cnn_pipeline.py

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# Define a thomas CNN for MNIST classification
class ThomasCNN(nn.Module):
    def __init__(self):
        super(ThomasCNN, self).__init__()
        self.conv_layers = nn.Sequential(
            nn.Conv2d(1, 32, kernel_size=3, padding=1),  # input channel = 1 for MNIST
            nn.ReLU(),
            nn.MaxPool2d(2),  # Downsampling by 2
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.fc_layers = nn.Sequential(
            nn.Linear(64 * 7 * 7, 128),
            nn.ReLU(),
            nn.Linear(128, 10)  # 10 classes for MNIST digits
        )

    def forward(self, x):
        x = self.conv_layers(x)
        x = x.view(x.size(0), -1)
        x = self.fc_layers(x)
        return x

# Placeholder for Perforated Backpropagation™
def perforated_backpropagation(loss, model, optimizer):
    """
    Placeholder function where modifications to the standard backpropagation
    procedure (i.e. Perforated Backpropagation™) should be implemented.
    
    For now, it performs the standard backpropagation.
    """
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

def train(model, device, train_loader, optimizer, epoch):
    model.train()
    criterion = nn.CrossEntropyLoss()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        output = model(data)
        loss = criterion(output, target)
        
        # Use the perforated backpropagation routine instead of the standard backpropagation
        perforated_backpropagation(loss, model, optimizer)
        
        if batch_idx % 100 == 0:
            print(f"Epoch {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)}] - Loss: {loss.item():.6f}")

def test(model, device, test_loader):
    model.eval()
    criterion = nn.CrossEntropyLoss(reduction='sum')
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()
    
    test_loss /= len(test_loader.dataset)
    accuracy = 100. * correct / len(test_loader.dataset)
    print(f"\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({accuracy:.2f}%)\n")

def main():
    # Training settings
    batch_size = 64
    epochs = 5  # Adjust epochs as required (ensure training is under 48 hours)
    learning_rate = 0.01
    use_cuda = torch.cuda.is_available()
    device = torch.device("cuda" if use_cuda else "cpu")
    
    # Data loaders for MNIST
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])
    train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
    test_dataset = datasets.MNIST('./data', train=False, transform=transform)
    
    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
    
    # Initialize model, optimizer
    model = ThomasCNN().to(device)
    optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)
    
    # Training loop
    for epoch in range(1, epochs + 1):
        train(model, device, train_loader, optimizer, epoch)
        test(model, device, test_loader)

if __name__ == '__main__':
    main()