使用 tff.learning.build_federated_evaluation 而不是 keras_evaluate

如何解决使用 tff.learning.build_federated_evaluation 而不是 keras_evaluate

我是 TFF 的新手，我正在研究这个 tutorial。我想用 TFF 的预定义函数替换 keras_evaluate 函数：evaluation = tff.learning.build_federated_evaluation(model)

那么我该如何编辑这些行：

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,verbose=0)

该函数仅对与 build_federated_evaluation 相反的数据集示例进行评估，它完全对 federated_test_data 进行评估。那么我如何修改这个函数来评估 federated_test_data 的整体，就像另一个 tutorial ： test_metrics = evaluation(state.model,federated_test_data)