What loss function should be used when training the model?

The choice of loss function depends on the machine learning task and the type of model:

• For regression tasks, Mean Squared Error (MSE) or Mean Absolute Error (MAE) are usually used.
• For binary classification tasks, Binary Cross-Entropy (logistic regression) is often applied.
• For multi-class classification, Categorical Cross-Entropy is used.
• For imbalanced classes, weighted versions of loss functions or Focal Loss may be employed.

For example, when training a neural network for image classification with 10 classes, Categorical Cross-Entropy is typically used:

loss = tf.keras.losses.CategoricalCrossentropy()

It is important to choose a loss function that matches the quality metric and the type of output data of the model.