def _prepare(self, **kwargs):
        '''Set up properties for optimization.

        This method can be overridden by base classes to provide parameters that
        are specific to a particular optimization technique (e.g., setting up a
        learning rate value).
        '''
        self.learning_rate = util.as_float(kwargs.pop('learning_rate', 1e-4))
        self.momentum = kwargs.pop('momentum', 0)
        self.nesterov = kwargs.pop('nesterov', False)
        self.patience = kwargs.get('patience', 5)
        self.validate_every = kwargs.pop('validate_every', 10)
        self.min_improvement = kwargs.pop('min_improvement', 0)
        self.max_gradient_norm = kwargs.pop('max_gradient_norm', 0)
        self.max_gradient_elem = kwargs.pop('max_gradient_elem', 0)

        util.log_param('patience', self.patience)
        util.log_param('validate_every', self.validate_every)
        util.log_param('min_improvement', self.min_improvement)
        util.log_param('max_gradient_norm', self.max_gradient_norm)
        util.log_param('max_gradient_elem', self.max_gradient_elem)
        util.log_param('learning_rate', self.learning_rate)
        util.log_param('momentum', self.momentum)
        util.log_param('nesterov', self.nesterov)
def _prepare ( self , ** kwargs ) : self . learning_rate = util . as_float ( kwargs . pop ( 'learning_rate' , 1e-4 ) ) self . momentum = kwargs . pop ( 'momentum' , 0 ) self . nesterov = kwargs . pop ( 'nesterov' , False ) self . patience = kwargs . get ( 'patience' , 5 ) self . validate_every = kwargs . pop ( 'validate_every' , 10 ) self . min_improvement = kwargs . pop ( 'min_improvement' , 0 ) self . max_gradient_norm = kwargs . pop ( 'max_gradient_norm' , 0 ) self . max_gradient_elem = kwargs . pop ( 'max_gradient_elem' , 0 ) util . log_param ( 'patience' , self . patience ) util . log_param ( 'validate_every' , self . validate_every ) util . log_param ( 'min_improvement' , self . min_improvement ) util . log_param ( 'max_gradient_norm' , self . max_gradient_norm ) util . log_param ( 'max_gradient_elem' , self . max_gradient_elem ) util . log_param ( 'learning_rate' , self . learning_rate ) util . log_param ( 'momentum' , self . momentum ) util . log_param ( 'nesterov' , self . nesterov )
Set up properties for optimization.

        This method can be overridden by base classes to provide parameters that
        are specific to a particular optimization technique (e.g., setting up a
        learning rate value).