你能给我一段用python运行tff训练fashionmnist的代码案例吗

当然可以，以下是一段用Python运行tff训练fashionmnist的代码案例：

import tensorflow as tf
import tensorflow_federated as tff

# 加载Fashion MNIST数据集
emnist_train, emnist_test = tff.simulation.datasets.emnist.load_data()

# 预处理Fashion MNIST数据集
def preprocess(dataset):
  def element_fn(element):
    return (tf.expand_dims(element['pixels'], -1), element['label'])
  return dataset.repeat(NUM_EPOCHS).map(element_fn).batch(BATCH_SIZE)

# 用来创建TensorFlow模型并编译使用的函数
def create_compiled_keras_model():
  model = tf.keras.models.Sequential([
      tf.keras.layers.Conv2D(32, 5, padding='same', activation='relu', input_shape=(28, 28, 1)),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(10, activation=tf.nn.softmax)
  ])
  model.compile(
      loss=tf.keras.losses.sparse_categorical_crossentropy,
      optimizer=tf.keras.optimizers.SGD(lr=0.02),
      metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
  return model

# 用来构建federated学习模型的函数
def model_fn():
  keras_model = create_compiled_keras_model()
  return tff.learning.from_compiled_keras_model(keras_model, sample_batch)

# 定义batch大小和迭代轮数
BATCH_SIZE = 100
NUM_EPOCHS = 10

# preprocess Fashion MNIST数据集
preprocessed_emnist_train = preprocess(emnist_train)
preprocessed_emnist_test = preprocess(emnist_test)

# 实例化一个tff.learning.build_federated_averaging_process调用，用于构建联邦学习过程
iterative_process = tff.learning.build_federated_averaging_process(
    model_fn,
    client_optimizer_fn=lambda: tf.keras.optimizers.SGD(lr=0.02),
    server_optimizer_fn=lambda: tf.keras.optimizers.SGD(lr=1.0))

# 训练模型
state = iterative_process.initialize()
for round_num in range(1, 11):
  state, metrics = iterative_process.next(state, [preprocessed_emnist_train] * 5)
  print('round {:2d}, metrics={}'.format(round_num, metrics))

# 评估模型
evaluation = tff.learning.build_federated_evaluation(model_fn)
test_metrics = evaluation(state.model, [preprocessed_emnist_test])
print('Test metrics: {}'.format(test_metrics))

请注意，这只是一个简单的示例。实际上，在训练神经网络时需要考虑许多其他因素，如运行时间、内存使用、调整学习率等。