def __init__(self, output_dim: Optional[int] = None, loss: types.LossType = "mean_squared_error", metrics: Optional[types.MetricsType] = None, project_name: str = "text_regressor", 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 = greedy.Greedy super().__init__(outputs=blocks.RegressionHead(output_dim=output_dim, 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[str, str]] = None, output_dim: Optional[int] = None, loss: types.LossType = "mean_squared_error", metrics: Optional[types.MetricsType] = None, project_name: str = "structured_data_regressor", max_trials: int = 100, directory: Union[str, pathlib.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.StructuredDataRegressorTuner super().__init__( outputs=blocks.RegressionHead( output_dim=output_dim, 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, max_model_size=max_model_size, **kwargs )
def __init__(self, output_dim=None, loss='mean_squared_error', metrics=None, project_name='text_regressor', max_trials=100, directory=None, objective='val_loss', tuner: Union[str, Type[tuner.AutoTuner]] = None, overwrite=False, seed=None, **kwargs): if tuner is None: tuner = greedy.Greedy super().__init__(outputs=blocks.RegressionHead(output_dim=output_dim, loss=loss, metrics=metrics), max_trials=max_trials, directory=directory, project_name=project_name, objective=objective, tuner=tuner, overwrite=overwrite, seed=seed, **kwargs)
def __init__(self, output_dim=None, column_names=None, column_types=None, lookback=None, predict_from=1, predict_until=None, loss='mean_squared_error', metrics=None, project_name='time_series_forecaster', max_trials=100, directory=None, objective='val_loss', overwrite=True, seed=None, **kwargs): super().__init__(outputs=blocks.RegressionHead(output_dim=output_dim, loss=loss, metrics=metrics), column_names=column_names, column_types=column_types, lookback=lookback, predict_from=predict_from, predict_until=predict_until, project_name=project_name, max_trials=max_trials, directory=directory, objective=objective, tuner=greedy.Greedy, overwrite=overwrite, seed=seed, **kwargs) self.lookback = lookback self.predict_from = predict_from self.predict_until = predict_until
def __init__(self, column_names: Optional[List[str]] = None, column_types: Optional[Dict[str, str]] = None, output_dim: Optional[int] = None, loss: types.LossType = 'mean_squared_error', metrics: Optional[types.MetricsType] = None, project_name: str = 'structured_data_regressor', 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.RegressionHead(output_dim=output_dim, 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=greedy.Greedy, overwrite=overwrite, seed=seed, **kwargs)
def __init__( self, output_dim=None, column_names: Optional[List[str]] = None, column_types: Optional[Dict[str, str]] = None, lookback: Optional[int] = None, predict_from: int = 1, predict_until: Optional[int] = None, loss: types.LossType = "mean_squared_error", metrics: Optional[types.MetricsType] = None, project_name: str = "time_series_forecaster", 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, **kwargs ): if tuner is None: tuner = greedy.Greedy super().__init__( outputs=blocks.RegressionHead( output_dim=output_dim, loss=loss, metrics=metrics ), column_names=column_names, column_types=column_types, lookback=lookback, predict_from=predict_from, predict_until=predict_until, project_name=project_name, max_trials=max_trials, directory=directory, objective=objective, tuner=tuner, overwrite=overwrite, seed=seed, **kwargs ) self.lookback = lookback self.predict_from = predict_from self.predict_until = predict_until
def __init__(self, output_dim=None, loss='mean_squared_error', metrics=None, project_name='text_regressor', max_trials=100, directory=None, objective='val_loss', overwrite=True, seed=None, **kwargs): super().__init__(outputs=blocks.RegressionHead(output_dim=output_dim, loss=loss, metrics=metrics), max_trials=max_trials, directory=directory, project_name=project_name, objective=objective, tuner=greedy.Greedy, overwrite=overwrite, seed=seed, **kwargs)