model.compile(optimizer=keras.optimizers.Adam(), loss=keras.losses.SparseCategoricalCrossentropy(), metrics=['accuracy'])

This code compiles a machine learning model using the Adam optimizer, the Sparse Categorical Crossentropy loss function, and accuracy as the evaluation metric.

The Adam optimizer is a popular optimization algorithm that uses adaptive learning rates to converge faster and more efficiently than other optimization algorithms.

The Sparse Categorical Crossentropy loss function is commonly used for multi-class classification problems where the target variable is represented as integers. It computes the cross-entropy loss between the predicted and true labels, and penalizes the model for incorrect predictions.

The accuracy metric measures how often the model makes correct predictions. It is a common evaluation metric for classification problems.