无法在keras中使用landmarks_classifier_oceania模型来检测希格斯玻色子粒子？
Unable to use landmarks_classifier_oceania model in keras to detect Higgs Boson particles?
我试图查看是否可以使用迁移学习检测 Higgs Boson，但我无法理解错误消息。
我想知道这是否与所提到的模型是为计算机视觉设计的事实有关，所以它只适用于此（我不认为是这种情况，但任何输入都值得赞赏）
这是代码和错误信息
import tensorflow.compat.v2 as tf
import tensorflow_hub as hub
m = hub.KerasLayer('https://tfhub.dev/google/on_device_vision/classifier/landmarks_classifier_oceania_antarctica_V1/1')
m = tf.keras.Sequential([
m,
tf.keras.layers.Dense(2, activation='softmax'),
])
m.compile(loss = 'binary_crossentropy',
optimizer = 'adam', metrics = ['accuracy','binary_accuracy'])
history = m.fit(ds_train,validation_data=ds_valid, epochs =12 ,steps_per_epoch=13)
错误：
ValueError Traceback (most recent call last)
<ipython-input-20-0c5a3b4a3d55> in <module>
11 m.compile(loss = 'binary_crossentropy',
12 optimizer = 'adam', metrics = ['accuracy','binary_accuracy'])
---> 13 history = m.fit(ds_train,validation_data=ds_valid, epochs =12 ,steps_per_epoch=13)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
64 def _method_wrapper(self, *args, **kwargs):
65 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 66 return method(self, *args, **kwargs)
67
68 # Running inside `run_distribute_coordinator` already.
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
846 batch_size=batch_size):
847 callbacks.on_train_batch_begin(step)
--> 848 tmp_logs = train_function(iterator)
849 # Catch OutOfRangeError for Datasets of unknown size.
850 # This blocks until the batch has finished executing.
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
578 xla_context.Exit()
579 else:
--> 580 result = self._call(*args, **kwds)
581
582 if tracing_count == self._get_tracing_count():
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
625 # This is the first call of __call__, so we have to initialize.
626 initializers = []
--> 627 self._initialize(args, kwds, add_initializers_to=initializers)
628 finally:
629 # At this point we know that the initialization is complete (or less
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
504 self._concrete_stateful_fn = (
505 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 506 *args, **kwds))
507
508 def invalid_creator_scope(*unused_args, **unused_kwds):
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2444 args, kwargs = None, None
2445 with self._lock:
-> 2446 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2447 return graph_function
2448
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2775
2776 self._function_cache.missed.add(call_context_key)
-> 2777 graph_function = self._create_graph_function(args, kwargs)
2778 self._function_cache.primary[cache_key] = graph_function
2779 return graph_function, args, kwargs
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2665 arg_names=arg_names,
2666 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2667 capture_by_value=self._capture_by_value),
2668 self._function_attributes,
2669 # Tell the ConcreteFunction to clean up its graph once it goes out of
/opt/conda/lib/python3.7/site-packages/tensorflow/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,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
439 # __wrapped__ allows AutoGraph to swap in a converted function. We give
440 # the function a weak reference to itself to avoid a reference cycle.
--> 441 return weak_wrapped_fn().__wrapped__(*args, **kwds)
442 weak_wrapped_fn = weakref.ref(wrapped_fn)
443
/opt/conda/lib/python3.7/site-packages/tensorflow/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
ValueError: in user code:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:571 train_function *
outputs = self.distribute_strategy.run(
/opt/conda/lib/python3.7/site-packages/tensorflow_hub/keras_layer.py:222 call *
result = f()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py:1605 __call__ **
return self._call_impl(args, kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py:1645 _call_impl
return self._call_flat(args, self.captured_inputs, cancellation_manager)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py:1730 _call_flat
arg.shape))
ValueError: The argument 'images' (value Tensor("IteratorGetNext:0", shape=(None, 28), dtype=float32, device=/job:worker/replica:0/task:0/device:CPU:0)) is not compatible with the shape this function was traced with. Expected shape (None, 321, 321, 3), but got shape (None, 28).
If you called get_concrete_function, you may need to pass a tf.TensorSpec(..., shape=...) with a less specific shape, having None on axes which can vary.
感谢任何努力
非常感谢
根据 Land Marks Classifier 的官方文档，
Inputs are expected to be 3-channel RGB color images of size 321 x
321, scaled to [0, 1].
但是从Your Dataset开始，文件格式是tfrecord.
