This project demonstrates the power of TensorFlow.js for implementing a Convolutional Neural Network (CNN) model to recognize and classify handwritten digits from 0 to 9. Leveraging the capabilities of modern web browsers and the TensorFlow.js library, this implementation showcases an interactive approach to machine learning, directly in the browser.
Handwritten digit recognition is a classic problem in the field of machine learning and computer vision, serving as a benchmark for new algorithms and techniques. This project specifically utilizes a CNN, a class of deep neural networks, most commonly applied to analyzing visual imagery. Our model is trained and evaluated using a subset of the MNIST dataset, which consists of thousands of labeled images of handwritten digits.
- Interactive Model Training: Users can initiate model training directly in their web browser, observing the process in real-time.
- Live Evaluation and Prediction: The trained model can be evaluated using a separate test dataset. Additionally, users can draw their own digits on a canvas element to test the model's predictions interactively.
- Visualization: Utilizing the tfjs-vis library, training progress and accuracy metrics are visualized, offering insights into the model's performance.
- Data Loading: The MNIST dataset is loaded and pre-processed to fit the model's input requirements.
- Model Architecture: A simple yet effective CNN architecture is defined, utilizing layers suited for image classification tasks.
- Training Process: The model is trained with a specified number of epochs, with performance monitored through real-time updates.
- Evaluation and Testing: Post-training, the model's accuracy is evaluated using a separate set of images that it hasn't seen during training.
- TensorFlow.js: For constructing and training the CNN model in the browser.
- tfjs-vis: For visualizing model metrics and training progress.
- HTML/CSS/JavaScript: For the web interface allowing users to interact with the model.