feat: Updated src/main.py

src/main.py

@@ -1,48 +1,65 @@
-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
-
-# Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize((0.5,), (0.5,))
-])
-
-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):
+class MNISTTrainer:
  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()
-
-# 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")
+ self.transform = transforms.Compose([
+ transforms.ToTensor(),
+ transforms.Normalize((0.5,), (0.5,))
+ ])
+ self.optimizer = None
+ self.criterion = nn.NLLLoss()
+ self.epochs = 3
+
+ def load_data(self):
+ """Load and preprocess MNIST data."""
+ trainset = datasets.MNIST('.', download=True, train=True, transform=self.transform)
+ trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+ return trainloader
+
+ class Net(nn.Module):
+ """Define the PyTorch Model."""
+ 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 define_model(self):
+ """Define the model."""
+ model = self.Net()
+ self.optimizer = optim.SGD(model.parameters(), lr=0.01)
+ return model
+
+ def train_model(self, model, trainloader):
+ """Train the model."""
+ 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()
+
+ def save_model(self, model):
+ """Save the trained model."""
+ torch.save(model.state_dict(), "mnist_model.pth")
+
+# Create an instance of MNISTTrainer
+trainer = MNISTTrainer()
+
+# Load the data
+trainloader = trainer.load_data()
+
+# Define the model
+model = trainer.define_model()
+
+# Train the model
+trainer.train_model(model, trainloader)
+
+# Save the model
+trainer.save_model(model)