Python Model Training: Digit Classifier

This challenge focuses on building a fundamental machine learning pipeline in Python. You will create a system to train a model capable of classifying handwritten digits from the MNIST dataset, a common benchmark for image recognition tasks. This skill is crucial for anyone looking to delve into areas like computer vision, pattern recognition, and predictive modeling.

Problem Description

Your task is to implement a Python script that trains a classification model on the MNIST dataset. The model should be able to predict which digit (0-9) an input image represents. You will be responsible for:

1. Data Loading and Preprocessing: Load the MNIST dataset and prepare it for training. This involves handling image data (typically represented as arrays) and their corresponding labels.
2. Model Definition: Define a suitable machine learning model architecture. For this challenge, a simple feed-forward neural network or a convolutional neural network (CNN) would be appropriate.
3. Model Training: Implement the training loop, which involves feeding the data to the model, calculating loss, and updating model weights using an optimizer.
4. Model Evaluation: Evaluate the trained model's performance on a separate test set to assess its accuracy.

Key Requirements:

• Use a popular machine learning library in Python (e.g., TensorFlow/Keras or PyTorch).
• The trained model should achieve a minimum accuracy of 90% on the MNIST test set.
• Your code should be well-commented and organized.

Expected Behavior: The script should execute without errors, load the MNIST dataset, train the model for a specified number of epochs, and print the final accuracy achieved on the test set.

Edge Cases:

• Consider how to handle potential issues with data loading or compatibility across different library versions.
• Ensure the model can generalize well to unseen data (i.e., the test set).

Examples

Example 1: (Conceptual Input/Output)

Input: (Implicitly, the MNIST dataset is loaded and preprocessed internally.)

Output:

Training complete.
Test accuracy: 92.5%

Explanation: The script loads MNIST, trains a model, and then reports the final accuracy on the held-out test data. The accuracy is a measure of how often the model correctly predicts the digit shown in an image.

Example 2: (Illustrative Model Architecture - not for implementation)

If a simple feed-forward neural network is chosen, it might look conceptually like this:

• Input Layer: Flattened image pixels (e.g., 28x28 = 784 features).
• Hidden Layer(s): One or more layers with activation functions (e.g., ReLU).
• Output Layer: 10 neurons (one for each digit) with a softmax activation to produce probabilities.

Explanation: This illustrates a common approach for digit classification. The input is transformed through layers of computation to output a prediction for each digit class.

Constraints

• The MNIST dataset should be used. You can use built-in functions from your chosen library to load it.
• The training process should complete within a reasonable time (e.g., under 10 minutes on a standard laptop).
• The solution must be implemented in Python.
• The final reported test accuracy must be at least 90%.

Notes

• Library Choice: You are free to choose between TensorFlow/Keras or PyTorch. Both are excellent choices for this task.
• Model Complexity: While CNNs often achieve higher accuracies on MNIST, a well-tuned feed-forward neural network can also meet the 90% requirement. Start simple and iterate.
• Hyperparameter Tuning: For optimal results, you may need to experiment with hyperparameters like learning rate, batch size, number of epochs, and the architecture of your model.
• Reproducibility: Consider setting random seeds for your chosen library to ensure your results are reproducible.
• Data Normalization: Remember to normalize your image pixel values (e.g., to the range [0, 1] or [-1, 1]) before feeding them into the model. This is a common preprocessing step that can significantly improve training.