The goal is to develop a deep learning model capable of accurately classifying handwritten digits from the MNIST dataset, which contains grayscale images of handwritten digits ranging from 0 to 9. The challenge lies in training a model that can effectively capture the subtle variations in writing styles across different individuals while maintaining robustness to noise and distortions in the images.
The MNIST dataset is a widely used benchmark in the field of machine learning, particularly for image classification tasks. It consists of 28x28 grayscale images of handwritten digits (0-9) collected from various sources.
- The dataset is divided into training and test sets, with 60,000 images for training and 10,000 images for testing.
For the handwritten digit recognition task, I've implemented several traditional and deep learning approaches and compared their performances. Here's an overview of my solution strategy and the comparisons made:
- Convolutional Neural Network (CNN): Utilized CNN architecture with appropriate convolutional layers, activation functions (e.g., ReLU), pooling layers (e.g., max pooling), and fully connected layers. Used suitable loss functions like cross-entropy and optimized the model using algorithms like stochastic gradient descent (SGD) or Adam.
- Multilayer Perceptron (MLP): Implemented an MLP architecture with multiple layers, activation functions (e.g., ReLU, sigmoid), and appropriate loss functions and optimizers for training, such as categorical cross-entropy and SGD.
- Evaluated the models based on their training and test losses (e g., mean squared error, categorical cross-entropy) to assess their convergence and generalization capabilities.
- Assessed model accuracies on both training and test sets to measure their performance in correctly classifying handwritten digits.
Accuracy Comparison:
- LeNet-5: Implemented the LeNet-5 architecture, which comprises convolutional layers followed by max-pooling and fully connected layers, designed specifically for handwritten digit recognition tasks.
- ResNet (Residual Network): Utilized ResNet architecture with residual connections, enabling training of very deep networks while mitigating the vanishing gradient problem.
- DenseNet (Densely Connected Convolutional Network): Implemented DenseNet architecture with densely connected layers, facilitating feature reuse and gradient flow throughout the network.
- Evaluated and compared the performances of LeNet-5, ResNet, and DenseNet based on their training and test losses to analyze their convergence rates and generalization capabilities.
- Assessed the accuracies of these models on both training and test datasets to determine their effectiveness in classifying handwritten digits accurately.
- Connectivity Visualization: Visualizing the connectivity of pixels forming a digit using the GNN model can provide qualitative insights into how the model processes and understands image data.
