如何解决如何解决值错误问题张量流？
使用model.fit（）时出现值错误，我不明白这是什么错误。我认为我正确地完成了所有过程。
这是我的模特，
model = Sequential（）
model.add(Dense(42,activation='relu')) # Input layer
model.add(Dropout(0.25))
model.add(Dense(21,activation='relu')) # Hidden layer
model.add(Dropout(0.25))
model.add(Dense(10,activation='relu')) # Hidden layer
model.add(Dropout(0.25))
model.add(Dense(5,activation='relu')) # Hidden layer
model.add(Dropout(0.25))
model.add(Dense(11,activation='softmax')) # Output layer
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
Epoch 1/100
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-26-9dd45f56d29e> in <module>
----> 1 model.fit(x=scaled_x_train,y=y_train,validation_data=(scaled_x_test,y_test),epochs=100)
~\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)
695 self._concrete_stateful_fn = (
696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 697 *args,**kwds))
698
699 def invalid_creator_scope(*unused_args,**unused_kwds):
~\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)
3073 arg_names=arg_names,3074 override_flat_arg_shapes=override_flat_arg_shapes,-> 3075 capture_by_value=self._capture_by_value),3076 self._function_attributes,3077 function_spec=self.function_spec,~\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\psiva\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function *
return step_function(self,iterator)
C:\Users\psiva\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\psiva\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\psiva\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\psiva\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
return fn(*args,**kwargs)
C:\Users\psiva\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step **
outputs = model.train_step(data)
C:\Users\psiva\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:749 train_step
y,y_pred,regularization_losses=self.losses)
C:\Users\psiva\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\psiva\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\losses.py:149 __call__
losses = ag_call(y_true,y_pred)
C:\Users\psiva\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\losses.py:253 call **
return ag_fn(y_true,**self._fn_kwargs)
C:\Users\psiva\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args,**kwargs)
C:\Users\psiva\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\psiva\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
return target(*args,**kwargs)
C:\Users\psiva\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\backend.py:4687 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
C:\Users\psiva\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,1) and (None,11) are incompatible
请帮助我解决此问题。 我写的这段代码是根据tensorflow 2.0和python 3.7编写的。 告诉我我要在此代码中进行的修复。 我的功能是42，输出目标变量有11个类。
解决方法
您需要确保标签是一次性编码的。试试：
y_train = tf.keras.utils.to_categorical(y_train,11)
y_test= tf.keras.utils.to_categorical(y_test,11)
绝对要确保最后一层中的神经元数量是标签中的列数。
assert model.layers[-1].units == y_train.shape[-1] == y_test.shape[-1]
我认为错误可能是由于错误说明标签的形状造成的。 含义： (,1) 应该改为 (,11)。我相信此代码可能对您有所帮助。
from sklearn.preprocessing import OneHotEncoder
onehot_encoder = OneHotEncoder(sparse=False)
labels_i = onehot_encoder.fit_transform(np.reshape(labels,(-1,1)))
此代码可用于标记您的标签。如果您有 11 个不同的类，请转换标签的形状 (_,11)。
,您是否在任何地方指定了输入形状？您可以通过使代码位于模型第一行下方来指定它。文档为here.
model.add(tf.keras.Input(shape=None,batch_size=None,name=None,dtype=None,sparse=False,tensor=None,ragged=False,**kwargs)
#alternatively you can specify it in the first dense layer with
model.add(layers.Dense(21,activation="relu",input_shape=(put your input dimensions here)))
还要检查训练和测试数据标签。标签的尺寸必须与最后一层中神经元（11）的数量匹配。由于您使用的是分类交叉熵，因此这些标签需要进行一次热编码。如果您的标签是整数编码，请使用稀疏分类交叉熵。文档为here.
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