Implemented learning rate decay.

climax/main/config.py

@@ -150,8 +150,7 @@ TRAIN_CONFIG = {
     'patience': 5,
     'multi_gpu': True,
     'classification': False,
-    'clip_gradients': True,
-    # 'lr_scheduler': torch.optim.lr_scheduler.
+    'clip_gradients': True
     }
 
 # whether to overwrite existing models

climax/main/downscale_train.py

@@ -162,7 +162,8 @@ if __name__ == '__main__':
 
     # initialize network trainer
     trainer = NetworkTrainer(net, optimizer, net.state_file, train_dl,
-                             valid_dl, loss_function=LOSS, **TRAIN_CONFIG)
+                             valid_dl, loss_function=LOSS,
+                             lr_scheduler=LR_SCHEDULER, **TRAIN_CONFIG)
 
     # train model
     state = trainer.train()