def test_trainer_can_resume_training_for_exponential_moving_average(self):
moving_average = ExponentialMovingAverage(self.model.named_parameters())
callbacks = self.default_callbacks() + [UpdateMovingAverage(moving_average)]
trainer = CallbackTrainer(self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=1, serialization_dir=self.TEST_DIR,
callbacks=callbacks)
trainer.train()
new_moving_average = ExponentialMovingAverage(self.model.named_parameters())
new_callbacks = self.default_callbacks() + [UpdateMovingAverage(new_moving_average)]
new_trainer = CallbackTrainer(self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=3, serialization_dir=self.TEST_DIR,
callbacks=new_callbacks)
new_trainer.handler.fire_event(Events.TRAINING_START) # pylint: disable=protected-access
assert new_trainer.epoch_number == 1
tracker = trainer.metric_tracker # pylint: disable=protected-access
assert tracker.is_best_so_far()
assert tracker._best_so_far is not None # pylint: disable=protected-access
new_trainer.train()
def test_trainer_can_run_ema_from_params(self):
uma_params = Params({"moving_average": {"decay": 0.9999}})
callbacks = self.default_callbacks() + [UpdateMovingAverage.from_params(uma_params, self.model)]
trainer = CallbackTrainer(model=self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=2,
callbacks=callbacks)
trainer.train()
def test_trainer_can_run_exponential_moving_average(self):
moving_average = ExponentialMovingAverage(self.model.named_parameters(), decay=0.9999)
callbacks = self.default_callbacks() + [UpdateMovingAverage(moving_average)]
trainer = CallbackTrainer(model=self.model,
training_data=self.instances,
iterator=self.iterator,
optimizer=self.optimizer,
num_epochs=2,
callbacks=callbacks)
trainer.train()