diff --git a/src/main.py b/src/main.py
index 243a31e..69793f1 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,3 +1,4 @@
+import logging
 from PIL import Image
 import torch
 import torch.nn as nn
@@ -6,6 +7,10 @@
 from torch.utils.data import DataLoader
 import numpy as np
 
+# Set up logging
+logging.basicConfig(level=logging.ERROR)
+logger = logging.getLogger('training')
+
 # Step 1: Load MNIST Data and Preprocess
 transform = transforms.Compose([
     transforms.ToTensor(),
@@ -35,14 +40,17 @@ def forward(self, x):
 optimizer = optim.SGD(model.parameters(), lr=0.01)
 criterion = nn.NLLLoss()
 
-# Training loop
+# Training loop with error logging
 epochs = 3
 for epoch in range(epochs):
-    for images, labels in trainloader:
-        optimizer.zero_grad()
-        output = model(images)
-        loss = criterion(output, labels)
-        loss.backward()
-        optimizer.step()
+    try:
+        for images, labels in trainloader:
+            optimizer.zero_grad()
+            output = model(images)
+            loss = criterion(output, labels)
+            loss.backward()
+            optimizer.step()
+    except Exception as e:
+        logger.error("Exception occurred", exc_info=True)
 
 torch.save(model.state_dict(), "mnist_model.pth")
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