def evaluate_model(model, num_classes, X_test, Y_test):
predicted = model.predict(X_test)
target_label = convert_prob_to_label(Y_test)
predicted_label = convert_prob_to_label(predicted)
target = np.ravel(target_label)
predicted = np.ravel(predicted_label)
evaluator = Evaluator(num_classes)
accuracy = evaluator.calculate_accuracy(target, predicted)
cm, precisions, recalls = evaluator.calculate_metrics(target, predicted)
precision = np.mean(precisions)
recall = np.mean(recalls)
f1 = 2 * precision * recall / (precision + recall)
return Result(cm, accuracy, precisions, precision, recalls, recall, f1)
