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MNIST Interactive Model Analyzer

An interactive tool for experimenting with MNIST handwriting recognition models using a custom-built 28x28 drawing grid. This application allows users to draw digits and get real-time predictions from a trained MNIST model.

Features

  • Interactive 28x28 drawing grid that matches MNIST input dimensions
  • Real-time prediction updates as you draw
  • Blur effect to simulate natural handwriting
  • Clear canvas functionality
  • Grid overlay for precise pixel manipulation
  • Support for custom MNIST-compatible models
  • Allows for custom conversion and output functions to allow for increased compatability with different model types
  • Display neuron activation values in Tkinter window given the drawing grid values

Installation

  1. Clone the repository:
git clone https://github.com/LucaLow/MNIST-Interactive-Model-Analyzer.git
cd MNIST-Interactive-Model-Analyzer

Or install through pip:

pip install -i https://test.pypi.org/simple/ mnist-interactive
  1. Install the required dependencies:
pip install numpy
pip install tensorflow

Requirements

  • Python >= 3.8
  • TensorFlow >= 2.0
  • NumPy
  • Tkinter (usually comes with Python)

Usage

  1. Basic usage with a pre-trained model:
import mnist_interactive.numberCreatorWindow as mi
import tensorflow as tf
import tkinter as tk
from tkinter import ttk


# Load your pre-trained MNIST model
model = tf.keras.models.load_model('sampleModel.keras')

# create the tkinter window
root = tk.Tk()


# Initialize the interactive grid
grid = mi.NumberCreatorWindow(root, model=model, blur=0.3)

root.mainloop()
  1. Customize the blur effect:
app = NumberCreatorWindow(root, model=model, blur=0.15)  # Adjust blur intensity

Controls

  • Left Click: Draw
  • Right Click: Erase
  • Clear Button: Reset the canvas
  • Real-time predictions displayed above the canvas

Project Structure

  • numberCreatorWindow.py: Main application window and drawing logic
  • utils.py: Utility functions for predictions and grid operations
  • example.py: Example implementation
  • requirements.txt: Required Python packages
  • setup.py: Package installation configuration
src/mnist_interactive/
├── backends/
│   ├── __init__.py
│   ├── tensorflow_backend.py
│   ├── pytorch_backend.py
│   └── base_backend.py
├── ui/
│   ├── __init__.py
│   ├── number_creator_window.py
│   ├── canvas.py
│   └── visualization.py
├── utils/
│   ├── __init__.py
│   ├── grid.py
│   └── blur.py
└── examples/
    ├── tensorflow_example.py
    └── pytorch_example.py

API Reference

NumberCreatorWindow

NumberCreatorWindow(root, model, blur=0.1, conversion_function, output_function)

Parameters:

  • root: Tkinter root window
  • model: TensorFlow model for digit prediction
  • blur: Blur intensity for drawing (default: 0.1)
  • conversion_function: Function receiving a 28×28 NumPy array and returning data in the shape/format the model accepts
  • output function: Function receiving the model’s raw output (e.g. probability array) and returning a tuple of (prediction int, confidence float)

Methods

  • clear_canvas(): Clear the drawing grid
  • predict(): Get prediction for current drawing

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

Luca Lowndes (Luca@Lowndes.net)

Acknowledgments

  • Built using TensorFlow and MNIST dataset
  • Inspired by the need for interactive MNIST model testing tools based upon the models you create