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
