model.fit () return ValueError: Ни одно значение не поддерживается. Ни один массив не имеет значения None

Question

Я проверил весь массив перед тем, как вводить их в функцию model.fit (), но функция model.fit () возвращает ValueError. Я приложил код ниже для вашей справки. Я также попытался однократным кодированием y_train и y_test, но все еще не смог соответствовать модели.

print(any(elem is None for elem in y_test))
print(any(elem is None for elem in x_traincnn))
print(any(elem is None for elem in x_testcnn))
print(any(elem is None for elem in y_train))

False
False
False
False

Эта часть кода не работает, и я понятия не имею, почему метод .fit не может распознать вектор x_traincnn

cnnhistory = model.fit(x=x_traincnn, y=y_train, batch_size=64, epochs=1000, validation_data=(x_testcnn, y_test))
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-51-b07b93617f0d> in <module>
----> 1 cnnhistory = model.fit(x=x_traincnn, y=y_train, batch_size=64, epochs=1000, validation_data=(x_testcnn, y_test))

D:\Anacona3\envs\tf_gpuu\lib\site-packages\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_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
   1211         else:
   1212             fit_inputs = x + y + sample_weights
-> 1213         self._make_train_function()
   1214         fit_function = self.train_function
   1215 

D:\Anacona3\envs\tf_gpuu\lib\site-packages\keras\engine\training.py in _make_train_function(self)
    314                     training_updates = self.optimizer.get_updates(
    315                         params=self._collected_trainable_weights,
--> 316                         loss=self.total_loss)
    317                 updates = self.updates + training_updates
    318 

D:\Anacona3\envs\tf_gpuu\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

D:\Anacona3\envs\tf_gpuu\lib\site-packages\keras\backend\tensorflow_backend.py in symbolic_fn_wrapper(*args, **kwargs)
     73         if _SYMBOLIC_SCOPE.value:
     74             with get_graph().as_default():
---> 75                 return func(*args, **kwargs)
     76         else:
     77             return func(*args, **kwargs)

D:\Anacona3\envs\tf_gpuu\lib\site-packages\keras\optimizers.py in get_updates(self, loss, params)
    274             new_a = self.rho * a + (1. - self.rho) * K.square(g)
    275             self.updates.append(K.update(a, new_a))
--> 276             new_p = p - lr * g / (K.sqrt(new_a) + self.epsilon)
    277 
    278             # Apply constraints.

D:\Anacona3\envs\tf_gpuu\lib\site-packages\tensorflow_core\python\ops\math_ops.py in binary_op_wrapper(x, y)
    901         try:
    902           y = ops.convert_to_tensor_v2(
--> 903               y, dtype_hint=x.dtype.base_dtype, name="y")
    904         except TypeError:
    905           # If the RHS is not a tensor, it might be a tensor aware object

D:\Anacona3\envs\tf_gpuu\lib\site-packages\tensorflow_core\python\framework\ops.py in convert_to_tensor_v2(value, dtype, dtype_hint, name)
   1240       name=name,
   1241       preferred_dtype=dtype_hint,
-> 1242       as_ref=False)
   1243 
   1244 

D:\Anacona3\envs\tf_gpuu\lib\site-packages\tensorflow_core\python\framework\ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, ctx, accept_composite_tensors)
   1294 
   1295     if ret is None:
-> 1296       ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
   1297 
   1298     if ret is NotImplemented:

D:\Anacona3\envs\tf_gpuu\lib\site-packages\tensorflow_core\python\framework\constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
    284                                          as_ref=False):
    285   _ = as_ref
--> 286   return constant(v, dtype=dtype, name=name)
    287 
    288 

D:\Anacona3\envs\tf_gpuu\lib\site-packages\tensorflow_core\python\framework\constant_op.py in constant(value, dtype, shape, name)
    225   """
    226   return _constant_impl(value, dtype, shape, name, verify_shape=False,
--> 227                         allow_broadcast=True)
    228 
    229 

D:\Anacona3\envs\tf_gpuu\lib\site-packages\tensorflow_core\python\framework\constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
    263       tensor_util.make_tensor_proto(
    264           value, dtype=dtype, shape=shape, verify_shape=verify_shape,
--> 265           allow_broadcast=allow_broadcast))
    266   dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
    267   const_tensor = g.create_op(

D:\Anacona3\envs\tf_gpuu\lib\site-packages\tensorflow_core\python\framework\tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape, allow_broadcast)
    435   else:
    436     if values is None:
--> 437       raise ValueError("None values not supported.")
    438     # if dtype is provided, forces numpy array to be the type
    439     # provided if possible.

ValueError: None values not supported.