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我是 TFF 的新手，我正在学习本教程。我想用keras_evaluateTFF 的预定义函数替换函数：evaluation = tff.learning.build_federated_evaluation(model)

那么如何编辑这些行：

def keras_evaluate(state, round_num):
  # Take our global model weights and push them back into a Keras model to
  # use its standard `.evaluate()` method.
  keras_model = load_model(batch_size=BATCH_SIZE)
  keras_model.compile(
      loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
      metrics=[FlattenedCategoricalAccuracy()])
  state.model.assign_weights_to(keras_model)
  loss, accuracy = keras_model.evaluate(example_dataset, steps=2, verbose=0)
  print('\tEval: loss={l:.3f}, accuracy={a:.3f}'.format(l=loss, a=accuracy))


for round_num in range(NUM_ROUNDS):
  print('Round {r}'.format(r=round_num))
  keras_evaluate(state, round_num)
  state, metrics = fed_avg.next(state, train_datasets)
  train_metrics = metrics['train']
  print('\tTrain: loss={l:.3f}, accuracy={a:.3f}'.format(
      l=train_metrics['loss'], a=train_metrics['accuracy']))

print('Final evaluation')
keras_evaluate(state, NUM_ROUNDS + 1)

在这一行：

loss, accuracy = keras_model.evaluate(example_dataset, steps=2, verbose=0)

该函数仅对与 相反的数据集示例进行build_federated_evaluation评估，它federated_test_data完全评估。那么如何修改此函数以评估federated_test_data其他教程中的总体情况： test_metrics = evaluation(state.model, federated_test_data)

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