def test_trainer_can_resume_with_lr_scheduler(self):
lr_scheduler = ExponentialLearningRateScheduler(self.optimizer, gamma=0.5)
trainer = Trainer(
model=self.model,
optimizer=self.optimizer,
iterator=self.iterator,
learning_rate_scheduler=lr_scheduler,
train_dataset=self.instances,
validation_dataset=self.instances,
num_epochs=2,
serialization_dir=self.TEST_DIR,
)
trainer.train()
new_lr_scheduler = ExponentialLearningRateScheduler(self.optimizer, gamma=0.5)
new_trainer = Trainer(
model=self.model,
optimizer=self.optimizer,
iterator=self.iterator,
learning_rate_scheduler=new_lr_scheduler,
train_dataset=self.instances,
validation_dataset=self.instances,
num_epochs=4,
serialization_dir=self.TEST_DIR,
)
epoch = new_trainer._restore_checkpoint()
assert epoch == 2
assert new_trainer._learning_rate_scheduler.lr_scheduler.last_epoch == 1
new_trainer.train()
def test_trainer_can_run_with_lr_scheduler(self):
lr_scheduler = ExponentialLearningRateScheduler(self.optimizer, gamma=0.5)
trainer = GradientDescentTrainer(
model=self.model,
optimizer=self.optimizer,
data_loader=self.data_loader,
learning_rate_scheduler=lr_scheduler,
validation_metric="-loss",
validation_data_loader=self.validation_data_loader,
num_epochs=2,
)
trainer.train()
def test_trainer_can_run_with_lr_scheduler(self):
lr_scheduler = ExponentialLearningRateScheduler(self.optimizer, gamma=0.5)
trainer = Trainer(
model=self.model,
optimizer=self.optimizer,
iterator=self.iterator,
learning_rate_scheduler=lr_scheduler,
validation_metric="-loss",
train_dataset=self.instances,
validation_dataset=self.instances,
num_epochs=2,
)
trainer.train()