feat: Updated src/main.py

Author: sweep-nightly[bot]

src/main.py

@@ -1,10 +1,10 @@
-from PIL import Image
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torchvision import datasets, transforms
+from PIL import Image
 from torch.utils.data import DataLoader
-import numpy as np
+from torchvision import datasets, transforms
 
 # Step 1: Load MNIST Data and Preprocess
 transform = transforms.Compose([
@@ -29,20 +29,29 @@ def forward(self, x):
         x = nn.functional.relu(self.fc2(x))
         x = self.fc3(x)
         return nn.functional.log_softmax(x, dim=1)
+class Trainer:
+    def __init__(self, learning_rate, model_path):
+        self.model = Net()
+        self.optimizer = optim.SGD(self.model.parameters(), lr=learning_rate)
+        self.criterion = nn.NLLLoss()
+        self.model_path = model_path
 
-# Step 3: Train the Model
-model = Net()
-optimizer = optim.SGD(model.parameters(), lr=0.01)
-criterion = nn.NLLLoss()
+    def train(self, epochs):
+        for epoch in range(epochs):
+            for images, labels in trainloader:
+                self.optimizer.zero_grad()
+                output = self.model(images)
+                loss = self.criterion(output, labels)
+                loss.backward()
+                self.optimizer.step()
+                
+    def save_model(self):
+        torch.save(self.model.state_dict(), self.model_path)
 
-# Training loop
-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()
 
-torch.save(model.state_dict(), "mnist_model.pth")
+# Step 3: Train the Model
+
+# Now let's create a Trainer instance and train and save the model
+trainer = Trainer(learning_rate=0.01, model_path="mnist_model.pth")
+trainer.train(epochs=3)
+trainer.save_model()