fix device

@@ -51,7 +51,8 @@ class MLPSolver(object):
     def train_loop(self):
     def train_loop(self):
         self.model.train()
         self.model.train()
 
 
-        train_loss, correct = 0
+        train_loss = torch.zeros(1).to(self.device)
+        correct = torch.zeros(1).to(self.device)
         for batch, (X, y) in enumerate(self.train_loader):
         for batch, (X, y) in enumerate(self.train_loader):
             X, y = X.to(self.device), y.to(self.device)
             X, y = X.to(self.device), y.to(self.device)
 
 
@@ -73,13 +74,14 @@ class MLPSolver(object):
 
 
         correct /= self.train_dataset_size
         correct /= self.train_dataset_size
         train_loss /= self.train_loader_size
         train_loss /= self.train_loader_size
-        self.logger.info(f'train accuracy: {correct :.4f}, train mean loss: {train_loss :.4f}')
+        self.logger.info(f'train accuracy: {correct.item() :.4f}, train mean loss: {train_loss.item() :.4f}')
 
 
     @torch.no_grad()
     @torch.no_grad()
     def val_loop(self, t):
     def val_loop(self, t):
         self.model.eval()
         self.model.eval()
 
 
-        val_loss, correct = 0, 0
+        val_loss = torch.zeros(1).to(self.device)
+        correct = torch.zeros(1).to(self.device)
         for X, y in self.val_loader:
         for X, y in self.val_loader:
             X, y = X.to(self.device), y.to(self.device)
             X, y = X.to(self.device), y.to(self.device)
 
 
@@ -93,7 +95,7 @@ class MLPSolver(object):
         correct /= self.val_dataset_size
         correct /= self.val_dataset_size
         val_loss /= self.val_loader_size
         val_loss /= self.val_loader_size
             
             
-        self.logger.info(f"val accuracy: {correct :.4f}, val mean loss: {val_loss :.4f}")
+        self.logger.info(f"val accuracy: {correct.item() :.4f}, val mean loss: {val_loss.item() :.4f}")
 
 
     def save_checkpoint(self, epoch_id):
     def save_checkpoint(self, epoch_id):
         self.model.eval()
         self.model.eval()