Refactor training loop from script to class

src/main.py

@@ -1,48 +1,46 @@
-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 torch.utils.data import DataLoader
-import numpy as np
+from torchvision import datasets, transforms
+
+from trainer import Trainer
 
 # 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()
+
+# Create Trainer instance
+trainer = Trainer(model, trainloader, optimizer)
 
 # 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")
+trainer.train(epochs)
+
+torch.save(model.state_dict(), "mnist_model.pth")

src/trainer.py

@@ -0,0 +1,21 @@
+import torch
+import torch.nn as nn
+
+
+class Trainer:
+ def __init__(self, model, dataloader, optimizer):
+ self.model = model
+ self.dataloader = dataloader
+ self.optimizer = optimizer
+ self.criterion = nn.NLLLoss()
+
+ def train(self, epochs):
+ for _epoch in range(epochs):
+ for images, labels in self.dataloader:
+ self.optimizer.zero_grad()
+ output = self.model(images)
+ loss = self.criterion(output, labels)
+ loss.backward()
+ self.optimizer.step()
+
+ torch.save(self.model.state_dict(), "mnist_model.pth")