def __init__(self,
inputs: Union[Input, List[Input]],
outputs: Union[head_module.Head, node_module.Node, list],
project_name: str = "auto_model",
max_trials: int = 100,
directory: Union[str, Path, None] = None,
objective: str = "val_loss",
tuner: Union[str, Type[tuner.AutoTuner]] = "greedy",
overwrite: bool = False,
seed: Optional[int] = None,
**kwargs):
self.inputs = nest.flatten(inputs)
self.outputs = nest.flatten(outputs)
self.seed = seed
if seed:
np.random.seed(seed)
tf.random.set_seed(seed)
# TODO: Support passing a tuner instance.
# Initialize the hyper_graph.
graph = self._build_graph()
if isinstance(tuner, str):
tuner = get_tuner_class(tuner)
self.tuner = tuner(hypermodel=graph,
overwrite=overwrite,
objective=objective,
max_trials=max_trials,
directory=directory,
seed=self.seed,
project_name=project_name,
**kwargs)
self.overwrite = overwrite
self._heads = [
output_node.in_blocks[0] for output_node in self.outputs
]
def __init__(self,
inputs: Union[Input, List[Input]],
outputs: Union[head_module.Head, node_module.Node, list],
preprocessors: Optional[
Union[preprocessor.Preprocessor,
List[preprocessor.Preprocessor]]] = None,
project_name: str = 'auto_model',
max_trials: int = 100,
directory: Union[str, Path, None] = None,
objective: str = 'val_loss',
tuner: Union[str, Type[tuner.AutoTuner]] = 'greedy',
overwrite: bool = False,
seed: Optional[int] = None,
**kwargs):
self.inputs = nest.flatten(inputs)
self.outputs = nest.flatten(outputs)
self.seed = seed
if seed:
np.random.seed(seed)
tf.random.set_seed(seed)
# TODO: Support passing a tuner instance.
# Initialize the hyper_graph.
graph = self._build_graph()
if isinstance(tuner, str):
tuner = get_tuner_class(tuner)
self.tuner = tuner(hypermodel=graph,
preprocessors=preprocessors,
overwrite=overwrite,
objective=objective,
max_trials=max_trials,
directory=directory,
seed=self.seed,
project_name=project_name,
**kwargs)
# Used by tuner to decide whether to use validation set for final fit.
self._split_dataset = False
self._heads = [
output_node.in_blocks[0] for output_node in self.outputs
]
self._input_adapters = [
input_node.get_adapter() for input_node in self.inputs
]
self._output_adapters = [head.get_adapter() for head in self._heads]