def build_model(self, monitor=Monitor.FILTERED_MEAN_RANK):
"""function to build the model"""
if self.config.load_from_data is not None:
self.load_model(self.config.load_from_data)
self.evaluator = Evaluator(self.model, self.config)
self.model.to(self.config.device)
if self.config.optimizer == "adam":
self.optimizer = optim.Adam(
self.model.parameters(),
lr=self.config.learning_rate,
)
elif self.config.optimizer == "sgd":
self.optimizer = optim.SGD(
self.model.parameters(),
lr=self.config.learning_rate,
)
elif self.config.optimizer == "adagrad":
self.optimizer = optim.Adagrad(
self.model.parameters(),
lr=self.config.learning_rate,
)
elif self.config.optimizer == "rms":
self.optimizer = optim.RMSprop(
self.model.parameters(),
lr=self.config.learning_rate,
)
else:
raise NotImplementedError("No support for %s optimizer" % self.config.optimizer)
self.config.summary()
self.early_stopper = EarlyStopper(self.config.patience, monitor)
def tune_model(self):
"""Function to tune the model."""
current_loss = float("inf")
self.generator = Generator(self.model, self.config)
self.evaluator = Evaluator(self.model, self.config, tuning=True)
for cur_epoch_idx in range(self.config.epochs):
current_loss = self.train_model_epoch(cur_epoch_idx, tuning=True)
self.evaluator.full_test(cur_epoch_idx)
self.generator.stop()
return current_loss
def enter_interactive_mode(self):
self.build_model()
self.load_model()
self.evaluator = Evaluator(self.model)
self._logger.info("""The training/loading of the model has finished!
Now enter interactive mode :)
-----
Example 1: trainer.infer_tails(1,10,topk=5)""")
self.infer_tails(1,10,topk=5)
self._logger.info("""-----
Example 2: trainer.infer_heads(10,20,topk=5)""")
self.infer_heads(10,20,topk=5)
self._logger.info("""-----
Example 3: trainer.infer_rels(1,20,topk=5)""")
self.infer_rels(1,20,topk=5)
def train_model(self, monitor=Monitor.FILTERED_MEAN_RANK):
"""Function to train the model."""
self.generator = Generator(self.model)
self.evaluator = Evaluator(self.model)
if self.config.loadFromData:
self.load_model()
for cur_epoch_idx in range(self.config.epochs):
self._logger.info("Epoch[%d/%d]" % (cur_epoch_idx, self.config.epochs))
self.train_model_epoch(cur_epoch_idx)
if cur_epoch_idx % self.config.test_step == 0:
metrics = self.evaluator.mini_test(cur_epoch_idx)
if self.early_stopper.should_stop(metrics):
### Early Stop Mechanism
### start to check if the metric is still improving after each mini-test.
### Example, if test_step == 5, the trainer will check metrics every 5 epoch.
break
self.evaluator.full_test(cur_epoch_idx)
self.evaluator.metric_calculator.save_test_summary(self.model.model_name)
self.generator.stop()
self.save_training_result()
if self.config.save_model:
self.save_model()
if self.config.disp_result:
self.display()
if self.config.disp_summary:
self.config.summary()
self.config.summary_hyperparameter(self.model.model_name)
self.export_embeddings()
return cur_epoch_idx # the runned epoches.