From a499f7430af66b77d81a0009fe8eeb1913862f63 Mon Sep 17 00:00:00 2001
From: "sweep-nightly[bot]"
 <131841235+sweep-nightly[bot]@users.noreply.github.com>
Date: Tue, 24 Oct 2023 20:47:26 +0000
Subject: [PATCH] Sandbox run src/main.py

---
 src/main.py | 33 ++++++++++++++++++++-------------
 1 file changed, 20 insertions(+), 13 deletions(-)

diff --git a/src/main.py b/src/main.py
index 3957829..d0fd614 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,21 +1,21 @@
-from PIL import Image
+import logging
+
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
-import numpy as np
-import logging
+from torchvision import datasets, transforms
 
 # Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+    [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
 
-trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
+trainset = datasets.MNIST(".", download=True, train=True, transform=transform)
 trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
 
+
 # Step 2: Define the PyTorch Model
 class Net(nn.Module):
     def __init__(self):
@@ -23,7 +23,7 @@ def __init__(self):
         self.fc1 = nn.Linear(28 * 28, 128)
         self.fc2 = nn.Linear(128, 64)
         self.fc3 = nn.Linear(64, 10)
-    
+
     def forward(self, x):
         x = x.view(-1, 28 * 28)
         x = nn.functional.relu(self.fc1(x))
@@ -31,12 +31,17 @@ def forward(self, x):
         x = self.fc3(x)
         return nn.functional.log_softmax(x, dim=1)
 
+
 # Step 3: Train the Model
 model = Net()
 optimizer = optim.SGD(model.parameters(), lr=0.01)
 criterion = nn.NLLLoss()
 
-logging.basicConfig(filename='training_errors.log', level=logging.ERROR, format='%(asctime)s %(levelname)s %(message)s')
+logging.basicConfig(
+    filename="training_errors.log",
+    level=logging.ERROR,
+    format="%(asctime)s %(levelname)s %(message)s",
+)
 
 # Training loop
 epochs = 3
@@ -49,6 +54,8 @@ def forward(self, x):
             loss.backward()
             optimizer.step()
         except Exception as e:
-            logging.error('Error at epoch %s, batch %s: %s', epoch, i, str(e), exc_info=True)
+            logging.error(
+                "Error at epoch %s, batch %s: %s", epoch, i, str(e), exc_info=True
+            )
 
-torch.save(model.state_dict(), "mnist_model.pth")
\ No newline at end of file
+torch.save(model.state_dict(), "mnist_model.pth")