def test_save_and_load(self):
self.test_fit()
model_path = tempfile.gettempdir()
self.model.save(model_path)
new_model = BLSTMModel.load_model(model_path)
self.assertIsNotNone(new_model)
sentence = list('语言学包含了几种分支领域。')
result = new_model.predict(sentence)
self.assertTrue(isinstance(result[0], str))
self.assertEqual(len(sentence), len(result))
def setUpClass(cls):
cls.epochs = 3
cls.model = BLSTMModel()
def setUpClass(cls):
cls.epochs = 1
embedding = EmbeddingManager.get_bert()
cls.model = BLSTMModel(embedding)
def main():
# parser config
config_file = "./config.ini"
cp = ConfigParser()
cp.read(config_file)
# default config
output_fold = cp["TRAIN"].get("output_fold")
epochs = cp["TRAIN"].getint("epochs")
batch_size = cp["TRAIN"].getint("batch_size")
generator_workers = cp["TRAIN"].getint("generator_workers")
output_weights_name = cp["TRAIN"].get("output_weights_name")
sequence_length_max = cp["TRAIN"].getint("sequence_length_max")
output_model_name = cp["TRAIN"].get("output_model_name")
save_weights_only = cp["TRAIN"].getboolean("save_weights_only")
cyclicLR_mode = cp["TRAIN"].get("cyclicLR_mode")
base_lr = cp["TRAIN"].getfloat("base_lr")
max_lr = cp["TRAIN"].getfloat("max_lr")
today = datetime.date.today()
formatted_today = today.strftime('%y%m%d')
output_dir = os.path.join('experiments', formatted_today, output_fold)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
output_dir_src = os.path.join(output_dir, 'src')
if not os.path.isdir(output_dir_src):
os.makedirs(output_dir_src)
print(f"backup config file to {output_dir_src}")
shutil.copy(config_file, os.path.join(output_dir_src, os.path.split(config_file)[1]))
train_file = os.path.basename(__file__)
shutil.copy(train_file, os.path.join(output_dir_src, train_file))
train_x, train_y = SMP2017ECDTClassificationCorpus.get_classification_data('train')
validate_x, validate_y = SMP2017ECDTClassificationCorpus.get_classification_data('validate')
test_x, test_y = SMP2017ECDTClassificationCorpus.get_classification_data('test')
#'bert-large-cased'
embedding = BERTEmbedding('bert-base-chinese', sequence_length=600)
# 还可以选择 CNNModel CNNLSTMModel
model = BLSTMModel(embedding)
# model.build_model(train_x, train_y)
# model.build_multi_gpu_model(gpus=2)
# print(model.summary())
if save_weights_only:
model_weights = os.path.join(output_dir, output_weights_name)
else:
model_weights = os.path.join(output_dir, output_model_name)
checkpoint = ModelCheckpoint(
model_weights,
save_weights_only=save_weights_only,
save_best_only=True,
verbose=1,
)
earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=20, verbose=0, mode='min')
csv_logger = CSVLogger(os.path.join(output_dir, 'training.csv'))
batch_size_cycliclr = ceil(len(train_x)/batch_size)
if cyclicLR_mode == 'exp_range':
gamma = 0.99994
else:
gamma = 1.
clr = CyclicLR(mode=cyclicLR_mode, step_size=batch_size_cycliclr, base_lr=base_lr, max_lr=max_lr, gamma=gamma)
save_min_loss = SaveMinLoss(filepath=output_dir)
tb = TensorBoard(log_dir=os.path.join(output_dir, "logs"), batch_size=batch_size)
callbacks = [
checkpoint,
tb,
csv_logger,
clr,
save_min_loss,
earlystop,
]
print("** start training **")
model.fit(train_x,
train_y,
x_validate=validate_x,
y_validate=validate_y,
epochs=epochs,
batch_size=batch_size,
labels_weight=True,
fit_kwargs={'callbacks': callbacks,
'workers': generator_workers,
'use_multiprocessing': True,
'class_weight': 'auto',
})
model_path = os.path.join(output_dir, 'model')
model.save(model_path)
report_evaluate = model.evaluate(test_x, test_y, debug_info=True)
with open(os.path.join(output_dir, 'report_evaluate.log'), 'w') as f:
f.write(f"The evaluate report is : \n{str(report_evaluate)}")