def exponential_decay_noise(xp, shape, dtype, hook, opt):
 std = numpy.sqrt((hook.eta / numpy.power((1 + opt.t), 0.55)))
 return xp.random.normal(0, std, shape).astype(dtype)

def exponential_decay_noise ( xp , shape , dtype , hook , opt ) : std = numpy . sqrt ( ( hook . eta / numpy . power ( ( 1 + opt . t ) , 0.55 ) ) ) return xp . random . normal ( 0 , std , shape ) . astype ( dtype )
time-dependent annealed gaussian noise function from the paper: adding gradient noise improves learning for very deep networks <URL .