(self, learning_rate, momentum_coef, stochastic_round=False,
                 wdecay=0.0, gradient_clip_norm=None, gradient_clip_value=None,
                 param_clip_value=None, name=None, schedule=Schedule(),
                 nesterov=False)
Class constructor.

        Arguments:
            learning_rate (float): Multiplicative coefficient of updates
            momentum_coef (float): Coefficient of momentum
            stochastic_round (bool, optional): Set this to True for stochastic
                                               rounding.  If False (default)
                                               rounding will be to nearest.  If
                                               True use default width
                                               stochastic rounding.  Note that
                                               this only affects the GPU
                                               backend.
            wdecay (float, optional): Amount of weight decay.  Defaults to 0
            gradient_clip_norm (float, optional): Target gradient norm.
                                                  Defaults to None.
            gradient_clip_value (float, optional): Value to element-wise clip
                                                   gradients.
                                                   Defaults to None.
            param_clip_value (float, optional): Value to element-wise clip
                                                parameters.
                                                Defaults to None.
            name (str, optional): the optimizer's layer's pretty-print name.
                                  Defaults to "gdm".
            schedule (neon.optimizers.optimizer.Schedule, optional): Learning
                        rate schedule.  Defaults to a constant learning rate.
            nesterov (bool, optional): Use nesterov accelerated gradient.
                                       Defaults to False.
Class constructor.
Class constructor .
def __init__(self, learning_rate, momentum_coef, stochastic_round=False,
                 wdecay=0.0, gradient_clip_norm=None, gradient_clip_value=None,
                 param_clip_value=None, name=None, schedule=Schedule(),
                 nesterov=False):
        """
        Class constructor.

        Arguments:
            learning_rate (float): Multiplicative coefficient of updates
            momentum_coef (float): Coefficient of momentum
            stochastic_round (bool, optional): Set this to True for stochastic
                                               rounding.  If False (default)
                                               rounding will be to nearest.  If
                                               True use default width
                                               stochastic rounding.  Note that
                                               this only affects the GPU
                                               backend.
            wdecay (float, optional): Amount of weight decay.  Defaults to 0
            gradient_clip_norm (float, optional): Target gradient norm.
                                                  Defaults to None.
            gradient_clip_value (float, optional): Value to element-wise clip
                                                   gradients.
                                                   Defaults to None.
            param_clip_value (float, optional): Value to element-wise clip
                                                parameters.
                                                Defaults to None.
            name (str, optional): the optimizer's layer's pretty-print name.
                                  Defaults to "gdm".
            schedule (neon.optimizers.optimizer.Schedule, optional): Learning
                        rate schedule.  Defaults to a constant learning rate.
            nesterov (bool, optional): Use nesterov accelerated gradient.
                                       Defaults to False.
        """
        super(GradientDescentMomentum, self).__init__(name=name)
        self.learning_rate, self.momentum_coef = (learning_rate, momentum_coef)
        self.gradient_clip_norm = gradient_clip_norm
        self.gradient_clip_value = gradient_clip_value
        self.param_clip_value = param_clip_value
        self.wdecay = wdecay
        self.schedule = schedule
        self.stochastic_round = stochastic_round
        self.nesterov = nesterov
        if self.momentum_coef == 0 and self.nesterov:
            raise ValueError("nesterov requires non-zero momentum")
def __init__ ( self , learning_rate , momentum_coef , stochastic_round = False , wdecay = 0.0 , gradient_clip_norm = None , gradient_clip_value = None , param_clip_value = None , name = None , schedule = Schedule ( ) , nesterov = False ) : super ( GradientDescentMomentum , self ) . __init__ ( name = name ) self . learning_rate , self . momentum_coef = ( learning_rate , momentum_coef ) self . gradient_clip_norm = gradient_clip_norm self . gradient_clip_value = gradient_clip_value self . param_clip_value = param_clip_value self . wdecay = wdecay self . schedule = schedule self . stochastic_round = stochastic_round self . nesterov = nesterov if self . momentum_coef == 0 and self . nesterov : raise ValueError ( "nesterov requires non-zero momentum" )