def __init__(self): Trainer.__init__(self) self.initial_budget_frac = 0.10 # fraction of samples that AL starts with self.val_frac = 0.05 self.test_frac = 0.05 self.budget_frac = 0.10 self.data_splits_frac = np.round( np.linspace(self.budget_frac, 1, num=10, endpoint=True), 1) self.batch_size = 64
def __init__(self): Trainer.__init__(self) config = load_config() self.config = config self.initial_budget_frac = config['Train']['init_budget_frac'] self.budget_frac = config['Train']['budget_frac'] self.data_splits_frac = np.round( np.linspace(self.budget_frac, self.budget_frac * 10, num=10, endpoint=True), 2) self.batch_size = config['Train']['batch_size'] self.max_runs = config['Train']['max_runs'] self.al_mode = config['Train']['al_mode'] self.run_no = 1 # tracker for running models over n trials (TODO: ensure that this is robust and doesn't index wildly)
def __init__(self, dataset, architecture='2stage_resolve_first', visualize_features=False): Trainer.__init__(self, dataset, architecture, visualize_features)