def update()

in train/lr_scheduler.py [0:0]

• 20 lines of code
• 5 McCabe index (conditional complexity)

    def update(self):
        self.step_counter += 1

        if self.cursor >= len(self.steps):
            return self.lr
        while self.steps[self.cursor] < self.step_counter:
            self.lr *= self.factor
            self.cursor += 1
            # message
            if self.cursor >= len(self.steps):
                logging.info("Iter: %d, change learning rate to %0.5e for step [%d:Inf)" \
                                % (self.step_counter-1, self.lr, self.step_counter-1))
                break # return self.lr
            else:
                logging.info("Iter: %d, change learning rate to %0.5e for step [%d:%d)" \
                                % (self.step_counter-1, self.lr, self.step_counter-1, \
                                   self.steps[self.cursor]))
        if self.step_counter < 1500:
            self.lr_normalized = self.lr * (1. + 9.*self.step_counter/1500.) / 10.
        else:
            self.lr_normalized = self.lr
        return self.lr_normalized