def main(config):
logger = config.get_logger('train')
# fix random seeds for reproducibility
seed_everything(seed=config.config['seed'])
metric_bests = []
# logger = config.get_logger('train')
for i,train_dataloader, valid_dataloader, test_dataloader in makeDataLoader(config):
model = makeModel(config)
# logger.info(model)
criterion = makeLoss(config)
metrics = makeMetrics(config)
optimizer = makeOptimizer(config, model)
lr_scheduler = makeLrSchedule(config, optimizer, train_dataloader)
trainer = Trainer(model, criterion, metrics, optimizer,
config=config,
i_fold=i,
data_loader=train_dataloader,
valid_data_loader=valid_dataloader,
test_data_loader=test_dataloader,
lr_scheduler=lr_scheduler)
trainer.train()
metric_bests.append(trainer.mnt_best)
logger.info('metric scores:{}'.format(metric_bests))
logger.info('metric mean score: {}'.format(sum(metric_bests) / float(len(metric_bests))))
def main(config):
from data_process import makeDataLoader
#
from trainer.htqe_trainer import Trainer
logger = config.get_logger('train')
train_dataloader, valid_dataloader, test_dataloader = makeDataLoader(
config)
model = makeModel(config)
logger.info(model)
criterion = makeLoss(config)
metrics = makeMetrics(config)
optimizer = makeOptimizer(config, model)
lr_scheduler = makeLrSchedule(config, optimizer, train_dataloader.dataset)
trainer = Trainer(model,
criterion,
metrics,
optimizer,
config=config,
data_loader=train_dataloader,
valid_data_loader=valid_dataloader,
test_data_loader=test_dataloader,
lr_scheduler=lr_scheduler)
trainer.train()
def main(config):
from data_process import makeDataLoader
# 针对不同的数据，训练过程的设置略有不同。
# from trainer.weibo_trainer import Trainer # weibo
# from trainer.cnews_trainer import Trainer # cnews
from trainer.medical_question_trainer import Trainer
logger = config.get_logger('train')
train_dataloader, valid_dataloader, test_dataloader = makeDataLoader(config)
model = makeModel(config)
logger.info(model)
criterion = makeLoss(config)
metrics = makeMetrics(config)
optimizer = makeOptimizer(config, model)
lr_scheduler = makeLrSchedule(config, optimizer, train_dataloader.dataset)
trainer = Trainer(model, criterion, metrics, optimizer,
config=config,
data_loader=train_dataloader,
valid_data_loader=valid_dataloader,
test_data_loader=test_dataloader,
lr_scheduler=lr_scheduler)
trainer.train()
def main(config):
logger = config.get_logger('train')
train_dataloader, valid_dataloader = makeDataLoader(config)
model = makeModel(config)
logger.info(model)
# criterion = makeLoss(config)
# metrics = makeMetrics(config)
optimizer = makeOptimizer(config, model)
lr_scheduler = makeLrSchedule(config, optimizer, train_dataloader)
trainer = Trainer(model,
None,
None,
optimizer,
config=config,
data_loader=train_dataloader,
valid_data_loader=valid_dataloader,
test_data_loader=None,
lr_scheduler=lr_scheduler)
trainer.train()