def __init__(self,
num_classes: Optional[int] = None,
multi_label: bool = False,
loss: types.LossType = None,
metrics: Optional[types.MetricsType] = None,
project_name: str = "text_classifier",
max_trials: int = 100,
directory: Union[str, Path, None] = None,
objective: str = "val_loss",
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite: bool = False,
seed: Optional[int] = None,
max_model_size: Optional[int] = None,
**kwargs):
if tuner is None:
tuner = task_specific.TextClassifierTuner
super().__init__(outputs=blocks.ClassificationHead(
num_classes=num_classes,
multi_label=multi_label,
loss=loss,
metrics=metrics,
),
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=tuner,
overwrite=overwrite,
seed=seed,
max_model_size=max_model_size,
**kwargs)
def __init__(self,
column_names: Optional[List[str]] = None,
column_types: Optional[Dict] = None,
num_classes: Optional[int] = None,
multi_label: bool = False,
loss: Optional[types.LossType] = None,
metrics: Optional[types.MetricsType] = None,
project_name: str = "structured_data_classifier",
max_trials: int = 100,
directory: Optional[Union[str, pathlib.Path]] = None,
objective: str = "val_accuracy",
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite: bool = False,
seed: Optional[int] = None,
**kwargs):
if tuner is None:
tuner = task_specific.StructuredDataClassifierTuner
super().__init__(outputs=blocks.ClassificationHead(
num_classes=num_classes,
multi_label=multi_label,
loss=loss,
metrics=metrics,
),
column_names=column_names,
column_types=column_types,
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=tuner,
overwrite=overwrite,
seed=seed,
**kwargs)
def __init__(self,
column_names=None,
column_types=None,
num_classes=None,
multi_label=False,
loss=None,
metrics=None,
project_name='structured_data_classifier',
max_trials=100,
directory=None,
objective='val_accuracy',
tuner: Union[str, Type[tuner.AutoTuner]] = None,
overwrite=False,
seed=None,
**kwargs):
if tuner is None:
tuner = greedy.Greedy
super().__init__(
outputs=blocks.ClassificationHead(num_classes=num_classes,
multi_label=multi_label,
loss=loss,
metrics=metrics),
column_names=column_names,
column_types=column_types,
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=tuner,
overwrite=overwrite,
seed=seed,
**kwargs)
def __init__(self,
num_classes: Optional[int] = None,
multi_label: bool = False,
loss: types.LossType = None,
metrics: Optional[types.MetricsType] = None,
project_name: str = 'text_classifier',
max_trials: int = 100,
directory: Union[str, pathlib.Path, None] = None,
objective: str = 'val_loss',
overwrite: bool = True,
seed: Optional[int] = None,
**kwargs):
super().__init__(outputs=blocks.ClassificationHead(
num_classes=num_classes,
multi_label=multi_label,
loss=loss,
metrics=metrics),
max_trials=max_trials,
directory=directory,
project_name=project_name,
objective=objective,
tuner=task_specific.TextClassifierTuner,
overwrite=overwrite,
seed=seed,
**kwargs)
