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Check out the documentation for more information.
🧠 MNIST Handwritten Digit Classifier
A simple yet effective neural network built using TensorFlow and Keras to classify handwritten digits (0–9) from the MNIST dataset.
This project serves as a beginner-friendly example of training, evaluating, and publishing a machine learning model on the Hugging Face Model Hub.
📌 Model Information
| Property | Value |
|---|---|
| Framework | TensorFlow / Keras |
| Model Type | Feedforward Neural Network (Fully Connected) |
| Dataset | MNIST |
| Input Shape | (784,) |
| Output Classes | 10 (Digits 0–9) |
| Training Accuracy | 98.24% |
| Test Accuracy | 96.86% |
🏗️ Model Architecture
Input Layer (784)
│
▼
Dense (128 neurons, ReLU)
│
▼
Dense (64 neurons, ReLU)
│
▼
Dense (10 neurons, Softmax)
📚 Dataset
The model was trained on the MNIST Handwritten Digits Dataset, containing:
- 60,000 training images
- 10,000 testing images
- Image Size: 28 × 28 pixels
- Classes: 0–9
Each image is flattened into a 784-dimensional vector before being passed into the neural network.
⚙️ Preprocessing
The following preprocessing steps were applied:
- Pixel values normalized to [0, 1]
- Images flattened from 28×28 → 784
- Labels encoded as integer class IDs
📈 Performance
| Metric | Score |
|---|---|
| Training Accuracy | 98.24% |
| Test Accuracy | 96.86% |
The small gap between training and test accuracy indicates that the model generalizes reasonably well on unseen data.
🚀 Loading the Model
import tensorflow as tf
model = tf.keras.models.load_model("mnist_classifier.keras")
🔍 Making Predictions
import numpy as np
import tensorflow as tf
model = tf.keras.models.load_model("mnist_classifier.keras")
# image should be normalized and reshaped
image = image.reshape(1, 784)
prediction = model.predict(image)
predicted_digit = np.argmax(prediction)
print(predicted_digit)
📦 Requirements
- Python 3.10+
- TensorFlow
- NumPy
Install dependencies:
pip install tensorflow numpy
📁 Repository Contents
.
├── mnist_classifier.keras
├── README.md
🎯 Intended Use
This model is intended for:
- Learning TensorFlow and Keras
- Educational purposes
- MNIST handwritten digit classification
- Demonstrating model deployment on Hugging Face
⚠️ Limitations
- Designed only for MNIST-style handwritten digits.
- Expects grayscale images of handwritten digits.
- Images should be preprocessed in the same way as the training data.
📄 License
This project is released under the MIT License, allowing free use, modification, and distribution.
👨💻 Author
Madhav Maheshwari
If you find this project useful, consider giving it a ⭐ on Hugging Face.
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