Refactor training loop from script to class

src/api.py

@@ -2,12 +2,14 @@
 from PIL import Image
 import torch
 from torchvision import transforms
-from main import Net  # Importing Net class from main.py
+from main import MNISTTrainer  # Importing MNISTTrainer class from main.py
 
-# Load the model
-model = Net()
-model.load_state_dict(torch.load("mnist_model.pth"))
-model.eval()
+# Create an instance of MNISTTrainer and train the model
+trainer = MNISTTrainer()
+trainloader = trainer.load_data()
+model = trainer.define_model()
+trainer.train_model(model, trainloader)
+trainer.save_model(model)
 
 # Transform used for preprocessing the image
 transform = transforms.Compose([

src/main.py

@@ -1,48 +1,45 @@
-from PIL import Image
-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
+class MNISTTrainer:
+    def __init__(self):
+        self.transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.5,), (0.5,))
+        ])
+        self.optimizer = None
+        self.criterion = nn.NLLLoss()
+        self.epochs = 3
 
-# Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
-    transforms.ToTensor(),
-    transforms.Normalize((0.5,), (0.5,))
-])
+    def load_data(self):
+        trainset = datasets.MNIST('.', download=True, train=True, transform=self.transform)
+        trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+        return trainloader
 
-trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
-trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+    def define_model(self):
+        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)
 
-# 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)
+            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()
+        model = Net()
+        self.optimizer = optim.SGD(model.parameters(), lr=0.01)
+        return model
 
-# 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()
+    def train_model(self, model, trainloader):
+        for epoch in range(self.epochs):
+            for images, labels in trainloader:
+                self.optimizer.zero_grad()
+                output = model(images)
+                loss = self.criterion(output, labels)
+                loss.backward()
+                self.optimizer.step()
 
-torch.save(model.state_dict(), "mnist_model.pth")
+    def save_model(self, model):
+        torch.save(model.state_dict(), "mnist_model.pth")