Keras Loss Functions: Everything You Need to Know

In the blog post by Derrick Mwiti, readers are introduced to the role and selection of loss functions in Keras for deep learning model development. The post explains that loss functions are integral to updating model weights through backpropagation until improvements in evaluation metrics like f1 score or AUC are no longer seen. It covers various built-in loss functions in Keras for different types of problems, such as BinaryCrossentropy for binary classification, CategoricalCrossentropy for multiclass classification, and MeanSquaredError for regression tasks. The article highlights how to implement custom loss functions and incorporate sample weighing to address observation sensitivity. Additionally, it discusses common issues such as NaNs in loss values, which can halt model training, and suggests potential solutions like proper data scaling and optimizer selection. The blog also emphasizes the importance of monitoring loss functions during training using tools like neptune.ai, which provides visualization and tracking capabilities to diagnose training issues effectively.

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