainable=False,
use_bert=False,
bert_config_file=self.bert_config_file,
bert_checkpoint_file=self.bert_model_file,
bert_trainable=True,
max_len=self.preprocessor.max_len,
max_word_len=self.preprocessor.max_word_len).build_model()
self.spm_trainer = SPMTrainer(self.spm_model, self.preprocessor)
self.json_file = os.path.join(self.checkpoint_dir,
'siamese_cnn_spm.json')
self.weights_file = os.path.join(self.checkpoint_dir,
'siamese_cnn_spm.hdf5')
def test_train(self):
self.spm_trainer.train(self.train_data,
self.train_labels,
self.valid_data,
self.valid_labels,
batch_size=6,
epochs=2)
assert not os.path.exists(self.json_file)
assert not os.path.exists(self.weights_file)
def test_train_no_word(self):
preprocessor = SPMPreprocessor(
(self.train_data[0] + self.valid_data[0],
self.train_data[1] + self.valid_data[1]),
self.train_labels + self.valid_labels,
use_word=False,
use_char=True,
use_bert=True,
bert_vocab_file=self.bert_vocab_file,
char_embed_type='word2vec',
max_len=10)
self.num_class = preprocessor.num_class
self.char_embeddings = preprocessor.char_embeddings
self.char_vocab_size = preprocessor.char_vocab_size
self.char_embed_dim = preprocessor.char_embed_dim
spm_model = SiameseCNN(num_class=self.num_class,
use_word=False,
use_char=True,
char_embeddings=self.char_embeddings,
char_vocab_size=self.char_vocab_size,
char_embed_dim=self.char_embed_dim,
char_embed_trainable=False,
use_bert=True,
bert_config_file=self.bert_config_file,
bert_checkpoint_file=self.bert_model_file,
max_len=preprocessor.max_len).build_model()
spm_trainer = SPMTrainer(spm_model, preprocessor)
spm_trainer.train(self.train_data,
self.train_labels,
self.valid_data,
self.valid_labels,
batch_size=6,
epochs=2)
assert not os.path.exists(self.json_file)
assert not os.path.exists(self.weights_file)
def test_train_bert_model(self):
preprocessor = SPMPreprocessor(
(self.train_data[0] + self.valid_data[0],
self.train_data[1] + self.valid_data[1]),
self.train_labels + self.valid_labels,
use_word=False,
use_char=False,
use_bert=True,
use_bert_model=True,
bert_vocab_file=self.bert_vocab_file,
max_len=10)
spm_model = BertSPM(num_class=self.num_class,
bert_config_file=self.bert_config_file,
bert_checkpoint_file=self.bert_model_file,
bert_trainable=True,
max_len=preprocessor.max_len).build_model()
spm_trainer = SPMTrainer(spm_model, preprocessor)
spm_trainer.train(self.train_data,
self.train_labels,
self.valid_data,
self.valid_labels,
batch_size=6,
epochs=2)
assert not os.path.exists(self.json_file)
assert not os.path.exists(self.weights_file)
def test_train_no_valid_data(self):
self.spm_trainer.train(self.train_data,
self.train_labels,
batch_size=6,
epochs=2)
assert not os.path.exists(self.json_file)
assert not os.path.exists(self.weights_file)
def test_train_callbacks(self):
self.spm_trainer.train(
self.train_data,
self.train_labels,
self.valid_data,
self.valid_labels,
batch_size=6,
epochs=2,
callback_list=['modelcheckpoint', 'earlystopping'],
checkpoint_dir=os.path.dirname(__file__),
model_name='siamese_cnn_spm')
assert not os.path.exists(self.json_file)
assert os.path.exists(self.weights_file)
os.remove(self.weights_file)
assert not os.path.exists(self.weights_file)
def test_train_swa(self):
self.spm_trainer.train(self.train_data,
self.train_labels,
self.valid_data,
self.valid_labels,
batch_size=6,
epochs=5,
callback_list=['swa'],
checkpoint_dir=os.path.dirname(__file__),
model_name='siamese_cnn_spm',
swa_model=self.swa_model,
load_swa_model=True)
assert not os.path.exists(self.json_file)
assert not os.path.exists(self.weights_file)
json_file = os.path.join(self.checkpoint_dir,
'siamese_cnn_spm_swa.json')
weights_file = os.path.join(self.checkpoint_dir,
'siamese_cnn_spm_swa.hdf5')
assert not os.path.exists(json_file)
assert os.path.exists(weights_file)
os.remove(weights_file)
assert not os.path.exists(weights_file)
def test_generator(self):
self.spm_trainer.train_generator(self.train_data,
self.train_labels,
self.valid_data,
self.valid_labels,
batch_size=6,
epochs=2)
assert not os.path.exists(self.json_file)
assert not os.path.exists(self.weights_file)