张量流数据集的顺序模型

我试图了解如何使用tensorflow小号Dataset的，而不是一个单独的喂养它■对于简单的回归模型，np.array用于训练的输入和输出。

这是一个简单的独立示例：

import tensorflow as tf
import numpy as np

# create training data
X_train_set = np.random.random(size=(1000,10))
y_train_set = np.random.random(size=(1000))

# convert to dataset
train_dataset = tf.data.Dataset.from_tensor_slices((X_train_set, y_train_set))

my_model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(10,)),
    tf.keras.layers.Dense(100, activation='relu'),
    tf.keras.layers.Dense(10, activation='relu'),
    tf.keras.layers.Dense(1)
])

my_model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.1),
loss='mean_absolute_error')

# train with np.array data
my_model.fit(X_train_set,y_train_set,epochs=2)
print('Success Training 1\n')

# train with datasets
my_model.fit(train_dataset,epochs=2)
print('Success Training 2\n')

运行该示例my_model.fit(X_train_set,y_train_set,epochs=2)确实可行。但是，my_model.fit(train_dataset,epochs=2)抛出错误：

Epoch 1/2
32/32 [==============================] - 0s 2ms/step - loss: 0.3424
Epoch 2/2
32/32 [==============================] - 0s 2ms/step - loss: 0.2501
Success Training 1

Epoch 1/2
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-82-31d0c7e586d8> in <module>
     21 
     22 # train with datasets
---> 23 my_model.fit(train_dataset,epochs=2)
     24 print('Success Training 2\n')

~/virtualEnv/py3_TF23/lib/python3.6/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.

~/virtualEnv/py3_TF23/lib/python3.6/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()

~/virtualEnv/py3_TF23/lib/python3.6/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()

~/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    805       # In this case we have created variables on the first call, so we run the
    806       # defunned version which is guaranteed to never create variables.
--> 807       return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
    808     elif self._stateful_fn is not None:
    809       # Release the lock early so that multiple threads can perform the call

~/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
   2826     """Calls a graph function specialized to the inputs."""
   2827     with self._lock:
-> 2828       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
   2829     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   2830 

~/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3208           and self.input_signature is None
   3209           and call_context_key in self._function_cache.missed):
-> 3210         return self._define_function_with_shape_relaxation(args, kwargs)
   3211 
   3212       self._function_cache.missed.add(call_context_key)

~/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/eager/function.py in _define_function_with_shape_relaxation(self, args, kwargs)
   3140 
   3141     graph_function = self._create_graph_function(
-> 3142         args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
   3143     self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
   3144 

~/virtualEnv/py3_TF23/lib/python3.6/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,

~/virtualEnv/py3_TF23/lib/python3.6/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,

~/virtualEnv/py3_TF23/lib/python3.6/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 

~/virtualEnv/py3_TF23/lib/python3.6/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:

    /home/max/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /home/max/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /home/max/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /home/max/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /home/max/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /home/max/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /home/max/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:747 train_step
        y_pred = self(x, training=True)
    /home/max/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py:976 __call__
        self.name)
    /home/max/virtualEnv/py3_TF23/lib/python3.6/site-packages/tensorflow/python/keras/engine/input_spec.py:216 assert_input_compatibility
        ' but received input with shape ' + str(shape))

    ValueError: Input 0 of layer sequential_26 is incompatible with the layer: expected axis -1 of input shape to have value 10 but received input with shape [10, 1]

问题是：我是否必须创建其他Sequential模型，还是我train_dataset完全不正确？我认为在培训步骤中anp.array应该可以与a互换Dataset吗？