AttributeError: Tensor.op is meaningless when eager execution is enabled when using multiple feature inputs in Tensorflow federated
AttributeError: Tensor.op is meaningless when eager execution is enabled when using multiple feature inputs in Tensorflow federated
输入多个特征输入时出现如下问题：
feature_layer = tf.keras.layers.DenseFeatures(feature_cols)
feature_layer_inputs = {}
feature_layer_inputs['a'] = tf.keras.Input(shape=(1,), name='a', dtype=tf.int32)
feature_layer_inputs['b'] = tf.keras.Input(shape=(1,), name='b', dtype=tf.int32)
model = feature_layer(feature_layer_inputs)
for units in [64, 64]:
model = tf.keras.layers.Dense(units, activation='relu')(model)
c_pred = tf.keras.layers.Dense(3, activation='softmax')(model) #classification 3 outputs
keras_model = tf.keras.Model(inputs=[v for v in feature_layer_inputs.values()], outputs=c_pred)
input_spec = collections.OrderedDict(
x=collections.OrderedDict(
a=tf.TensorSpec(shape=[None,], dtype=tf.int32),
b=tf.TensorSpec(shape=[None,], dtype=tf.int32),
y=tf.TensorSpec(shape=[None,], dtype=tf.int32))
def model_fn():
# We _must_ create a new model here, and _not_ capture it from an external
# scope. TFF will call this within different graph contexts.
return tff.learning.from_keras_model(
keras_model,
#input_spec=preprocessed_sample_dataset.element_spec,
input_spec=input_spec,
loss=losses.SparseCategoricalCrossentropy(),
metrics=[metrics.SparseCategoricalAccuracy()])
iterative_process = tff.learning.build_federated_averaging_process(
model_fn,
client_optimizer_fn=lambda: optimizers.Adam(learning_rate=client_lr),
server_optimizer_fn=lambda: optimizers.SGD(learning_rate=server_lr))
state = iterative_process.initialize()
for round_num in range(1, NUM_ROUNDS+1):
#state, tff_metrics = iterative_process.next(state, federated_train_data)
state, tff_metrics = iterative_process.next(state, train_data)
eval_model = keras_model
eval_model.compile(optimizer=optimizers.Adam(learning_rate=client_lr),
loss=losses.SparseCategoricalCrossentropy(),
metrics=[metrics.SparseCategoricalAccuracy()])
tff.learning.assign_weights_to_keras_model(eval_model, state.model)
ev_result = eval_model.evaluate(x_test, y_test, verbose=0)
我从这个 solution 得到这个代码。但是，我得到了如下完整的回溯。最终，我得到一个错误 'AttributeError: Tensor.op is meaningless when eager execution'。似乎构建的模型有问题，尤其是 tf.keras.model 函数中的输入。
Traceback (most recent call last):
File "accident_modify_uk_final3b.py", line 323, in <module>
server_optimizer_fn=lambda: optimizers.SGD(learning_rate=server_lr))
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/federated_averaging.py", line 212, in build_federated_averaging_process
stateful_delta_aggregate_fn, stateful_model_broadcast_fn)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py", line 360, in build_model_delta_optimizer_process
@tff.tf_computation
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/api/computations.py", line 152, in tf_computation
return computation_wrapper_instances.tensorflow_wrapper(*args)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/impl/wrappers/computation_wrapper.py", line 333, in __call__
self._wrapper_fn)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/impl/wrappers/computation_wrapper.py", line 91, in _wrap
concrete_fn = wrapper_fn(fn, parameter_type, unpack=None)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/impl/wrappers/computation_wrapper_instances.py", line 52, in _tf_wrapper_fn
target_fn, parameter_type, ctx_stack)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/impl/tensorflow_serialization.py", line 275, in serialize_py_fn_as_tf_computation
result = target(*args)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/impl/utils/function_utils.py", line 455, in <lambda>
return lambda: fn() # pylint: disable=unnecessary-lambda
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py", line 364, in tf_init_fn
stateful_model_broadcast_fn.initialize())
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py", line 227, in server_init
_, optimizer_vars = _build_server_optimizer(model, optimizer)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py", line 123, in _build_server_optimizer
apply_delta(delta=weights_delta)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 580, in __call__
result = self._call(*args, **kwds)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 627, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 506, in _initialize
*args, **kwds))
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2446, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2777, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2667, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 981, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 441, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
AttributeError: in user code:
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py:112 apply_delta *
optimizer.apply_gradients(grads_and_vars, name='server_update')
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:508 apply_gradients **
"name": name,
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2420 merge_call
return self._merge_call(merge_fn, args, kwargs)
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2427 _merge_call
return merge_fn(self._strategy, *args, **kwargs)
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:590 _distributed_apply **
"update_" + var.op.name, skip_on_eager=True):
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:581 op
return self._handle.op
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/framework/ops.py:1113 op
"Tensor.op is meaningless when eager execution is enabled.")
