Cyclical Learning Rates - Keras

clr.py

def clr(self):
        cycle = np.floor(1 + self.clr_iterations / (2 * self.step_size))
        x = np.abs(self.clr_iterations / self.step_size - 2 * cycle + 1)
        if self.scale_mode == 'cycle':
            return self.base_lr + (self.max_lr - self.base_lr) * \
                np.maximum(0, (1 - x)) * self.scale_fn(cycle)
        else:
            return self.base_lr + (self.max_lr - self.base_lr) * \
                np.maximum(0, (1 - x)) * self.scale_fn(self.clr_iterations)