def __init__(self, inputs, outputs, name='auto_model', max_trials=100, directory=None, objective='val_loss', tuner='greedy', overwrite=False, seed=None): 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. if isinstance(tuner, str): tuner = tuner_module.get_tuner_class(tuner) self.tuner = tuner( hypermodel=lambda hp: None, overwrite=overwrite, objective=objective, max_trials=max_trials, directory=directory, seed=self.seed, project_name=name) self._split_dataset = False if all([isinstance(output_node, base.Head) for output_node in self.outputs]): self.heads = self.outputs else: self.heads = [output_node.in_blocks[0] for output_node in self.outputs]
def __init__(self, inputs, outputs, name='auto_model', max_trials=100, directory=None, objective='val_loss', tuner='greedy', overwrite=False, seed=None): self.inputs = nest.flatten(inputs) self.outputs = nest.flatten(outputs) self.name = name self.max_trials = max_trials self.directory = directory self.seed = seed self.hyper_graph = None self.objective = objective # TODO: Support passing a tuner instance. self.tuner = tuner_module.get_tuner_class(tuner) self.overwrite = overwrite self._split_dataset = False if all([ isinstance(output_node, base.Head) for output_node in self.outputs ]): self.heads = self.outputs else: self.heads = [ output_node.in_blocks[0] for output_node in self.outputs ]