def allocate_worker(self):
"""Allocates the AveragingWorker for internal use."""
worker = SequentialWorker(model=self.master_model, features_col=self.features_column,
label_col=self.label_column, batch_size=self.batch_size,
optimizer=self.worker_optimizer, loss=self.loss, metrics = self.metrics)
return worker
def allocate_worker(self):
"""Allocates the EnsembleWorker for internal use."""
worker = SequentialWorker(model=self.master_model, features_col=self.features_column,
label_col=self.label_column, batch_size=self.batch_size, num_epoch = self.num_epoch,
optimizer=self.worker_optimizer, loss=self.loss, loss_weights=self.loss_weights, metrics=self.metrics)
return worker
def allocate_worker(self):
"""Allocates a worker for the Single Trainer instance.
Only for internal use.
"""
worker = SequentialWorker(model=self.master_model, features_col=self.features_column,
label_col=self.label_column, batch_size=self.batch_size,
optimizer=self.worker_optimizer, loss=self.loss, metrics = self.metrics)
return worker
