Keras, as part of the TensorFlow 2.0 ecosystem, is a powerful and user-friendly deep learning framework used for training and evaluating neural networks. When evaluating neural networks, selecting appropriate performance metrics like the Macro F1 Score is crucial, especially for imbalanced classification problems where accuracy alone can be misleading. The blog explains the difference between model performance metrics and loss functions, emphasizing that while loss functions like cross-entropy are minimized during training, performance metrics such as accuracy and F1 score are maximized. In cases of class imbalance, metrics like Recall, Precision, and F1 score are preferred. The article dives into implementing the F1 score in Keras, both using built-in functions and custom implementations, and demonstrates how Neptune.ai can be utilized for effective experiment tracking. Through examples, it highlights the importance of using the right metrics for model evaluation and provides insights into creating custom metrics for more accurate assessments in imbalanced datasets.