def _fit(model: Estimator,
iterator: BasicDatasetIterator,
train_config={}) -> Estimator:
x, y = iterator.iter_all('train')
model.fit(x, y)
model.save()
return model
def _fit_batches(model: Estimator, iterator: DataFittingIterator,
train_config) -> Estimator:
model.fit_batches(iterator, batch_size=train_config['batch_size'])
model.save()
return model
def _fit(model: Estimator, iterator: DataLearningIterator,
train_config) -> Estimator:
x, y = iterator.get_instances('train')
model.fit(x, y)
model.save()
return model
def _fit_batches(model: Estimator, iterator: DataFittingIterator, train_config) -> Estimator:
model.fit_batches(iterator, batch_size=train_config['batch_size'])
model.save()
return model
def _fit(model: Estimator, iterator: DataLearningIterator, train_config) -> Estimator:
x, y = iterator.get_instances('train')
model.fit(x, y)
model.save()
return model
def _fit(model: Estimator, dataset: Dataset, train_config={}):
x, y = dataset.iter_all('train')
model.fit(x, y)
model.save()
return model