Keras Metrics: Everything You Need to Know

Keras metrics serve as essential tools for evaluating deep learning model performance, with options spanning built-in metrics and customizable functions to cater to specific needs. The distinction between Keras and tf.keras lies in their origins and compatibility with multiple backends, as Keras was designed to operate independently, supporting TensorFlow, PyTorch, and JAX, while tf.keras is integrated into TensorFlow. To effectively manage and track experiments, tools like Neptune are recommended, offering capabilities to log and visualize metrics, including custom metrics like precision, recall, and F1 score, through Python functions or classes. Practical examples demonstrate how to set up a development environment, implement and utilize various Keras metrics, and visualize model performance using Neptune, enhancing model evaluation through comprehensive tracking and charting tools.