Multi-Layer Perceptron, MNIST

In this notebook, I will train an Multi-Layer Perceptron (MLP, i.e., modern feedforward artificial neural network, consisting of fully connected neurons) to classify images from the MNIST database hand-written digit database.

Project Overview

The process will be broken down into the following steps:

1. Load Libraries

• Importing essential libraries including PyTorch and NumPy for data manipulation and machine learning tasks.

2. Load and Visualize the Data

• Utilizing PyTorch to download and preprocess the MNIST dataset.
• Dividing the dataset into training and testing sets, and allocating a portion for validation.
• Creating data loaders for efficient loading of batches during training, validation, and testing.

3. Visualize a Batch of Training Data

• Visualizing a batch of training images to gain insights into the nature of the data.
• Displaying 20 images at a time, each labeled with the correct digit.

4. View an Image in More Detail

• Selecting and visualizing a single image in grayscale.
• Overlaying pixel values on the image in a 2D grid for a detailed view of intensity values.

5. Define the Network Architecture

• Defining an MLP architecture named "Net" for digit classification.
• Consists of three fully connected layers with ReLU activations and dropout layers to prevent overfitting.

6. Specify Loss Function and Optimizer

• Loading necessary libraries and specifying categorical cross-entropy as the loss function.
• Choosing stochastic gradient descent (SGD) as the optimizer with a learning rate of 0.01.

7. Train the Network

• Training the MLP on the MNIST dataset for 50 epochs.
• Monitoring and printing training and validation losses after each epoch.
• Saving the model state if the validation loss decreases, ensuring the best-performing model is retained.

8. Load the Model with the Lowest Validation Loss

• Loading the model state from the file 'model.pt,' representing the model with the lowest validation loss.

9. Test the Trained Network

• Evaluating the performance of the trained MLP on the test set.
• Calculating and printing the average test loss, test accuracy for each digit class, and overall test accuracy.

10. Visualize Sample Test Results

• Visualizing a batch of test images along with their predicted and true labels.
• Displaying images in a grid with color-coded titles (green for correct predictions, red for incorrect), providing a quick overview of the model's performance.