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