TensorBoard is an open-source tool developed by Google for visualizing machine learning experiments, particularly with TensorFlow. It provides a comprehensive suite of features to monitor training processes, analyze model performance, and visualize data.

🔍 Key Features

  • Dashboard: Real-time tracking of metrics like loss, accuracy, and learning rate.
  • Graph Visualization: Interactive visualization of computational graphs.
  • Histograms: Monitor the distribution of weights and biases during training.
  • Projector: Explore high-dimensional data via 2D/3D projections.
  • Scalars & Images: Log and visualize scalar values, images, audio, and more.

📚 How to Use

  1. Installation: 
    pip install tensorboard
  2. Integration: Add summaries to your TensorFlow code using tf.summary.
  3. Launch: Run tensorboard --logdir=PATH to start the server.
  4. Access: Open http://localhost:6006 in your browser.

🌐 Related Resources

For deeper exploration, check out our TensorFlow guide to understand how TensorBoard integrates with model training.

TensorBoard_Interface
TensorFlow_Logo

📌 Tip: Use TensorBoard to debug and optimize your models efficiently!