Always use the same dropout scheduler function

Closes #159 (closed)

dan/manager/training.py

@@ -32,7+32,7 @@
         self.type = None
         self.is_master = False
         self.params = params
         self.dropout_scheduler = None
         self.models = {}
         self.dataset = None
         self.dataset_name = list(self.params["dataset_params"]["datasets"].values())[0]
@@ -196,7+196,7 @@
                 )
 
         # Handle curriculum dropout
-        if "dropout_scheduler" in self.params["model_params"]:
-            func = self.params["model_params"]["dropout_scheduler"]["function"]
-            T = self.params["model_params"]["dropout_scheduler"]["T"]
-            self.dropout_scheduler = DropoutScheduler(self.models, func, T)
+        self.dropout_scheduler = DropoutScheduler(self.models)
 
         self.scaler = GradScaler(enabled=self.params["training_params"]["use_amp"])
 
@@ -658,7+655,7 @@
                     # Update dropout scheduler if used
                     if self.dropout_scheduler:
                         self.dropout_scheduler.step(len(batch_metrics["names"]))
                         self.dropout_scheduler.update_dropout_rate()
 
                     # Add batch metrics values to epoch metrics values
                     self.metric_manager["train"].update_metrics(batch_metrics)