странная ошибка нейронной сети с размерами

Question

хорошо, поэтому я получил следующую сеть:

#building model
def build_model():
    model = models.Sequential()
    model.add(layers.InputLayer(input_shape=(100,28)))
    model.add(layers.Dense(28,activation = 'relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(0.1))
    model.add(layers.Conv1D(filters=16,kernel_size=3,strides=1,padding='same',activation='relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(0.1))
    model.add(layers.Conv1D(filters=32,kernel_size=3,strides=1,padding='same',activation='relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(0.1))
    model.add(layers.Conv1D(filters=64,kernel_size=3,strides=1,padding='same',activation='relu'))
    model.add(BatchNormalization(momentum = 0.99))
    model.add(Dropout(0.1))
    model.add(layers.Flatten())
    model.add(layers.Dense(1, activation = 'linear'))

    model.compile(
        optimizer='adam',
        loss=['mean_squared_error'],
        metrics=[tf.keras.metrics.RootMeanSquaredError()]
    )
    return model


model = build_model()


#train model and output
history = model.fit(
    dataframes,
    target_fx,
    epochs=50,
    callbacks=[keras.callbacks.EarlyStopping(
        patience = 4)
    ]
)

с фреймами данных (1101, 100, 28) np.array и target_fx a (1101, 100, 1) np.array

но когда я запускаю его, я получаю следующую ошибку:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-9-b02e21827529> in <module>
     35     epochs=50,
     36     callbacks=[keras.callbacks.EarlyStopping(
---> 37         patience = 4)
     38     ]
     39 )

~\Anaconda3\envs\deeplearning\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)
   1152             sample_weight=sample_weight,
   1153             class_weight=class_weight,
-> 1154             batch_size=batch_size)
   1155 
   1156         # Prepare validation data.

~\Anaconda3\envs\deeplearning\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
    619                 feed_output_shapes,
    620                 check_batch_axis=False,  # Don't enforce the batch size.
--> 621                 exception_prefix='target')
    622 
    623             # Generate sample-wise weight values given the `sample_weight` and

~\Anaconda3\envs\deeplearning\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    133                         ': expected ' + names[i] + ' to have ' +
    134                         str(len(shape)) + ' dimensions, but got array '
--> 135                         'with shape ' + str(data_shape))
    136                 if not check_batch_axis:
    137                     data_shape = data_shape[1:]

ValueError: Error when checking target: expected dense_10 to have 2 dimensions, but got array with shape (1101, 100, 1)

, что я нахожу невероятно странным, поскольку ошибка касается цели, а не обучающих функций. У кого-нибудь есть предложения?