当我们使用 Transfer Learning 并且我们想重用来自 TF Hub 或 tf.keras.applications 的 Models 时，我们的数据应该在 predefined-format 如文档中所述。
因此，请确保您的 Dataset 由 Images 组成，并将 Image Array 大小调整为 (321,321,3) 以使 TF Hub Module 正常工作。
我试图查看是否可以使用迁移学习检测 Higgs Boson，但我无法理解错误消息。 我想知道这是否与所提到的模型是为计算机视觉设计的事实有关，所以它只适用于此（我不认为是这种情况，但任何输入都值得赞赏） 这是代码和错误信息
import tensorflow.compat.v2 as tf
import tensorflow_hub as hub
m = hub.KerasLayer('https://tfhub.dev/google/on_device_vision/classifier/landmarks_classifier_oceania_antarctica_V1/1')
m = tf.keras.Sequential([
m,
tf.keras.layers.Dense(2, activation='softmax'),
])
m.compile(loss = 'binary_crossentropy',
optimizer = 'adam', metrics = ['accuracy','binary_accuracy'])
history = m.fit(ds_train,validation_data=ds_valid, epochs =12 ,steps_per_epoch=13)
错误：
ValueError Traceback (most recent call last)
<ipython-input-20-0c5a3b4a3d55> in <module>
11 m.compile(loss = 'binary_crossentropy',
12 optimizer = 'adam', metrics = ['accuracy','binary_accuracy'])
---> 13 history = m.fit(ds_train,validation_data=ds_valid, epochs =12 ,steps_per_epoch=13)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
64 def _method_wrapper(self, *args, **kwargs):
65 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 66 return method(self, *args, **kwargs)
67
68 # Running inside `run_distribute_coordinator` already.
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
846 batch_size=batch_size):
847 callbacks.on_train_batch_begin(step)
--> 848 tmp_logs = train_function(iterator)
849 # Catch OutOfRangeError for Datasets of unknown size.
850 # This blocks until the batch has finished executing.
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
578 xla_context.Exit()
579 else:
--> 580 result = self._call(*args, **kwds)
581
582 if tracing_count == self._get_tracing_count():
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
625 # This is the first call of __call__, so we have to initialize.
626 initializers = []
--> 627 self._initialize(args, kwds, add_initializers_to=initializers)
628 finally:
629 # At this point we know that the initialization is complete (or less
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
504 self._concrete_stateful_fn = (
505 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 506 *args, **kwds))
507
508 def invalid_creator_scope(*unused_args, **unused_kwds):
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2444 args, kwargs = None, None
2445 with self._lock:
-> 2446 graph_function, _, _ = self._maybe_define_function(args, kwargs)
2447 return graph_function
2448
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
2775
2776 self._function_cache.missed.add(call_context_key)
-> 2777 graph_function = self._create_graph_function(args, kwargs)
2778 self._function_cache.primary[cache_key] = graph_function
2779 return graph_function, args, kwargs
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2665 arg_names=arg_names,
2666 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2667 capture_by_value=self._capture_by_value),
2668 self._function_attributes,
2669 # Tell the ConcreteFunction to clean up its graph once it goes out of
/opt/conda/lib/python3.7/site-packages/tensorflow/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,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
439 # __wrapped__ allows AutoGraph to swap in a converted function. We give
440 # the function a weak reference to itself to avoid a reference cycle.
--> 441 return weak_wrapped_fn().__wrapped__(*args, **kwds)
442 weak_wrapped_fn = weakref.ref(wrapped_fn)
443
/opt/conda/lib/python3.7/site-packages/tensorflow/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
ValueError: in user code:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:571 train_function *
outputs = self.distribute_strategy.run(
/opt/conda/lib/python3.7/site-packages/tensorflow_hub/keras_layer.py:222 call *
result = f()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py:1605 __call__ **
return self._call_impl(args, kwargs)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py:1645 _call_impl
return self._call_flat(args, self.captured_inputs, cancellation_manager)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py:1730 _call_flat
arg.shape))
ValueError: The argument 'images' (value Tensor("IteratorGetNext:0", shape=(None, 28), dtype=float32, device=/job:worker/replica:0/task:0/device:CPU:0)) is not compatible with the shape this function was traced with. Expected shape (None, 321, 321, 3), but got shape (None, 28).
If you called get_concrete_function, you may need to pass a tf.TensorSpec(..., shape=...) with a less specific shape, having None on axes which can vary.
感谢任何努力 非常感谢
根据 Land Marks Classifier 的官方文档，
Inputs are expected to be 3-channel RGB color images of size 321 x 321, scaled to [0, 1].
但是从Your Dataset开始，文件格式是tfrecord.
当我们使用 Transfer Learning 并且我们想重用来自 TF Hub 或 tf.keras.applications 的 Models 时，我们的数据应该在 predefined-format 如文档中所述。
因此，请确保您的 Dataset 由 Images 组成，并将 Image Array 大小调整为 (321,321,3) 以使 TF Hub Module 正常工作。