当使用构建一个联邦平均进程时- TypeError:期望一个可调用的.建立增强模型

Question

1整个发行

我正在通过tff.learning.build_federated_averaging_process().生成一个迭代过程。并接收错误：

    Traceback (most recent call last):
      File "B:\tools and software\Anaconda\envs\bookProjects\lib\site-packages\IPython\core\interactiveshell.py", line 3331, in run_code
        exec(code_obj, self.user_global_ns, self.user_ns)
      File "<ipython-input-2-47998fd56829>", line 1, in <module>
        runfile('B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/train_v04.py', args=['--experiment_name=temp', '--client_batch_size=20', '--client_optimizer=sgd', '--client_learning_rate=0.2', '--server_optimizer=sgd', '--server_learning_rate=1.0', '--total_rounds=200', '--rounds_per_eval=1', '--rounds_per_checkpoint=50', '--rounds_per_profile=0', '--root_output_dir=B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/logs/fed_out/'], wdir='B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection')
      File "B:\tools and software\PyCharm 2020.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
        pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
      File "B:\tools and software\PyCharm 2020.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
        exec(compile(contents+"\n", file, 'exec'), glob, loc)
      File "B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/train_v04.py", line 306, in <module>
        app.run(main)
      File "B:\tools and software\Anaconda\envs\bookProjects\lib\site-packages\absl\app.py", line 299, in run
        _run_main(main, args)
      File "B:\tools and software\Anaconda\envs\bookProjects\lib\site-packages\absl\app.py", line 250, in _run_main
        sys.exit(main(argv))
      File "B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/train_v04.py", line 299, in main
        train_main()
      File "B:/projects/openProjects/githubprojects/BotnetTrafficAnalysisFederaedLearning/anomaly-detection/train_v04.py", line 262, in train_main
        server_optimizer_fn=server_optimizer_fn,
      File "B:\tools and software\Anaconda\envs\bookProjects\lib\site-packages\tensorflow_federated\python\learning\federated_averaging.py", line 211, in build_federated_averaging_process
        stateful_delta_aggregate_fn, stateful_model_broadcast_fn)
      File "B:\tools and software\Anaconda\envs\bookProjects\lib\site-packages\tensorflow_federated\python\learning\framework\optimizer_utils.py", line 498, in build_model_delta_optimizer_process
        py_typecheck.check_callable(model_fn)
      File "B:\tools and software\Anaconda\envs\bookProjects\lib\site-packages\tensorflow_federated\python\common_libs\py_typecheck.py", line 106, in check_callable
        type_string(type(target))))
    TypeError: Expected a callable, found non-callable tensorflow_federated.python.learning.model_utils.EnhancedModel.

突出说明：

in build_federated_averaging_process
    stateful_delta_aggregate_fn, stateful_model_broadcast_fn)

和

TypeError: Expected a callable, found non-callable tensorflow_federated.python.learning.model_utils.EnhancedModel.

两人尝试过

1. 研究了here试图使model_fn成为collection.abc可调用的另一个类似问题，model_fn=Callable[[], model_fn]只创建了一个新的错误。

3一些代码：

• 迭代过程：

model_fn = model_builder(input_dim=sysarg，input_spec=input_spec) iterative_process =model_builder model_fn=model_fn，client_optimizer_fn=client_optimizer_fn，server_optimizer_fn=server_optimizer_fn，) iterative_process =model_builder bulder:

    def model_builder(input_dim, input_spec):
           model = create_model(input_dim)
            return tff.learning.from_keras_model(keras_model=model,
                                                 loss=tf.keras.losses.MeanSquaredError(),
                                                 input_spec=input_spec,
                                                 metrics=[tf.keras.metrics.Accuracy()],
                                                 )

• 创建模型(用于很好的度量)

    def create_model(input_dim):
           autoencoder = Sequential([
                tf.keras.layers.Dense(int(0.75 * input_dim), activation="tanh", input_shape=(input_dim,)),
                tf.keras.layers.Dense(int(0.5 * input_dim), activation="tanh"),
                tf.keras.layers.Dense(int(0.33 * input_dim), activation="tanh"),
                tf.keras.layers.Dense(int(0.25 * input_dim), activation="tanh"),
                tf.keras.layers.Dense(int(0.33 * input_dim), activation="tanh"),
                tf.keras.layers.Dense(int(0.5 * input_dim), activation="tanh"),
                tf.keras.layers.Dense(int(0.75 * input_dim), activation="tanh"),
                tf.keras.layers.Dense(input_dim)
                ])

Answer 1

model_fn参数的tff.learning.build_federated_averaging_process需要是一个可调用的(What is a callable?)，它不带参数并返回tff.learning.Model。

从代码(为可读性在此转载)：

def model_builder(input_dim, input_spec):
  model = create_model(input_dim)
  return tff.learning.from_keras_model(
    keras_model=model,
    loss=tf.keras.losses.MeanSquaredError(),
    input_spec=input_spec,
    metrics=[tf.keras.metrics.Accuracy()])

model_fn = model_builder(input_dim=sysarg, input_spec=input_spec)

iterative_process = tff.learning.build_federated_averaging_process(
  model_fn=model_fn,
  client_optimizer_fn=client_optimizer_fn,
  server_optimizer_fn=server_optimizer_fn)

model_fn实际上是tff.learning.Model的一个实例，而不是构造和返回模型的可调用的实例。

model_builder应该被传递给tff.learning.build_federated_averaging_process，但是由于它使用参数，所以不能按原样工作。

另一种选择是使用functools.partial

iterative_process = tff.learning.build_federated_averaging_process(
  model_fn=functools.partial(model_builder, input_dim=sysarg, input_spec=input_spec),
  client_optimizer_fn=client_optimizer_fn,
  server_optimizer_fn=server_optimizer_fn)

甚至使用无arg朗姆达：

iterative_process = tff.learning.build_federated_averaging_process(
  model_fn=lambda: model_builder(input_dim=sysarg, input_spec=input_spec),
  client_optimizer_fn=client_optimizer_fn,
  server_optimizer_fn=server_optimizer_fn)

以下问题很好地讨论了lambdas和partials之间的区别：Python: Why is functools.partial necessary?