import learnergy4video.utils.logging as l from learnergy4video.visual.image import _rasterize from learnergy4video.utils.collate import collate_fn from learnergy4video.core.dataset import Dataset from learnergy4video.core.model import Model from learnergy4video.models.real import GaussianRBM, FRRBM from learnergy4video.utils.fft_utils import fftshift, ifftshift workers = os.cpu_count() if workers == None: workers = 0 else: workers -= 2 logger = l.get_logger(__name__) class MultFRRBM(Model): """A class provides the basic implementation for Deep Belief Networks. References: Roder 2021 """ def __init__(self, n_visible=(72, 96), n_hidden=(128, 128), steps=(1, 1), learning_rate=(0.0001, 0.0001), momentum=(0, 0), decay=(0, 0), temperature=(1, 1),
def test_get_logger(): logger = logging.get_logger(__name__) assert logger.name == 'test_logging' assert logger.hasHandlers() == True