Sandbox run src/main.py

src/main.py

@@ -2,36 +2,21 @@
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from cnn import CNN, train
 from PIL import Image
 from torch.utils.data import DataLoader
 from torchvision import datasets, transforms
 
+from cnn import CNN, train
+
 # 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)
 
 
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 model = Net()
 model = CNN()
 optimizer = optim.SGD(model.parameters(), lr=0.01)
@@ -41,4 +26,4 @@
 train(model, trainloader, optimizer)
 
 
-torch.save(model.state_dict(), "mnist_model.pth")
+torch.save(model.state_dict(), "mnist_model.pth")