在Tensorflow中拟合文本分类的BERT模型时出现类型错误

我一直遵循这个教程，无论我如何尝试切碎东西，在调用model fit函数后，我总是会遇到这个错误，如下所示：

bert\u history=model.fit（ds\u train\u encoded，epoch=number\u of\u epoch，validation\u data=ds\u test\u encoded）

警告：tensorflow:顺序模型中的层应该只有一个输入张量，但我们收到一个输入：{'input_id'：，'token_type_id'：，'attention_mask'：}
考虑用函数API重写这个模型。
警告：tensorflow：顺序模型中的层应该只有一个输入张量，但我们收到一个输入：{'input_ID'：，'token_type_ID'：，'attention_mask'：}
考虑用函数API重写这个模型。
警告：tensorflow:调用模型时无法更新参数'output\u attentions'、'output\u hidden\u State'和'use\u cache'。必须在config对象中将它们设置为True/False（即：'config=XConfig.from_pretrained（'name'，output\u attentions=True）`）。
警告：tensorflow:调用模型时无法更新参数'output\u attentions'、'output\u hidden\u State'和'use\u cache'。必须在config对象中将它们设置为True/False（即：'config=XConfig.from_pretrained（'name'，output\u attentions=True）`）。
警告：tensorflow:无法在图形模式下设置参数'return\u dict'，参数将始终设置为'True'。
警告：tensorflow:无法在图形模式下设置参数'return\u dict'，参数将始终设置为'True'。
---------------------------------------------------------------------------
TypeError回溯（最近一次调用上次）
在里面
---->1 bert_history=model.fit（ds_train_编码，历代数=历代数，验证数据=ds_test_编码）
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in_method_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。
/opt/conda/lib/python3.7/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、无序、类权重、样本权重、初始历元、每个历元的步数、验证步骤、验证批量大小、验证频率、最大队列大小、工作人员、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（）
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in___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（）
/opt/conda/lib/python3.7/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）
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in_initialize（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）：
/opt/conda/lib/python3.7/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
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in_-maybe_-define_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
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in_create_graph_function（self、args、kwargs、override_flat_arg_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，
/opt/conda/lib/python3.7/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、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、，
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_-fu
WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'input_ids': <tf.Tensor 'ExpandDims_1:0' shape=(512, 1) dtype=int32>, 'token_type_ids': <tf.Tensor 'ExpandDims_2:0' shape=(512, 1) dtype=int32>, 'attention_mask': <tf.Tensor 'ExpandDims:0' shape=(512, 1) dtype=int32>}
    Consider rewriting this model with the Functional API.
    WARNING:tensorflow:Layers in a Sequential model should only have a single input tensor, but we receive a <class 'dict'> input: {'input_ids': <tf.Tensor 'ExpandDims_1:0' shape=(512, 1) dtype=int32>, 'token_type_ids': <tf.Tensor 'ExpandDims_2:0' shape=(512, 1) dtype=int32>, 'attention_mask': <tf.Tensor 'ExpandDims:0' shape=(512, 1) dtype=int32>}
    Consider rewriting this model with the Functional API.
    WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).
    WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).
    WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.
    WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.
    ---------------------------------------------------------------------------
    TypeError                                 Traceback (most recent call last)
    <ipython-input-257-7e871179b930> in <module>
    ----> 1 bert_history = model.fit(ds_train_encoded, epochs=number_of_epochs, validation_data=ds_test_encoded)
    
    /opt/conda/lib/python3.7/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.
    
    /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)
       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()
    
    /opt/conda/lib/python3.7/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()
    
    /opt/conda/lib/python3.7/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
    
    /opt/conda/lib/python3.7/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):
    
    /opt/conda/lib/python3.7/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 
    
    /opt/conda/lib/python3.7/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
    
    /opt/conda/lib/python3.7/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,
    
    /opt/conda/lib/python3.7/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,
    
    /opt/conda/lib/python3.7/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 
    
    /opt/conda/lib/python3.7/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
    
    TypeError: in user code:
    
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
            return step_function(self, iterator)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
            outputs = model.distribute_strategy.run(run_step, args=(data,))
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
            return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
            return self._call_for_each_replica(fn, args, kwargs)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
            return fn(*args, **kwargs)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
            outputs = model.train_step(data)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:747 train_step
            y_pred = self(x, training=True)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
            outputs = call_fn(inputs, *args, **kwargs)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/sequential.py:386 call
            outputs = layer(inputs, **kwargs)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
            outputs = call_fn(inputs, *args, **kwargs)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py:218 call
            lambda: array_ops.identity(inputs))
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/utils/tf_utils.py:65 smart_cond
            pred, true_fn=true_fn, false_fn=false_fn, name=name)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/smart_cond.py:54 smart_cond
            return true_fn()
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/layers/core.py:214 dropped_inputs
            rate=self.rate)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
            return target(*args, **kwargs)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py:507 new_func
            return func(*args, **kwargs)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py:4941 dropout
            return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:201 wrapper
            return target(*args, **kwargs)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py:5023 dropout_v2
            x = ops.convert_to_tensor(x, name="x")
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py:1499 convert_to_tensor
            ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py:338 _constant_tensor_conversion_function
            return constant(v, dtype=dtype, name=name)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py:264 constant
            allow_broadcast=True)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py:282 _constant_impl
            allow_broadcast=allow_broadcast))
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/tensor_util.py:456 make_tensor_proto
            _AssertCompatible(values, dtype)
        /opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/tensor_util.py:333 _AssertCompatible
            raise TypeError("Expected any non-tensor type, got a tensor instead.")
    
        TypeError: Expected any non-tensor type, got a tensor instead.