def run(self):
best_err1 = 100.
best_epoch = 0
logger.info('==> creating model "{}"'.format(args.model_name))
model = Util.getModel(**vars(args))
model = model.to(DEVICE)
# 大部分情况下，设置这个flag可以让内置的cuDNN的auto - tuner自动寻找最适合当前配置的高效算法，来达到优化运行效率的问题。
cudnn.benchmark = True
# define loss function (criterion) and pptimizer
# criterion = nn.CrossEntropyLoss().to(DEVICE)
# 标签平滑
criterion = LabelSmoothingLoss(classes=self.args.num_classes, smoothing=0.2)
# define optimizer
optimizer = Util.getOptimizer(model=model, args=self.args)
trainer = Trainer(dataset=self.dataset, criterion=criterion, optimizer=optimizer, args=self.args, logger=logger)
logger.info('train: {} test: {}'.format(self.dataset.get_train_length(), self.dataset.get_validation_length()))
for epoch in range(0, self.args.EPOCHS):
# train for one epoch
model = trainer.train(model=model, epoch=epoch)
# evaluate on validation set
model, val_loss, val_err1 = trainer.test(model=model, epoch=epoch)
# remember best err@1 and save checkpoint
is_best = val_err1 < best_err1
if is_best:
best_err1 = val_err1
best_epoch = epoch
logger.info('Best var_err1 {}'.format(best_err1))
Util.save_checkpoint(model.state_dict(), is_best, args.output_models_dir)
if not is_best and epoch - best_epoch >= args.patience > 0:
break
logger.info('Best val_err1: {:.4f} at epoch {}'.format(best_err1, best_epoch))
from models import ConvModel
dataset = KPIDataset(
'../data/train_preprocessed.csv',
seq_length=1001,
step_width=1
)
model = ConvModel(1001)
args = {
"lr": 0.5e-4,
"betas": (0.9, 0.999),
"eps": 1e-8,
"weight_decay": 0.0
}
trainer = Trainer(
model,
dataset,
batch_size=512,
epochs=100,
log_nth=800,
validation_size=0.2,
optim_args=args,
loss_func=CrossEntropyLoss()
)
trainer.train()
