Add support for multiple optimizers

prototorch/models/abstract.py

@@ -1,11 +1,20 @@
 import pytorch_lightning as pl
 import torch
+from torch.optim.lr_scheduler import ExponentialLR
 
 
 class AbstractLightningModel(pl.LightningModule):
     def configure_optimizers(self):
         optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
-        return optimizer
+        scheduler = ExponentialLR(optimizer,
+                                  gamma=0.99,
+                                  last_epoch=-1,
+                                  verbose=False)
+        sch = {
+            "scheduler": scheduler,
+            "interval": "step",
+        }  # called after each training step
+        return [optimizer], [sch]
 
 
 class AbstractPrototypeModel(AbstractLightningModel):