LSTM Cannot convert a symbolic Tensor (lstm_1/strided_slice:0) to a numpy array

Question

I'm using LSTM to train moving MNIST dataset

I got this error, how to fix it?

Full error message

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_21316\248295683.py in <module>
     11 regressor = Sequential()
     12 # Adding the first LSTM layer and some Dropout regularisation
---> 13 regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (x_train.shape[1], 1)))
     14 regressor.add(Dropout(0.2))
     15 

c:\Programs\Python\Python37\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    520     self._self_setattr_tracking = False  # pylint: disable=protected-access
    521     try:
--> 522       result = method(self, *args, **kwargs)
    523     finally:
    524       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

c:\Programs\Python\Python37\lib\site-packages\tensorflow\python\keras\engine\sequential.py in add(self, layer)
    211           # and create the node connecting the current layer
    212           # to the input layer we just created.
--> 213           layer(x)
    214           set_inputs = True
    215 

c:\Python\Python37\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
    666 
...
--> 870         " a NumPy call, which is not supported".format(self.name))
    871 
    872   def __len__(self):

NotImplementedError: Cannot convert a symbolic Tensor (lstm_1/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported

Edited

I updated my training code and error message

Training Dataset Shapes: (900, 19, 4096), (900, 19, 4096)

Validation Dataset Shapes: (100, 19, 4096), (100, 19, 4096)

regressor = Sequential()
regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (x_train.shape[1], 1)))
regressor.add(Dropout(0.2))

regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))

regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))

regressor.add(LSTM(units = 50))
regressor.add(Dropout(0.2))
regressor.add(Dense(units = 1))
regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')


regressor.fit(x_train, y_train, epochs = 100, batch_size = 32)