Subsampler and qr

modules/optimizers.py

@@ -10,18 +10,21 @@ from AccelerationModule import AccelerationModule
 
 # Abstract class that provides basic guidelines to implement an acceleration
 class Optimizer(object, metaclass=ABCMeta):
-    def __init__(self, data_loader: torch.utils.data.dataloader.DataLoader, learning_rate: float,
-                 weight_decay: float = 0.0, verbose: bool = False):
+    def __init__(self, training_dataloader: torch.utils.data.dataloader.DataLoader, validation_dataloader: torch.utils.data.dataloader.DataLoader,
+        learning_rate: float, weight_decay: float = 0.0, verbose: bool = False):
         """
-
-        :type data_loader: torch.utils.data.dataloader.DataLoader
+        
+        :type training_dataloader: torch.utils.data.dataloader.DataLoader
+        :type validation_dataloader: torch.utils.data.dataloader.DataLoader        
         :type learning_rate: float
         :type weight_decay: float
         """
         self.iteration_counter = 0
 
-        assert isinstance(data_loader, torch.utils.data.dataloader.DataLoader)
-        self.data_loader = data_loader
+        assert isinstance(training_dataloader, torch.utils.data.dataloader.DataLoader)
+        assert isinstance(validation_dataloader, torch.utils.data.dataloader.DataLoader)
+        self.training_dataloader = training_dataloader
+        self.validation_dataloader = validation_dataloader
 
         assert isinstance(learning_rate, float)
         self.lr = learning_rate
@@ -33,8 +36,10 @@ class Optimizer(object, metaclass=ABCMeta):
         self.model = None
 
         self.training_loss_history = []
+        self.validation_loss_history = []
         self.criterion_specified = False
         self.criterion = None
+        self.optimizer_str = None
         self.optimizer_specified = False
         self.optimizer = None
         self.loss_name = None
@@ -65,8 +70,8 @@ class Optimizer(object, metaclass=ABCMeta):
         if criterion_string.lower() == 'mse':
             self.criterion = torch.nn.MSELoss()
             self.criterion_specified = True
-        elif criterion_string.lower() == 'ce':
-            self.criterion = torch.nn.CrossEntropyLoss()
+        elif criterion_string.lower() == 'nll':
+            self.criterion = torch.nn.functional.nll_loss
             self.criterion_specified = True
         else:
             raise ValueError("Loss function is not recognized: currently only MSE and CE are allowed")