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)
