diff --git a/onn.py b/onn.py
index fa718c1..eeb209c 100755
--- a/onn.py
+++ b/onn.py
@@ -886,9 +886,13 @@ def optim_from_config(config):
         optim = RMSprop(mu=mu)
     elif config.optim == 'sgd':
         d1 = config.optim_decay1 if 'optim_decay1' in config else 0
-        if d1 > 0:
-            b1 = np.exp(-1/d1)
-            optim = Momentum(mu=b1, nesterov=config.nesterov)
+        clip = config.gradient_clip if 'gradient_clip' in config else 0.0
+        if d1 > 0 or clip > 0:
+            b1 = np.exp(-1/d1) if d1 > 0 else 0
+            if clip > 0:
+                optim = MomentumClip(mu=b1, nesterov=config.nesterov, clip=clip)
+            else:
+                optim = Momentum(mu=b1, nesterov=config.nesterov)
         else:
             optim = Optimizer()
     else:
@@ -904,6 +908,20 @@ def learner_from_config(config, optim, rscb):
                        callback=rscb, expando=expando)
         # final learning rate isn't of interest here; it's gonna be close to 0.
         log('total epochs', learner.epochs)
+    elif config.learner in ('sin', 'sine'):
+        lower_rate = config.learn * 1e-5 # TODO: allow access to this.
+        epochs = config.epochs * (config.restarts + 1)
+        frequency = config.epochs
+        learner = SineCLR(optim, epochs=epochs, frequency=frequency,
+                          upper_rate=config.learn, lower_rate=lower_rate,
+                          callback=rscb)
+    elif config.learner == 'wave':
+        lower_rate = config.learn * 1e-5 # TODO: allow access to this.
+        epochs = config.epochs * (config.restarts + 1)
+        frequency = config.epochs
+        learner = WaveCLR(optim, epochs=epochs, frequency=frequency,
+                          upper_rate=config.learn, lower_rate=lower_rate,
+                          callback=rscb)
     elif config.learner == 'anneal':
         learner = AnnealingLearner(optim, epochs=config.epochs, rate=config.learn,
                                    halve_every=config.learn_halve_every)