Answer: Keras calculates accuracy by comparing the predicted labels with the true labels, counting the proportion of correct predictions to total predictions.

In Keras, accuracy is calculated through a process that quantitatively measures how well the model’s predictions match the actual labels. This metric is especially common in classification tasks, where the goal is to predict a label or class from a set of predefined options. The calculation of accuracy involves several steps:

1. Model Predictions: The model generates predictions for the test dataset. For classification tasks, these predictions are often in the form of probabilities for each class.
2. Threshold Application: In binary classification, a threshold (typically 0.5 for sigmoid output layers) is applied to convert probabilities into binary class labels. In multi-class classification, the class with the highest probability is selected as the prediction.
3. Comparison: The predicted class labels are then compared to the true labels. Each instance where the predicted label matches the true label is counted as a correct prediction.

This process is summarized in the table below: