问题描述
我尝试开发一个 CNN 模型来从静脉图像中提取特征，但我无法解决显示的 ValueError。
model = Sequential()
model.add(Conv2D(64,kernel_size=(2,2),activation='relu',padding='same',input_shape=(48,64,3)))
model.add(Conv2D(64,(2,padding='same'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(5,activation='softmax'))
model.summary()
model.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.Adadelta(),metrics=['accuracy'])
datagen.fit(x_train)
model.fit(datagen.flow(x_train,y_train,batch_size=3),steps_per_epoch=len(x_train)/3,epochs=12,verbose=1)
score = model.evaluate(x_test,y_test,verbose=0)
print('Test loss:',score[0])
print('Test accuracy:',score[1])
错误跟踪如下：
ValueError Traceback (most recent call last)
<ipython-input-14-b07132f37f80> in <module>
12 datagen.fit(x_train)
13
---> 14 model.fit(datagen.flow(x_train,verbose=1)
15 score = model.evaluate(x_test,verbose=0)
16 print('Test loss:',score[0])
~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self,*args,**kwargs)
106 def _method_wrapper(self,**kwargs):
107 if not self._in_multi_worker_mode(): # pylint: disable=protected-access
--> 108 return method(self,**kwargs)
109
110 # Running inside `run_distribute_coordinator` already.
~\anaconda3\envs\tensorflow\lib\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)
1096 batch_size=batch_size):
1097 callbacks.on_train_batch_begin(step)
-> 1098 tmp_logs = train_function(iterator)
1099 if data_handler.should_sync:
1100 context.async_wait()
~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self,**kwds)
778 else:
779 compiler = "nonXla"
--> 780 result = self._call(*args,**kwds)
781
782 new_tracing_count = self._get_tracing_count()
~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self,**kwds)
821 # This is the first call of __call__,so we have to initialize.
822 initializers = []
--> 823 self._initialize(args,kwds,add_initializers_to=initializers)
824 finally:
825 # At this point we kNow that the initialization is complete (or less
~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self,args,add_initializers_to)
694 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
695 self._concrete_stateful_fn = (
--> 696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
697 *args,**kwds))
698
~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self,**kwargs)
2853 args,kwargs = None,None
2854 with self._lock:
-> 2855 graph_function,_,_ = self._maybe_define_function(args,kwargs)
2856 return graph_function
2857
~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self,kwargs)
3211
3212 self._function_cache.missed.add(call_context_key)
-> 3213 graph_function = self._create_graph_function(args,kwargs)
3214 self._function_cache.primary[cache_key] = graph_function
3215 return graph_function,kwargs
~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self,kwargs,override_flat_arg_shapes)
3063 arg_names = base_arg_names + missing_arg_names
3064 graph_function = ConcreteFunction(
-> 3065 func_graph_module.func_graph_from_py_func(
3066 self._name,3067 self._python_function,~\anaconda3\envs\tensorflow\lib\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)
984 _,original_func = tf_decorator.unwrap(python_func)
985
--> 986 func_outputs = python_func(*func_args,**func_kwargs)
987
988 # invariant: `func_outputs` contains only Tensors,CompositeTensors,~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args,**kwds)
598 # __wrapped__ allows AutoGraph to swap in a converted function. We give
599 # the function a weak reference to itself to avoid a reference cycle.
--> 600 return weak_wrapped_fn().__wrapped__(*args,**kwds)
601 weak_wrapped_fn = weakref.ref(wrapped_fn)
602
~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args,**kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e,"ag_error_Metadata"):
--> 973 raise e.ag_error_Metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self,iterator)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step,args=(data,))
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn,args=args,kwargs=kwargs)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn,kwargs)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args,**kwargs)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:748 train_step
loss = self.compiled_loss(
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:204 __call__
loss_value = loss_obj(y_t,y_p,sample_weight=sw)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\losses.py:149 __call__
losses = ag_call(y_true,y_pred)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\losses.py:253 call **
return ag_fn(y_true,y_pred,**self._fn_kwargs)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args,**kwargs)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\losses.py:1535 categorical_crossentropy
return K.categorical_crossentropy(y_true,from_logits=from_logits)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args,**kwargs)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\backend.py:4687 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
C:\Users\Asus\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1134 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self,other))
ValueError: Shapes (None,None,None) and (None,5) are incompatible
解决方法
我可以从您的代码中看到的一个问题，在这里：
model.add(Dense(8,activation='sigmoid'))
model.compile(loss=keras.losses.categorical_crossentropy,如果您使用损失函数 softmax，您应该使用激活 categorical cross entropy。或者，如果激活需要 binary_cross_entropy
sigmoid