AttributeError: Tensor.op is meaningless when eager execution is enabled.
如何解决这个问题？
我认为问题在于 model_fn() 从全局范围捕获 keras_model，而不是在调用时构建新实例。
来自 tff.learning.build_federated_averaging_process 的文档：
model_fn: A no-arg function that returns a tff.learning.Model. This method must not capture TensorFlow tensors or variables and use them. The model must be constructed entirely from scratch on each invocation, returning the same pre-constructed model each call will result in an error.
上面的代码中有一段代码注释暗示了这一点（复制在这里）：
def model_fn():
# We _must_ create a new model here, and _not_ capture it from an external
# scope. TFF will call this within different graph contexts.
return tff.learning.from_keras_model(
keras_model,
#input_spec=preprocessed_sample_dataset.element_spec,
input_spec=input_spec,
loss=losses.SparseCategoricalCrossentropy(),
metrics=[metrics.SparseCategoricalAccuracy()])
要解决此问题，请尝试在 model_fn() 中调用 tf.keras.Model 构造函数 。模型的层可能也需要在内部创建，因此使用 create_keras_model() 函数 returns a new tf.keras.Model 并从 model_fn().
中调用该助手
输入多个特征输入时出现如下问题：
feature_layer = tf.keras.layers.DenseFeatures(feature_cols)
feature_layer_inputs = {}
feature_layer_inputs['a'] = tf.keras.Input(shape=(1,), name='a', dtype=tf.int32)
feature_layer_inputs['b'] = tf.keras.Input(shape=(1,), name='b', dtype=tf.int32)
model = feature_layer(feature_layer_inputs)
for units in [64, 64]:
model = tf.keras.layers.Dense(units, activation='relu')(model)
c_pred = tf.keras.layers.Dense(3, activation='softmax')(model) #classification 3 outputs
keras_model = tf.keras.Model(inputs=[v for v in feature_layer_inputs.values()], outputs=c_pred)
input_spec = collections.OrderedDict(
x=collections.OrderedDict(
a=tf.TensorSpec(shape=[None,], dtype=tf.int32),
b=tf.TensorSpec(shape=[None,], dtype=tf.int32),
y=tf.TensorSpec(shape=[None,], dtype=tf.int32))
def model_fn():
