base_lr=0.001, max_lr=0.006,
                                step_size=2000., mode=&#39;triangular&#39;)
            model.fit(X_train, Y_train, callbacks=[clr])
        ```

    Class also supports custom scaling functions:
        ```python
            clr_fn = lambda x: 0.5*(1+np.sin(x*np.pi/2.))
            clr = CyclicLR(base_lr=0.001, max_lr=0.006,
                                step_size=2000., scale_fn=clr_fn,
                                scale_mode=&#39;cycle&#39;)
            model.fit(X_train