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我想在本教程中尝试这种TFF方法，但我发现我无法理解的错误女巫。我使用assign_weight，然后评估我的模型这是我的代码：

emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data()
...
def create_compiled_keras_model():
    model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(
          10, activation=tf.nn.softmax, kernel_initializer='zeros', input_shape=(784,))])

    model.compile(
    loss=tf.keras.losses.SparseCategoricalCrossentropy(),
     optimizer=tf.keras.optimizers.SGD(learning_rate=0.02),
     metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
    return model

def model_fn():
    keras_model = create_compiled_keras_model()
    return tff.learning.from_compiled_keras_model(keras_model, sample_batch)

iterative_process = tff.learning.build_federated_averaging_process(model_fn)
state = iterative_process.initialize()
state, metrics = iterative_process.next(state, train_data)

NUM_ROUNDS = 11
for round_num in range(1, NUM_ROUNDS):
    state, metrics = iterative_process.next(state, train_data)
    print('round {:2d}, metrics={}'.format(round_num, metrics))

evaluation = tff.learning.build_federated_evaluation(model_fn)
train_metrics = evaluation(state.model, train_data)


keras_model = create_compiled_keras_model()
keras_model.compile(
    loss=tf.keras.losses.SparseCategoricalCrossentropy(),
     optimizer=tf.keras.optimizers.SGD(learning_rate=0.02),
     metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
tff.learning.assign_weights_to_keras_model(keras_model, state.model)
centralized_data= emnist_test.create_tf_dataset_from_all_clients()
loss, accuracy = keras_model.evaluate(centralized_data, verbose =1)
print('loss={}, accuracy={}'.format(loss, accuracy))

错误信息 ：

loss, accuracy = keras_model.evaluate(centralized_data, verbose =1)
ValueError: No data provided for "dense_input". Need data for each key in: ['dense_input']
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