Python 如何修复tensorflow的值错误问题？

我在使用model.fit（）时遇到一个值错误，我无法理解错误是什么。我认为所有的过程我都做对了

这是我的模型

模型=顺序（）

model.add（密集（42，activation='relu'））#输入层
模型。添加（辍学率（0.25））
model.add（密集（21，activation='relu'））#隐藏层
模型。添加（辍学率（0.25））
model.add（密集（10，activation='relu'））#隐藏层
模型。添加（辍学率（0.25））
model.add（密集（5，activation='relu'））#隐藏层
模型。添加（辍学率（0.25））
model.add（密集（11，activation='softmax'））#输出层
compile（loss='classifical_crossentropy'，optimizer='adam'，metrics=['accurity']）
纪元1/100
---------------------------------------------------------------------------
ValueError回溯（最近一次调用上次）
在里面
---->1模型拟合（x=scaled\u x\u序列，y=y\u序列，验证数据=（scaled\u x\u测试，y\u测试），历元=100）
~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\keras\engine\training.py in\u method\u包装（self，*args，**kwargs）
106定义方法包装（self，*args，**kwargs）：
107如果不是self._处于_multi_worker_模式（）：#pylint:disable=受保护的访问
-->108返回方法（self、*args、**kwargs）
109
110#已经在“运行分配协调器”内部运行了。
~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\keras\engine\training.py-in-fit（self、x、y、批大小、历元、冗余、回调、验证拆分、验证数据、洗牌、类权重、样本权重、初始历元、每个历元的步骤、验证步骤、验证批次大小、验证频率、最大队列大小、工人、使用多处理）
1096批次大小=批次大小）：
1097回拨。列车上批次开始（步骤）
->1098 tmp_日志=训练函数（迭代器）
1099如果数据处理程序应同步：
1100 context.async_wait（）
~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\eager\def\u function.py in\uuuuu调用（self，*args，**kwds）
778其他：
779 compiler=“nonXla”
-->780结果=自身调用（*args，**kwds）
781
782 new_tracing_count=self._get_tracing_count（）
~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\eager\def_function.py in_调用（self，*args，**kwds）
821#这是"调用"的第一个调用，因此我们必须初始化。
822个初始值设定项=[]
-->823自身初始化（参数、KWD、添加初始化器到=初始化器）
824最后：
825#此时我们知道初始化已完成（或更少）
初始化中的~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\eager\def_function.py（self、args、kwds、add_initializers_to）
695自具体状态fn=(
696 self._stateful_fn._get_concrete_function_internal_garbage_collected（#pylint:disable=protected access
-->697*args，**科威特第纳尔）
698
699 def无效的创建者范围（*未使用的参数，**未使用的参数）：
~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\eager\function.py in\u get\u concrete\u function\u internal\u garbage\u collected（self，*args，**kwargs）
2853 args，kwargs=None，None
2854带自锁：
->2855图形函数，可能定义函数（args，kwargs）
2856返回图函数
2857
~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\eager\function.py in\u maybe\u define\u函数（self、args、kwargs）
3211
3212自.\u函数\u缓存.missed.add（调用上下文\u键）
->3213图形函数=self.\u创建图形函数（args，kwargs）
3214自.\u函数\u缓存.primary[缓存\u键]=图形\u函数
3215返回图_函数，args，kwargs
~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\eager\function.py in\u create\u graph\u函数（self、args、kwargs、override\u flat\u arg\u shapes）
3073参数名称=参数名称，
3074覆盖平面形状=覆盖平面形状，
->3075按值捕获=自身。_按值捕获），
3076自我功能属性，
3077功能规格=自身功能规格，
来自func py func的~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\framework\func\u graph.py in func\u graph\u from\u py func（名称、python\u func、args、kwargs、签名、func\u图、autograph\u选项、添加控制依赖项、arg\u名称、op\u返回值、集合、按值捕获、覆盖平面arg\u形状）
984，original_func=tf_decorator.unwrap（python_func）
985
-->986 func_输出=python_func（*func_参数，**func_参数）
987
988#不变量：`func_outputs`只包含张量、复合传感器、，
~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\eager\def_function.py in wrapped_fn（*args，**kwds）
598#uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu。我们给予
599#函数对自身进行弱引用以避免引用循环。
-->600返回弱_-wrapped_-fn（）
601弱包层=弱包层参考（包层）
602
包装中的~\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\framework\func\u graph.py（*args，**kwargs）
971例外情况为e:#pylint:disable=broad except
972如果hasattr（e，“AGU错误元数据”）：
-->973将e.ag\u错误\u元数据引发到\u异常（e）
974其他：
975提高
ValueError:在用户代码中：
C:\Users\psiva\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\keras\engine\training.py:806 train\u函数*
返回步骤_函数（self、迭代器）
C:\Users\psiva\anaconda3\envs\tensorflow\lib\site packages\tensorflow\python\keras\engine\training.py:796 step\u函数**
输出=模型。分配策略。运行（运行）
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, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **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, *args, **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, *args, **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, kwds, 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, *args, **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, args, 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, args, kwargs

~\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, 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, args, kwargs, 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, args, 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, sample_weight, 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, y_pred, **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, y_pred, 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

   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)))

y_train = tf.keras.utils.to_categorical(y_train, 11)
y_test= tf.keras.utils.to_categorical(y_test, 11)

assert model.layers[-1].units == y_train.shape[-1] == y_test.shape[-1]

from sklearn.preprocessing import OneHotEncoder
onehot_encoder = OneHotEncoder(sparse=False)
labels_i = onehot_encoder.fit_transform(np.reshape(labels, (-1, 1)))