# We _must_ create a new model here, and _not_ capture it from an external
# scope. TFF will call this within different graph contexts.
return tff.learning.from_keras_model(
keras_model,
#input_spec=preprocessed_sample_dataset.element_spec,
input_spec=input_spec,
loss=losses.SparseCategoricalCrossentropy(),
metrics=[metrics.SparseCategoricalAccuracy()])
iterative_process = tff.learning.build_federated_averaging_process(
model_fn,
client_optimizer_fn=lambda: optimizers.Adam(learning_rate=client_lr),
server_optimizer_fn=lambda: optimizers.SGD(learning_rate=server_lr))
state = iterative_process.initialize()
for round_num in range(1, NUM_ROUNDS+1):
#state, tff_metrics = iterative_process.next(state, federated_train_data)
state, tff_metrics = iterative_process.next(state, train_data)
eval_model = keras_model
eval_model.compile(optimizer=optimizers.Adam(learning_rate=client_lr),
loss=losses.SparseCategoricalCrossentropy(),
metrics=[metrics.SparseCategoricalAccuracy()])
tff.learning.assign_weights_to_keras_model(eval_model, state.model)
ev_result = eval_model.evaluate(x_test, y_test, verbose=0)
我从这个 solution 得到这个代码。但是，我得到了如下完整的回溯。最终，我得到一个错误 'AttributeError: Tensor.op is meaningless when eager execution'。似乎构建的模型有问题，尤其是 tf.keras.model 函数中的输入。
Traceback (most recent call last):
File "accident_modify_uk_final3b.py", line 323, in <module>
server_optimizer_fn=lambda: optimizers.SGD(learning_rate=server_lr))
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/federated_averaging.py", line 212, in build_federated_averaging_process
stateful_delta_aggregate_fn, stateful_model_broadcast_fn)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py", line 360, in build_model_delta_optimizer_process
@tff.tf_computation
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/api/computations.py", line 152, in tf_computation
return computation_wrapper_instances.tensorflow_wrapper(*args)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/impl/wrappers/computation_wrapper.py", line 333, in __call__
self._wrapper_fn)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/impl/wrappers/computation_wrapper.py", line 91, in _wrap
concrete_fn = wrapper_fn(fn, parameter_type, unpack=None)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/impl/wrappers/computation_wrapper_instances.py", line 52, in _tf_wrapper_fn
target_fn, parameter_type, ctx_stack)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/impl/tensorflow_serialization.py", line 275, in serialize_py_fn_as_tf_computation
result = target(*args)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/core/impl/utils/function_utils.py", line 455, in <lambda>
return lambda: fn() # pylint: disable=unnecessary-lambda
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py", line 364, in tf_init_fn
stateful_model_broadcast_fn.initialize())
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py", line 227, in server_init
_, optimizer_vars = _build_server_optimizer(model, optimizer)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py", line 123, in _build_server_optimizer
apply_delta(delta=weights_delta)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 580, in __call__
result = self._call(*args, **kwds)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 627, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 506, in _initialize
*args, **kwds))
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2446, in _get_concrete_function_internal_garbage_collected
graph_function, _, _ = self._maybe_define_function(args, kwargs)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2777, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/function.py", line 2667, in _create_graph_function
capture_by_value=self._capture_by_value),
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 981, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py", line 441, in wrapped_fn
return weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py", line 968, in wrapper
raise e.ag_error_metadata.to_exception(e)
AttributeError: in user code:
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py:112 apply_delta *
optimizer.apply_gradients(grads_and_vars, name='server_update')
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:508 apply_gradients **
"name": name,
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2420 merge_call
return self._merge_call(merge_fn, args, kwargs)
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2427 _merge_call
return merge_fn(self._strategy, *args, **kwargs)
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:590 _distributed_apply **
"update_" + var.op.name, skip_on_eager=True):
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:581 op
return self._handle.op
/home/anaconda3/envs/env1_TF2.1/lib/python3.7/site-packages/tensorflow/python/framework/ops.py:1113 op
"Tensor.op is meaningless when eager execution is enabled.")
AttributeError: Tensor.op is meaningless when eager execution is enabled.
如何解决这个问题？
我认为问题在于 model_fn() 从全局范围捕获 keras_model，而不是在调用时构建新实例。
来自 tff.learning.build_federated_averaging_process 的文档：
model_fn: A no-arg function that returns a tff.learning.Model. This method must not capture TensorFlow tensors or variables and use them. The model must be constructed entirely from scratch on each invocation, returning the same pre-constructed model each call will result in an error.
上面的代码中有一段代码注释暗示了这一点（复制在这里）：
def model_fn():
# We _must_ create a new model here, and _not_ capture it from an external
# scope. TFF will call this within different graph contexts.
return tff.learning.from_keras_model(
keras_model,
#input_spec=preprocessed_sample_dataset.element_spec,
input_spec=input_spec,
loss=losses.SparseCategoricalCrossentropy(),
metrics=[metrics.SparseCategoricalAccuracy()])
要解决此问题，请尝试在 model_fn() 中调用 tf.keras.Model 构造函数 。模型的层可能也需要在内部创建，因此使用 create_keras_model() 函数 returns a new tf.keras.Model 并从 model_fn().