What are the best practices for logging in TensorFlow?

What are the best practices for logging in TensorFlow?

When logging in TensorFlow, it's important to use the built-in logging module effectively. You should set the logging level appropriately, use informative messages, and log relevant metrics during training.

Can you give me examples of what to log during model training?

You should log metrics like loss and accuracy, the learning rate, and any hyperparameter changes. Additionally, logging the model's architecture and training duration can be helpful.

How can I visualize the logs?

You can use TensorBoard to visualize your logs. Just ensure you write your logs in a format that TensorBoard can read, and then launch TensorBoard to see the visualizations.

Are there any specific logging libraries you recommend?

Yes, besides TensorFlow's built-in logging, you can use libraries like logging for Python or loguru for more advanced logging features.