问题描述
我尝试在Tensorflow Federated的图像分类"教程中自定义模型. (它最初使用的是顺序模型)我使用Keras ResNet50，但是当它开始训练时，总是出现错误形状不兼容"
I tried to customize the model in "Image classification" tutorial in Tensorflow Federated. (It originally used a sequential model)I use Keras ResNet50 but when it began to train, there is always an error "Incompatible shapes"
这是我的代码:
NUM_CLIENTS = 4
NUM_EPOCHS = 10
BATCH_SIZE = 2
SHUFFLE_BUFFER = 5
def create_compiled_keras_model():
model = tf.keras.applications.resnet.ResNet50(include_top=False, weights='imagenet',
input_tensor=tf.keras.layers.Input(shape=(100,
300, 3)), pooling=None)
model.compile(
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
optimizer=tf.keras.optimizers.SGD(learning_rate=0.02),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
return model
def model_fn():
keras_model = create_compiled_keras_model()
return tff.learning.from_compiled_keras_model(keras_model, sample_batch)
iterative_process = tff.learning.build_federated_averaging_process(model_fn)
错误信息:在此处输入图片描述
我觉得形状不兼容，因为时代和客户信息丢失了.如果有人能给我一个提示，将非常感激.
I feel that the shape is incompatible because the epoch and clients information were somehow missing. Would be very thankful if someone could give me a hint.
更新:
tff.learning.build_federated_averaging_process
---------------------------------------------------------------------------
AssertionError Traceback (most recent call last)
<ipython-input-164-dac26193d9d8> in <module>()
----> 1 iterative_process = tff.learning.build_federated_averaging_process(model_fn)
2
3 # iterative_process = build_federated_averaging_process(model_fn)
13 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_federated/python/learning/federated_averaging.py in build_federated_averaging_process(model_fn, server_optimizer_fn, client_weight_fn, stateful_delta_aggregate_fn, stateful_model_broadcast_fn)
165 return optimizer_utils.build_model_delta_optimizer_process(
166 model_fn, client_fed_avg, server_optimizer_fn,
--> 167 stateful_delta_aggregate_fn, stateful_model_broadcast_fn)
/usr/local/lib/python3.6/dist-packages/tensorflow_federated/python/learning/framework/optimizer_utils.py in build_model_delta_optimizer_process(model_fn, model_to_client_delta_fn, server_optimizer_fn, stateful_delta_aggregate_fn, stateful_model_broadcast_fn)
349 # still need this.
350 with tf.Graph().as_default():
--> 351 dummy_model_for_metadata = model_utils.enhance(model_fn())
352
353 # ===========================================================================
<ipython-input-159-b2763ace8e5b> in model_fn()
1 def model_fn():
2 keras_model = model
----> 3 return tff.learning.from_compiled_keras_model(keras_model, sample_batch)
/usr/local/lib/python3.6/dist-packages/tensorflow_federated/python/learning/keras_utils.py in from_compiled_keras_model(keras_model, dummy_batch)
211 # Model.test_on_batch() once before asking for metrics.
212 if isinstance(dummy_tensors, collections.Mapping):
--> 213 keras_model.test_on_batch(**dummy_tensors)
214 else:
215 keras_model.test_on_batch(*dummy_tensors)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py in test_on_batch(self, x, y, sample_weight, reset_metrics)
1007 sample_weight=sample_weight,
1008 reset_metrics=reset_metrics,
-> 1009 standalone=True)
1010 outputs = (
1011 outputs['total_loss'] + outputs['output_losses'] + outputs['metrics'])
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in test_on_batch(model, x, y, sample_weight, reset_metrics, standalone)
503 y,
504 sample_weights=sample_weights,
--> 505 output_loss_metrics=model._output_loss_metrics)
506
507 if reset_metrics:
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
568 xla_context.Exit()
569 else:
--> 570 result = self._call(*args, **kwds)
571
572 if tracing_count == self._get_tracing_count():
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
606 # In this case we have not created variables on the first call. So we can
607 # run the first trace but we should fail if variables are created.
--> 608 results = self._stateful_fn(*args, **kwds)
609 if self._created_variables:
610 raise ValueError("Creating variables on a non-first call to a function"
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
2407 """Calls a graph function specialized to the inputs."""
2408 with self._lock:
-> 2409 graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
2410 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access
2411
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2765
2766 self._function_cache.missed.add(call_context_key)
-> 2767 graph_function = self._create_graph_function(args, kwargs)
2768 self._function_cache.primary[cache_key] = graph_function
2769 return graph_function, args, kwargs
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2655 arg_names=arg_names,
2656 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2657 capture_by_value=self._capture_by_value),
2658 self._function_attributes,
2659 # Tell the ConcreteFunction to clean up its graph once it goes out of
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
979 _, original_func = tf_decorator.unwrap(python_func)
980
--> 981 func_outputs = python_func(*func_args, **func_kwargs)
982
983 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, **kwds)
437 # __wrapped__ allows AutoGraph to swap in a converted function. We give
438 # the function a weak reference to itself to avoid a reference cycle.
--> 439 return weak_wrapped_fn().__wrapped__(*args, **kwds)
440 weak_wrapped_fn = weakref.ref(wrapped_fn)
441
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/func_graph.py in wrapper(*args, **kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise
AssertionError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_eager.py:345 test_on_batch *
with backend.eager_learning_phase_scope(0):
/usr/lib/python3.6/contextlib.py:81 __enter__
return next(self.gen)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/backend.py:425 eager_learning_phase_scope
assert ops.executing_eagerly_outside_functions()
AssertionError:
推荐答案
我有同样的问题:如果我执行此行状态，指标= iterative_process.next(状态，federated_train_data)print('第1轮，metrics = {}'.format(metrics))
I have same problem:if I execute this linestate, metrics = iterative_process.next(state, federated_train_data)print('round 1, metrics={}'.format(metrics))
我发现此错误InvalidArgumentError:找到2个根错误. (0)无效的参数:默认MaxPoolingOp仅在设备类型CPU上支持NHWC [[{{node StatefulPartitionedCall/StatefulPartitionedCall/sequential/vgg16/block1_pool/MaxPool}}]]] [[subcomputation/StatefulPartitionedCall_1/ReduceDataset] [[subcomputation/StatefulPartitionedCall_1/ReduceDataset/_140]] (1)无效的参数:默认MaxPoolingOp仅在设备类型CPU上支持NHWC [[{{node StatefulPartitionedCall/StatefulPartitionedCall/sequential/vgg16/block1_pool/MaxPool}}]]] [[subcomputation/StatefulPartitionedCall_1/ReduceDataset]0次成功操作.忽略0个派生错误.
I find this errorInvalidArgumentError: 2 root error(s) found. (0) Invalid argument: Default MaxPoolingOp only supports NHWC on device type CPU [[{{node StatefulPartitionedCall/StatefulPartitionedCall/sequential/vgg16/block1_pool/MaxPool}}]] [[subcomputation/StatefulPartitionedCall_1/ReduceDataset]] [[subcomputation/StatefulPartitionedCall_1/ReduceDataset/_140]] (1) Invalid argument: Default MaxPoolingOp only supports NHWC on device type CPU [[{{node StatefulPartitionedCall/StatefulPartitionedCall/sequential/vgg16/block1_pool/MaxPool}}]] [[subcomputation/StatefulPartitionedCall_1/ReduceDataset]]0 successful operations.0 derived errors ignored.
知道我雇用了VGG16您对这种类型的错误有任何想法吗
knowin that I employe VGG16have you any idea on this type of error
这篇关于Tensorflow联合中的ResNet模型的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！