Process the inputs for fit/eval/predict().
(model,
x,
y,
batch_size=None,
epochs=1,
sample_weights=None,
class_weights=None,
shuffle=False,
steps=None,
distribution_strategy=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False)
| 583 | |
| 584 | |
| 585 | def _process_inputs(model, |
| 586 | x, |
| 587 | y, |
| 588 | batch_size=None, |
| 589 | epochs=1, |
| 590 | sample_weights=None, |
| 591 | class_weights=None, |
| 592 | shuffle=False, |
| 593 | steps=None, |
| 594 | distribution_strategy=None, |
| 595 | max_queue_size=10, |
| 596 | workers=1, |
| 597 | use_multiprocessing=False): |
| 598 | """Process the inputs for fit/eval/predict().""" |
| 599 | adapter_cls = data_adapter.select_data_adapter(x, y) |
| 600 | if adapter_cls in _ADAPTER_FOR_STANDARDIZE_USER_DATA: |
| 601 | x, y, sample_weights = model._standardize_user_data( |
| 602 | x, |
| 603 | y, |
| 604 | sample_weight=sample_weights, |
| 605 | class_weight=class_weights, |
| 606 | batch_size=batch_size, |
| 607 | check_steps=False, |
| 608 | steps=steps) |
| 609 | adapter = adapter_cls( |
| 610 | x, |
| 611 | y, |
| 612 | batch_size=batch_size, |
| 613 | epochs=epochs, |
| 614 | steps=steps, |
| 615 | sample_weights=sample_weights, |
| 616 | shuffle=shuffle, |
| 617 | distribution_strategy=distribution_strategy, |
| 618 | max_queue_size=max_queue_size, |
| 619 | workers=workers, |
| 620 | use_multiprocessing=use_multiprocessing) |
| 621 | # As a fallback for the data type that does not work with |
| 622 | # _standardize_user_data, use the _prepare_model_with_inputs. |
| 623 | if adapter_cls not in _ADAPTER_FOR_STANDARDIZE_USER_DATA: |
| 624 | training_v2_utils._prepare_model_with_inputs(model, adapter.get_dataset()) |
| 625 | return adapter |
| 626 | |
| 627 | |
| 628 | def _get_total_number_of_samples(adapter): |
no test coverage detected