问题描述
我试图查看我是否可以使用迁移学习检测到Higgs Boson，但无法理解错误消息。 我想知道所提到的模型是为计算机视觉设计的，是否只能用于该模型（我不认为是这种情况，但是可以接受任何输入）是否与事实有关？ 这是代码和错误消息
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','binary_accuracy'])
---> 13 history = m.fit(ds_train,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,**kwargs):
65 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
---> 66 return method(self,**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,**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,**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,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,**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,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,kwargs
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self,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,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,321,3),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.
任何努力都值得赞赏 非常感谢
解决方法
根据Land Marks Classifier的官方文档
输入应为大小为321 x的3通道RGB彩色图像 321，缩放为[0，1]。
但是从Your Dataset开始，文件格式为tfrecord。
当我们使用Transfer Learning并想从Models或TF Hub重用tf.keras.applications时，我们的数据应采用预定义格式，如文档中所述。
因此，请确保您的Dataset由Images组成，并将Image Array调整为(321,321,3)，以使TF Hub Module正常工作。