(self, learning_rate, epoch)
return float(learning_rate * self.change ** self.steps)
Returns the current learning rate given the epoch and initial learning rate.

        Arguments:
            learning_rate (float): Initial learning rate
            epoch (int): Current epoch, used to calculate the adjusted learning rate

        Returns:
            (float): The adjusted learning rate
Returns the current learning rate given the epoch and initial learning rate.
Returns the current learning rate given the epoch and initial learning rate .
def get_learning_rate(self, learning_rate, epoch):
        """
        Returns the current learning rate given the epoch and initial learning rate.

        Arguments:
            learning_rate (float): Initial learning rate
            epoch (int): Current epoch, used to calculate the adjusted learning rate

        Returns:
            (float): The adjusted learning rate
        """

        # will be moved to StepSchedule in the future
        if isinstance(self.step_config, list) and isinstance(self.change, list):
            if epoch in self.step_config:
                # steps will store the current lr
                self.steps = self.change[self.step_config.index(epoch)]
            if self.steps == 0:
                return learning_rate
            else:
                return self.steps

        # will be moved to PowerSchedule in the future
        elif isinstance(self.step_config, int):
            self.steps = np.floor(epoch / self.step_config)

        elif isinstance(self.step_config, list):
            self.steps = np.sum(epoch >= np.array(self.step_config))

        return float(learning_rate * self.change ** self.steps)
def get_learning_rate ( self , learning_rate , epoch ) : # will be moved to StepSchedule in the future if isinstance ( self . step_config , list ) and isinstance ( self . change , list ) : if epoch in self . step_config : # steps will store the current lr self . steps = self . change [ self . step_config . index ( epoch ) ] if self . steps == 0 : return learning_rate else : return self . steps # will be moved to PowerSchedule in the future elif isinstance ( self . step_config , int ) : self . steps = np . floor ( epoch / self . step_config ) elif isinstance ( self . step_config , list ) : self . steps = np . sum ( epoch >= np . array ( self . step_config ) ) return float ( learning_rate * self . change ** self . steps )