When using building a federated averaging process - TypeError: Expected a callable.... found Enhanced Model
When using building a federated averaging process - TypeError: Expected a callable.... found Enhanced Model
1 个问题
我正在通过 tff.learning.build_federated_averaging_process() 生成迭代过程。并收到错误：
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.
突出显示：
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.
2 人尝试过
- 查看了另一个类似的问题
已尝试制作
model_fn 一个 collection.abc 可调用，model_fn=Callable[[], model_fn]
只会产生一个新错误。
3 一些代码：
迭代过程：
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,
)
iterative_process = compression_process_adapter.CompressionProcessAdapter(iterative_process)```
模特儿：
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（好的措施）
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)
])
tff.learning.build_federated_averaging_process needs to be a callable (What is a callable?) 的 model_fn 参数不带参数，returns 一个 tff.learning.Model.
来自代码（为了便于阅读而在此处复制）：
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 实例 ，而不是构造和 returns 模型的可调用对象。
model_builder 应传递给 tff.learning.build_federated_averaging_process，但由于它需要参数，因此不能按原样工作。
另一种方法是使用 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)
或者甚至使用无参数 lambda：
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)
下面的问题很好地讨论了lambdas和partials之间的区别：Python: Why is functools.partial necessary?
1 个问题
我正在通过 tff.learning.build_federated_averaging_process() 生成迭代过程。并收到错误：
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.
突出显示：
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.
2 人尝试过
- 查看了另一个类似的问题
model_fn=Callable[[], model_fn]只会产生一个新错误。
3 一些代码：
迭代过程：
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, ) iterative_process = compression_process_adapter.CompressionProcessAdapter(iterative_process)```模特儿：
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（好的措施）
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)
])
tff.learning.build_federated_averaging_process needs to be a callable (What is a callable?) 的 model_fn 参数不带参数，returns 一个 tff.learning.Model.
来自代码（为了便于阅读而在此处复制）：
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 实例 ，而不是构造和 returns 模型的可调用对象。
model_builder 应传递给 tff.learning.build_federated_averaging_process，但由于它需要参数，因此不能按原样工作。
另一种方法是使用 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)
或者甚至使用无参数 lambda：
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)
下面的问题很好地讨论了lambdas和partials之间的区别：Python: Why is functools.partial necessary?