Introduction to Handwritten Digit Classification

Classifying handwritten numerals represents a foundational challenge in computer vision. This guide demonstrates implementing a Convolutional Neural Network (CNN) to solve this task using the PyTorch framework. By leveraging the MNIST dataset, we construct a specific architecture to process image data and evaluate performance through accuracy metrics and confusion matrix analysis.

Data Preparation and Preprocessing

The MNIST benchmark remains a standard validation tool for image classification algorithms. It consists of grayscale images sized 28x28 pixels, divided into 60,000 training examples and 10,000 testing examples across ten distinct classes (0-9). Its simplicity allows for rapid iteration on model architectures while providing meaningful performance baselines.

To ensure compatibility with neural network inputs, raw images undergo transformation. Pixel values are converted into tensors and normalized to a range between 0 and 1. This standardization stabilizes gradient descent during the optimization process. The following libraries facilitate data handling and visualization:

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms as T
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

Designing the Network Architecture

CNNs excel at capturing spatial hierarchies in images through convolutional operations. The proposed model utilizes alternating convolutional and pooling layers to extract features, followed by fully connected layers for classification.

The architecture includes:

• Feature Extraction: Two convolutional blocks utilizing ReLU activation and Max Pooling to reduce dimensionality while retaining key patterns.
• Classification Head: Fully connected layers that flatten the feature maps and output logits for the ten digit classes.

Below is the implementation using PyTorch's module system. Note the use of sequential blocks to organize layer operations:

class DigitNet(nn.Module):
    def __init__(self):
        super(DigitNet, self).__init__()
        # First feature block
        self.feature_stage_1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
        )
        # Second feature block
        self.feature_stage_2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2)
        )
        # Fully connected layers
        self.classifier_1 = nn.Linear(32 * 7 * 7, 128)
        self.output_layer = nn.Linear(128, 10)
        self.activation = nn.ReLU()

    def forward(self, input_tensor):
        features = self.feature_stage_1(input_tensor)
        features = self.feature_stage_2(features)
        # Flatten the tensor
        features = features.view(features.size(0), -1)
        hidden = self.classifier_1(features)
        hidden = self.activation(hidden)
        logits = self.output_layer(hidden)
        return logits

Training Procedure and Performance Evaluation

Once the architecture is defined, the model undergoes optimization using the training split. The process involves iterating through batches, computing gradients, and updating weights. Cross-Entropy Loss is selected as the objective function since it effectively penalizes incorrect class probabilities in multi-class scenarios.

Data loaders are initialized to handle batching and shuffling. An optimizer, such as Adam, manages the learning rate adjustments. After training epochs are completed, the model is switched to evaluation mode to generate predictions on the test set.

To visualize specific class performance, a confusion matrix is generated. Thiss tool highlights which digits are frequently misclassified, providing deeper insight than overall accuracy alone. The evaluation script computes predictions and renders the matrix:

def evaluate_model(model, loader):
    model.eval()
    all_preds = []
    all_labels = []
    
    with torch.no_grad():
        for data, target in loader:
            output = model(data)
            pred = output.argmax(dim=1)
            all_preds.extend(pred.cpu().numpy())
            all_labels.extend(target.numpy())
            
    cm = confusion_matrix(all_labels, all_preds)
    disp = ConfusionMatrixDisplay(confusion_matrix=cm)
    disp.plot(cmap='Blues')
    plt.show()