, callbacks=[clr])
```
</code></pre>
<p>Class also supports custom scaling functions:
<code>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='cycle')
model.fit(X_train, Y_train, callbacks=[clr])</code></p>
<h1 id="arguments">Arguments</h1>
<pre><code>base_lr: initial learning rate which is the
    lower boundary in the cycle.
max_lr: upper boundary in the cycle. Functionally,
    it defines the cycle amplitude (max_