Example #1
0
def _fit(model: Estimator,
         iterator: BasicDatasetIterator,
         train_config={}) -> Estimator:
    x, y = iterator.iter_all('train')
    model.fit(x, y)
    model.save()
    return model
Example #2
0
def _fit_batches(model: Estimator, iterator: DataFittingIterator,
                 train_config) -> Estimator:
    model.fit_batches(iterator, batch_size=train_config['batch_size'])
    model.save()
    return model
Example #3
0
def _fit(model: Estimator, iterator: DataLearningIterator,
         train_config) -> Estimator:
    x, y = iterator.get_instances('train')
    model.fit(x, y)
    model.save()
    return model
Example #4
0
def _fit_batches(model: Estimator, iterator: DataFittingIterator, train_config) -> Estimator:
    model.fit_batches(iterator, batch_size=train_config['batch_size'])
    model.save()
    return model
Example #5
0
def _fit(model: Estimator, iterator: DataLearningIterator, train_config) -> Estimator:
    x, y = iterator.get_instances('train')
    model.fit(x, y)
    model.save()
    return model
Example #6
0
def _fit(model: Estimator, dataset: Dataset, train_config={}):
    x, y = dataset.iter_all('train')
    model.fit(x, y)
    model.save()
    return model