我正在尝试使用Keras和TensorFlow安装一个简单的LSTM网络。尝试拟合时，我不断收到以下错误或其他错误。我试过了89和1的批处理大小，并且都返回相同的错误，只是显示的形状有冲突而不同。
我的Xtrain的形状是：
In [9]: print(Xtrain.shape)
(6853, 89, 250)
In [10]: print(ytrain.shape)
(6853, 89, 1)
In [11]: model = Sequential()^M
...: model.add(LSTM(units=89,^M
...: batch_input_shape=(Xtrain.shape[0],timesteps,len(features)),^M
...: activation='relu',^M
...: return_sequences=True,^M
...: stateful=True))^M
...: ^M
...: model.add(Dropout(0.2))^M
...: ^M
...: model.add(Dense(21))^M
...: ^M
...: model.add(Dropout(0.2))^M
...: ^M
...: model.add(TimeDistributed(Dense(1)))^M
...: #model.add(Dense(1))^M
...: model.compile(loss='mean_squared_error', optimizer=opt, metrics=['mae'])^M
...: model.summary()^M
...: model.fit(Xtrain,ytrain,batch_size=89,verbose=1,shuffle=False,epochs=1)
...:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_5 (LSTM) (6853, 89, 89) 121040
_________________________________________________________________
dropout_9 (Dropout) (6853, 89, 89) 0
_________________________________________________________________
dense_9 (Dense) (6853, 89, 21) 1890
_________________________________________________________________
dropout_10 (Dropout) (6853, 89, 21) 0
_________________________________________________________________
time_distributed_5 (TimeDist (6853, 89, 1) 22
=================================================================
Total params: 122,952
Trainable params: 122,952
Non-trainable params: 0
_________________________________________________________________
Epoch 1/1
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-9-9ecc3d11a1f7> in <module>()
16 model.compile(loss='mean_squared_error', optimizer=opt, metrics=['mae'])
17 model.summary()
---> 18 model.fit(Xtrain,ytrain,batch_size=89,verbose=1,shuffle=False,epochs=1)
C:\Users\asus\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_dat
1035 initial_epoch=initial_epoch,
1036 steps_per_epoch=steps_per_epoch,
-> 1037 validation_steps=validation_steps)
1038
1039 def evaluate(self, x=None, y=None,
C:\Users\asus\Anaconda3\lib\site-packages\keras\engine\training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f
197 ins_batch[i] = ins_batch[i].toarray()
198
--> 199 outs = f(ins_batch)
200 outs = to_list(outs)
201 for l, o in zip(out_labels, outs):
C:\Users\asus\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs)
2664 return self._legacy_call(inputs)
2665
-> 2666 return self._call(inputs)
2667 else:
2668 if py_any(is_tensor(x) for x in inputs):
C:\Users\asus\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in _call(self, inputs)
2634 symbol_vals,
2635 session)
-> 2636 fetched = self._callable_fn(*array_vals)
2637 return fetched[:len(self.outputs)]
2638
C:\Users\asus\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args)
1452 else:
1453 return tf_session.TF_DeprecatedSessionRunCallable(
-> 1454 self._session._session, self._handle, args, status, None)
1455
1456 def __del__(self):
C:\Users\asus\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
517 None, None,
518 compat.as_text(c_api.TF_Message(self.status.status)),
--> 519 c_api.TF_GetCode(self.status.status))
520 # Delete the underlying status object from memory otherwise it stays alive
521 # as there is a reference to status from this from the traceback due to
InvalidArgumentError: Incompatible shapes: [6853] vs. [89]
[[Node: training_4/Adam/gradients/loss_4/time_distributed_5_loss/mul_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _class=["loc:@tra
我已经检查了github和SO上的其他一些类似错误的问题，但所有问题都与自定义层或多个GPU上的并行处理有关，在我的情况下都不适用。
我收到的唯一有用的错误消息是batch_size必须为
我在这里做什么错了？