(self, stochastic_round=False, learning_rate=0.002, beta_1=0.9, beta_2=0.999,
                 epsilon=1e-8, schedule=Schedule(), name="ShiftAdaMax")
Args:
            stochastic_round (bool): Set this to True for stochastic rounding.
                                     If False rounding will be to nearest.
                                     If True will perform stochastic rounding using default width.
                                     Only affects the gpu backend.
            learning_rate (float): the multiplicative coefficient of updates
            beta_1 (float): Adam parameter beta1
            beta_2 (float): Adam parameter beta2
            epsilon (float): numerical stability parameter
            schedule (neon.optimizers.optimizer.Schedule, optional): Learning rate schedule.
                                                                     Defaults to a constant.
Args:
            stochastic_round (bool): Set this to True for stochastic rounding.
                                     If False rounding will be to nearest.
                                     If True will perform stochastic rounding using default width.
                                     Only affects the gpu backend.
            learning_rate (float): the multiplicative coefficient of updates
            beta_1 (float): Adam parameter beta1
            beta_2 (float): Adam parameter beta2
            epsilon (float): numerical stability parameter
            schedule (neon.optimizers.optimizer.Schedule, optional): Learning rate schedule.
                                                                     Defaults to a constant.
Args : stochastic_round ( bool ) : Set this to True for stochastic rounding . If False rounding will be to nearest . If True will perform stochastic rounding using default width . Only affects the gpu backend . learning_rate ( float ) : the multiplicative coefficient of updates beta_1 ( float ) : Adam parameter beta1 beta_2 ( float ) : Adam parameter beta2 epsilon ( float ) : numerical stability parameter schedule ( neon . optimizers . optimizer . Schedule optional ) : Learning rate schedule . Defaults to a constant .
def __init__(self, stochastic_round=False, learning_rate=0.002, beta_1=0.9, beta_2=0.999,
                 epsilon=1e-8, schedule=Schedule(), name="ShiftAdaMax"):
        """
        Args:
            stochastic_round (bool): Set this to True for stochastic rounding.
                                     If False rounding will be to nearest.
                                     If True will perform stochastic rounding using default width.
                                     Only affects the gpu backend.
            learning_rate (float): the multiplicative coefficient of updates
            beta_1 (float): Adam parameter beta1
            beta_2 (float): Adam parameter beta2
            epsilon (float): numerical stability parameter
            schedule (neon.optimizers.optimizer.Schedule, optional): Learning rate schedule.
                                                                     Defaults to a constant.
        """
        super(ShiftAdaMax, self).__init__(name=name)
        self.beta_1 = beta_1
        self.beta_2 = beta_2
        self.epsilon = epsilon
        self.learning_rate = learning_rate
        self.stochastic_round = stochastic_round
        self.schedule = schedule
def __init__ ( self , stochastic_round = False , learning_rate = 0.002 , beta_1 = 0.9 , beta_2 = 0.999 , epsilon = 1e-8 , schedule = Schedule ( ) , name = "ShiftAdaMax" ) : super ( ShiftAdaMax , self ) . __init__ ( name = name ) self . beta_1 = beta_1 self . beta_2 = beta_2 self . epsilon = epsilon self . learning_rate = learning_rate self . stochastic_round = stochastic_round self . schedule = schedule