def __init__(self, searcher, dispatcher=None, callbacks=[], reward_metric=None, max_model_size=0, cache_preprocessed_data=False, cache_home=None, **config_kwargs): self.config_kwargs = config_kwargs metrics = config_kwargs.get('metrics') if metrics is None and reward_metric is None: raise ValueError('Must specify `reward_metric` or `metrics`.') if reward_metric is None: reward_metric = metrics[0] if metrics is None: metrics = [reward_metric] config_kwargs['metrics'] = metrics if reward_metric not in metrics: metrics.append(reward_metric) config_kwargs['metrics'] = metrics self.cache_preprocessed_data = cache_preprocessed_data self.cache_home = cache_home HyperModel.__init__(self, searcher, dispatcher=dispatcher, callbacks=callbacks, reward_metric=reward_metric)
def __init__(self, searcher, optimizer, loss, metrics, dispatcher=None, callbacks=[], reward_metric=None, max_model_size=0, one_shot_mode=False, one_shot_train_sampler=None, visualization=False): self.optimizer = optimizer self.loss = loss self.metrics = metrics self.max_model_size = max_model_size if reward_metric is None: reward_metric = metrics[0] if one_shot_mode: self.weights_cache = LayerWeightsCache() else: self.weights_cache = None self.one_shot_mode = one_shot_mode self.one_shot_train_sampler = one_shot_train_sampler if one_shot_train_sampler is not None else searcher self.visualization = visualization HyperModel.__init__(self, searcher, dispatcher=dispatcher, callbacks=callbacks, reward_metric=reward_metric)
def __init__(self, searcher, task='classification', dispatcher=None, callbacks=None, reward_metric='accuracy', data_cleaner_params=None, cache_dir=None, clear_cache=True): if callbacks is None: callbacks = [] self.task = task self.data_cleaner_params = data_cleaner_params self.cache_dir = cache_dir self.clear_cache = clear_cache HyperModel.__init__(self, searcher, dispatcher=dispatcher, callbacks=callbacks, reward_metric=reward_metric)