import nest_asyncio nest_asyncio.apply() import collections import numpy as np import tensorflow as tf import tensorflow_federated as tff np.random.seed(0) tff.federated_computation(lambda: 'Hello, World!')() emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data(cache_dir = '/home/cqx/PycharmProjects/cache/fed_emnist_digitsonly') example_dataset = emnist_train.create_tf_dataset_for_client( emnist_train.client_ids[0]) example_element = next(iter(example_dataset)) example_element['label'].numpy()当把数据集换成本地fashion_mnist时，代码该如何修改

如果要将数据集从emnist_train更改为fashion_mnist，则需要对代码进行以下更改：

1. 导入fashion_mnist数据集

from tensorflow.keras.datasets import fashion_mnist

2. 加载fashion_mnist数据集

(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()

3. 将数据集转换为TFF格式

def preprocess_fn(images, labels):
  images = tf.expand_dims(images, axis=-1)
  images = tf.cast(images, tf.float32) / 255.0
  labels = tf.one_hot(labels, 10)
  return collections.OrderedDict(x=images, y=labels)

def make_federated_data(client_data, client_ids):
  return [client_data.create_tf_dataset_for_client(client_id)
          .map(preprocess_fn)
          .shuffle(500)
          .repeat(NUM_EPOCHS)
          .batch(BATCH_SIZE)
          .prefetch(PREFETCH_BUFFER)
          for client_id in client_ids]

train_data = make_federated_data(fashion_mnist, client_ids)

在替换原始数据集之后，这些更改将确保新数据集与代码一起工作。