Sandbox run src/main.py

src/main.py

@@ -1,42 +1,47 @@
-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):
  super().__init__()
  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))
  x = nn.functional.relu(self.fc2(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")
+torch.save(model.state_dict(), "mnist_model.pth")